diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..7932d6cdb91c82fa2b698d5f36ff250ec8185a31
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+.DS_Store
+.ipynb_checkpoints/
+__pycache__/
\ No newline at end of file
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..28d8009f90794cd95bb3a5f3d58253b61c6fadb9
--- /dev/null
+++ b/app.py
@@ -0,0 +1,206 @@
+def read_and_split_file(filename, chunk_size=1200, chunk_overlap=200):
+ with open(filename, 'r') as f:
+ text = f.read()
+
+ text_splitter = RecursiveCharacterTextSplitter(
+ chunk_size=chunk_size, chunk_overlap=chunk_overlap,
+ length_function = len, separators=[" ", ",", "\n"]
+ )
+
+ # st.write(f'Financial report char len: {len(text)}')
+ texts = text_splitter.create_documents([text])
+ return texts
+
+
+def get_label_prediction(selected_predictor, texts):
+ predicted_labels = []
+ replies = []
+
+
+ emdedding_model_name = predictors[selected_predictor]['embedding_model']
+ emdedding_model = SentenceTransformer(emdedding_model_name)
+
+ texts_str = [text.page_content for text in texts]
+ embeddings = emdedding_model.encode(texts_str, show_progress_bar=True).tolist()
+
+ # dataset = load_dataset(predictors[selected_predictor]['dataset_name'])
+ label_encoder = LabelEncoder()
+ encoded_labels = label_encoder.fit_transform([label.upper() for label in labels])
+
+ input_size = predictors[selected_predictor]['embedding_dim']
+ hidden_size = 256
+ output_size = len(label_encoder.classes_)
+ dropout_rate = 0.5
+ batch_size = 8
+
+
+ model = MLP(input_size, hidden_size, output_size, dropout_rate)
+ load_model(model, predictors[selected_predictor]['mlp_model'])
+
+ embeddings_tensor = torch.tensor(embeddings)
+
+ data = TensorDataset(embeddings_tensor)
+ dataloader = DataLoader(data, batch_size=batch_size, shuffle=True)
+
+ with torch.no_grad():
+ model.eval()
+ for inputs in dataloader:
+ # st.write(inputs[0])
+ outputs = model(inputs[0])
+
+ # _, predicted = torch.max(outputs, 1)
+
+ probabilities = F.softmax(outputs, dim=1)
+ predicted_indices = torch.argmax(probabilities, dim=1).tolist()
+ predicted_labels_list = label_encoder.inverse_transform(predicted_indices)
+ for pred_label in predicted_labels_list:
+ predicted_labels.append(pred_label)
+ # st.write(pred_label)
+
+ predicted_labels_counter = Counter(predicted_labels)
+ predicted_label = predicted_labels_counter.most_common(1)[0][0]
+ return predicted_label
+
+
+
+
+
+if __name__ == '__main__':
+ # Comments and ideas to implement:
+ # 1. Try sending list of inputs to the Inference API.
+
+
+
+ from config import (
+ labels, headers_inference_api, headers_inference_endpoint,
+ # summarization_prompt_template,
+ prompt_template,
+ # task_explain_for_predictor_model,
+ summarizers, predictors, summary_scores_template,
+ summarization_system_msg, summarization_user_prompt, prediction_user_prompt, prediction_system_msg,
+ # prediction_prompt,
+ chat_prompt, instruction_prompt
+ )
+
+ import streamlit as st
+ from sys import exit
+ from pprint import pprint
+ from collections import Counter
+ from itertools import zip_longest
+ from random import choice
+ import requests
+ from re import sub
+ from rouge import Rouge
+ from time import sleep, perf_counter
+ import os
+ from textwrap import wrap
+ from multiprocessing import Pool, freeze_support
+ from tqdm import tqdm
+ from stqdm import stqdm
+ from langchain.document_loaders import TextLoader
+ from langchain.text_splitter import RecursiveCharacterTextSplitter
+ from langchain.schema.document import Document
+ # from langchain.schema import Document
+ from langchain.chat_models import ChatOpenAI
+ from langchain.llms import OpenAI
+ from langchain.schema import AIMessage, HumanMessage, SystemMessage
+ from langchain.prompts import PromptTemplate
+ from datasets import Dataset, load_dataset
+ from sklearn.preprocessing import LabelEncoder
+ from test_models.train_classificator import MLP
+ from safetensors.torch import load_model, save_model
+ from sentence_transformers import SentenceTransformer
+ from torch.utils.data import DataLoader, TensorDataset
+ import torch.nn.functional as F
+ import torch
+ import torch.nn as nn
+ import sys
+
+ sys.path.append(os.path.abspath(os.path.join(os.getcwd(), 'test_models/')))
+ sys.path.append(os.path.abspath(os.path.join(os.getcwd(), 'test_models/financial-roberta')))
+
+ st.set_page_config(
+ page_title="Financial advisor",
+ page_icon="đłđ°",
+ layout="wide",
+ )
+ # st.session_state.summarized = False
+
+
+
+
+
+
+ with st.sidebar:
+ "# How to useđ"
+
+
+ """
+ â¨This is a holiday version of the web-UI with the magic đ, allowing you to unwrap
+ label predictions for a company based on its financial report text! đ⨠The prediction
+ enchantment is performed using the sophisticated embedding classifier approach. đđŽ
+ """
+
+
+ center_style = "{}
"
+ st.markdown(center_style.format('Load the financial report'), unsafe_allow_html=True)
+
+
+ upload_types = ['Text input', 'File upload']
+ upload_captions = ['Paste the text', 'Upload a text file']
+ upload_type = st.radio('Select how to upload the financial report', upload_types,
+ captions=upload_captions)
+
+
+ match upload_type:
+ case 'Text input':
+ financial_report_text = st.text_area('Something', label_visibility='collapsed',
+ placeholder='Financial report as TEXT')
+
+
+ case 'File upload':
+ uploaded_files = st.file_uploader("Choose a a text file", type=['.txt', '.docx'],
+ label_visibility='collapsed', accept_multiple_files=True)
+
+ if not bool(uploaded_files):
+ st.stop()
+
+ financial_report_text = ''
+ for uploaded_file in uploaded_files:
+ if uploaded_file.name.endswith("docx"):
+ document = Document(uploaded_file)
+ document.save('./utils/texts/' + uploaded_file.name)
+ document = Document(uploaded_file.name)
+ financial_report_text += "".join([paragraph.text for paragraph in document.paragraphs]) + '\n'
+ else:
+ financial_report_text += "".join([line.decode() for line in uploaded_file]) + '\n'
+
+ # with open('./utils/texts/financial_report_text.txt', 'w') as file:
+ # file.write(financial_report_text)
+
+ if st.button('Get label'):
+ with st.spinner("Thinking..."):
+ text_splitter = RecursiveCharacterTextSplitter(
+ chunk_size=3200, chunk_overlap=200,
+ length_function = len, separators=[" ", ",", "\n"]
+ )
+
+ # st.write(f'Financial report char len: {len(financial_report_text)}')
+ documents = text_splitter.create_documents([financial_report_text])
+ # st.write(f'Num chunks: {len(documents)}')
+ texts = [document.page_content for document in documents]
+ # st.write(f'Each chunk char length: {[len(text) for text in texts]}')
+
+ # predicted_label = get_label_prediction(texts)
+ from test_models.create_setfit_model import model
+
+ with torch.no_grad():
+ model.model_head.eval()
+ predicted_labels = model(texts)
+ # st.write(predicted_labels)
+
+ predicted_labels_counter = Counter(predicted_labels)
+ predicted_label = predicted_labels_counter.most_common(1)[0][0]
+
+ font_style = 'The predicted label is **{}**.'
+ st.markdown(font_style.format(predicted_label), unsafe_allow_html=True)
\ No newline at end of file
diff --git a/config.py b/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..51033c715c2851dc42fe211a3713faf5325ff71d
--- /dev/null
+++ b/config.py
@@ -0,0 +1,212 @@
+import os
+from langchain.prompts import PromptTemplate
+
+
+labels = ['buy', 'sell', 'hold']
+headers_inference_api = {"Authorization": f"Bearer {os.environ['HG_api_key']}"}
+# headers_inference_endpoint = {
+# "Authorization": f"Bearer {os.environ['HG_api_key_personal']}",
+# "Content-Type": "application/json"
+#}
+
+summarization_system_msg = """You are the best financial advisor and expert broker. You are \
+reading Item 7 from Form 10-K of some company and you want to summarize it into 10 sentences the best as \
+possible, so that then the human will analyze your summary and take on serious decisions, whether to \
+buy, sell or hold the holdings of that company. There is no need to copy messages from the original text. \
+Don't write general things, which aren't important to the investor. Include the most important parts, \
+which describes the business growth, predictions for next years etc."""
+summarization_user_msg = "Company's description: {company_description}"
+
+summarization_user_prompt = PromptTemplate.from_template(
+ template=summarization_user_msg
+)
+
+# summarization_template = """ You are the best financial advisor and expert broker. You are \
+# reading Item 7 from Form 10-K of some company and you want to summarize it into 2-3 sentences the best as \
+# possible, so that then the human will analyze your summary and take on serious decisions, whether to \
+# buy, sell or hold the holdings of that company. There is no need to copy messages from the original text
+
+# Company's description: {company_description}"""
+
+# summarization_prompt_template = PromptTemplate.from_template(
+# template=summarization_template
+# )
+
+prediction_system_msg = """You are the best financial advisor and expert broker. I am an investor, who seek \
+for your help. Below is the description of one big company. You need to reply to me with a \
+single word, either 'sell', 'buy' or 'hold'. This word should best describe your recommendation \
+on what is the best action for me with the company's holdings."""
+prediction_user_msg = """Company's description: {company_description}
+
+So what do you think? Sell, buy or hold?"""
+prediction_user_prompt = PromptTemplate.from_template(
+ template=prediction_user_msg
+)
+
+prediction_template = ' ' + prediction_system_msg + ' \n\n' + prediction_user_msg
+prediction_prompt = PromptTemplate.from_template(
+ template=prediction_template
+)
+
+
+
+
+
+template = """ You are the best financial advisor and expert broker. I am an investor, who seek \
+for your help. Below is the description of one big company. You need to reply to me with a \
+single word, either 'sell', 'buy' or 'hold'. This word should best describe your recommendation \
+on what is the best action for me with the company's holdings.
+
+Company's description: {company_description}
+
+So what do you think? Sell, buy or hold?"""
+prompt_template = PromptTemplate.from_template(
+ template=template
+)
+
+
+chat_structure = """
+### Instruction:
+{instruction}
+
+### Response:
+"""
+chat_prompt = PromptTemplate.from_template(
+ template=chat_structure
+)
+
+
+instruction = """You are the best financial advisor and expert broker. I am an investor, who seek \
+for your help. Below is the description of one big company. You need to reply to me with a \
+single word, either 'sell', 'buy' or 'hold'. This word should best describe your recommendation \
+on what is the best action for me with the company's holdings.
+
+Company's description: {company_description}
+
+So what do you think? Sell, buy or hold?"""
+# text_gen_prompt = PromptTemplate.from_template(
+# template=chat_prompt.format(instruction=instruction_prompt.format(company_description=text.page_content))
+# )
+instruction_prompt = PromptTemplate.from_template(
+ template=instruction
+)
+
+
+
+
+
+# predictor_system_message = """You are the preeminent financial advisor and expert broker,
+# renowned for your unparalleled market acumen. As you meticulously analyze the summary of Item 7 from
+# Form 10-K of some company, your task is to distill your profound insights into a single decisive word,
+# choosing from the options: 'sell', 'buy', or 'hold'. This word reflects your beliefs about the company's
+# future. Your selection should be astutely founded on a
+# comprehensive understanding of all economic facets and nuanced considerations. Remember, your
+# recommendation carries significant weight, influencing critical decisions on whether to divest, invest,
+# or maintain positions in that company. If you predict "buy" it means that the company is a good investment
+# option and is likely to grow in the next year. If you predict "sell" it means that you think that the
+# company won't perform wellduring the upcoming year. Approach this task with the sagacity and expertise that
+# has earned you your esteemed reputation. Please, don't include any warnings that it is difficult to make
+# a definitive recommendation, based on the information provided. Please, don't include any additional text
+# before your answer, don't write 'based on the information provided, I recommend ...'."""
+
+
+
+
+
+summarizers = {
+# 'financial-summarization-pegasus': {
+# 'model_name': 'human-centered-summarization/financial-summarization-pegasus',
+# 'api_url' : 'https://api-inference.huggingface.co/models/human-centered-summarization/financial-summarization-pegasus',
+# 'chunk_size': 1_400,
+# 'size': 'large'
+# },
+ 'bart-finance-pegasus': {
+ 'model_name': 'amitesh11/bart-finance-pegasus',
+ 'api_url': 'https://api-inference.huggingface.co/models/amitesh11/bart-finance-pegasus',
+ 'chunk_size': 2_600,
+ 'size': 'medium'
+ },
+# 'financial-summary': {
+# 'model_name': 'Spacetimetravel/autotrain-financial-conversation_financial-summary-90517144315',
+# 'api_url' : "https://api-inference.huggingface.co/models/Spacetimetravel/autotrain-financial-conversation_financial-summary-90517144315",
+# 'chunk_size': 1_800,
+# 'size': 'small'
+# },
+ 'gpt-3.5-turbo': {
+ 'model_name': 'gpt-3.5-turbo',
+ 'api_url' : "",
+ 'chunk_size': 6_000,
+ 'size': ''
+ }
+}
+
+
+
+# There are 3 inference_types: chatGPT, Inference API and Inference Endpoint
+# Add captions to display inference_type
+predictors = {
+ 'gpt-3.5-turbo': {
+ 'model_name': 'OpenAI-gpt-3.5-turbo',
+ 'inference_type': 'chatGPT',
+ 'model_task': 'text-generation'
+ },
+
+
+ 'blenderbot-3B': {
+ 'model_name': 'facebook/blenderbot-3B',
+ 'api_url' : 'https://api-inference.huggingface.co/models/facebook/blenderbot-3B',
+ 'inference_type': 'Inference API',
+ 'model_task': 'conversational'
+ },
+ 'TinyLlama-1.1B': {
+ 'model_name': 'tog/TinyLlama-1.1B-alpaca-chat-v1.0',
+ 'api_url' : 'https://api-inference.huggingface.co/models/tog/TinyLlama-1.1B-alpaca-chat-v1.0',
+ 'inference_type': 'Inference API',
+ 'model_task': 'conversational'
+ },
+
+
+ 'open-llama-7b-v2': {
+ 'model_name': 'VMware/open-llama-7b-v2-open-instruct',
+ 'api_url' : 'https://audqis4a3tk9s0li.us-east-1.aws.endpoints.huggingface.cloud',
+ 'inference_type': 'Inference Endpoint',
+ 'model_task': 'conversational'
+ },
+
+
+ 'gpt2-xl': {
+ 'model_name': 'gpt2-xl',
+ 'api_url' : 'https://api-inference.huggingface.co/models/gpt2-xl',
+ 'inference_type': 'Inference API',
+ 'model_task': 'text-generation'
+ },
+ 'distilgpt2-finance': {
+ 'model_name': 'lxyuan/distilgpt2-finetuned-finance',
+ 'api_url' : 'https://api-inference.huggingface.co/models/lxyuan/distilgpt2-finetuned-finance',
+ 'inference_type': 'Inference API',
+ 'model_task': 'text-generation'
+ },
+
+
+ 'embedding_mlp_classifier': {
+ 'dataset_name': 'CabraVC/vector_dataset_2023-12-02_00-32',
+ 'embedding_model': 'all-distilroberta-v1',
+ 'embedding_dim': 768,
+ 'mlp_model': 'embedding_mlp.safetensors',
+
+ },
+ 'embedding_mlp_classifier_gtr-t5-xxl': {
+ 'dataset_name': 'CabraVC/vector_dataset_2023-12-02_00-32',
+ 'embedding_model': 'gtr-t5-xxl',
+ 'embedding_dim': 768,
+ 'mlp_model': 'embedding_mlp.safetensors',
+ }
+}
+
+
+
+summary_scores_template = {
+ 'rouge-1': [],
+ 'rouge-2': [],
+ 'rouge-l': []
+}
\ No newline at end of file
diff --git a/current_requirements.txt b/current_requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..496ede7e755377339764cc2851cf20e16bd345a8
--- /dev/null
+++ b/current_requirements.txt
@@ -0,0 +1,225 @@
+accelerate==0.24.1
+aiohttp==3.9.0
+aiosignal==1.3.1
+altair==5.1.2
+annotated-types==0.6.0
+anyio==3.7.1
+appnope==0.1.3
+argcomplete @ file:///private/tmp/python-argcomplete-20231119-5267-f5ylk/argcomplete-3.1.6
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asgiref==3.7.2
+asttokens==2.4.1
+async-lru==2.0.4
+attrs==23.1.0
+Babel==2.13.1
+backoff==2.2.1
+bcrypt==4.0.1
+beautifulsoup4==4.12.2
+bleach==6.1.0
+blinker==1.7.0
+cachetools==5.3.2
+certifi==2023.11.17
+cffi==1.15.1
+charset-normalizer==3.3.2
+chroma-hnswlib==0.7.3
+chromadb==0.4.18
+click==8.1.7
+coloredlogs==15.0.1
+comm==0.2.0
+dataclasses-json==0.5.14
+debugpy==1.8.0
+decorator==5.1.1
+defusedxml==0.7.1
+Deprecated==1.2.14
+distro==1.8.0
+docutils==0.19
+et-xmlfile==1.1.0
+executing==2.0.1
+fastapi==0.104.1
+fastjsonschema==2.19.0
+filelock==3.13.1
+flatbuffers==23.5.26
+fqdn==1.5.1
+frozenlist==1.4.0
+fsspec==2023.10.0
+fvalues==0.0.3
+gitdb==4.0.11
+GitPython==3.1.40
+google-auth==2.23.4
+googleapis-common-protos==1.61.0
+greenlet==3.0.1
+grpcio==1.59.3
+h11==0.14.0
+httpcore==1.0.2
+httptools==0.6.1
+httpx==0.25.1
+huggingface-hub==0.19.4
+humanfriendly==10.0
+idna==3.4
+importlib-metadata==6.8.0
+importlib-resources==6.1.1
+ipykernel==6.26.0
+ipython==8.17.2
+ipywidgets==8.1.1
+isoduration==20.11.0
+jedi==0.19.1
+Jinja2==3.1.2
+json5==0.9.14
+jsonpatch==1.33
+jsonpointer==2.4
+jsonschema==4.20.0
+jsonschema-specifications==2023.11.1
+jupyter-events==0.9.0
+jupyter-lsp==2.2.0
+jupyter_client==8.6.0
+jupyter_core==5.5.0
+jupyter_server==2.10.1
+jupyter_server_terminals==0.4.4
+jupyterlab==4.0.9
+jupyterlab-pygments==0.2.2
+jupyterlab-widgets==3.0.9
+jupyterlab_server==2.25.2
+kubernetes==28.1.0
+langchain==0.0.281
+langsmith==0.0.65
+linkify-it-py==2.0.2
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+marshmallow==3.20.1
+matplotlib-inline==0.1.6
+mdit-py-plugins==0.4.0
+mdurl==0.1.2
+mistune==3.0.2
+mmh3==4.0.1
+monotonic==1.6
+mpmath==1.3.0
+multidict==6.0.4
+mypy-extensions==1.0.0
+nbclient==0.9.0
+nbconvert==7.11.0
+nbformat==5.9.2
+nest-asyncio==1.5.8
+networkx==3.2.1
+notebook==7.0.6
+notebook_shim==0.2.3
+numexpr==2.8.7
+numpy==1.26.2
+oauthlib==3.2.2
+onnxruntime==1.16.2
+openai==0.28.1
+openpyxl==3.1.2
+opentelemetry-api==1.21.0
+opentelemetry-exporter-otlp-proto-common==1.21.0
+opentelemetry-exporter-otlp-proto-grpc==1.21.0
+opentelemetry-instrumentation==0.42b0
+opentelemetry-instrumentation-asgi==0.42b0
+opentelemetry-instrumentation-fastapi==0.42b0
+opentelemetry-proto==1.21.0
+opentelemetry-sdk==1.21.0
+opentelemetry-semantic-conventions==0.42b0
+opentelemetry-util-http==0.42b0
+outcome==1.3.0.post0
+overrides==7.4.0
+packaging==23.2
+pandas==2.1.3
+pandocfilters==1.5.0
+parso==0.8.3
+peft==0.6.2
+pep8==1.7.1
+pexpect==4.8.0
+Pillow==10.1.0
+platformdirs==4.0.0
+posthog==3.0.2
+prometheus-client==0.18.0
+prompt-toolkit==3.0.41
+protobuf==4.25.1
+psutil==5.9.6
+ptyprocess==0.7.0
+pulsar-client==3.3.0
+pure-eval==0.2.2
+pyarrow==14.0.1
+pyasn1==0.5.0
+pyasn1-modules==0.3.0
+pycparser==2.21
+pydantic==1.10.13
+pydantic_core==2.14.3
+pydeck==0.8.1b0
+Pygments==2.17.1
+PyPika==0.48.9
+PySocks==1.7.1
+python-dateutil==2.8.2
+python-dotenv==1.0.0
+python-json-logger==2.0.7
+pytz==2023.3.post1
+PyYAML==6.0
+pyzmq==25.1.1
+recoverpy==2.1.4
+referencing==0.31.0
+regex==2023.10.3
+requests==2.31.0
+requests-oauthlib==1.3.1
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rich==13.7.0
+rouge==1.0.1
+rpds-py==0.13.0
+rsa==4.9
+safetensors==0.4.0
+selenium==4.15.2
+Send2Trash==1.8.2
+simple-term-menu==1.6.3
+six==1.16.0
+smmap==5.0.1
+sniffio==1.3.0
+sortedcontainers==2.4.0
+soupsieve==2.5
+SQLAlchemy==1.4.50
+stack-data==0.6.3
+starlette==0.27.0
+stqdm==0.0.5
+streamlit==1.28.2
+sympy==1.12
+tenacity==8.2.3
+terminado==0.18.0
+textual==0.42.0
+tiktoken==0.5.1
+tinycss2==1.2.1
+tokenizers==0.15.0
+toml==0.10.2
+toolz==0.12.0
+torch==2.1.1
+torchaudio==2.1.1
+torchvision==0.16.1
+tornado==6.3.3
+tqdm==4.66.1
+traitlets==5.13.0
+transformers==4.35.2
+trio==0.23.1
+trio-websocket==0.11.1
+typer==0.9.0
+types-python-dateutil==2.8.19.14
+typing-inspect==0.9.0
+typing_extensions==4.8.0
+tzdata==2023.3
+tzlocal==5.2
+uc-micro-py==1.0.2
+uri-template==1.3.0
+urllib3==1.26.18
+uvicorn==0.24.0.post1
+uvloop==0.19.0
+validators==0.22.0
+watchdog==3.0.0
+watchfiles==0.21.0
+wcwidth==0.2.10
+webcolors==1.13
+webdriver-manager==4.0.1
+webencodings==0.5.1
+websocket-client==1.6.4
+websockets==12.0
+widgetsnbextension==4.0.9
+wrapt==1.16.0
+wsproto==1.2.0
+yarl==1.9.2
+zipp==3.17.0
diff --git a/embedding_mlp.pth b/embedding_mlp.pth
new file mode 100644
index 0000000000000000000000000000000000000000..6767ad33fae1776c789e615ec364019bbd2949e7
--- /dev/null
+++ b/embedding_mlp.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07f9d57532b6a1d2f75dbbc24bf1cb07c00dad72115d709f1d69dda8a58167d5
+size 792704
diff --git a/embedding_mlp.safetensors b/embedding_mlp.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..372b30fc800434b43f62c68d5b2e831b559b2daf
--- /dev/null
+++ b/embedding_mlp.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6904be304f3772eea874d1354add73c90aa79f575ff7cd50fdb70555a4c0e90a
+size 790836
diff --git a/gitattributes b/gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b
--- /dev/null
+++ b/gitattributes
@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..32b5297b30b07720d60ebc709ea8e2f6ef872f9d
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,18 @@
+streamlit==1.28.2
+transformers==4.35.2
+sentence-transformers==2.2.2
+datasets==2.15.0
+torch==2.1.1
+accelerate==0.24.1
+openai==0.28.1
+tiktoken==0.5.1
+chromadb==0.4.18
+langchain==0.0.281
+stqdm==0.0.5
+peft==0.6.2
+rouge==1.0.1
+watchdog==3.0.0
+huggingface_hub==0.19.4
+matplotlib==3.8.2
+seaborn==0.13.0
+setfit==1.0.1
\ No newline at end of file
diff --git a/test_models/EDA.ipynb b/test_models/EDA.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..fe362dcb78bf890334fc7b5b7bb2898303bac896
--- /dev/null
+++ b/test_models/EDA.ipynb
@@ -0,0 +1,106 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "7d34f1af-07e7-4320-9cc8-085bc1848b2f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "from statistics import mean\n",
+ "import os\n",
+ "dataset_dir = os.path.abspath(os.path.join(os.getcwd(), '..', '..', 'financial_dataset'))\n",
+ "sys.path.append(dataset_dir)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "99b69912-8c9e-49b5-abf1-229217ac5e5e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from load_test_data import get_labels_df, get_texts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "53b202a1-13c9-4ba0-9cba-bf6f207af9a1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "132 132\n",
+ "92249.56060606061\n"
+ ]
+ }
+ ],
+ "source": [
+ "labels_dir = dataset_dir + '/csvs/'\n",
+ "df = get_labels_df(labels_dir)\n",
+ "texts_dir = dataset_dir + '/txts/'\n",
+ "texts = get_texts(texts_dir)\n",
+ "print(len(df), len(texts))\n",
+ "print(mean(list(map(len, texts))))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "b1f8d856-8204-4a42-ab73-3af2ff7a728e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Label\n",
+ "SELL 53.8\n",
+ "HOLD 28.0\n",
+ "BUY 18.2\n",
+ "Name: proportion, dtype: float64"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.Label.value_counts(normalize=True).round(3)s * 100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "683fe2a9-6b9e-442b-a740-313482c96424",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/test_models/create_setfit_model.py b/test_models/create_setfit_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..07b78c446897d0d5f33719cf75a32005a306cc6a
--- /dev/null
+++ b/test_models/create_setfit_model.py
@@ -0,0 +1,99 @@
+import torch
+from torch import nn
+from sentence_transformers import SentenceTransformer
+from datasets import load_dataset
+from sklearn.utils.class_weight import compute_class_weight
+from safetensors.torch import load_model
+from setfit.__init__ import SetFitModel
+
+
+
+
+DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
+
+class MLP(nn.Module):
+ def __init__(self, input_size=768, output_size=3, dropout_rate=.2, class_weights=None):
+ super(MLP, self).__init__()
+ self.class_weights = class_weights
+
+ # self.bn1 = nn.BatchNorm1d(hidden_size)
+ self.dropout = nn.Dropout(dropout_rate)
+
+ self.linear = nn.Linear(input_size, output_size)
+
+ # nn.init.kaiming_normal_(self.fc1.weight, nonlinearity='relu')
+ # nn.init.kaiming_normal_(self.fc2.weight)
+
+ def forward(self, x):
+ # return self.linear(self.dropout(x))
+ return self.dropout(self.linear(x))
+
+ def predict(self, x):
+ _, predicted = torch.max(self.forward(x), 1)
+ return predicted
+
+ def predict_proba(self, x):
+ return self.forward(x)
+
+ def get_loss_fn(self):
+ return nn.CrossEntropyLoss(weight=self.class_weights, reduction='mean')
+
+dataset = load_dataset("CabraVC/vector_dataset_roberta-fine-tuned")
+
+class_weights = torch.tensor(compute_class_weight('balanced', classes=[0, 1, 2], y=dataset['train']['labels']), dtype=torch.float) ** .5
+
+model_head = MLP(class_weights=class_weights)
+
+if __name__ == '__main__' or __name__ == 'create_setfit_model':
+ model_body = SentenceTransformer('financial-roberta')
+ load_model(model_head, f'models/linear_head.pth')
+elif __name__ == 'test_models.create_setfit_model':
+ model_body = SentenceTransformer('test_models/financial-roberta')
+ load_model(model_head, f'/test_models/models/linear_head.pth')
+
+
+model = SetFitModel(model_body=model_body,
+ model_head=model_head,
+ labels=dataset['train'].features['labels'].names).to(DEVICE)
+
+
+if __name__ == '__main__':
+ from time import perf_counter
+ start = perf_counter()
+ test_sentences = [
+ """Two thousand and six was a very good year for The Coca-Cola Company. We achieved our 52nd
+consecutive year of unit case volume growth. Volume reached a record high of 2.4 billion unit cases.
+Net operating revenues grew 4 percent to $24.billion, and operating income grew
+4 percent to $6.3 billion. Our total return to shareowners was 23 percent, outperforming the Dow
+Jones Industrial Average and the S&P 500. By virtually every measure, we met or exceeded our
+objectivesâa strong ending for the year with great momentum for entering 2007.""",
+
+ """
+ The secret formula to our success in 2006? There is no one answer. Our inspiration comes from
+many sourcesâour bottling partners, retail customers and consumers, as well as our critics. And the
+men and women of The Coca-Cola Company have a passion for what they do that ignites this
+inspiration every day, everywhere we do business. We remain fresh, relevant and original by knowing
+what
+to change without changing what we know. We are asking more questions, listening more closely and
+collaborating more effectively with our bottling partners, suppliers and retail customers to give
+consumers what they want.
+ """,
+
+ """
+ And we continue to strengthen our bench, nurturing leaders and promoting from within our
+organization. As 2006 came to a close, our Board of Directors elected Muhtar Kent as president and
+chief operating officer of our Company. Muhtar is a 28-year veteran of the Coca-Cola system (the
+Company and our bottling partners). Muhtarâs close working relationships with our bottling partners
+will enable us to continue capturing marketplace opportunities and improving our business. Other
+system veterans promoted and now leading operating groups include Ahmet Bozer, Eurasia; Sandy
+Douglas, North America; and Glenn Jordan, Pacific. Combined, these leaders have 65 years of Coca-
+Cola system experience.
+ """
+ ]
+
+ # for sentence in test_sentences:
+ # print(model(sentence))
+ # print('-' * 50)
+ print(model(test_sentences))
+ print(f'It took me: {(perf_counter() - start) // 60:.0f} mins {(perf_counter() - start) % 60:.0f} secs')
\ No newline at end of file
diff --git a/test_models/financial-roberta/1_Pooling/config.json b/test_models/financial-roberta/1_Pooling/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..4e09f293dfe90bba49f87cfe7996271f07be2666
--- /dev/null
+++ b/test_models/financial-roberta/1_Pooling/config.json
@@ -0,0 +1,7 @@
+{
+ "word_embedding_dimension": 768,
+ "pooling_mode_cls_token": false,
+ "pooling_mode_mean_tokens": true,
+ "pooling_mode_max_tokens": false,
+ "pooling_mode_mean_sqrt_len_tokens": false
+}
\ No newline at end of file
diff --git a/test_models/financial-roberta/README.md b/test_models/financial-roberta/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..af078fb9a9dc199cc98cc79d1df1b85dd1995f51
--- /dev/null
+++ b/test_models/financial-roberta/README.md
@@ -0,0 +1,497 @@
+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+datasets:
+- CabraVC/vector_dataset_stratified_ttv_split_2023-12-05_21-07
+metrics:
+- accuracy
+widget:
+- text: "30, 2006, we adopted the provisions of SFAS No. 123(R), which establishes\
+ \ accounting for stock-based awards exchanged for employee services. Accordingly,\
+ \ stock-based compensation cost is measured at grant date, based on the fair value\
+ \ of the awards, and is recognized as expense over the requisite employee service\
+ \ period. Stock-based compensation expense recognized during fiscal years 2008\
+ \ and 2007 was $133.4 million and $116.7 million, respectively, which consisted\
+ \ of stock-based compensation expense related to stock options and our employee\
+ \ stock purchase plan. Please refer to Note 2 of the Notes to Consolidated Financial\
+ \ Statements for further information.\n\n \n\n We elected to adopt the modified\
+ \ prospective application method beginning January 30, 2006 as provided by SFAS\
+ \ No. 123(R). We recognize stock-based compensation expense using the straight-line\
+ \ attribution method. We estimate the value of employee stock options on the date\
+ \ of grant using a binomial model. Prior to the adoption of SFAS No. 123(R), we\
+ \ recorded stock-based compensation expense equal to the amount that would have\
+ \ been recognized if the fair value method was used, for the purpose of the pro\
+ \ forma financial information provided in accordance with Statement of Financial\
+ \ Accounting Standards No. 123, or SFAS No. 123, Accounting for Stock-Based Compensation,\
+ \ as amended by SFAS No. 148, Accounting for Stock-Based Compensation - Transition\
+ \ and Disclosures.\n\n \n\n At the beginning of fiscal year 2006, we transitioned\
+ \ from a Black-Scholes model to a binomial model for calculating the estimated\
+ \ fair value of new stock-based compensation awards granted under our stock option\
+ \ plans. The determination of fair value of share-based payment awards on the\
+ \ date of grant using an option-pricing model is affected by our stock price as\
+ \ well as assumptions regarding a number of highly complex and subjective variables.\
+ \ These variables include, but are not limited to, the expected stock price volatility\
+ \ over the term of the awards, actual and projected employee stock option exercise\
+ \ behaviors, vesting schedules, death and disability probabilities, expected volatility\
+ \ and risk-free interest. Our management determined that the use of implied volatility\
+ \ is expected to be more reflective of market conditions and, therefore, could\
+ \ reasonably be expected to be a better indicator of our expected volatility than\
+ \ historical volatility. The risk-free interest rate assumption is based upon\
+ \ observed interest rates appropriate for the term of our employee stock options.\
+ \ The dividend yield assumption is based on the history and expectation of dividend\
+ \ payouts. We began segregating options into groups for employees with relatively\
+ \ homogeneous exercise behavior in order to calculate the best estimate of fair\
+ \ value using the binomial valuation model.\n\nUsing the binomial model, the fair\
+ \ value of the stock options granted under our stock option plans have been estimated\
+ \ using the following assumptions during the year ended January 27, 2008:\n\n\
+ \ \n\n For our employee stock purchase plan we continue to use the Black-Scholes\
+ \ model. The fair value of the shares issued under the employee stock purchase\
+ \ plan has"
+- text: "local resources; help focus the bottler's sales and marketing programs; assist\
+ \ in the development of the bottler's business and information systems; and establish\
+ \ an appropriate capital structure for the bottler. \n\nOur Company has a long\
+ \ history of providing world-class customer service, demonstrating leadership\
+ \ in the marketplace and leveraging the talent of our global workforce. In addition,\
+ \ we have an experienced bottler management team. All of these factors are critical\
+ \ to build upon as we manage our growing bottling and distribution operations.\
+ \ \n\nThe Company has a deep commitment to continuously improving our business.\
+ \ This includes our efforts to develop innovative packaging and merchandising\
+ \ solutions which help drive demand for our beverages and meet the growing needs\
+ \ of our consumers. As we further transform the way we go to market, the Company\
+ \ continues to seek out ways to be more efficient. \n\nChallenges and Risks \n\
+ \nBeing global provides unique opportunities for our Company. Challenges and risks\
+ \ accompany those opportunities. Our management has identified certain challenges\
+ \ and risks that demand the attention of the nonalcoholic beverage segment of\
+ \ the commercial beverage industry and our Company. Of these, five key challenges\
+ \ and risks are discussed below. \n\nObesity and Inactive Lifestyles \n\nIncreasing\
+ \ concern among consumers, public health professionals and government agencies\
+ \ of the potential health problems associated with obesity and inactive lifestyles\
+ \ represents a significant challenge to our industry. We recognize that obesity\
+ \ is a complex public health problem and are committed to being a part of the\
+ \ solution. This commitment is reflected through our broad portfolio, with a beverage\
+ \ to suit every caloric and hydration need. \n\nAll of our beverages can be consumed\
+ \ as part of a balanced diet. Consumers who want to reduce the calories they consume\
+ \ from beverages can choose from our continuously expanding portfolio of more\
+ \ than 800 low- and no-calorie beverages, nearly 25 percent of our global portfolio,\
+ \ as well as our regular beverages in smaller portion sizes. We believe in the\
+ \ importance and power of âinformed choice,â and we continue to support the fact-based\
+ \ nutrition labeling and education initiatives that encourage people to live active,\
+ \ healthy lifestyles. Our commitment also includes creating and adhering to responsible\
+ \ policies in schools and in the marketplace; supporting programs to encourage\
+ \ physical activity and promote nutrition education; and continuously meeting\
+ \ changing consumer needs through beverage innovation, choice and variety. We\
+ \ recognize the health of our business is interwoven with the well-being of our\
+ \ consumers, our employees and the communities we serve, and we are working in\
+ \ cooperation with governments, educators and consumers. \n\nWater Quality and\
+ \ Quantity \n\nWater quality and quantity is an issue that increasingly requires\
+ \ our Company's attention and collaboration with other companies, suppliers, governments,\
+ \ nongovernmental organizations and communities where we operate. Water is the\
+ \ main ingredient in substantially all of our products and is needed to produce\
+ \ the agricultural ingredients on"
+- text: "over a fixed 17-year period and is calculated using an 8.85% interest rate.\
+ \ \n\n \n\nWhile the Pension Protection Act makes our funding obligations for\
+ \ these plans more predictable, factors outside our control continue to have an\
+ \ impact on the funding requirements. Estimates of future funding requirements\
+ \ are based on various assumptions and can vary materially from actual funding\
+ \ requirements. Assumptions include, among other things, the actual and projected\
+ \ market performance of assets; statutory requirements; and demographic data for\
+ \ participants. For additional information, see Note 10 of the Notes to the Consolidated\
+ \ Financial Statements. \n\n\n\nRecent Accounting Standards \n\n \n\nRevenue\
+ \ Arrangements with Multiple Deliverables. In October 2009, the Financial Accounting\
+ \ Standards Board (\"FASB\") issued ASU 200913. The standard (1) revises guidance\
+ \ on when individual deliverables may be treated as separate units of accounting,\
+ \ (2) establishes a selling price hierarchy for determining the selling price\
+ \ of a deliverable, (3) eliminates the residual method for revenue recognition\
+ \ and (4) provides guidance on allocating consideration among separate deliverables.\
+ \ It applies only to contracts entered into or materially modified after December\
+ \ 31, 2010. We adopted this standard on a prospective basis beginning January\
+ \ 1, 2011. We determined that the only revenue arrangements impacted by the adoption\
+ \ of this standard are those associated with our SkyMiles Program. \n\n \n\n\
+ Fair Value Measurement and Disclosure Requirements. In May 2011, the FASB issued\
+ \ \"Amendments to Achieve Common Fair Value Measurement and Disclosure Requirements\
+ \ in U.S. GAAP and IFRSs.\" The standard revises guidance for fair value measurement\
+ \ and expands the disclosure requirements. It is effective prospectively for fiscal\
+ \ years beginning after December 15, 2011. We are currently evaluating the impact\
+ \ the adoption of this standard will have on our Consolidated Financial Statements.\
+ \ \n\n \n\nSupplemental Information \n\n \n\nWe sometimes use information that\
+ \ is derived from the Consolidated Financial Statements, but that is not presented\
+ \ in accordance with accounting principles generally accepted in the U.S. (âGAAPâ).\
+ \ Certain of this information are considered to be ânon-GAAP financial measuresâ\
+ \ under the U.S. Securities and Exchange Commission rules. The non-GAAP financial\
+ \ measures should be considered in addition to results prepared in accordance\
+ \ with GAAP, but should not be considered a substitute for or superior to GAAP\
+ \ results. \n\n \n\nThe following tables show reconciliations of non-GAAP financial\
+ \ measures to the most directly comparable GAAP financial measures. \n\n \n\n\
+ We exclude the following items from CASM to determine CASM-Ex: \n\n \n\nâ˘\tAircraft\
+ \ fuel and related taxes. Management believes the volatility in fuel prices impacts\
+ \ the comparability of year-over-year financial performance. \n\n \n\nâ˘\tAncillary\
+ \ businesses . Ancillary businesses are not related to the generation of a seat\
+ \ mile. These businesses include aircraft maintenance and staffing services we\
+ \ provide to third parties and our vacation wholesale operations. \n\n \n\nâ˘\t\
+ Profit sharing. Management believes the exclusion of this item"
+- text: 'Organic local-currency sales increased 4.0 percent and acquisitions added
+ 1.4 percent. Acquisition growth was largely due to the October 2011 acquisition
+ of the do-it-yourself and professional business of GPI Group and the April 2010
+ acquisition of the A-One branded label business and related operations. A-One
+ is the largest branded label business in Asia and the second largest worldwide.
+ 3M also acquired Hybrivet Systems Inc. in the first quarter of 2011, a provider
+ of instant-read products to detect lead and other contaminants and toxins. Foreign
+ currency impacts contributed 2.4 percent to sales growth in the Consumer and Office
+ segment.
+
+
+ Â
+
+
+ On a geographic basis, sales increased in all regions, led by Asia Pacific, Latin
+ America/Canada and Europe, which all had sales growth rates in excess of 10 percent.
+ U.S. sales also grew, albeit at a slower rate.
+
+
+ Â
+
+
+ Consumer and Office operating income was flat when comparing 2011 to 2010, reflecting
+ continued ongoing investments in developing economies in brand development and
+ marketing and sales coverage. Even with these investments, Consumer and Office
+ generated operating income margins of 20.2 percent.
+
+
+
+
+ Safety, Security and Protection Services Business (12.7% of consolidated sales):
+
+
+ Â
+
+
+ The Safety, Security and Protection Services segment serves a broad range of markets
+ that increase the safety, security and productivity of workers, facilities and
+ systems. Major product offerings include personal protection products, cleaning
+ and protection products for commercial establishments, safety and security products
+ (including border and civil security solutions), roofing granules for asphalt
+ shingles, infrastructure protection products used in the oil and gas pipeline
+ markets, and track and trace solutions.
+
+
+ Â
+
+
+ Year 2012 results:
+
+
+ Â
+
+
+ Safety, Security and Protection Services sales totaled $3.8 billion, down 0.5
+ percent in U.S. dollars. Organic local-currency sales grew 2.2 percent and foreign
+ currency translation reduced sales by 2.7 percent. Organic local-currency sales
+ growth was led by infrastructure protection and personal safety, with growth also
+ in building and commercial services and roofing granules.
+
+
+ Â
+
+
+ 2012 organic local-currency sales declined 18 percent in security systems, as
+ government spending for security solutions has been declining over the last few
+ years. As discussed later in the âCritical Accounting Estimatesâ section, 3M will
+ continue to monitor this business to assess whether long-term expectations have
+ been significantly impacted such that an asset or goodwill impairment test would
+ be required. The Company completed its annual goodwill impairment test in the
+ fourth quarter of 2012, with no impairment indicated.
+
+
+ Â
+
+
+ Geographically, organic local-currency sales increased 19 percent in Latin America/Canada.
+ Organic local-currency sales were flat in Asia Pacific and the United States,
+ and declined 2 percent in EMEA.
+
+
+ Â
+
+
+ The combination of selling price increases and raw material cost reductions, plus
+ factory efficiencies, drove a 4.1 percent increase in operating income. Operating
+ income margins increased 1.0 percentage points to 22.3 percent.
+
+
+ Â
+
+
+ Year 2011 results:
+
+
+ Â
+
+
+ Safety,'
+- text: "but are generally subject to refinement during the purchase price allocation\
+ \ period (generally within one year of the acquisition date). To estimate restructuring\
+ \ expenses, management utilizes assumptions of the number of employees that would\
+ \ be involuntarily terminated and of future costs to operate and eventually vacate\
+ \ duplicate facilities. Estimated restructuring expenses may change as management\
+ \ executes the approved plan. Decreases to the cost estimates of executing the\
+ \ currently approved plans associated with pre-merger activities of the companies\
+ \ we acquire are recorded as an adjustment to goodwill indefinitely, whereas increases\
+ \ to the estimates are recorded as an adjustment to goodwill during the purchase\
+ \ price allocation period and as operating expenses thereafter.\n\n \n\nFor a\
+ \ given acquisition, we may identify certain pre-acquisition contingencies. If,\
+ \ during the purchase price allocation period, we are able to determine the fair\
+ \ value of a pre-acquisition contingency, we will include that amount in the purchase\
+ \ price allocation. If, as of the end of the purchase price allocation period,\
+ \ we are unable to determine the fair value of a pre-acquisition contingency,\
+ \ we will evaluate whether to include an amount in the purchase price allocation\
+ \ based on whether it is probable a liability had been incurred and whether an\
+ \ amount can be reasonably estimated. Through fiscal 2009, after the end of the\
+ \ purchase price allocation period, any adjustment to amounts recorded for a pre-acquisition\
+ \ contingency, with the exception of unresolved income tax matters, were included\
+ \ in our operating results in the period in which the adjustment was determined.\n\
+ \n\n\nFiscal 2010\n\n \n\nIn fiscal 2010, we will adopt FASB Statement No. 141\
+ \ (revised 2007), Business Combinations . For any business combination that is\
+ \ consummated pursuant to Statement 141(R), including our proposed acquisition\
+ \ of Sun described above, we will recognize separately from goodwill, the identifiable\
+ \ assets acquired, the liabilities assumed, and any noncontrolling interests in\
+ \ the acquiree generally at their acquisition date fair values as defined by FASB\
+ \ Statement No. 157, Fair Value Measurements . Goodwill as of the acquisition\
+ \ date is measured as the excess of consideration transferred, which is also generally\
+ \ measured at fair value, and the net of the acquisition date amounts of the identifiable\
+ \ assets acquired and the liabilities assumed.\n\n \n\nThe determination of fair\
+ \ value will require our management to make significant estimates and assumptions,\
+ \ with respect to intangible assets acquired, support obligations assumed, and\
+ \ pre-acquisition contingencies. The assumptions and estimates used in determining\
+ \ the fair values of these items will be substantially similar upon our adoption\
+ \ of Statement 141(R) as they were under Statement 141 (see above).\n\n \n\nThe\
+ \ below discussion lists those areas of Statement 141(R) that we believe, upon\
+ \ our adoption, require us to apply additional, significant estimates and assumptions.\n\
+ \n \n\nUpon our adoption of Statement 141(R), any changes to deferred tax asset\
+ \ valuation allowances and liabilities related to uncertain tax positions will\
+ \ be recorded in current"
+pipeline_tag: text-classification
+inference: true
+model-index:
+- name: SetFit
+ results:
+ - task:
+ type: text-classification
+ name: Text Classification
+ dataset:
+ name: CabraVC/vector_dataset_stratified_ttv_split_2023-12-05_21-07
+ type: CabraVC/vector_dataset_stratified_ttv_split_2023-12-05_21-07
+ split: test
+ metrics:
+ - type: accuracy
+ value: 0.5833333333333334
+ name: Accuracy
+---
+
+# SetFit
+
+This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [CabraVC/vector_dataset_stratified_ttv_split_2023-12-05_21-07](https://huggingface.co/datasets/CabraVC/vector_dataset_stratified_ttv_split_2023-12-05_21-07) dataset that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 3 classes
+- **Training Dataset:** [CabraVC/vector_dataset_stratified_ttv_split_2023-12-05_21-07](https://huggingface.co/datasets/CabraVC/vector_dataset_stratified_ttv_split_2023-12-05_21-07)
+
+
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label | Examples |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| BUY | - 'mix, the mix of sales by us and by other sellers, our continuing focus on in-stock inventory availability, our investment in new geographies and product lines, and the extent to which we choose to utilize outsource fulfillment providers. Accounts payable days were 62, 57, and 53 for 2008, 2007 and 2006. We expect some variability in accounts payable days over time since they are affected by several factors, including the mix of product sales, the mix of sales by other sellers, the mix of suppliers, seasonality, and changes in payment terms over time, including the effect of balancing pricing and timing of payment terms with suppliers. \n\nWe expect spending in technology and content will increase over time as we add computer scientists, software engineers, and employees involved in category expansion, editorial content, buying, merchandising selection, and systems support. We seek to efficiently invest in several areas of technology and content, including seller platforms, web services, digital initiatives, and expansion of new and existing product categories, as well as in technology infrastructure to enhance the customer experience, improve our process efficiencies and support our infrastructure web services. We believe that advances in technology, specifically the speed and reduced cost of processing power, the improved consumer experience of the Internet outside of the workplace through lower-cost broadband service to the home, and the advances of wireless connectivity, will continue to improve the consumer experience on the Internet and increase its ubiquity in peopleâs lives. We are investing in Amazon Web Services, which provides technology services that give developers access to technology infrastructure that they can use to enable virtually any type of business. A continuing challenge will be to continue to build and deploy innovative and efficient software that will best take advantage of continued advances in technology. \n\nOur financial reporting currency is the U.S. Dollar and changes in exchange rates significantly affect our reported results and consolidated trends. For example, if the U.S. Dollar weakens year-over-year relative to currencies in our international locations, our consolidated net sales, gross profit, and operating expenses will be higher than if currencies had remained constant. Likewise, if the U.S. Dollar strengthens year-over-year relative to currencies in our international locations, our consolidated net sales, gross profit, and operating expenses will be lower than if currencies had remained constant. We believe that our increasing diversification beyond the U.S. economy through our growing international businesses benefits our shareholders over the long term. We also believe it is important to evaluate our operating results and growth rates before and after the effect of currency changes. \n\nIn addition, the remeasurement of our 6.875% PEACS and intercompany balances can result in significant gains and charges associated with the effect of movements in currency exchange rates. Currency volatilities may continue, which may significantly impact (either positively or negatively) our reported results and'
- 'equal to the entire difference between the debt instrumentsâ amortized cost basis and its fair value. For available-for-sale debt instruments that are considered other-than-temporarily impaired due to the existence of a credit loss, if we do not intend to sell and it is more likely than not that we will not be required to sell the instrument before recovery of its remaining amortized cost basis (amortized cost basis less any current-period credit loss), we separate the amount of the impairment into the amount that is credit related and the amount due to all other factors. The credit loss component is recognized in earnings.\n\n\n\nWe performed an impairment review of our investment portfolio as of January 25, 2015. We concluded that our investments were appropriately valued and that no other than temporary impairment charges were necessary on our portfolio of available-for-sale investments as of January 25, 2015.\n\n\n\nStock-based Compensation\n\n\n\nOur stock-based compensation expense is associated with stock options, restricted stock units, or RSUs, performance stock units, or PSUs, and our employee stock purchase plan, or ESPP.\n\n\n\nDuring fiscal year 2015, we shifted away from granting stock options and toward granting RSUs and PSUs to reflect changing market trends for equity incentives at our peer companies. The number of PSUs that will ultimately vest is contingent on the Companyâs level of achievement compared with the corporate financial performance target established by our Compensation Committee in the beginning of each fiscal year. The number of shares of our stock to be received at vesting ranges from 0% to 200% of the target amount.\n\n\n\nWe use the closing trading price of our common stock on the date of grant, minus a dividend yield discount, as the fair value of awards of RSUs and PSUs. The compensation expense for RSUs is recognized using a straight-line attribution method over the requisite employee service period, while compensation expense for PSUs is recognized using an accelerated amortization model. We estimate the fair value of shares to be issued under our ESPP using the Black-Scholes model at the commencement of an offering period in March and September of each year. Stock-based compensation for our ESPP is expensed using an accelerated amortization model.\n\n\n\nOur RSU and PSU awards are not eligible for cash dividends prior to vesting; therefore, the fair value of RSUs and PSUs is discounted by the dividend yield. Additionally, we estimate forfeitures annually based on historical experience and revise the estimates of forfeiture in subsequent periods if actual forfeitures differ from those estimates. If factors change, the compensation expense that we record under these accounting standards may differ significantly from what we have recorded in the current period.\n\n\n\nLitigation, Investigation and Settlement Costs\n\n\n\nFrom time to time, we are involved in legal actions and/or investigations by regulatory bodies. We are aggressively defending our current litigation matters. However, there are many uncertainties associated with any litigation or investigations, and we cannot be certain that these actions or other third-party claims'
- 'local, and foreign tax authorities. Management believes that adequate provision has been made for any adjustments that may result from tax examinations. However, the outcome of tax audits cannot be predicted with certainty. If any issues addressed in the Companyâs tax audits are resolved in a manner not consistent with managementâs expectations, the Company could be required to adjust its provision for income taxes in the period such resolution occurs. \n\nAs of September 25, 2010, the Company had $51 billion in cash, cash equivalents and marketable securities, an increase of $17 billion from September 26, 2009. The principal component of this net increase was the cash generated by operating activities of $18.6 billion, which was partially offset by payments for acquisition of property, plant and equipment of $2 billion and payments made in connection with business acquisitions, net of cash acquired, of $638 million. \n\nThe Companyâs marketable securities investment portfolio is invested primarily in highly rated securities, generally with a minimum rating of single-A or equivalent. As of September 25, 2010 and September 26, 2009, $30.8 billion and $17.4 billion, respectively, of the Companyâs cash, cash equivalents and marketable securities were held by foreign subsidiaries and are generally based in U.S. dollar-denominated holdings. The Company believes its existing balances of cash, cash equivalents and marketable securities will be sufficient to satisfy its working capital needs, capital asset purchases, outstanding commitments and other liquidity requirements associated with its existing operations over the next 12 months. \n\nCapital Assets \n\nThe Companyâs capital expenditures were $2.6 billion during 2010, consisting of approximately $404 million for retail store facilities and $2.2 billion for other capital expenditures, including product tooling and manufacturing process equipment and corporate facilities and infrastructure. The Companyâs actual cash payments for capital expenditures during 2010 were $2 billion. \n\nThe Company anticipates utilizing approximately $4.0 billion for capital expenditures during 2011, including approximately $600 million for retail store facilities and approximately $3.4 billion for product tooling and manufacturing process equipment, and corporate facilities and infrastructure, including information systems hardware, software and enhancements. \n\nHistorically the Company has opened between 25 and 50 new retail stores per year. During 2011, the Company expects to open 40 to 50 new stores, over half of which are expected to be located outside of the U.S. \n\nOff-Balance Sheet Arrangements and Contractual Obligations \n\nThe Company has not entered into any transactions with unconsolidated entities whereby the Company has financial guarantees, subordinated retained interests, derivative instruments, or other contingent arrangements that expose the Company to material continuing risks, contingent liabilities, or any other obligation under a variable interest in an unconsolidated entity that provides financing, liquidity, market risk, or credit risk support to the Company. \n\nAs of September 25, 2010, the Company had'
|
+| SELL | - 'estimates and changes to these estimates will cause the fair values of our stock awards and related stock-based compensation expense that we record to vary.\n\nWe record deferred tax assets for stock-based compensation awards that result in deductions on our income tax returns, based on the amount of stock-based compensation recognized and the fair values attributable to the vested portion of stock awards assumed in connection with a business combination, at the statutory tax rate in the jurisdiction in which we will receive a tax deduction. Because the deferred tax assets we record are based upon the stock-based compensation expenses in a particular jurisdiction, the aforementioned inputs that affect the fair values of our stock awards may also indirectly affect our income tax expense. In addition, differences between the deferred tax assets recognized for financial reporting purposes and the actual tax deduction reported on our income tax returns are recorded in additional paid-in capital. If the tax deduction is less than the deferred tax asset, the calculated shortfall reduces our pool of excess tax benefits. If the pool of excess tax benefits is reduced to zero, then subsequent shortfalls would increase our income tax expense.\n\nTo the extent we change the terms of our employee stock-based compensation programs, experience market volatility in the pricing of our common stock that increases the implied volatility calculation of publicly traded options in our stock, refine different assumptions in future periods such as forfeiture rates that differ from our current estimates, or assume stock awards from acquired companies that are different in nature than our stock award arrangements, among other potential impacts, the stock-based compensation expense that we record in future periods and the tax benefits that we realize may differ significantly from what we have recorded in previous reporting periods.\n\nResults of Operations\n\nImpact of Acquisitions\n\nThe comparability of our operating results in fiscal 2014 compared to fiscal 2013 is impacted by our acquisitions, primarily our acquisitions of Responsys in the third quarter of fiscal 2014, Tekelec in the first quarter of fiscal 2014 and Acme Packet in the fourth quarter of fiscal 2013.\n\nThe comparability of our operating results in fiscal 2013 compared to fiscal 2012 is impacted by our acquisitions, primarily our acquisitions of Acme Packet in the fourth quarter of fiscal 2013, Taleo Corporation (Taleo) in the fourth quarter of fiscal 2012 and RightNow Technologies, Inc. (RightNow) during the third quarter of fiscal 2012.\n\nIn our discussion of changes in our results of operations from fiscal 2014 compared to fiscal 2013 and fiscal 2013 compared to fiscal 2012, we may qualitatively disclose the impacts of our acquired products (for the one year period subsequent to the acquisition date) to the growth in our new software licenses revenues, cloud SaaS and PaaS revenues, software license updates and product support revenues, hardware systems products revenues and hardware systems support revenues where such qualitative discussions would be meaningful for an understanding of the factors that'
- 'the share repurchase program for 2015.\n\nResults of Operations\n\nThe following section presents the results of operations and variances on an after-tax basis for the companyâs business segments â Upstream and Downstream â as well as for âAll Other.â Earnings are also presented for the U.S. and international geographic areas of the Upstream and Downstream business segments. Refer to Note 12, beginning on page FS-37, for a discussion of the companyâs âreportable segments.â This section should also be read in conjunction with the discussion in âBusiness Environment and Outlookâ on pages FS-2 through FS-5.\n\nU.S. upstream earnings of $3.3 billion in 2014 decreased $717 million from 2013, primarily due to lower crude oil prices of $950 million. Higher depreciation expenses of $440 million and higher operating expenses of $210 million also contributed to the decline. Partially offsetting the decrease were higher gains on asset sales of $700 million in the current period compared with $60 million in 2013, higher natural gas realizations of $150 million and higher crude oil production of $100 million.\n\nU.S. upstream earnings of $4.0 billion in 2013 decreased $1.3 billion from 2012, primarily due to higher operating, depreciation and exploration expenses of $420 million, $350 million, and $190 million, respectively, and lower crude oil production of $170 million. Higher natural gas realizations of approximately $200 million were mostly offset by lower crude oil realizations of $170 million.\n\nThe companyâs average realization for U.S. crude oil and natural gas liquids in 2014 was $84.13 per barrel, compared with $93.46 in 2013 and $95.21 in 2012. The average natural gas realization was $3.90 per thousand cubic feet in 2014, compared with $3.37 and $2.64 in 2013 and 2012, respectively.\n\nNet oil-equivalent production in 2014 averaged 664,000 barrels per day, up 1 percent from both 2013 and 2012. Between 2014 and 2013, production increases in the Permian Basin in Texas and New Mexico and the Marcellus Shale in western Pennsylvania were partially offset by normal field declines. Between 2013 and 2012, new production in the Marcellus Shale in western Pennsylvania and the Delaware Basin in New Mexico, along with the absence of weather-related downtime in the Gulf of Mexico, was largely offset by normal field declines.\n\nThe net liquids component of oil-equivalent production for 2014 averaged 456,000 barrels per day, up 2 percent from 2013 and largely unchanged from 2012. Net natural gas production averaged about 1.3 billion cubic feet per day in 2014, largely unchanged from 2013 and up 4 percent from 2012. Refer to the âSelected Operating Dataâ table on page FS-11 for a three-year comparative of production volumes in the United States.\n\nInternational Upstream\n\nInternational upstream earnings were $13.6 billion in 2014 compared with $16.8 billion in 2013. The decrease between periods was primarily due to lower crude oil prices and sales volumes of $2.0 billion and $400 million, respectively. Also contributing to the decrease were higher depreciation expenses of $1.0 billion, mainly related to impairments and other asset writeoffs, and higher operating and tax'
- 'hypothetical royalties generated from using our tradename) or (4)\xa0projected discounted future cash flows. We recognize an impairment charge if the assetâs carrying value exceeds its estimated fair value.\n\n\xa0\xa0\xa0\xa0\xa0Changes in assumptions or circumstances could result in an additional impairment in the period in which the change occurs and in future years. Factors which could cause impairment include, but are not limited to, (1)\xa0negative trends in our market capitalization, (2)\xa0volatile fuel prices, (3)\xa0declining passenger mile yields, (4)\xa0lower passenger demand as a result of the weakened U.S. and global economy, (5)\xa0interruption to our operations due to an employee strike, terrorist attack, or other reasons, (6)\xa0changes to the regulatory environment and (7)\xa0consolidation of competitors in the industry.\n\n\xa0\xa0\xa0\xa0\xa0 Long-Lived Assets . Our flight equipment and other long-lived assets have a recorded value of $20.4\xa0billion on our Consolidated Balance Sheet at December\xa031, 2009. This value is based on various factors, including the assetsâ estimated useful lives and their estimated salvage values. We record impairment losses on long-lived assets used in operations when events and circumstances indicate the assets may be impaired and the estimated future cash flows generated by those assets are less than their carrying amounts. If we decide to permanently remove flight equipment or other long-lived assets from operations, we will evaluate those assets for impairment. For long-lived assets held for sale, we record impairment losses when the carrying amount is greater than the fair value less the cost to sell. We discontinue depreciation of long-lived assets when these assets are classified as held for sale.\n\n\xa0\xa0\xa0\xa0\xa0To determine impairments for aircraft used in operations, we group assets at the fleet-type level (the lowest level for which there are identifiable cash flows) and then estimate future cash flows based on projections of capacity, passenger mile yield, fuel costs, labor costs and other relevant factors. If impairment occurs, the impairment loss recognized is the amount by which the aircraftâs carrying amount exceeds its estimated fair value. We estimate aircraft fair values using published sources, appraisals and bids received from third parties, as available. For additional information about our accounting policy for the impairment of long-lived assets, see Note 1 of the Notes to the Consolidated Financial Statements. \n\n\n\n\xa0\xa0\xa0\xa0\xa0 Income Tax Valuation Allowance and Contingencies . We periodically assess whether it is more likely than not that we will generate sufficient taxable income to realize our deferred income tax assets, and we establish valuation allowances if recovery is deemed not likely. In making this determination, we consider all available positive and negative evidence and make certain assumptions. We consider, among other things, our deferred tax liabilities, the overall business environment, our historical earnings and losses, our industryâs historically cyclical periods of earnings and losses and potential, current and future tax planning strategies. We cannot presently determine when we will be able to generate sufficient taxable'
|
+| HOLD | - 'both historical and projected future operating results, the reversal of existing taxable temporary differences, taxable income in prior carryback years (if permitted) and the availability of tax planning strategies. A valuation allowance is required to be established unless management determines that it is more likely than not that the Company will ultimately realize the tax benefit associated with a deferred tax asset. \n\nAdditionally, undistributed earnings of a subsidiary are accounted for as a temporary difference, except that deferred tax liabilities are not recorded for undistributed earnings of a foreign subsidiary that are deemed to be indefinitely reinvested in the foreign jurisdiction. The Company has formulated a specific plan for reinvestment of undistributed earnings of its foreign subsidiaries which demonstrates that such earnings will be indefinitely reinvested in the applicable tax jurisdictions. Should we change our plans, we would be required to record a significant amount of deferred tax liabilities. \n\nThe Company\'s effective tax rate is expected to be approximately 23.0 percent to 24.0 percent in 2009. This estimated tax rate does not reflect the impact of any unusual or special items that may affect our tax rate in 2009. \n\nContingencies \n\nOur Company is subject to various claims and contingencies, mostly related to legal proceedings and tax matters (both income taxes and indirect taxes). Due to their nature, such legal proceedings and tax matters involve inherent uncertainties including, but not limited to, court rulings, negotiations between affected parties and governmental actions. Management assesses the probability of loss for such contingencies and accrues a liability and/or discloses the relevant circumstances, as appropriate. Management believes that any liability to the Company that may arise as a result of currently pending legal proceedings, tax matters or other contingencies will not have a material adverse effect on the financial condition of the Company taken as a whole. Refer to Note 13 of Notes to Consolidated Financial Statements. \n\nRecent Accounting Standards and Pronouncements \n\nRefer to Note 1 of Notes to Consolidated Financial Statements for a discussion of recent accounting standards and pronouncements. \n\nOperations Review \n\nWe manufacture, distribute and market nonalcoholic beverage concentrates and syrups. We also manufacture, distribute and market finished beverages. Our organizational structure as of December 31, 2008, consisted of the following operating segments, the first six of which are sometimes referred to as "operating groups" or "groups": Eurasia and Africa; Europe; Latin America; North America; Pacific; Bottling Investments; and Corporate. We revised previously reported group information to conform to our operating structure in effect as of December 31, 2008. For further information regarding our operating segments, including a discussion of changes made to our operating segments effective July 1, 2008, refer to Note 21 of Notes to Consolidated Financial Statements. \n\nBeverage Volume \n\nWe measure our sales volume in two ways: (1) unit cases of finished products and (2) concentrate'
- "the last month of each quarter, we may not be able to reduce our inventory purchase commitments in a timely manner in response to customer cancellations or deferrals.\n\n\n\nCharges to cost of sales for inventory provisions totaled $50.1 million, $89.9 million and $53.0 million, unfavorably impacting our gross margin by 1.2%, 2.1% and 1.3%, in fiscal years 2014, 2013 and 2012, respectively. Sales of inventory that was previously written-off or written-down totaled $43.4 million, $53.7 million and $71.1 million, favorably impacting our gross margin by 1.1%, 1.3% and 1.8% in fiscal years 2014, 2013 and 2012, respectively. As a result, the overall net effect on our gross margin from charges to cost of sales for inventory provisions and sales of items previously written-off or written-down was a 0.1% unfavorable impact in fiscal year 2014, a 0.8% unfavorable impact in fiscal year 2013 and a 0.5% favorable impact in fiscal year 2012.\n\nDuring fiscal years 2014, 2013 and 2012, the charges we took to cost of sales for inventory provisions were primarily related to the write-off of excess quantities of certain older generations of GPU and Tegra Processor products whose inventory levels were higher than our updated forecasts of future demand for those products. As a fabless semiconductor company, we must make commitments to purchase inventory based on forecasts of future customer demand. In doing so, we must account for our third-party manufacturers' lead times and constraints. We also adjust to other market factors, such as product offerings and pricing actions by our competitors, new product transitions, and macroeconomic conditions - all of which may impact demand for our products.\n\n\n\nPlease refer to the Gross Profit and Gross Margin discussion below in this Management's Discussion and Analysis for further discussion.\n\n\n\nWarranty Liabilities\n\n\n\nCost of revenue includes the estimated cost of product warranties that are calculated at the point of revenue recognition. Under limited circumstances, we may offer an extended limited warranty to customers for certain products. Our products are complex and may contain defects or experience failures due to any number of issues in design, fabrication, packaging, materials and/or use within a system. If any of our products or technologies contains a defect, compatibility issue or other error, we may have to invest additional research and development efforts to find and correct the issue. In addition, an error or defect in new products or releases or related software drivers after commencement of commercial shipments could result in failure to achieve market acceptance or loss of design wins. Also, we may be required to reimburse customers, including our customersâ costs to repair or replace products in the field.\n\n \n\nIncome Taxes\n\nWe recognize federal, state and foreign current tax liabilities or assets based on our estimate of taxes payable or refundable in the current fiscal year by tax jurisdiction. We recognize federal, state and foreign deferred tax assets or liabilities, as appropriate, for our estimate of future tax effects attributable to temporary differences and carryforwards; and we record a"
- 'the euro. The Europe segment represented approximately 23% and 26% of the Companyâs total net sales for 2012 and 2011, respectively. \n\nNet sales in the Europe segment increased $9.1 billion or 49% during 2011 compared to 2010. The increase in net sales during 2011 was attributable primarily to the continued year-over-year increase in iPhone sales from carrier expansion and strong demand for iPhone 4, and increased sales of iPad and Mac, partially offset by a decrease in iPod sales. The Europe segment represented 26% and 29% of the Companyâs total net sales for 2011 and 2010, respectively. \n\nJapan \n\nNet sales in the Japan segment increased $5.1 billion or 94% during 2012 compared to 2011. The growth in net sales during 2012 was primarily driven by increased demand for iPhone following the launches of iPhone 4S and iPhone 5, expanded distribution with a new iPhone carrier, strong demand for the new iPad and iPad 2, higher sales from the iTunes Store, and strength in the Japanese Yen relative to the U.S. dollar. The Japan segment represented approximately 7% and 5% of the Companyâs total net sales for 2012 and 2011, respectively. \n\nNet sales in the Japan segment increased $1.5 billion or 37% during 2011 compared to 2010. The key contributors to Japanâs net sales growth were increased iPhone sales, strong sales of iPad, increased sales of Mac, and strength in the Japanese Yen relative to the U.S. dollar. The Japan segment represented 5% and 6% of the Companyâs total net sales for 2011 and 2010, respectively. \n\nAsia-Pacific \n\nNet sales in the Asia-Pacific segment increased $10.7 billion or 47% during 2012 compared to 2011. The growth in net sales during 2012 was mainly due to increased demand for iPhone from the launch of iPhone 4S, strong demand for the new iPad and iPad 2, and higher Mac sales. Growth in the Asia Pacific segment was affected by the timing of iPhone and iPad product launches. iPhone 5 was launched in a limited number of countries in the Asia Pacific segment during the fourth quarter of 2012 and was not launched in China during 2012, and the new iPad that was introduced by the Company in March 2012 was not launched in China until the fourth quarter of 2012. The Asia-Pacific segment represented approximately 21% of the Companyâs total net sales in both 2012 and 2011. \n\nNet sales in the Asia Pacific segment increased $14.3 billion or 174% during 2011 compared to 2010. The Company experienced particularly strong year-over-year net sales growth in its Asia Pacific segment during 2011, especially in Greater China, which includes Hong Kong and Taiwan. Korea and Australia also experienced strong year-over-year revenue growth. Higher net sales in the Asia Pacific segment were due mainly to the increase in iPhone sales primarily attributable to the strong demand for iPhone 4 and carrier expansion, strong sales of iPad, and increased Mac sales. The Asia Pacific segment represented 21% and 13% of the Companyâs total net sales in 2011 and 2010, respectively. \n\nRetail \n\nNet sales in the Retail segment increased $4.7 billion or 33% during 2012 compared to 2011. The growth in net sales during 2012 was driven primarily by increased demand for'
|
+
+## Evaluation
+
+### Metrics
+| Label | Accuracy |
+|:--------|:---------|
+| **all** | 0.5833 |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the đ¤ Hub
+model = SetFitModel.from_pretrained("setfit_model_id")
+# Run inference
+preds = model("Organic local-currency sales increased 4.0 percent and acquisitions added 1.4 percent. Acquisition growth was largely due to the October 2011 acquisition of the do-it-yourself and professional business of GPI Group and the April 2010 acquisition of the A-One branded label business and related operations. A-One is the largest branded label business in Asia and the second largest worldwide. 3M also acquired Hybrivet Systems Inc. in the first quarter of 2011, a provider of instant-read products to detect lead and other contaminants and toxins. Foreign currency impacts contributed 2.4 percent to sales growth in the Consumer and Office segment.
+
+Â
+
+On a geographic basis, sales increased in all regions, led by Asia Pacific, Latin America/Canada and Europe, which all had sales growth rates in excess of 10 percent. U.S. sales also grew, albeit at a slower rate.
+
+Â
+
+Consumer and Office operating income was flat when comparing 2011 to 2010, reflecting continued ongoing investments in developing economies in brand development and marketing and sales coverage. Even with these investments, Consumer and Office generated operating income margins of 20.2 percent.
+
+
+
+Safety, Security and Protection Services Business (12.7% of consolidated sales):
+
+Â
+
+The Safety, Security and Protection Services segment serves a broad range of markets that increase the safety, security and productivity of workers, facilities and systems. Major product offerings include personal protection products, cleaning and protection products for commercial establishments, safety and security products (including border and civil security solutions), roofing granules for asphalt shingles, infrastructure protection products used in the oil and gas pipeline markets, and track and trace solutions.
+
+Â
+
+Year 2012 results:
+
+Â
+
+Safety, Security and Protection Services sales totaled $3.8 billion, down 0.5 percent in U.S. dollars. Organic local-currency sales grew 2.2 percent and foreign currency translation reduced sales by 2.7 percent. Organic local-currency sales growth was led by infrastructure protection and personal safety, with growth also in building and commercial services and roofing granules.
+
+Â
+
+2012 organic local-currency sales declined 18 percent in security systems, as government spending for security solutions has been declining over the last few years. As discussed later in the âCritical Accounting Estimatesâ section, 3M will continue to monitor this business to assess whether long-term expectations have been significantly impacted such that an asset or goodwill impairment test would be required. The Company completed its annual goodwill impairment test in the fourth quarter of 2012, with no impairment indicated.
+
+Â
+
+Geographically, organic local-currency sales increased 19 percent in Latin America/Canada. Organic local-currency sales were flat in Asia Pacific and the United States, and declined 2 percent in EMEA.
+
+Â
+
+The combination of selling price increases and raw material cost reductions, plus factory efficiencies, drove a 4.1 percent increase in operating income. Operating income margins increased 1.0 percentage points to 22.3 percent.
+
+Â
+
+Year 2011 results:
+
+Â
+
+Safety,")
+```
+
+
+
+
+
+
+
+
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median | Max |
+|:-------------|:----|:---------|:----|
+| Word count | 431 | 475.4792 | 532 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| BUY | 6 |
+| HOLD | 12 |
+| SELL | 30 |
+
+### Training Hyperparameters
+- batch_size: (6, 8)
+- num_epochs: (0, 32)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (0.0, 0.0)
+- head_learning_rate: 0.0002
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.08
+- max_length: 512
+- seed: 1003200212
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Framework Versions
+- Python: 3.11.6
+- SetFit: 1.0.1
+- Sentence Transformers: 2.2.2
+- Transformers: 4.35.2
+- PyTorch: 2.1.1
+- Datasets: 2.15.0
+- Tokenizers: 0.15.0
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+ doi = {10.48550/ARXIV.2209.11055},
+ url = {https://arxiv.org/abs/2209.11055},
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+ title = {Efficient Few-Shot Learning Without Prompts},
+ publisher = {arXiv},
+ year = {2022},
+ copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+
+
+
+
+
\ No newline at end of file
diff --git a/test_models/financial-roberta/config.json b/test_models/financial-roberta/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a9168e16a3f72f92592fb9e929631ed026b50bbb
--- /dev/null
+++ b/test_models/financial-roberta/config.json
@@ -0,0 +1,28 @@
+{
+ "_name_or_path": "/root/.cache/torch/sentence_transformers/CabraVC_emb_classifier_model/",
+ "architectures": [
+ "RobertaModel"
+ ],
+ "attention_probs_dropout_prob": 0.1,
+ "bos_token_id": 0,
+ "classifier_dropout": null,
+ "eos_token_id": 2,
+ "gradient_checkpointing": false,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 768,
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "layer_norm_eps": 1e-05,
+ "max_position_embeddings": 514,
+ "model_type": "roberta",
+ "num_attention_heads": 12,
+ "num_hidden_layers": 6,
+ "pad_token_id": 1,
+ "position_embedding_type": "absolute",
+ "torch_dtype": "float32",
+ "transformers_version": "4.36.1",
+ "type_vocab_size": 1,
+ "use_cache": true,
+ "vocab_size": 50265
+}
diff --git a/test_models/financial-roberta/config_sentence_transformers.json b/test_models/financial-roberta/config_sentence_transformers.json
new file mode 100644
index 0000000000000000000000000000000000000000..fd1b291129c607e5d49799f87cb219b27f98acdf
--- /dev/null
+++ b/test_models/financial-roberta/config_sentence_transformers.json
@@ -0,0 +1,7 @@
+{
+ "__version__": {
+ "sentence_transformers": "2.0.0",
+ "transformers": "4.6.1",
+ "pytorch": "1.8.1"
+ }
+}
\ No newline at end of file
diff --git a/test_models/financial-roberta/merges.txt b/test_models/financial-roberta/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..226b0752cac7789c48f0cb3ec53eda48b7be36cc
--- /dev/null
+++ b/test_models/financial-roberta/merges.txt
@@ -0,0 +1,50001 @@
+#version: 0.2
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+{%- endif %}
+{% if head_class -%}
+ - **Classification head:** a {{ head_class }} instance
+{%- else -%}
+
+{%- endif %}
+{%- if spacy_model %}
+- **spaCy Model:** {{ spacy_model }}
+{%- endif %}
+{%- if aspect_model %}
+- **SetFitABSA Aspect Model:** [{{ aspect_model }}](https://huggingface.co/{{ aspect_model }})
+{%- endif %}
+{%- if polarity_model %}
+- **SetFitABSA Polarity Model:** [{{ polarity_model }}](https://huggingface.co/{{ polarity_model }})
+{%- endif %}
+- **Maximum Sequence Length:** {{ model_max_length }} tokens
+{% if num_classes -%}
+ - **Number of Classes:** {{ num_classes }} classes
+{%- else -%}
+
+{%- endif %}
+{% if dataset_id -%}
+ - **Training Dataset:** [{{ dataset_name if dataset_name else dataset_id }}](https://huggingface.co/datasets/{{ dataset_id }})
+{%- else -%}
+
+{%- endif %}
+{% if language -%}
+ - **Language{{"s" if language is not string and language | length > 1 else ""}}:**
+ {%- if language is string %} {{ language }}
+ {%- else %} {% for lang in language -%}
+ {{ lang }}{{ ", " if not loop.last else "" }}
+ {%- endfor %}
+ {%- endif %}
+{%- else -%}
+
+{%- endif %}
+{% if license -%}
+ - **License:** {{ license }}
+{%- else -%}
+
+{%- endif %}
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+{% if label_examples %}
+### Model Labels
+{{ label_examples }}{% endif -%}
+{% if metrics_table %}
+## Evaluation
+
+### Metrics
+{{ metrics_table }}{% endif %}
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+{% if is_absa %}
+```python
+from setfit import AbsaModel
+
+# Download from the {{ hf_emoji }} Hub
+model = AbsaModel.from_pretrained(
+ "{{ aspect_model }}",
+ "{{ polarity_model }}",
+)
+# Run inference
+preds = model("The food was great, but the venue is just way too busy.")
+```
+{%- else %}
+```python
+from setfit import SetFitModel
+
+# Download from the {{ hf_emoji }} Hub
+model = SetFitModel.from_pretrained("{{ model_id | default('setfit_model_id', true) }}")
+# Run inference
+preds = model("{{ predict_example | default("I loved the spiderman movie!", true) | replace('"', '\\"') }}")
+```
+{%- endif %}
+
+
+
+
+
+
+
+
+
+## Training Details
+{% if train_set_metrics %}
+### Training Set Metrics
+{{ train_set_metrics }}{% if train_set_sentences_per_label_list %}
+{{ train_set_sentences_per_label_list }}{% endif %}{% endif %}{% if hyperparameters %}
+### Training Hyperparameters
+{% for name, value in hyperparameters.items() %}- {{ name }}: {{ value }}
+{% endfor %}{% endif %}{% if eval_lines %}
+### Training Results
+{{ eval_lines }}{% if explain_bold_in_eval %}
+* The bold row denotes the saved checkpoint.{% endif %}{% endif %}{% if co2_eq_emissions %}
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Carbon Emitted**: {{ "%.3f"|format(co2_eq_emissions["emissions"] / 1000) }} kg of CO2
+- **Hours Used**: {{ co2_eq_emissions["hours_used"] }} hours
+
+### Training Hardware
+- **On Cloud**: {{ "Yes" if co2_eq_emissions["on_cloud"] else "No" }}
+- **GPU Model**: {{ co2_eq_emissions["hardware_used"] or "No GPU used" }}
+- **CPU Model**: {{ co2_eq_emissions["cpu_model"] }}
+- **RAM Size**: {{ "%.2f"|format(co2_eq_emissions["ram_total_size"]) }} GB
+{% endif %}
+### Framework Versions
+- Python: {{ version["python"] }}
+- SetFit: {{ version["setfit"] }}
+- Sentence Transformers: {{ version["sentence_transformers"] }}
+{%- if "spacy" in version %}
+- spaCy: {{ version["spacy"] }}
+{%- endif %}
+- Transformers: {{ version["transformers"] }}
+- PyTorch: {{ version["torch"] }}
+- Datasets: {{ version["datasets"] }}
+- Tokenizers: {{ version["tokenizers"] }}
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+ doi = {10.48550/ARXIV.2209.11055},
+ url = {https://arxiv.org/abs/2209.11055},
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+ title = {Efficient Few-Shot Learning Without Prompts},
+ publisher = {arXiv},
+ year = {2022},
+ copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+
+
+
+
+
\ No newline at end of file
diff --git a/test_models/setfit/modeling.py b/test_models/setfit/modeling.py
new file mode 100644
index 0000000000000000000000000000000000000000..29b39909b9c03f96d9e74a96b537a00e44ecb2c1
--- /dev/null
+++ b/test_models/setfit/modeling.py
@@ -0,0 +1,908 @@
+import json
+import os
+import tempfile
+import warnings
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Dict, List, Optional, Set, Tuple, Union
+
+
+# For Python 3.7 compatibility
+try:
+ from typing import Literal
+except ImportError:
+ from typing_extensions import Literal
+
+import joblib
+import numpy as np
+import requests
+import torch
+from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
+from huggingface_hub.utils import validate_hf_hub_args
+from sentence_transformers import SentenceTransformer, models
+from sklearn.linear_model import LogisticRegression
+from sklearn.multiclass import OneVsRestClassifier
+from sklearn.multioutput import ClassifierChain, MultiOutputClassifier
+from torch import nn
+from torch.utils.data import DataLoader
+from tqdm.auto import tqdm, trange
+from transformers.utils import copy_func
+
+from . import logging
+from .data import SetFitDataset
+from .model_card import SetFitModelCardData, generate_model_card
+from .utils import set_docstring
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+MODEL_HEAD_NAME = "model_head.pkl"
+CONFIG_NAME = "config_setfit.json"
+
+
+class SetFitHead(models.Dense):
+ """
+ A SetFit head that supports multi-class classification for end-to-end training.
+ Binary classification is treated as 2-class classification.
+
+ To be compatible with Sentence Transformers, we inherit `Dense` from:
+ https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/models/Dense.py
+
+ Args:
+ in_features (`int`, *optional*):
+ The embedding dimension from the output of the SetFit body. If `None`, defaults to `LazyLinear`.
+ out_features (`int`, defaults to `2`):
+ The number of targets. If set `out_features` to 1 for binary classification, it will be changed to 2 as 2-class classification.
+ temperature (`float`, defaults to `1.0`):
+ A logits' scaling factor. Higher values make the model less confident and lower values make
+ it more confident.
+ eps (`float`, defaults to `1e-5`):
+ A value for numerical stability when scaling logits.
+ bias (`bool`, *optional*, defaults to `True`):
+ Whether to add bias to the head.
+ device (`torch.device`, str, *optional*):
+ The device the model will be sent to. If `None`, will check whether GPU is available.
+ multitarget (`bool`, defaults to `False`):
+ Enable multi-target classification by making `out_features` binary predictions instead
+ of a single multinomial prediction.
+ """
+
+ def __init__(
+ self,
+ in_features: Optional[int] = None,
+ out_features: int = 2,
+ temperature: float = 1.0,
+ eps: float = 1e-5,
+ bias: bool = True,
+ device: Optional[Union[torch.device, str]] = None,
+ multitarget: bool = False,
+ ) -> None:
+ super(models.Dense, self).__init__() # init on models.Dense's parent: nn.Module
+
+ if out_features == 1:
+ logger.warning(
+ "Change `out_features` from 1 to 2 since we use `CrossEntropyLoss` for binary classification."
+ )
+ out_features = 2
+
+ if in_features is not None:
+ self.linear = nn.Linear(in_features, out_features, bias=bias)
+ else:
+ self.linear = nn.LazyLinear(out_features, bias=bias)
+
+ self.in_features = in_features
+ self.out_features = out_features
+ self.temperature = temperature
+ self.eps = eps
+ self.bias = bias
+ self._device = device or "cuda" if torch.cuda.is_available() else "cpu"
+ self.multitarget = multitarget
+
+ self.to(self._device)
+ self.apply(self._init_weight)
+
+ def forward(
+ self,
+ features: Union[Dict[str, torch.Tensor], torch.Tensor],
+ temperature: Optional[float] = None,
+ ) -> Union[Dict[str, torch.Tensor], Tuple[torch.Tensor]]:
+ """
+ SetFitHead can accept embeddings in:
+ 1. Output format (`dict`) from Sentence-Transformers.
+ 2. Pure `torch.Tensor`.
+
+ Args:
+ features (`Dict[str, torch.Tensor]` or `torch.Tensor):
+ The embeddings from the encoder. If using `dict` format,
+ make sure to store embeddings under the key: 'sentence_embedding'
+ and the outputs will be under the key: 'prediction'.
+ temperature (`float`, *optional*):
+ A logits' scaling factor. Higher values make the model less
+ confident and lower values make it more confident.
+ Will override the temperature given during initialization.
+ Returns:
+ [`Dict[str, torch.Tensor]` or `Tuple[torch.Tensor]`]
+ """
+ temperature = temperature or self.temperature
+ is_features_dict = False # whether `features` is dict or not
+ if isinstance(features, dict):
+ assert "sentence_embedding" in features
+ is_features_dict = True
+ x = features["sentence_embedding"] if is_features_dict else features
+ logits = self.linear(x)
+ logits = logits / (temperature + self.eps)
+ if self.multitarget: # multiple targets per item
+ probs = torch.sigmoid(logits)
+ else: # one target per item
+ probs = nn.functional.softmax(logits, dim=-1)
+ if is_features_dict:
+ features.update(
+ {
+ "logits": logits,
+ "probs": probs,
+ }
+ )
+ return features
+
+ return logits, probs
+
+ def predict_proba(self, x_test: torch.Tensor) -> torch.Tensor:
+ self.eval()
+
+ return self(x_test)[1]
+
+ def predict(self, x_test: torch.Tensor) -> torch.Tensor:
+ probs = self.predict_proba(x_test)
+
+ if self.multitarget:
+ return torch.where(probs >= 0.5, 1, 0)
+ return torch.argmax(probs, dim=-1)
+
+ def get_loss_fn(self) -> nn.Module:
+ if self.multitarget: # if sigmoid output
+ return torch.nn.BCEWithLogitsLoss()
+ return torch.nn.CrossEntropyLoss()
+
+ @property
+ def device(self) -> torch.device:
+ """
+ `torch.device`: The device on which the model is placed.
+
+ Reference from: https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/SentenceTransformer.py#L869
+ """
+ return next(self.parameters()).device
+
+ def get_config_dict(self) -> Dict[str, Optional[Union[int, float, bool]]]:
+ return {
+ "in_features": self.in_features,
+ "out_features": self.out_features,
+ "temperature": self.temperature,
+ "bias": self.bias,
+ "device": self.device.type, # store the string of the device, instead of `torch.device`
+ }
+
+ @staticmethod
+ def _init_weight(module) -> None:
+ if isinstance(module, nn.Linear):
+ nn.init.xavier_uniform_(module.weight)
+ if module.bias is not None:
+ nn.init.constant_(module.bias, 1e-2)
+
+ def __repr__(self) -> str:
+ return "SetFitHead({})".format(self.get_config_dict())
+
+
+@dataclass
+class SetFitModel(PyTorchModelHubMixin):
+ """A SetFit model with integration to the [Hugging Face Hub](https://huggingface.co).
+
+ Example::
+
+ >>> from setfit import SetFitModel
+ >>> model = SetFitModel.from_pretrained("tomaarsen/setfit-bge-small-v1.5-sst2-8-shot")
+ >>> model.predict([
+ ... "It's a charming and often affecting journey.",
+ ... "It's slow -- very, very slow.",
+ ... "A sometimes tedious film.",
+ ... ])
+ ['positive', 'negative', 'negative']
+ """
+
+ model_body: Optional[SentenceTransformer] = None
+ model_head: Optional[Union[SetFitHead, LogisticRegression]] = None
+ multi_target_strategy: Optional[str] = None
+ normalize_embeddings: bool = False
+ labels: Optional[List[str]] = None
+ model_card_data: Optional[SetFitModelCardData] = field(default_factory=SetFitModelCardData)
+
+ attributes_to_save: Set[str] = field(
+ init=False, repr=False, default_factory=lambda: {"normalize_embeddings", "labels"}
+ )
+
+ def __post_init__(self):
+ self.model_card_data.register_model(self)
+
+ @property
+ def has_differentiable_head(self) -> bool:
+ # if False, sklearn is assumed to be used instead
+ return isinstance(self.model_head, nn.Module)
+
+ @property
+ def id2label(self) -> Dict[int, str]:
+ """Return a mapping from integer IDs to string labels."""
+ if self.labels is None:
+ return {}
+ return dict(enumerate(self.labels))
+
+ @property
+ def label2id(self) -> Dict[str, int]:
+ """Return a mapping from string labels to integer IDs."""
+ if self.labels is None:
+ return {}
+ return {label: idx for idx, label in enumerate(self.labels)}
+
+ def fit(
+ self,
+ x_train: List[str],
+ y_train: Union[List[int], List[List[int]]],
+ num_epochs: int,
+ batch_size: Optional[int] = None,
+ body_learning_rate: Optional[float] = None,
+ head_learning_rate: Optional[float] = None,
+ end_to_end: bool = False,
+ l2_weight: Optional[float] = None,
+ max_length: Optional[int] = None,
+ show_progress_bar: bool = True,
+ ) -> None:
+ """Train the classifier head, only used if a differentiable PyTorch head is used.
+
+ Args:
+ x_train (`List[str]`): A list of training sentences.
+ y_train (`Union[List[int], List[List[int]]]`): A list of labels corresponding to the training sentences.
+ num_epochs (`int`): The number of epochs to train for.
+ batch_size (`int`, *optional*): The batch size to use.
+ body_learning_rate (`float`, *optional*): The learning rate for the `SentenceTransformer` body
+ in the `AdamW` optimizer. Disregarded if `end_to_end=False`.
+ head_learning_rate (`float`, *optional*): The learning rate for the differentiable torch head
+ in the `AdamW` optimizer.
+ end_to_end (`bool`, defaults to `False`): If True, train the entire model end-to-end.
+ Otherwise, freeze the `SentenceTransformer` body and only train the head.
+ l2_weight (`float`, *optional*): The l2 weight for both the model body and head
+ in the `AdamW` optimizer.
+ max_length (`int`, *optional*): The maximum token length a tokenizer can generate. If not provided,
+ the maximum length for the `SentenceTransformer` body is used.
+ show_progress_bar (`bool`, defaults to `True`): Whether to display a progress bar for the training
+ epochs and iterations.
+ """
+ if self.has_differentiable_head: # train with pyTorch
+ self.model_body.train()
+ self.model_head.train()
+ if not end_to_end:
+ self.freeze("body")
+
+ dataloader = self._prepare_dataloader(x_train, y_train, batch_size, max_length)
+ criterion = self.model_head.get_loss_fn()
+ optimizer = self._prepare_optimizer(head_learning_rate, body_learning_rate, l2_weight)
+ #
+ #
+ #
+ #
+ scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.2)
+ # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=.25, patience=10, threshold=5 * 1e-5, min_lr=1e-7, verbose=True)
+ #
+ #
+ #
+ #
+ # Need to replace with ReduceOnPlateauLR()
+ #
+ #
+ #
+ #
+ for epoch_idx in trange(num_epochs, desc="Epoch", disable=not show_progress_bar):
+ total_loss = 0.
+ for batch in tqdm(dataloader, desc="Iteration", disable=not show_progress_bar, leave=False):
+ features, labels = batch
+ optimizer.zero_grad()
+
+ # to model's device
+ features = {k: v.to(self.device) for k, v in features.items()}
+ labels = labels.to(self.device)
+
+ outputs = self.model_body(features)
+ if self.normalize_embeddings:
+ outputs["sentence_embedding"] = nn.functional.normalize(
+ outputs["sentence_embedding"], p=2, dim=1
+ )
+ outputs = self.model_head(outputs)
+ logits = outputs["logits"]
+
+ loss: torch.Tensor = criterion(logits, labels)
+ total_loss += loss.item()
+ loss.backward()
+ optimizer.step()
+ if epoch_idx % 5 == 0:
+ print()
+ print(epoch_idx + 1, total_loss / len(dataloader))
+ print()
+
+ scheduler.step()
+
+ if not end_to_end:
+ self.unfreeze("body")
+ else: # train with sklearn
+ print()
+ print('I am using LogisticRegression!')
+ print()
+ embeddings = self.model_body.encode(x_train, normalize_embeddings=self.normalize_embeddings)
+ self.model_head.fit(embeddings, y_train)
+
+ def _prepare_dataloader(
+ self,
+ x_train: List[str],
+ y_train: Union[List[int], List[List[int]]],
+ batch_size: Optional[int] = None,
+ max_length: Optional[int] = None,
+ shuffle: bool = True,
+ ) -> DataLoader:
+ max_acceptable_length = self.model_body.get_max_seq_length()
+ if max_length is None:
+ max_length = max_acceptable_length
+ logger.warning(
+ f"The `max_length` is `None`. Using the maximum acceptable length according to the current model body: {max_length}."
+ )
+
+ if max_length > max_acceptable_length:
+ logger.warning(
+ (
+ f"The specified `max_length`: {max_length} is greater than the maximum length of the current model body: {max_acceptable_length}. "
+ f"Using {max_acceptable_length} instead."
+ )
+ )
+ max_length = max_acceptable_length
+
+ dataset = SetFitDataset(
+ x_train,
+ y_train,
+ tokenizer=self.model_body.tokenizer,
+ max_length=max_length,
+ )
+ dataloader = DataLoader(
+ dataset,
+ batch_size=batch_size,
+ collate_fn=dataset.collate_fn,
+ shuffle=shuffle,
+ pin_memory=True,
+ #
+ #
+ #
+ #
+ #
+ drop_last=True
+ #
+ #
+ #
+ #
+ #
+ )
+
+ return dataloader
+
+ def _prepare_optimizer(
+ self,
+ head_learning_rate: float,
+ body_learning_rate: Optional[float],
+ l2_weight: float,
+ ) -> torch.optim.Optimizer:
+ body_learning_rate = body_learning_rate or head_learning_rate
+ l2_weight = l2_weight or 1e-2
+ optimizer = torch.optim.Adam(
+ [
+ {
+ "params": self.model_body.parameters(),
+ "lr": body_learning_rate,
+ "weight_decay": l2_weight,
+ },
+ {"params": self.model_head.parameters(), "lr": head_learning_rate, "weight_decay": l2_weight},
+ ],
+ )
+
+ return optimizer
+
+ def freeze(self, component: Optional[Literal["body", "head"]] = None) -> None:
+ """Freeze the model body and/or the head, preventing further training on that component until unfrozen.
+
+ Args:
+ component (`Literal["body", "head"]`, *optional*): Either "body" or "head" to freeze that component.
+ If no component is provided, freeze both. Defaults to None.
+ """
+ if component is None or component == "body":
+ self._freeze_or_not(self.model_body, to_freeze=True)
+
+ if (component is None or component == "head") and self.has_differentiable_head:
+ self._freeze_or_not(self.model_head, to_freeze=True)
+
+ def unfreeze(
+ self, component: Optional[Literal["body", "head"]] = None, keep_body_frozen: Optional[bool] = None
+ ) -> None:
+ """Unfreeze the model body and/or the head, allowing further training on that component.
+
+ Args:
+ component (`Literal["body", "head"]`, *optional*): Either "body" or "head" to unfreeze that component.
+ If no component is provided, unfreeze both. Defaults to None.
+ keep_body_frozen (`bool`, *optional*): Deprecated argument, use `component` instead.
+ """
+ if keep_body_frozen is not None:
+ warnings.warn(
+ "`keep_body_frozen` is deprecated and will be removed in v2.0.0 of SetFit. "
+ 'Please either pass "head", "body" or no arguments to unfreeze both.',
+ DeprecationWarning,
+ stacklevel=2,
+ )
+ # If the body must stay frozen, only unfreeze the head. Eventually, this entire if-branch
+ # can be removed.
+ if keep_body_frozen and not component:
+ component = "head"
+
+ if component is None or component == "body":
+ self._freeze_or_not(self.model_body, to_freeze=False)
+
+ if (component is None or component == "head") and self.has_differentiable_head:
+ self._freeze_or_not(self.model_head, to_freeze=False)
+
+ def _freeze_or_not(self, model: nn.Module, to_freeze: bool) -> None:
+ """Set `requires_grad=not to_freeze` for all parameters in `model`"""
+ for param in model.parameters():
+ param.requires_grad = not to_freeze
+
+ def encode(
+ self, inputs: List[str], batch_size: int = 32, show_progress_bar: Optional[bool] = None
+ ) -> Union[torch.Tensor, np.ndarray]:
+ """Convert input sentences to embeddings using the `SentenceTransformer` body.
+
+ Args:
+ inputs (`List[str]`): The input sentences to embed.
+ batch_size (`int`, defaults to `32`): The batch size to use in encoding the sentences to embeddings.
+ Higher often means faster processing but higher memory usage.
+ show_progress_bar (`Optional[bool]`, defaults to `None`): Whether to show a progress bar while encoding.
+
+ Returns:
+ Union[torch.Tensor, np.ndarray]: A matrix with shape [INPUT_LENGTH, EMBEDDING_SIZE], as a
+ torch Tensor if this model has a differentiable Torch head, or otherwise as a numpy array.
+ """
+ return self.model_body.encode(
+ inputs,
+ batch_size=batch_size,
+ normalize_embeddings=self.normalize_embeddings,
+ convert_to_tensor=self.has_differentiable_head,
+ show_progress_bar=show_progress_bar,
+ )
+
+ def _output_type_conversion(
+ self, outputs: Union[torch.Tensor, np.ndarray], as_numpy: bool = False
+ ) -> Union[torch.Tensor, np.ndarray]:
+ """Return `outputs` in the desired type:
+ * Numpy array if no differentiable head is used.
+ * Torch tensor if a differentiable head is used.
+
+ Note:
+ If the model is trained with string labels, which is only possible with a non-differentiable head,
+ then we cannot output using torch Tensors, but only using a numpy array.
+
+ Returns:
+ Union[torch.Tensor, "ndarray"]: The input, correctly converted to the desired type.
+ """
+ if as_numpy and self.has_differentiable_head:
+ outputs = outputs.detach().cpu().numpy()
+ elif not as_numpy and not self.has_differentiable_head and outputs.dtype.char != "U":
+ # Only output as tensor if the output isn't a string
+ outputs = torch.from_numpy(outputs)
+ return outputs
+
+ def predict_proba(
+ self,
+ inputs: Union[str, List[str]],
+ batch_size: int = 32,
+ as_numpy: bool = False,
+ show_progress_bar: Optional[bool] = None,
+ ) -> Union[torch.Tensor, np.ndarray]:
+ """Predict the probabilities of the various classes.
+
+ Args:
+ inputs (`Union[str, List[str]]`): The input sentences to predict class probabilities for.
+ batch_size (`int`, defaults to `32`): The batch size to use in encoding the sentences to embeddings.
+ Higher often means faster processing but higher memory usage.
+ as_numpy (`bool`, defaults to `False`): Whether to output as numpy array instead.
+ show_progress_bar (`Optional[bool]`, defaults to `None`): Whether to show a progress bar while encoding.
+
+ Example::
+
+ >>> model = SetFitModel.from_pretrained(...)
+ >>> model.predict_proba(["What a boring display", "Exhilarating through and through", "I'm wowed!"])
+ tensor([[0.9367, 0.0633],
+ [0.0627, 0.9373],
+ [0.0890, 0.9110]], dtype=torch.float64)
+ >>> model.predict_proba("That was cool!")
+ tensor([0.8421, 0.1579], dtype=torch.float64)
+
+ Returns:
+ `Union[torch.Tensor, np.ndarray]`: A matrix with shape [INPUT_LENGTH, NUM_CLASSES] denoting
+ probabilities of predicting an input as a class. If the input is a string, then the output
+ is a vector with shape [NUM_CLASSES,].
+ """
+ is_singular = isinstance(inputs, str)
+ if is_singular:
+ inputs = [inputs]
+ embeddings = self.encode(inputs, batch_size=batch_size, show_progress_bar=show_progress_bar)
+ probs = self.model_head.predict_proba(embeddings)
+ outputs = self._output_type_conversion(probs, as_numpy=as_numpy)
+ return outputs[0] if is_singular else outputs
+
+ def predict(
+ self,
+ inputs: Union[str, List[str]],
+ batch_size: int = 32,
+ as_numpy: bool = False,
+ use_labels: bool = True,
+ show_progress_bar: Optional[bool] = None,
+ ) -> Union[torch.Tensor, np.ndarray, List[str], int, str]:
+ """Predict the various classes.
+
+ Args:
+ inputs (`Union[str, List[str]]`): The input sentence or sentences to predict classes for.
+ batch_size (`int`, defaults to `32`): The batch size to use in encoding the sentences to embeddings.
+ Higher often means faster processing but higher memory usage.
+ as_numpy (`bool`, defaults to `False`): Whether to output as numpy array instead.
+ use_labels (`bool`, defaults to `True`): Whether to try and return elements of `SetFitModel.labels`.
+ show_progress_bar (`Optional[bool]`, defaults to `None`): Whether to show a progress bar while encoding.
+
+ Example::
+
+ >>> model = SetFitModel.from_pretrained(...)
+ >>> model.predict(["What a boring display", "Exhilarating through and through", "I'm wowed!"])
+ ["negative", "positive", "positive"]
+ >>> model.predict("That was cool!")
+ "positive"
+
+ Returns:
+ `Union[torch.Tensor, np.ndarray, List[str], int, str]`: A list of string labels with equal length to the
+ inputs if `use_labels` is `True` and `SetFitModel.labels` has been defined. Otherwise a vector with
+ equal length to the inputs, denoting to which class each input is predicted to belong. If the inputs
+ is a single string, then the output is a single label as well.
+ """
+ is_singular = isinstance(inputs, str)
+ if is_singular:
+ inputs = [inputs]
+ embeddings = self.encode(inputs, batch_size=batch_size, show_progress_bar=show_progress_bar)
+ preds = self.model_head.predict(embeddings)
+ # If labels are defined, we don't have multilabels & the output is not already strings, then we convert to string labels
+ if (
+ use_labels
+ and self.labels
+ and preds.ndim == 1
+ and (self.has_differentiable_head or preds.dtype.char != "U")
+ ):
+ outputs = [self.labels[int(pred)] for pred in preds]
+ else:
+ outputs = self._output_type_conversion(preds, as_numpy=as_numpy)
+ return outputs[0] if is_singular else outputs
+
+ def __call__(
+ self,
+ inputs: Union[str, List[str]],
+ batch_size: int = 32,
+ as_numpy: bool = False,
+ use_labels: bool = True,
+ show_progress_bar: Optional[bool] = None,
+ ) -> Union[torch.Tensor, np.ndarray, List[str], int, str]:
+ """Predict the various classes.
+
+ Args:
+ inputs (`Union[str, List[str]]`): The input sentence or sentences to predict classes for.
+ batch_size (`int`, defaults to `32`): The batch size to use in encoding the sentences to embeddings.
+ Higher often means faster processing but higher memory usage.
+ as_numpy (`bool`, defaults to `False`): Whether to output as numpy array instead.
+ use_labels (`bool`, defaults to `True`): Whether to try and return elements of `SetFitModel.labels`.
+ show_progress_bar (`Optional[bool]`, defaults to `None`): Whether to show a progress bar while encoding.
+
+ Example::
+
+ >>> model = SetFitModel.from_pretrained(...)
+ >>> model(["What a boring display", "Exhilarating through and through", "I'm wowed!"])
+ ["negative", "positive", "positive"]
+ >>> model("That was cool!")
+ "positive"
+
+ Returns:
+ `Union[torch.Tensor, np.ndarray, List[str], int, str]`: A list of string labels with equal length to the
+ inputs if `use_labels` is `True` and `SetFitModel.labels` has been defined. Otherwise a vector with
+ equal length to the inputs, denoting to which class each input is predicted to belong. If the inputs
+ is a single string, then the output is a single label as well.
+ """
+ return self.predict(
+ inputs,
+ batch_size=batch_size,
+ as_numpy=as_numpy,
+ use_labels=use_labels,
+ show_progress_bar=show_progress_bar,
+ )
+
+ @property
+ def device(self) -> torch.device:
+ """Get the Torch device that this model is on.
+
+ Returns:
+ torch.device: The device that the model is on.
+ """
+ return self.model_body._target_device
+
+ def to(self, device: Union[str, torch.device]) -> "SetFitModel":
+ """Move this SetFitModel to `device`, and then return `self`. This method does not copy.
+
+ Args:
+ device (Union[str, torch.device]): The identifier of the device to move the model to.
+
+ Example::
+
+ >>> model = SetFitModel.from_pretrained(...)
+ >>> model.to("cpu")
+ >>> model(["cats are cute", "dogs are loyal"])
+
+ Returns:
+ SetFitModel: Returns the original model, but now on the desired device.
+ """
+ # Note that we must also set _target_device, or any SentenceTransformer.fit() call will reset
+ # the body location
+ self.model_body._target_device = device if isinstance(device, torch.device) else torch.device(device)
+ self.model_body = self.model_body.to(device)
+
+ if self.has_differentiable_head:
+ self.model_head = self.model_head.to(device)
+
+ return self
+
+ def create_model_card(self, path: str, model_name: Optional[str] = "SetFit Model") -> None:
+ """Creates and saves a model card for a SetFit model.
+
+ Args:
+ path (str): The path to save the model card to.
+ model_name (str, *optional*): The name of the model. Defaults to `SetFit Model`.
+ """
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+ # If the model_path is a folder that exists locally, i.e. when create_model_card is called
+ # via push_to_hub, and the path is in a temporary folder, then we only take the last two
+ # directories
+ model_path = Path(model_name)
+ if model_path.exists() and Path(tempfile.gettempdir()) in model_path.resolve().parents:
+ self.model_card_data.model_id = "/".join(model_path.parts[-2:])
+
+ with open(os.path.join(path, "README.md"), "w", encoding="utf-8") as f:
+ f.write(self.generate_model_card())
+
+ def generate_model_card(self) -> str:
+ """Generate and return a model card string based on the model card data.
+
+ Returns:
+ str: The model card string.
+ """
+ return generate_model_card(self)
+
+ def _save_pretrained(self, save_directory: Union[Path, str]) -> None:
+ save_directory = str(save_directory)
+ # Save the config
+ config_path = os.path.join(save_directory, CONFIG_NAME)
+ with open(config_path, "w") as f:
+ json.dump(
+ {
+ attr_name: getattr(self, attr_name)
+ for attr_name in self.attributes_to_save
+ if hasattr(self, attr_name)
+ },
+ f,
+ indent=2,
+ )
+ # Save the body
+ self.model_body.save(path=save_directory, create_model_card=False)
+ # Save the README
+ #
+ #
+ #
+ #
+ #
+ # self.create_model_card(path=save_directory, model_name=save_directory)
+ #
+ #
+ #
+ #
+ #
+ # Move the head to the CPU before saving
+ if self.has_differentiable_head:
+ self.model_head.to("cpu")
+ # Save the classification head
+ joblib.dump(self.model_head, str(Path(save_directory) / MODEL_HEAD_NAME))
+ if self.has_differentiable_head:
+ self.model_head.to(self.device)
+
+ @classmethod
+ @validate_hf_hub_args
+ def _from_pretrained(
+ cls,
+ model_id: str,
+ revision: Optional[str] = None,
+ cache_dir: Optional[str] = None,
+ force_download: Optional[bool] = None,
+ proxies: Optional[Dict] = None,
+ resume_download: Optional[bool] = None,
+ local_files_only: Optional[bool] = None,
+ token: Optional[Union[bool, str]] = None,
+ multi_target_strategy: Optional[str] = None,
+ use_differentiable_head: bool = False,
+ device: Optional[Union[torch.device, str]] = None,
+ **model_kwargs,
+ ) -> "SetFitModel":
+ model_body = SentenceTransformer(model_id, cache_folder=cache_dir, use_auth_token=token, device=device)
+ device = model_body._target_device
+ model_body.to(device) # put `model_body` on the target device
+
+ # Try to load a SetFit config file
+ config_file: Optional[str] = None
+ if os.path.isdir(model_id):
+ if CONFIG_NAME in os.listdir(model_id):
+ config_file = os.path.join(model_id, CONFIG_NAME)
+ else:
+ try:
+ config_file = hf_hub_download(
+ repo_id=model_id,
+ filename=CONFIG_NAME,
+ revision=revision,
+ cache_dir=cache_dir,
+ force_download=force_download,
+ proxies=proxies,
+ resume_download=resume_download,
+ token=token,
+ local_files_only=local_files_only,
+ )
+ except requests.exceptions.RequestException:
+ pass
+
+ model_kwargs = {key: value for key, value in model_kwargs.items() if value is not None}
+
+ if config_file is not None:
+ with open(config_file, "r", encoding="utf-8") as f:
+ config = json.load(f)
+ # Update model_kwargs + warnings
+ for setting, value in config.items():
+ if setting in model_kwargs:
+ if model_kwargs[setting] != value:
+ logger.warning(
+ f"Overriding {setting} in model configuration from {value} to {model_kwargs[setting]}."
+ )
+ else:
+ model_kwargs[setting] = value
+
+ # Try to load a model head file
+ if os.path.isdir(model_id):
+ if MODEL_HEAD_NAME in os.listdir(model_id):
+ model_head_file = os.path.join(model_id, MODEL_HEAD_NAME)
+ else:
+ logger.info(
+ f"{MODEL_HEAD_NAME} not found in {Path(model_id).resolve()},"
+ " initialising classification head with random weights."
+ " You should TRAIN this model on a downstream task to use it for predictions and inference."
+ )
+ model_head_file = None
+ else:
+ try:
+ model_head_file = hf_hub_download(
+ repo_id=model_id,
+ filename=MODEL_HEAD_NAME,
+ revision=revision,
+ cache_dir=cache_dir,
+ force_download=force_download,
+ proxies=proxies,
+ resume_download=resume_download,
+ token=token,
+ local_files_only=local_files_only,
+ )
+ except requests.exceptions.RequestException:
+ logger.info(
+ f"{MODEL_HEAD_NAME} not found on HuggingFace Hub, initialising classification head with random weights."
+ " You should TRAIN this model on a downstream task to use it for predictions and inference."
+ )
+ model_head_file = None
+
+ model_card_data: SetFitModelCardData = model_kwargs.pop("model_card_data", SetFitModelCardData())
+
+ if model_head_file is not None:
+ model_head = joblib.load(model_head_file)
+ if isinstance(model_head, torch.nn.Module):
+ model_head.to(device)
+ model_card_data.infer_st_id(model_id)
+ else:
+ head_params = model_kwargs.pop("head_params", {})
+ if use_differentiable_head:
+ if multi_target_strategy is None:
+ use_multitarget = False
+ else:
+ if multi_target_strategy in ["one-vs-rest", "multi-output"]:
+ use_multitarget = True
+ else:
+ raise ValueError(
+ f"multi_target_strategy '{multi_target_strategy}' is not supported for differentiable head"
+ )
+ # Base `model_head` parameters
+ # - get the sentence embedding dimension from the `model_body`
+ # - follow the `model_body`, put `model_head` on the target device
+ base_head_params = {
+ "in_features": model_body.get_sentence_embedding_dimension(),
+ "device": device,
+ "multitarget": use_multitarget,
+ }
+ model_head = SetFitHead(**{**head_params, **base_head_params})
+ else:
+ clf = LogisticRegression(**head_params)
+ if multi_target_strategy is not None:
+ if multi_target_strategy == "one-vs-rest":
+ multilabel_classifier = OneVsRestClassifier(clf)
+ elif multi_target_strategy == "multi-output":
+ multilabel_classifier = MultiOutputClassifier(clf)
+ elif multi_target_strategy == "classifier-chain":
+ multilabel_classifier = ClassifierChain(clf)
+ else:
+ raise ValueError(f"multi_target_strategy {multi_target_strategy} is not supported.")
+
+ model_head = multilabel_classifier
+ else:
+ model_head = clf
+
+ model_card_data.set_st_id(model_id if "/" in model_id else f"sentence-transformers/{model_id}")
+
+ # Remove the `transformers` config
+ model_kwargs.pop("config", None)
+ return cls(
+ model_body=model_body,
+ model_head=model_head,
+ multi_target_strategy=multi_target_strategy,
+ model_card_data=model_card_data,
+ **model_kwargs,
+ )
+
+
+docstring = SetFitModel.from_pretrained.__doc__
+cut_index = docstring.find("model_kwargs")
+if cut_index != -1:
+ docstring = (
+ docstring[:cut_index]
+ + """labels (`List[str]`, *optional*):
+ If the labels are integers ranging from `0` to `num_classes-1`, then these labels indicate
+ the corresponding labels.
+ model_card_data (`SetFitModelCardData`, *optional*):
+ A `SetFitModelCardData` instance storing data such as model language, license, dataset name,
+ etc. to be used in the automatically generated model cards.
+ multi_target_strategy (`str`, *optional*):
+ The strategy to use with multi-label classification. One of "one-vs-rest", "multi-output",
+ or "classifier-chain".
+ use_differentiable_head (`bool`, *optional*):
+ Whether to load SetFit using a differentiable (i.e., Torch) head instead of Logistic Regression.
+ normalize_embeddings (`bool`, *optional*):
+ Whether to apply normalization on the embeddings produced by the Sentence Transformer body.
+ device (`Union[torch.device, str]`, *optional*):
+ The device on which to load the SetFit model, e.g. `"cuda:0"`, `"mps"` or `torch.device("cuda")`.
+
+ Example::
+
+ >>> from setfit import SetFitModel
+ >>> model = SetFitModel.from_pretrained(
+ ... "sentence-transformers/paraphrase-mpnet-base-v2",
+ ... labels=["positive", "negative"],
+ ... )
+ """
+ )
+ SetFitModel.from_pretrained = set_docstring(SetFitModel.from_pretrained, docstring)
+
+SetFitModel.save_pretrained = copy_func(SetFitModel.save_pretrained)
+SetFitModel.save_pretrained.__doc__ = SetFitModel.save_pretrained.__doc__.replace(
+ "~ModelHubMixin._from_pretrained", "SetFitModel.push_to_hub"
+)
diff --git a/test_models/setfit/sampler.py b/test_models/setfit/sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bb9ce35c1791373950b1c12b74cc95f89708709
--- /dev/null
+++ b/test_models/setfit/sampler.py
@@ -0,0 +1,168 @@
+from itertools import zip_longest
+from typing import Generator, Iterable, List, Optional
+
+import numpy as np
+import torch
+from sentence_transformers import InputExample
+from torch.utils.data import IterableDataset
+
+from . import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def shuffle_combinations(iterable: Iterable, replacement: bool = True) -> Generator:
+ """Generates shuffled pair combinations for any iterable data provided.
+
+ Args:
+ iterable: data to generate pair combinations from
+ replacement: enable to include combinations of same samples,
+ equivalent to itertools.combinations_with_replacement
+
+ Returns:
+ Generator of shuffled pairs as a tuple
+ """
+ n = len(iterable)
+ k = 1 if not replacement else 0
+ idxs = np.stack(np.triu_indices(n, k), axis=-1)
+ for i in np.random.RandomState(seed=42).permutation(len(idxs)):
+ _idx, idx = idxs[i, :]
+ yield iterable[_idx], iterable[idx]
+
+
+class ContrastiveDataset(IterableDataset):
+ def __init__(
+ self,
+ examples: List[InputExample],
+ multilabel: bool,
+ num_iterations: Optional[None] = None,
+ sampling_strategy: str = "oversampling",
+ max_pairs: int = -1,
+ ) -> None:
+ """Generates positive and negative text pairs for contrastive learning.
+
+ Args:
+ examples (InputExample): text and labels in a text transformer dataclass
+ multilabel: set to process "multilabel" labels array
+ sampling_strategy: "unique", "oversampling", or "undersampling"
+ num_iterations: if provided explicitly sets the number of pairs to be generated
+ where n_pairs = n_iterations * n_sentences * 2 (for pos & neg pairs)
+ max_pairs: If not -1, then we only sample pairs until we have certainly reached
+ max_pairs pairs.
+ """
+ super().__init__()
+ self.pos_index = 0
+ self.neg_index = 0
+ self.pos_pairs = []
+ self.neg_pairs = []
+ self.sentences = np.array([s.texts[0] for s in examples])
+ self.labels = np.array([s.label for s in examples])
+ self.sentence_labels = list(zip(self.sentences, self.labels))
+ self.max_pairs = max_pairs
+
+ if multilabel:
+ self.generate_multilabel_pairs()
+ else:
+ self.generate_pairs()
+
+ if num_iterations is not None and num_iterations > 0:
+ self.len_pos_pairs = num_iterations * len(self.sentences)
+ self.len_neg_pairs = num_iterations * len(self.sentences)
+
+ elif sampling_strategy == "unique":
+ self.len_pos_pairs = len(self.pos_pairs)
+ self.len_neg_pairs = len(self.neg_pairs)
+
+ elif sampling_strategy == "undersampling":
+ self.len_pos_pairs = min(len(self.pos_pairs), len(self.neg_pairs))
+ self.len_neg_pairs = min(len(self.pos_pairs), len(self.neg_pairs))
+
+ elif sampling_strategy == "oversampling":
+ self.len_pos_pairs = max(len(self.pos_pairs), len(self.neg_pairs))
+ self.len_neg_pairs = max(len(self.pos_pairs), len(self.neg_pairs))
+
+ else:
+ raise ValueError("Invalid sampling strategy. Must be one of 'unique', 'oversampling', or 'undersampling'.")
+
+ def generate_pairs(self) -> None:
+ for (_text, _label), (text, label) in shuffle_combinations(self.sentence_labels):
+ if _label == label:
+ self.pos_pairs.append(InputExample(texts=[_text, text], label=1.0))
+ else:
+ self.neg_pairs.append(InputExample(texts=[_text, text], label=0.0))
+ if self.max_pairs != -1 and len(self.pos_pairs) > self.max_pairs and len(self.neg_pairs) > self.max_pairs:
+ break
+
+ def generate_multilabel_pairs(self) -> None:
+ for (_text, _label), (text, label) in shuffle_combinations(self.sentence_labels):
+ if any(np.logical_and(_label, label)):
+ # logical_and checks if labels are both set for each class
+ self.pos_pairs.append(InputExample(texts=[_text, text], label=1.0))
+ else:
+ self.neg_pairs.append(InputExample(texts=[_text, text], label=0.0))
+ if self.max_pairs != -1 and len(self.pos_pairs) > self.max_pairs and len(self.neg_pairs) > self.max_pairs:
+ break
+
+ def get_positive_pairs(self) -> List[InputExample]:
+ pairs = []
+ for _ in range(self.len_pos_pairs):
+ if self.pos_index >= len(self.pos_pairs):
+ self.pos_index = 0
+ pairs.append(self.pos_pairs[self.pos_index])
+ self.pos_index += 1
+ return pairs
+
+ def get_negative_pairs(self) -> List[InputExample]:
+ pairs = []
+ for _ in range(self.len_neg_pairs):
+ if self.neg_index >= len(self.neg_pairs):
+ self.neg_index = 0
+ pairs.append(self.neg_pairs[self.neg_index])
+ self.neg_index += 1
+ return pairs
+
+ def __iter__(self):
+ for pos_pair, neg_pair in zip_longest(self.get_positive_pairs(), self.get_negative_pairs()):
+ if pos_pair is not None:
+ yield pos_pair
+ if neg_pair is not None:
+ yield neg_pair
+
+ def __len__(self) -> int:
+ return self.len_pos_pairs + self.len_neg_pairs
+
+
+class ContrastiveDistillationDataset(ContrastiveDataset):
+ def __init__(
+ self,
+ examples: List[InputExample],
+ cos_sim_matrix: torch.Tensor,
+ num_iterations: Optional[None] = None,
+ sampling_strategy: str = "oversampling",
+ max_pairs: int = -1,
+ ) -> None:
+ self.cos_sim_matrix = cos_sim_matrix
+ super().__init__(
+ examples,
+ multilabel=False,
+ num_iterations=num_iterations,
+ sampling_strategy=sampling_strategy,
+ max_pairs=max_pairs,
+ )
+ # Internally we store all pairs in pos_pairs, regardless of sampling strategy.
+ # After all, without labels, there isn't much of a strategy.
+ self.sentence_labels = list(enumerate(self.sentences))
+
+ self.len_neg_pairs = 0
+ if num_iterations is not None and num_iterations > 0:
+ self.len_pos_pairs = num_iterations * len(self.sentences)
+ else:
+ self.len_pos_pairs = len(self.pos_pairs)
+
+ def generate_pairs(self) -> None:
+ for (text_one, id_one), (text_two, id_two) in shuffle_combinations(self.sentence_labels):
+ self.pos_pairs.append(InputExample(texts=[text_one, text_two], label=self.cos_sim_matrix[id_one][id_two]))
+ if self.max_pairs != -1 and len(self.pos_pairs) > self.max_pairs:
+ break
diff --git a/test_models/setfit/span/__init__.py b/test_models/setfit/span/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ec75b8cfa70a7258f38ce210c7bbf9b8804280e
--- /dev/null
+++ b/test_models/setfit/span/__init__.py
@@ -0,0 +1,3 @@
+from .aspect_extractor import AspectExtractor
+from .modeling import AbsaModel, AspectModel, PolarityModel
+from .trainer import AbsaTrainer
diff --git a/test_models/setfit/span/aspect_extractor.py b/test_models/setfit/span/aspect_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..325b845b5c4f4df2111ec77d3d54beed9276e35c
--- /dev/null
+++ b/test_models/setfit/span/aspect_extractor.py
@@ -0,0 +1,34 @@
+from typing import TYPE_CHECKING, List, Tuple
+
+
+if TYPE_CHECKING:
+ from spacy.tokens import Doc
+
+
+class AspectExtractor:
+ def __init__(self, spacy_model: str) -> None:
+ super().__init__()
+ import spacy
+
+ self.nlp = spacy.load(spacy_model)
+
+ def find_groups(self, aspect_mask: List[bool]):
+ start = None
+ for idx, flag in enumerate(aspect_mask):
+ if flag:
+ if start is None:
+ start = idx
+ else:
+ if start is not None:
+ yield slice(start, idx)
+ start = None
+ if start is not None:
+ yield slice(start, idx + 1)
+
+ def __call__(self, texts: List[str]) -> Tuple[List["Doc"], List[slice]]:
+ aspects_list = []
+ docs = list(self.nlp.pipe(texts))
+ for doc in docs:
+ aspect_mask = [token.pos_ in ("NOUN", "PROPN") for token in doc]
+ aspects_list.append(list(self.find_groups(aspect_mask)))
+ return docs, aspects_list
diff --git a/test_models/setfit/span/modeling.py b/test_models/setfit/span/modeling.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bdfb9581b5617e7aae4ea3a11178a5acc0caa18
--- /dev/null
+++ b/test_models/setfit/span/modeling.py
@@ -0,0 +1,292 @@
+import copy
+import os
+import tempfile
+import types
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple, Union
+
+import torch
+from huggingface_hub.utils import SoftTemporaryDirectory
+
+from setfit.utils import set_docstring
+
+from .. import logging
+from ..modeling import SetFitModel
+from .aspect_extractor import AspectExtractor
+
+
+if TYPE_CHECKING:
+ from spacy.tokens import Doc
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+class SpanSetFitModel(SetFitModel):
+ spacy_model: str = "en_core_web_lg"
+ span_context: int = 0
+
+ attributes_to_save: Set[str] = field(
+ init=False,
+ repr=False,
+ default_factory=lambda: {"normalize_embeddings", "labels", "span_context", "spacy_model"},
+ )
+
+ def prepend_aspects(self, docs: List["Doc"], aspects_list: List[List[slice]]) -> List[str]:
+ for doc, aspects in zip(docs, aspects_list):
+ for aspect_slice in aspects:
+ aspect = doc[max(aspect_slice.start - self.span_context, 0) : aspect_slice.stop + self.span_context]
+ # TODO: Investigate performance difference of different formats
+ yield aspect.text + ":" + doc.text
+
+ def __call__(self, docs: List["Doc"], aspects_list: List[List[slice]]) -> List[bool]:
+ inputs_list = list(self.prepend_aspects(docs, aspects_list))
+ preds = self.predict(inputs_list, as_numpy=True)
+ iter_preds = iter(preds)
+ return [[next(iter_preds) for _ in aspects] for aspects in aspects_list]
+
+ def create_model_card(self, path: str, model_name: Optional[str] = None) -> None:
+ """Creates and saves a model card for a SetFit model.
+
+ Args:
+ path (str): The path to save the model card to.
+ model_name (str, *optional*): The name of the model. Defaults to `SetFit Model`.
+ """
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+ # If the model_path is a folder that exists locally, i.e. when create_model_card is called
+ # via push_to_hub, and the path is in a temporary folder, then we only take the last two
+ # directories
+ model_path = Path(model_name)
+ if model_path.exists() and Path(tempfile.gettempdir()) in model_path.resolve().parents:
+ model_name = "/".join(model_path.parts[-2:])
+
+ is_aspect = isinstance(self, AspectModel)
+ aspect_model = "setfit-absa-aspect"
+ polarity_model = "setfit-absa-polarity"
+ if model_name is not None:
+ if is_aspect:
+ aspect_model = model_name
+ if model_name.endswith("-aspect"):
+ polarity_model = model_name[: -len("-aspect")] + "-polarity"
+ else:
+ polarity_model = model_name
+ if model_name.endswith("-polarity"):
+ aspect_model = model_name[: -len("-polarity")] + "-aspect"
+
+ # Only once:
+ if self.model_card_data.absa is None and self.model_card_data.model_name:
+ from spacy import __version__ as spacy_version
+
+ self.model_card_data.model_name = self.model_card_data.model_name.replace(
+ "SetFit", "SetFit Aspect Model" if is_aspect else "SetFit Polarity Model", 1
+ )
+ self.model_card_data.tags.insert(1, "absa")
+ self.model_card_data.version["spacy"] = spacy_version
+ self.model_card_data.absa = {
+ "is_absa": True,
+ "is_aspect": is_aspect,
+ "spacy_model": self.spacy_model,
+ "aspect_model": aspect_model,
+ "polarity_model": polarity_model,
+ }
+ if self.model_card_data.task_name is None:
+ self.model_card_data.task_name = "Aspect Based Sentiment Analysis (ABSA)"
+ self.model_card_data.inference = False
+ with open(os.path.join(path, "README.md"), "w", encoding="utf-8") as f:
+ f.write(self.generate_model_card())
+
+
+docstring = SpanSetFitModel.from_pretrained.__doc__
+cut_index = docstring.find("multi_target_strategy")
+if cut_index != -1:
+ docstring = (
+ docstring[:cut_index]
+ + """model_card_data (`SetFitModelCardData`, *optional*):
+ A `SetFitModelCardData` instance storing data such as model language, license, dataset name,
+ etc. to be used in the automatically generated model cards.
+ use_differentiable_head (`bool`, *optional*):
+ Whether to load SetFit using a differentiable (i.e., Torch) head instead of Logistic Regression.
+ normalize_embeddings (`bool`, *optional*):
+ Whether to apply normalization on the embeddings produced by the Sentence Transformer body.
+ span_context (`int`, defaults to `0`):
+ The number of words before and after the span candidate that should be prepended to the full sentence.
+ By default, 0 for Aspect models and 3 for Polarity models.
+ device (`Union[torch.device, str]`, *optional*):
+ The device on which to load the SetFit model, e.g. `"cuda:0"`, `"mps"` or `torch.device("cuda")`."""
+ )
+ SpanSetFitModel.from_pretrained = set_docstring(SpanSetFitModel.from_pretrained, docstring, cls=SpanSetFitModel)
+
+
+class AspectModel(SpanSetFitModel):
+ def __call__(self, docs: List["Doc"], aspects_list: List[List[slice]]) -> List[bool]:
+ sentence_preds = super().__call__(docs, aspects_list)
+ return [
+ [aspect for aspect, pred in zip(aspects, preds) if pred == "aspect"]
+ for aspects, preds in zip(aspects_list, sentence_preds)
+ ]
+
+
+# The set_docstring magic has as a consequences that subclasses need to update the cls in the from_pretrained
+# classmethod, otherwise the wrong instance will be instantiated.
+AspectModel.from_pretrained = types.MethodType(AspectModel.from_pretrained.__func__, AspectModel)
+
+
+@dataclass
+class PolarityModel(SpanSetFitModel):
+ span_context: int = 3
+
+
+PolarityModel.from_pretrained = types.MethodType(PolarityModel.from_pretrained.__func__, PolarityModel)
+
+
+@dataclass
+class AbsaModel:
+ aspect_extractor: AspectExtractor
+ aspect_model: AspectModel
+ polarity_model: PolarityModel
+
+ def predict(self, inputs: Union[str, List[str]]) -> List[Dict[str, Any]]:
+ is_str = isinstance(inputs, str)
+ inputs_list = [inputs] if is_str else inputs
+ docs, aspects_list = self.aspect_extractor(inputs_list)
+ if sum(aspects_list, []) == []:
+ return aspects_list
+
+ aspects_list = self.aspect_model(docs, aspects_list)
+ if sum(aspects_list, []) == []:
+ return aspects_list
+
+ polarity_list = self.polarity_model(docs, aspects_list)
+ outputs = []
+ for docs, aspects, polarities in zip(docs, aspects_list, polarity_list):
+ outputs.append(
+ [
+ {"span": docs[aspect_slice].text, "polarity": polarity}
+ for aspect_slice, polarity in zip(aspects, polarities)
+ ]
+ )
+ return outputs if not is_str else outputs[0]
+
+ @property
+ def device(self) -> torch.device:
+ return self.aspect_model.device
+
+ def to(self, device: Union[str, torch.device]) -> "AbsaModel":
+ self.aspect_model.to(device)
+ self.polarity_model.to(device)
+
+ def __call__(self, inputs: Union[str, List[str]]) -> List[Dict[str, Any]]:
+ return self.predict(inputs)
+
+ def save_pretrained(
+ self,
+ save_directory: Union[str, Path],
+ polarity_save_directory: Optional[Union[str, Path]] = None,
+ push_to_hub: bool = False,
+ **kwargs,
+ ) -> None:
+ if polarity_save_directory is None:
+ base_save_directory = Path(save_directory)
+ save_directory = base_save_directory.parent / (base_save_directory.name + "-aspect")
+ polarity_save_directory = base_save_directory.parent / (base_save_directory.name + "-polarity")
+ self.aspect_model.save_pretrained(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)
+ self.polarity_model.save_pretrained(save_directory=polarity_save_directory, push_to_hub=push_to_hub, **kwargs)
+
+ @classmethod
+ def from_pretrained(
+ cls,
+ model_id: str,
+ polarity_model_id: Optional[str] = None,
+ spacy_model: Optional[str] = None,
+ span_contexts: Tuple[Optional[int], Optional[int]] = (None, None),
+ force_download: bool = None,
+ resume_download: bool = None,
+ proxies: Optional[Dict] = None,
+ token: Optional[Union[str, bool]] = None,
+ cache_dir: Optional[str] = None,
+ local_files_only: bool = None,
+ use_differentiable_head: bool = None,
+ normalize_embeddings: bool = None,
+ **model_kwargs,
+ ) -> "AbsaModel":
+ revision = None
+ if len(model_id.split("@")) == 2:
+ model_id, revision = model_id.split("@")
+ if spacy_model:
+ model_kwargs["spacy_model"] = spacy_model
+ aspect_model = AspectModel.from_pretrained(
+ model_id,
+ span_context=span_contexts[0],
+ revision=revision,
+ force_download=force_download,
+ resume_download=resume_download,
+ proxies=proxies,
+ token=token,
+ cache_dir=cache_dir,
+ local_files_only=local_files_only,
+ use_differentiable_head=use_differentiable_head,
+ normalize_embeddings=normalize_embeddings,
+ labels=["no aspect", "aspect"],
+ **model_kwargs,
+ )
+ if polarity_model_id:
+ model_id = polarity_model_id
+ revision = None
+ if len(model_id.split("@")) == 2:
+ model_id, revision = model_id.split("@")
+ # If model_card_data was provided, "separate" the instance between the Aspect
+ # and Polarity models.
+ model_card_data = model_kwargs.pop("model_card_data", None)
+ if model_card_data:
+ model_kwargs["model_card_data"] = copy.deepcopy(model_card_data)
+ polarity_model = PolarityModel.from_pretrained(
+ model_id,
+ span_context=span_contexts[1],
+ revision=revision,
+ force_download=force_download,
+ resume_download=resume_download,
+ proxies=proxies,
+ token=token,
+ cache_dir=cache_dir,
+ local_files_only=local_files_only,
+ use_differentiable_head=use_differentiable_head,
+ normalize_embeddings=normalize_embeddings,
+ **model_kwargs,
+ )
+ if aspect_model.spacy_model != polarity_model.spacy_model:
+ logger.warning(
+ "The Aspect and Polarity models are configured to use different spaCy models:\n"
+ f"* {repr(aspect_model.spacy_model)} for the aspect model, and\n"
+ f"* {repr(polarity_model.spacy_model)} for the polarity model.\n"
+ f"This model will use {repr(aspect_model.spacy_model)}."
+ )
+
+ aspect_extractor = AspectExtractor(spacy_model=aspect_model.spacy_model)
+
+ return cls(aspect_extractor, aspect_model, polarity_model)
+
+ def push_to_hub(self, repo_id: str, polarity_repo_id: Optional[str] = None, **kwargs) -> None:
+ if "/" not in repo_id:
+ raise ValueError(
+ '`repo_id` must be a full repository ID, including organisation, e.g. "tomaarsen/setfit-absa-restaurant".'
+ )
+ if polarity_repo_id is not None and "/" not in polarity_repo_id:
+ raise ValueError(
+ '`polarity_repo_id` must be a full repository ID, including organisation, e.g. "tomaarsen/setfit-absa-restaurant".'
+ )
+ commit_message = kwargs.pop("commit_message", "Add SetFit ABSA model")
+
+ # Push the files to the repo in a single commit
+ with SoftTemporaryDirectory() as tmp_dir:
+ save_directory = Path(tmp_dir) / repo_id
+ polarity_save_directory = None if polarity_repo_id is None else Path(tmp_dir) / polarity_repo_id
+ self.save_pretrained(
+ save_directory=save_directory,
+ polarity_save_directory=polarity_save_directory,
+ push_to_hub=True,
+ commit_message=commit_message,
+ **kwargs,
+ )
diff --git a/test_models/setfit/span/trainer.py b/test_models/setfit/span/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..93d0051a3623b7523e31ff667c89eab7334d5afb
--- /dev/null
+++ b/test_models/setfit/span/trainer.py
@@ -0,0 +1,337 @@
+from collections import defaultdict
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
+
+from datasets import Dataset
+from transformers.trainer_callback import TrainerCallback
+
+from setfit.span.modeling import AbsaModel, AspectModel, PolarityModel
+from setfit.training_args import TrainingArguments
+
+from .. import logging
+from ..trainer import ColumnMappingMixin, Trainer
+
+
+if TYPE_CHECKING:
+ import optuna
+
+logger = logging.get_logger(__name__)
+
+
+class AbsaTrainer(ColumnMappingMixin):
+ """Trainer to train a SetFit ABSA model.
+
+ Args:
+ model (`AbsaModel`):
+ The AbsaModel model to train.
+ args (`TrainingArguments`, *optional*):
+ The training arguments to use. If `polarity_args` is not defined, then `args` is used for both
+ the aspect and the polarity model.
+ polarity_args (`TrainingArguments`, *optional*):
+ The training arguments to use for the polarity model. If not defined, `args` is used for both
+ the aspect and the polarity model.
+ train_dataset (`Dataset`):
+ The training dataset. The dataset must have "text", "span", "label" and "ordinal" columns.
+ eval_dataset (`Dataset`, *optional*):
+ The evaluation dataset. The dataset must have "text", "span", "label" and "ordinal" columns.
+ metric (`str` or `Callable`, *optional*, defaults to `"accuracy"`):
+ The metric to use for evaluation. If a string is provided, we treat it as the metric
+ name and load it with default settings.
+ If a callable is provided, it must take two arguments (`y_pred`, `y_test`).
+ metric_kwargs (`Dict[str, Any]`, *optional*):
+ Keyword arguments passed to the evaluation function if `metric` is an evaluation string like "f1".
+ For example useful for providing an averaging strategy for computing f1 in a multi-label setting.
+ callbacks (`List[`[`~transformers.TrainerCallback`]`]`, *optional*):
+ A list of callbacks to customize the training loop. Will add those to the list of default callbacks
+ detailed in [here](https://huggingface.co/docs/transformers/main/en/main_classes/callback).
+ If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method.
+ column_mapping (`Dict[str, str]`, *optional*):
+ A mapping from the column names in the dataset to the column names expected by the model.
+ The expected format is a dictionary with the following format:
+ `{"text_column_name": "text", "span_column_name": "span", "label_column_name: "label", "ordinal_column_name": "ordinal"}`.
+ """
+
+ _REQUIRED_COLUMNS = {"text", "span", "label", "ordinal"}
+
+ def __init__(
+ self,
+ model: AbsaModel,
+ args: Optional[TrainingArguments] = None,
+ polarity_args: Optional[TrainingArguments] = None,
+ train_dataset: Optional["Dataset"] = None,
+ eval_dataset: Optional["Dataset"] = None,
+ metric: Union[str, Callable[["Dataset", "Dataset"], Dict[str, float]]] = "accuracy",
+ metric_kwargs: Optional[Dict[str, Any]] = None,
+ callbacks: Optional[List[TrainerCallback]] = None,
+ column_mapping: Optional[Dict[str, str]] = None,
+ ) -> None:
+ self.model = model
+ self.aspect_extractor = model.aspect_extractor
+
+ if train_dataset is not None and column_mapping:
+ train_dataset = self._apply_column_mapping(train_dataset, column_mapping)
+ aspect_train_dataset, polarity_train_dataset = self.preprocess_dataset(
+ model.aspect_model, model.polarity_model, train_dataset
+ )
+ if eval_dataset is not None and column_mapping:
+ eval_dataset = self._apply_column_mapping(eval_dataset, column_mapping)
+ aspect_eval_dataset, polarity_eval_dataset = self.preprocess_dataset(
+ model.aspect_model, model.polarity_model, eval_dataset
+ )
+
+ self.aspect_trainer = Trainer(
+ model.aspect_model,
+ args=args,
+ train_dataset=aspect_train_dataset,
+ eval_dataset=aspect_eval_dataset,
+ metric=metric,
+ metric_kwargs=metric_kwargs,
+ callbacks=callbacks,
+ )
+ self.aspect_trainer._set_logs_mapper(
+ {
+ "eval_embedding_loss": "eval_aspect_embedding_loss",
+ "embedding_loss": "aspect_embedding_loss",
+ }
+ )
+ self.polarity_trainer = Trainer(
+ model.polarity_model,
+ args=polarity_args or args,
+ train_dataset=polarity_train_dataset,
+ eval_dataset=polarity_eval_dataset,
+ metric=metric,
+ metric_kwargs=metric_kwargs,
+ callbacks=callbacks,
+ )
+ self.polarity_trainer._set_logs_mapper(
+ {
+ "eval_embedding_loss": "eval_polarity_embedding_loss",
+ "embedding_loss": "polarity_embedding_loss",
+ }
+ )
+
+ def preprocess_dataset(
+ self, aspect_model: AspectModel, polarity_model: PolarityModel, dataset: Dataset
+ ) -> Dataset:
+ if dataset is None:
+ return dataset, dataset
+
+ # Group by "text"
+ grouped_data = defaultdict(list)
+ for sample in dataset:
+ text = sample.pop("text")
+ grouped_data[text].append(sample)
+
+ def index_ordinal(text: str, target: str, ordinal: int) -> Tuple[int, int]:
+ find_from = 0
+ for _ in range(ordinal + 1):
+ start_idx = text.index(target, find_from)
+ find_from = start_idx + 1
+ return start_idx, start_idx + len(target)
+
+ def overlaps(aspect: slice, aspects: List[slice]) -> bool:
+ for test_aspect in aspects:
+ overlapping_indices = set(range(aspect.start, aspect.stop + 1)) & set(
+ range(test_aspect.start, test_aspect.stop + 1)
+ )
+ if overlapping_indices:
+ return True
+ return False
+
+ docs, aspects_list = self.aspect_extractor(grouped_data.keys())
+ aspect_aspect_list = []
+ aspect_labels = []
+ polarity_aspect_list = []
+ polarity_labels = []
+ for doc, aspects, text in zip(docs, aspects_list, grouped_data):
+ # Collect all of the gold aspects
+ gold_aspects = []
+ gold_polarity_labels = []
+ for annotation in grouped_data[text]:
+ try:
+ start, end = index_ordinal(text, annotation["span"], annotation["ordinal"])
+ except ValueError:
+ logger.info(
+ f"The ordinal of {annotation['ordinal']} for span {annotation['span']!r} in {text!r} is too high. "
+ "Skipping this sample."
+ )
+ continue
+
+ gold_aspect_span = doc.char_span(start, end)
+ if gold_aspect_span is None:
+ continue
+ gold_aspects.append(slice(gold_aspect_span.start, gold_aspect_span.end))
+ gold_polarity_labels.append(annotation["label"])
+
+ # The Aspect model uses all gold aspects as "True", and all non-overlapping predicted
+ # aspects as "False"
+ aspect_labels.extend([True] * len(gold_aspects))
+ aspect_aspect_list.append(gold_aspects[:])
+ for aspect in aspects:
+ if not overlaps(aspect, gold_aspects):
+ aspect_labels.append(False)
+ aspect_aspect_list[-1].append(aspect)
+
+ # The Polarity model uses only the gold aspects and labels
+ polarity_labels.extend(gold_polarity_labels)
+ polarity_aspect_list.append(gold_aspects)
+
+ aspect_texts = list(aspect_model.prepend_aspects(docs, aspect_aspect_list))
+ polarity_texts = list(polarity_model.prepend_aspects(docs, polarity_aspect_list))
+ return Dataset.from_dict({"text": aspect_texts, "label": aspect_labels}), Dataset.from_dict(
+ {"text": polarity_texts, "label": polarity_labels}
+ )
+
+ def train(
+ self,
+ args: Optional[TrainingArguments] = None,
+ polarity_args: Optional[TrainingArguments] = None,
+ trial: Optional[Union["optuna.Trial", Dict[str, Any]]] = None,
+ **kwargs,
+ ) -> None:
+ """
+ Main training entry point.
+
+ Args:
+ args (`TrainingArguments`, *optional*):
+ Temporarily change the aspect training arguments for this training call.
+ polarity_args (`TrainingArguments`, *optional*):
+ Temporarily change the polarity training arguments for this training call.
+ trial (`optuna.Trial` or `Dict[str, Any]`, *optional*):
+ The trial run or the hyperparameter dictionary for hyperparameter search.
+ """
+ self.train_aspect(args=args, trial=trial, **kwargs)
+ self.train_polarity(args=polarity_args, trial=trial, **kwargs)
+
+ def train_aspect(
+ self,
+ args: Optional[TrainingArguments] = None,
+ trial: Optional[Union["optuna.Trial", Dict[str, Any]]] = None,
+ **kwargs,
+ ) -> None:
+ """
+ Train the aspect model only.
+
+ Args:
+ args (`TrainingArguments`, *optional*):
+ Temporarily change the aspect training arguments for this training call.
+ trial (`optuna.Trial` or `Dict[str, Any]`, *optional*):
+ The trial run or the hyperparameter dictionary for hyperparameter search.
+ """
+ self.aspect_trainer.train(args=args, trial=trial, **kwargs)
+
+ def train_polarity(
+ self,
+ args: Optional[TrainingArguments] = None,
+ trial: Optional[Union["optuna.Trial", Dict[str, Any]]] = None,
+ **kwargs,
+ ) -> None:
+ """
+ Train the polarity model only.
+
+ Args:
+ args (`TrainingArguments`, *optional*):
+ Temporarily change the aspect training arguments for this training call.
+ trial (`optuna.Trial` or `Dict[str, Any]`, *optional*):
+ The trial run or the hyperparameter dictionary for hyperparameter search.
+ """
+ self.polarity_trainer.train(args=args, trial=trial, **kwargs)
+
+ def add_callback(self, callback: Union[type, TrainerCallback]) -> None:
+ """
+ Add a callback to the current list of [`~transformers.TrainerCallback`].
+
+ Args:
+ callback (`type` or [`~transformers.TrainerCallback`]):
+ A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the
+ first case, will instantiate a member of that class.
+ """
+ self.aspect_trainer.add_callback(callback)
+ self.polarity_trainer.add_callback(callback)
+
+ def pop_callback(self, callback: Union[type, TrainerCallback]) -> Tuple[TrainerCallback, TrainerCallback]:
+ """
+ Remove a callback from the current list of [`~transformers.TrainerCallback`] and returns it.
+
+ If the callback is not found, returns `None` (and no error is raised).
+
+ Args:
+ callback (`type` or [`~transformers.TrainerCallback`]):
+ A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the
+ first case, will pop the first member of that class found in the list of callbacks.
+
+ Returns:
+ `Tuple[`[`~transformers.TrainerCallback`], [`~transformers.TrainerCallback`]`]`: The callbacks removed from the
+ aspect and polarity trainers, if found.
+ """
+ return self.aspect_trainer.pop_callback(callback), self.polarity_trainer.pop_callback(callback)
+
+ def remove_callback(self, callback: Union[type, TrainerCallback]) -> None:
+ """
+ Remove a callback from the current list of [`~transformers.TrainerCallback`].
+
+ Args:
+ callback (`type` or [`~transformers.TrainerCallback`]):
+ A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the
+ first case, will remove the first member of that class found in the list of callbacks.
+ """
+ self.aspect_trainer.remove_callback(callback)
+ self.polarity_trainer.remove_callback(callback)
+
+ def push_to_hub(self, repo_id: str, polarity_repo_id: Optional[str] = None, **kwargs) -> None:
+ """Upload model checkpoint to the Hub using `huggingface_hub`.
+
+ See the full list of parameters for your `huggingface_hub` version in the\
+ [huggingface_hub documentation](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.ModelHubMixin.push_to_hub).
+
+ Args:
+ repo_id (`str`):
+ The full repository ID to push to, e.g. `"tomaarsen/setfit-aspect"`.
+ repo_id (`str`):
+ The full repository ID to push to, e.g. `"tomaarsen/setfit-sst2"`.
+ config (`dict`, *optional*):
+ Configuration object to be saved alongside the model weights.
+ commit_message (`str`, *optional*):
+ Message to commit while pushing.
+ private (`bool`, *optional*, defaults to `False`):
+ Whether the repository created should be private.
+ api_endpoint (`str`, *optional*):
+ The API endpoint to use when pushing the model to the hub.
+ token (`str`, *optional*):
+ The token to use as HTTP bearer authorization for remote files.
+ If not set, will use the token set when logging in with
+ `transformers-cli login` (stored in `~/.huggingface`).
+ branch (`str`, *optional*):
+ The git branch on which to push the model. This defaults to
+ the default branch as specified in your repository, which
+ defaults to `"main"`.
+ create_pr (`boolean`, *optional*):
+ Whether or not to create a Pull Request from `branch` with that commit.
+ Defaults to `False`.
+ allow_patterns (`List[str]` or `str`, *optional*):
+ If provided, only files matching at least one pattern are pushed.
+ ignore_patterns (`List[str]` or `str`, *optional*):
+ If provided, files matching any of the patterns are not pushed.
+ """
+ return self.model.push_to_hub(repo_id=repo_id, polarity_repo_id=polarity_repo_id, **kwargs)
+
+ def evaluate(self, dataset: Optional[Dataset] = None) -> Dict[str, Dict[str, float]]:
+ """
+ Computes the metrics for a given classifier.
+
+ Args:
+ dataset (`Dataset`, *optional*):
+ The dataset to compute the metrics on. If not provided, will use the evaluation dataset passed via
+ the `eval_dataset` argument at `Trainer` initialization.
+
+ Returns:
+ `Dict[str, Dict[str, float]]`: The evaluation metrics.
+ """
+ aspect_eval_dataset = polarity_eval_dataset = None
+ if dataset:
+ aspect_eval_dataset, polarity_eval_dataset = self.preprocess_dataset(
+ self.model.aspect_model, self.model.polarity_model, dataset
+ )
+ return {
+ "aspect": self.aspect_trainer.evaluate(aspect_eval_dataset),
+ "polarity": self.polarity_trainer.evaluate(polarity_eval_dataset),
+ }
diff --git a/test_models/setfit/trainer.py b/test_models/setfit/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa0f8dbe9197ad0d88c95fc9a3f5298dceb2ac39
--- /dev/null
+++ b/test_models/setfit/trainer.py
@@ -0,0 +1,1058 @@
+import math
+import os
+import shutil
+import time
+import warnings
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
+
+import evaluate
+import torch
+from datasets import Dataset, DatasetDict
+from sentence_transformers import InputExample, SentenceTransformer, losses
+from sentence_transformers.datasets import SentenceLabelDataset
+from sentence_transformers.losses.BatchHardTripletLoss import BatchHardTripletLossDistanceFunction
+from sentence_transformers.util import batch_to_device
+from sklearn.preprocessing import LabelEncoder
+from torch import nn
+from torch.cuda.amp import autocast
+from torch.utils.data import DataLoader
+from tqdm.autonotebook import tqdm
+from transformers.integrations import WandbCallback, get_reporting_integration_callbacks
+from transformers.trainer_callback import (
+ CallbackHandler,
+ DefaultFlowCallback,
+ IntervalStrategy,
+ PrinterCallback,
+ ProgressCallback,
+ TrainerCallback,
+ TrainerControl,
+ TrainerState,
+)
+from transformers.trainer_utils import (
+ HPSearchBackend,
+ default_compute_objective,
+ number_of_arguments,
+ set_seed,
+ speed_metrics,
+)
+from transformers.utils.import_utils import is_in_notebook
+
+from setfit.model_card import ModelCardCallback
+
+from . import logging
+from .integrations import default_hp_search_backend, is_optuna_available, run_hp_search_optuna
+from .losses import SupConLoss
+from .sampler import ContrastiveDataset
+from .training_args import TrainingArguments
+from .utils import BestRun, default_hp_space_optuna
+
+
+# For Python 3.7 compatibility
+try:
+ from typing import Literal
+except ImportError:
+ from typing_extensions import Literal
+
+
+if TYPE_CHECKING:
+ import optuna
+
+ from .modeling import SetFitModel
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+DEFAULT_CALLBACKS = [DefaultFlowCallback]
+DEFAULT_PROGRESS_CALLBACK = ProgressCallback
+
+if is_in_notebook():
+ from transformers.utils.notebook import NotebookProgressCallback
+
+ DEFAULT_PROGRESS_CALLBACK = NotebookProgressCallback
+
+
+class ColumnMappingMixin:
+ _REQUIRED_COLUMNS = {"text", "label"}
+
+ def _validate_column_mapping(self, dataset: "Dataset") -> None:
+ """
+ Validates the provided column mapping against the dataset.
+ """
+ column_names = set(dataset.column_names)
+ if self.column_mapping is None and not self._REQUIRED_COLUMNS.issubset(column_names):
+ # Issue #226: load_dataset will automatically assign points to "train" if no split is specified
+ if column_names == {"train"} and isinstance(dataset, DatasetDict):
+ raise ValueError(
+ "SetFit expected a Dataset, but it got a DatasetDict with the split ['train']. "
+ "Did you mean to select the training split with dataset['train']?"
+ )
+ elif isinstance(dataset, DatasetDict):
+ raise ValueError(
+ f"SetFit expected a Dataset, but it got a DatasetDict with the splits {sorted(column_names)}. "
+ "Did you mean to select one of these splits from the dataset?"
+ )
+ else:
+ raise ValueError(
+ f"SetFit expected the dataset to have the columns {sorted(self._REQUIRED_COLUMNS)}, "
+ f"but only the columns {sorted(column_names)} were found. "
+ "Either make sure these columns are present, or specify which columns to use with column_mapping in Trainer."
+ )
+ if self.column_mapping is not None:
+ missing_columns = set(self._REQUIRED_COLUMNS)
+ # Remove columns that will be provided via the column mapping
+ missing_columns -= set(self.column_mapping.values())
+ # Remove columns that will be provided because they are in the dataset & not mapped away
+ missing_columns -= set(dataset.column_names) - set(self.column_mapping.keys())
+ if missing_columns:
+ raise ValueError(
+ f"The following columns are missing from the column mapping: {missing_columns}. "
+ "Please provide a mapping for all required columns."
+ )
+ if not set(self.column_mapping.keys()).issubset(column_names):
+ raise ValueError(
+ f"The column mapping expected the columns {sorted(self.column_mapping.keys())} in the dataset, "
+ f"but the dataset had the columns {sorted(column_names)}."
+ )
+
+ def _apply_column_mapping(self, dataset: "Dataset", column_mapping: Dict[str, str]) -> "Dataset":
+ """
+ Applies the provided column mapping to the dataset, renaming columns accordingly.
+ Extra features not in the column mapping are prefixed with `"feat_"`.
+ """
+ dataset = dataset.rename_columns(
+ {
+ **column_mapping,
+ **{
+ col: f"feat_{col}"
+ for col in dataset.column_names
+ if col not in column_mapping and col not in self._REQUIRED_COLUMNS
+ },
+ }
+ )
+ dset_format = dataset.format
+ dataset = dataset.with_format(
+ type=dset_format["type"],
+ columns=dataset.column_names,
+ output_all_columns=dset_format["output_all_columns"],
+ **dset_format["format_kwargs"],
+ )
+ return dataset
+
+
+class Trainer(ColumnMappingMixin):
+ """Trainer to train a SetFit model.
+
+ Args:
+ model (`SetFitModel`, *optional*):
+ The model to train. If not provided, a `model_init` must be passed.
+ args (`TrainingArguments`, *optional*):
+ The training arguments to use.
+ train_dataset (`Dataset`):
+ The training dataset.
+ eval_dataset (`Dataset`, *optional*):
+ The evaluation dataset.
+ model_init (`Callable[[], SetFitModel]`, *optional*):
+ A function that instantiates the model to be used. If provided, each call to
+ [`Trainer.train`] will start from a new instance of the model as given by this
+ function when a `trial` is passed.
+ metric (`str` or `Callable`, *optional*, defaults to `"accuracy"`):
+ The metric to use for evaluation. If a string is provided, we treat it as the metric
+ name and load it with default settings. If a callable is provided, it must take two arguments
+ (`y_pred`, `y_test`) and return a dictionary with metric keys to values.
+ metric_kwargs (`Dict[str, Any]`, *optional*):
+ Keyword arguments passed to the evaluation function if `metric` is an evaluation string like "f1".
+ For example useful for providing an averaging strategy for computing f1 in a multi-label setting.
+ callbacks (`List[`[`~transformers.TrainerCallback`]`]`, *optional*):
+ A list of callbacks to customize the training loop. Will add those to the list of default callbacks
+ detailed in [here](https://huggingface.co/docs/transformers/main/en/main_classes/callback).
+ If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method.
+ column_mapping (`Dict[str, str]`, *optional*):
+ A mapping from the column names in the dataset to the column names expected by the model.
+ The expected format is a dictionary with the following format:
+ `{"text_column_name": "text", "label_column_name: "label"}`.
+ """
+
+ def __init__(
+ self,
+ model: Optional["SetFitModel"] = None,
+ args: Optional[TrainingArguments] = None,
+ train_dataset: Optional["Dataset"] = None,
+ eval_dataset: Optional["Dataset"] = None,
+ model_init: Optional[Callable[[], "SetFitModel"]] = None,
+ metric: Union[str, Callable[["Dataset", "Dataset"], Dict[str, float]]] = "accuracy",
+ metric_kwargs: Optional[Dict[str, Any]] = None,
+ callbacks: Optional[List[TrainerCallback]] = None,
+ column_mapping: Optional[Dict[str, str]] = None,
+ ) -> None:
+ if args is not None and not isinstance(args, TrainingArguments):
+ raise ValueError("`args` must be a `TrainingArguments` instance imported from `setfit`.")
+ self.args = args or TrainingArguments()
+ self.column_mapping = column_mapping
+ if train_dataset:
+ self._validate_column_mapping(train_dataset)
+ if self.column_mapping is not None:
+ logger.info("Applying column mapping to the training dataset")
+ train_dataset = self._apply_column_mapping(train_dataset, self.column_mapping)
+ self.train_dataset = train_dataset
+
+ if eval_dataset:
+ self._validate_column_mapping(eval_dataset)
+ if self.column_mapping is not None:
+ logger.info("Applying column mapping to the evaluation dataset")
+ eval_dataset = self._apply_column_mapping(eval_dataset, self.column_mapping)
+ self.eval_dataset = eval_dataset
+
+ self.model_init = model_init
+ self.metric = metric
+ self.metric_kwargs = metric_kwargs
+ self.logs_mapper = {}
+
+ # Seed must be set before instantiating the model when using model_init.
+ set_seed(12)
+
+ if model is None:
+ if model_init is not None:
+ model = self.call_model_init()
+ else:
+ raise RuntimeError("`Trainer` requires either a `model` or `model_init` argument.")
+ else:
+ if model_init is not None:
+ raise RuntimeError("`Trainer` requires either a `model` or `model_init` argument, but not both.")
+
+ self.model = model
+ self.hp_search_backend = None
+
+ # Setup the callbacks
+ default_callbacks = DEFAULT_CALLBACKS + get_reporting_integration_callbacks(self.args.report_to)
+ callbacks = default_callbacks if callbacks is None else default_callbacks + callbacks
+ if WandbCallback in callbacks:
+ # Set the W&B project via environment variables if it's not already set
+ os.environ.setdefault("WANDB_PROJECT", "setfit")
+ # TODO: Observe optimizer and scheduler by wrapping SentenceTransformer._get_scheduler
+ self.callback_handler = CallbackHandler(callbacks, self.model, self.model.model_body.tokenizer, None, None)
+ self.state = TrainerState()
+ self.control = TrainerControl()
+ self.add_callback(DEFAULT_PROGRESS_CALLBACK if self.args.show_progress_bar else PrinterCallback)
+ self.control = self.callback_handler.on_init_end(self.args, self.state, self.control)
+
+ # Add the callback for filling the model card data with hyperparameters
+ # and evaluation results
+ self.add_callback(ModelCardCallback(self))
+
+ self.callback_handler.on_init_end(args, self.state, self.control)
+
+ def add_callback(self, callback: Union[type, TrainerCallback]) -> None:
+ """
+ Add a callback to the current list of [`~transformers.TrainerCallback`].
+
+ Args:
+ callback (`type` or [`~transformers.TrainerCallback`]):
+ A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the
+ first case, will instantiate a member of that class.
+ """
+ self.callback_handler.add_callback(callback)
+
+ def pop_callback(self, callback: Union[type, TrainerCallback]) -> TrainerCallback:
+ """
+ Remove a callback from the current list of [`~transformers.TrainerCallback`] and returns it.
+
+ If the callback is not found, returns `None` (and no error is raised).
+
+ Args:
+ callback (`type` or [`~transformers.TrainerCallback`]):
+ A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the
+ first case, will pop the first member of that class found in the list of callbacks.
+
+ Returns:
+ [`~transformers.TrainerCallback`]: The callback removed, if found.
+ """
+ return self.callback_handler.pop_callback(callback)
+
+ def remove_callback(self, callback: Union[type, TrainerCallback]) -> None:
+ """
+ Remove a callback from the current list of [`~transformers.TrainerCallback`].
+
+ Args:
+ callback (`type` or [`~transformers.TrainerCallback`]):
+ A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the
+ first case, will remove the first member of that class found in the list of callbacks.
+ """
+ self.callback_handler.remove_callback(callback)
+
+ def apply_hyperparameters(self, params: Dict[str, Any], final_model: bool = False) -> None:
+ """Applies a dictionary of hyperparameters to both the trainer and the model
+
+ Args:
+ params (`Dict[str, Any]`): The parameters, usually from `BestRun.hyperparameters`
+ final_model (`bool`, *optional*, defaults to `False`): If `True`, replace the `model_init()` function with a fixed model based on the parameters.
+ """
+
+ if self.args is not None:
+ self.args = self.args.update(params, ignore_extra=True)
+ else:
+ self.args = TrainingArguments.from_dict(params, ignore_extra=True)
+
+ # Seed must be set before instantiating the model when using model_init.
+ set_seed(self.args.seed)
+ self.model = self.model_init(params)
+ if final_model:
+ self.model_init = None
+
+ def _hp_search_setup(self, trial: Union["optuna.Trial", Dict[str, Any]]) -> None:
+ """HP search setup code"""
+
+ # Heavily inspired by transformers.Trainer._hp_search_setup
+ if self.hp_search_backend is None or trial is None:
+ return
+
+ if isinstance(trial, Dict): # For passing a Dict to train() -- mostly unused for now
+ params = trial
+ elif self.hp_search_backend == HPSearchBackend.OPTUNA:
+ params = self.hp_space(trial)
+ else:
+ raise ValueError("Invalid trial parameter")
+
+ logger.info(f"Trial: {params}")
+ self.apply_hyperparameters(params, final_model=False)
+
+ def call_model_init(self, params: Optional[Dict[str, Any]] = None) -> "SetFitModel":
+ model_init_argcount = number_of_arguments(self.model_init)
+ if model_init_argcount == 0:
+ model = self.model_init()
+ elif model_init_argcount == 1:
+ model = self.model_init(params)
+ else:
+ raise RuntimeError("`model_init` should have 0 or 1 argument.")
+
+ if model is None:
+ raise RuntimeError("`model_init` should not return None.")
+
+ return model
+
+ def freeze(self, component: Optional[Literal["body", "head"]] = None) -> None:
+ """Freeze the model body and/or the head, preventing further training on that component until unfrozen.
+
+ This method is deprecated, use `SetFitModel.freeze` instead.
+
+ Args:
+ component (`Literal["body", "head"]`, *optional*): Either "body" or "head" to freeze that component.
+ If no component is provided, freeze both. Defaults to None.
+ """
+ warnings.warn(
+ f"`{self.__class__.__name__}.freeze` is deprecated and will be removed in v2.0.0 of SetFit. "
+ "Please use `SetFitModel.freeze` directly instead.",
+ DeprecationWarning,
+ stacklevel=2,
+ )
+ return self.model.freeze(component)
+
+ def unfreeze(
+ self, component: Optional[Literal["body", "head"]] = None, keep_body_frozen: Optional[bool] = None
+ ) -> None:
+ """Unfreeze the model body and/or the head, allowing further training on that component.
+
+ This method is deprecated, use `SetFitModel.unfreeze` instead.
+
+ Args:
+ component (`Literal["body", "head"]`, *optional*): Either "body" or "head" to unfreeze that component.
+ If no component is provided, unfreeze both. Defaults to None.
+ keep_body_frozen (`bool`, *optional*): Deprecated argument, use `component` instead.
+ """
+ warnings.warn(
+ f"`{self.__class__.__name__}.unfreeze` is deprecated and will be removed in v2.0.0 of SetFit. "
+ "Please use `SetFitModel.unfreeze` directly instead.",
+ DeprecationWarning,
+ stacklevel=2,
+ )
+ return self.model.unfreeze(component, keep_body_frozen=keep_body_frozen)
+
+ def train(
+ self,
+ args: Optional[TrainingArguments] = None,
+ trial: Optional[Union["optuna.Trial", Dict[str, Any]]] = None,
+ **kwargs,
+ ) -> None:
+ """
+ Main training entry point.
+
+ Args:
+ args (`TrainingArguments`, *optional*):
+ Temporarily change the training arguments for this training call.
+ trial (`optuna.Trial` or `Dict[str, Any]`, *optional*):
+ The trial run or the hyperparameter dictionary for hyperparameter search.
+ """
+ if len(kwargs):
+ warnings.warn(
+ f"`{self.__class__.__name__}.train` does not accept keyword arguments anymore. "
+ f"Please provide training arguments via a `TrainingArguments` instance to the `{self.__class__.__name__}` "
+ f"initialisation or the `{self.__class__.__name__}.train` method.",
+ DeprecationWarning,
+ stacklevel=2,
+ )
+
+ if trial: # Trial and model initialization
+ self._hp_search_setup(trial) # sets trainer parameters and initializes model
+
+ args = args or self.args or TrainingArguments()
+
+ if self.train_dataset is None:
+ raise ValueError(
+ f"Training requires a `train_dataset` given to the `{self.__class__.__name__}` initialization."
+ )
+
+ train_parameters = self.dataset_to_parameters(self.train_dataset)
+ full_parameters = (
+ train_parameters + self.dataset_to_parameters(self.eval_dataset) if self.eval_dataset else train_parameters
+ )
+
+ self.train_embeddings(*full_parameters, args=args)
+ self.train_classifier(*train_parameters, args=args)
+
+ def dataset_to_parameters(self, dataset: Dataset) -> List[Iterable]:
+ return [dataset["text"], dataset["label"]]
+
+ def train_embeddings(
+ self,
+ x_train: List[str],
+ y_train: Optional[Union[List[int], List[List[int]]]] = None,
+ x_eval: Optional[List[str]] = None,
+ y_eval: Optional[Union[List[int], List[List[int]]]] = None,
+ args: Optional[TrainingArguments] = None,
+ ) -> None:
+ """
+ Method to perform the embedding phase: finetuning the `SentenceTransformer` body.
+
+ Args:
+ x_train (`List[str]`): A list of training sentences.
+ y_train (`Union[List[int], List[List[int]]]`): A list of labels corresponding to the training sentences.
+ args (`TrainingArguments`, *optional*):
+ Temporarily change the training arguments for this training call.
+ """
+ args = args or self.args or TrainingArguments()
+ # Since transformers v4.32.0, the log/eval/save steps should be saved on the state instead
+ self.state.logging_steps = args.logging_steps
+ self.state.eval_steps = args.eval_steps
+ self.state.save_steps = args.save_steps
+ # Reset the state
+ self.state.global_step = 0
+ self.state.total_flos = 0
+
+ train_max_pairs = -1 if args.max_steps == -1 else args.max_steps * args.embedding_batch_size
+ train_dataloader, loss_func, batch_size = self.get_dataloader(
+ x_train, y_train, args=args, max_pairs=train_max_pairs
+ )
+ if x_eval is not None and args.evaluation_strategy != IntervalStrategy.NO:
+ eval_max_pairs = -1 if args.eval_max_steps == -1 else args.eval_max_steps * args.embedding_batch_size
+ eval_dataloader, _, _ = self.get_dataloader(x_eval, y_eval, args=args, max_pairs=eval_max_pairs)
+ else:
+ eval_dataloader = None
+
+ if args.max_steps > 0:
+ total_train_steps = args.max_steps
+ else:
+ total_train_steps = len(train_dataloader) * args.embedding_num_epochs
+ logger.info("***** Running training *****")
+ logger.info(f" Num examples = {len(train_dataloader)}")
+ logger.info(f" Num epochs = {args.embedding_num_epochs}")
+ logger.info(f" Total optimization steps = {total_train_steps}")
+ logger.info(f" Total train batch size = {batch_size}")
+
+ warmup_steps = math.ceil(total_train_steps * args.warmup_proportion)
+ self._train_sentence_transformer(
+ self.model.model_body,
+ train_dataloader=train_dataloader,
+ eval_dataloader=eval_dataloader,
+ args=args,
+ loss_func=loss_func,
+ warmup_steps=warmup_steps,
+ )
+
+ def get_dataloader(
+ self, x: List[str], y: Union[List[int], List[List[int]]], args: TrainingArguments, max_pairs: int = -1
+ ) -> Tuple[DataLoader, nn.Module, int]:
+ # sentence-transformers adaptation
+ input_data = [InputExample(texts=[text], label=label) for text, label in zip(x, y)]
+
+ if args.loss in [
+ losses.BatchAllTripletLoss,
+ losses.BatchHardTripletLoss,
+ losses.BatchSemiHardTripletLoss,
+ losses.BatchHardSoftMarginTripletLoss,
+ SupConLoss,
+ ]:
+ data_sampler = SentenceLabelDataset(input_data, samples_per_label=args.samples_per_label)
+ batch_size = min(args.embedding_batch_size, len(data_sampler))
+ dataloader = DataLoader(data_sampler, batch_size=batch_size, drop_last=True)
+
+ if args.loss is losses.BatchHardSoftMarginTripletLoss:
+ loss = args.loss(
+ model=self.model.model_body,
+ distance_metric=args.distance_metric,
+ )
+ elif args.loss is SupConLoss:
+ loss = args.loss(model=self.model.model_body)
+ else:
+ loss = args.loss(
+ model=self.model.model_body,
+ distance_metric=args.distance_metric,
+ margin=args.margin,
+ )
+ else:
+ data_sampler = ContrastiveDataset(
+ input_data,
+ self.model.multi_target_strategy,
+ args.num_iterations,
+ args.sampling_strategy,
+ max_pairs=max_pairs,
+ )
+ # shuffle_sampler = True can be dropped in for further 'randomising'
+ shuffle_sampler = True if args.sampling_strategy == "unique" else False
+ batch_size = min(args.embedding_batch_size, len(data_sampler))
+ dataloader = DataLoader(data_sampler, batch_size=batch_size, shuffle=shuffle_sampler, drop_last=False)
+ loss = args.loss(self.model.model_body)
+
+ return dataloader, loss, batch_size
+
+ def log(self, args: TrainingArguments, logs: Dict[str, float]) -> None:
+ """
+ Log `logs` on the various objects watching training.
+
+ Subclass and override this method to inject custom behavior.
+
+ Args:
+ logs (`Dict[str, float]`):
+ The values to log.
+ """
+ logs = {self.logs_mapper.get(key, key): value for key, value in logs.items()}
+ if self.state.epoch is not None:
+ logs["epoch"] = round(self.state.epoch, 2)
+
+ output = {**logs, **{"step": self.state.global_step}}
+ self.state.log_history.append(output)
+ return self.callback_handler.on_log(args, self.state, self.control, logs)
+
+ def _set_logs_mapper(self, logs_mapper: Dict[str, str]) -> None:
+ """Set the logging mapper.
+
+ Args:
+ logs_mapper (str): The logging mapper, e.g. {"eval_embedding_loss": "eval_aspect_embedding_loss"}.
+ """
+ self.logs_mapper = logs_mapper
+
+ def _train_sentence_transformer(
+ self,
+ model_body: SentenceTransformer,
+ train_dataloader: DataLoader,
+ eval_dataloader: Optional[DataLoader],
+ args: TrainingArguments,
+ loss_func: nn.Module,
+ warmup_steps: int = 10000,
+ ) -> None:
+ """
+ Train the model with the given training objective
+ Each training objective is sampled in turn for one batch.
+ We sample only as many batches from each objective as there are in the smallest one
+ to make sure of equal training with each dataset.
+ """
+ # TODO: args.gradient_accumulation_steps
+ # TODO: fp16/bf16, etc.
+ # TODO: Safetensors
+
+ # Hardcoded training arguments
+ max_grad_norm = 1
+ #
+ #
+ #
+ #
+ #
+ weight_decay = 5e-3 # 5e-3 best
+ #
+ #
+ #
+ #
+ #
+
+ self.state.epoch = 0
+ start_time = time.time()
+ if args.max_steps > 0:
+ self.state.max_steps = args.max_steps
+ else:
+ self.state.max_steps = len(train_dataloader) * args.embedding_num_epochs
+ self.control = self.callback_handler.on_train_begin(args, self.state, self.control)
+ steps_per_epoch = len(train_dataloader)
+
+ if args.use_amp:
+ scaler = torch.cuda.amp.GradScaler()
+
+ model_body.to(model_body._target_device)
+ loss_func.to(model_body._target_device)
+
+ # Use smart batching
+ train_dataloader.collate_fn = model_body.smart_batching_collate
+ if eval_dataloader:
+ eval_dataloader.collate_fn = model_body.smart_batching_collate
+
+ # Prepare optimizers
+ param_optimizer = list(loss_func.named_parameters())
+
+ no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
+ optimizer_grouped_parameters = [
+ {
+ "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
+ "weight_decay": weight_decay,
+ },
+ {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
+ ]
+
+ optimizer = torch.optim.AdamW(optimizer_grouped_parameters, **{"lr": args.body_embedding_learning_rate})
+ scheduler_obj = model_body._get_scheduler(
+ optimizer, scheduler="WarmupLinear", warmup_steps=warmup_steps, t_total=self.state.max_steps
+ )
+ self.callback_handler.optimizer = optimizer
+ self.callback_handler.lr_scheduler = scheduler_obj
+ self.callback_handler.train_dataloader = train_dataloader
+ self.callback_handler.eval_dataloader = eval_dataloader
+
+ self.callback_handler.on_train_begin(args, self.state, self.control)
+
+ data_iterator = iter(train_dataloader)
+ skip_scheduler = False
+ for epoch in range(args.embedding_num_epochs):
+ self.control = self.callback_handler.on_epoch_begin(args, self.state, self.control)
+
+ loss_func.zero_grad()
+ loss_func.train()
+
+ for step in range(steps_per_epoch):
+ self.control = self.callback_handler.on_step_begin(args, self.state, self.control)
+
+ try:
+ data = next(data_iterator)
+ except StopIteration:
+ data_iterator = iter(train_dataloader)
+ data = next(data_iterator)
+
+ features, labels = data
+ labels = labels.to(model_body._target_device)
+ features = list(map(lambda batch: batch_to_device(batch, model_body._target_device), features))
+
+ if args.use_amp:
+ with autocast():
+ loss_value = loss_func(features, labels)
+
+ scale_before_step = scaler.get_scale()
+ scaler.scale(loss_value).backward()
+ scaler.unscale_(optimizer)
+ torch.nn.utils.clip_grad_norm_(loss_func.parameters(), max_grad_norm)
+ scaler.step(optimizer)
+ scaler.update()
+
+ skip_scheduler = scaler.get_scale() != scale_before_step
+ else:
+ loss_value = loss_func(features, labels)
+ loss_value.backward()
+ torch.nn.utils.clip_grad_norm_(loss_func.parameters(), max_grad_norm)
+ optimizer.step()
+
+ optimizer.zero_grad()
+
+ if not skip_scheduler:
+ scheduler_obj.step()
+
+ self.state.global_step += 1
+ self.state.epoch = epoch + (step + 1) / steps_per_epoch
+ self.control = self.callback_handler.on_step_end(args, self.state, self.control)
+
+ self.maybe_log_eval_save(model_body, eval_dataloader, args, scheduler_obj, loss_func, loss_value)
+
+ if self.control.should_epoch_stop or self.control.should_training_stop:
+ break
+
+ self.control = self.callback_handler.on_epoch_end(args, self.state, self.control)
+
+ self.maybe_log_eval_save(model_body, eval_dataloader, args, scheduler_obj, loss_func, loss_value)
+
+ if self.control.should_training_stop:
+ break
+
+ if self.args.load_best_model_at_end and self.state.best_model_checkpoint:
+ dir_name = Path(self.state.best_model_checkpoint).name
+ if dir_name.startswith("step_"):
+ step_to_load = dir_name[5:]
+ logger.info(f"Loading best SentenceTransformer model from step {step_to_load}.")
+ self.model.model_card_data.set_best_model_step(int(step_to_load))
+ self.model.model_body = SentenceTransformer(
+ self.state.best_model_checkpoint, device=model_body._target_device
+ )
+ self.model.model_body.to(model_body._target_device)
+
+ # Ensure logging the speed metrics
+ num_train_samples = self.state.max_steps * args.embedding_batch_size # * args.gradient_accumulation_steps
+ metrics = speed_metrics("train", start_time, num_samples=num_train_samples, num_steps=self.state.max_steps)
+ self.control.should_log = True
+ self.log(args, metrics)
+
+ self.control = self.callback_handler.on_train_end(args, self.state, self.control)
+
+ def maybe_log_eval_save(
+ self,
+ model_body: SentenceTransformer,
+ eval_dataloader: Optional[DataLoader],
+ args: TrainingArguments,
+ scheduler_obj,
+ loss_func,
+ loss_value: torch.Tensor,
+ ) -> None:
+ if self.control.should_log:
+ learning_rate = scheduler_obj.get_last_lr()[0]
+ metrics = {"embedding_loss": round(loss_value.item(), 4), "learning_rate": learning_rate}
+ self.control = self.log(args, metrics)
+
+ eval_loss = None
+ if self.control.should_evaluate and eval_dataloader is not None:
+ eval_loss = self._evaluate_with_loss(model_body, eval_dataloader, args, loss_func)
+ learning_rate = scheduler_obj.get_last_lr()[0]
+ metrics = {"eval_embedding_loss": round(eval_loss, 4), "learning_rate": learning_rate}
+ self.control = self.log(args, metrics)
+
+ self.control = self.callback_handler.on_evaluate(args, self.state, self.control, metrics)
+
+ loss_func.zero_grad()
+ loss_func.train()
+
+ if self.control.should_save:
+ checkpoint_dir = self._checkpoint(self.args.output_dir, args.save_total_limit, self.state.global_step)
+ self.control = self.callback_handler.on_save(self.args, self.state, self.control)
+
+ if eval_loss is not None and (self.state.best_metric is None or eval_loss < self.state.best_metric):
+ self.state.best_metric = eval_loss
+ self.state.best_model_checkpoint = checkpoint_dir
+
+ def _evaluate_with_loss(
+ self,
+ model_body: SentenceTransformer,
+ eval_dataloader: DataLoader,
+ args: TrainingArguments,
+ loss_func: nn.Module,
+ ) -> float:
+ model_body.eval()
+ losses = []
+ eval_steps = (
+ min(len(eval_dataloader), args.eval_max_steps) if args.eval_max_steps != -1 else len(eval_dataloader)
+ )
+ for step, data in enumerate(
+ tqdm(iter(eval_dataloader), total=eval_steps, leave=False, disable=not args.show_progress_bar), start=1
+ ):
+ features, labels = data
+ labels = labels.to(model_body._target_device)
+ features = list(map(lambda batch: batch_to_device(batch, model_body._target_device), features))
+
+ if args.use_amp:
+ with autocast():
+ loss_value = loss_func(features, labels)
+
+ losses.append(loss_value.item())
+ else:
+ losses.append(loss_func(features, labels).item())
+
+ if step >= eval_steps:
+ break
+
+ model_body.train()
+ return sum(losses) / len(losses)
+
+ def _checkpoint(self, checkpoint_path: str, checkpoint_save_total_limit: int, step: int) -> None:
+ # Delete old checkpoints
+ if checkpoint_save_total_limit is not None and checkpoint_save_total_limit > 0:
+ old_checkpoints = []
+ for subdir in Path(checkpoint_path).glob("step_*"):
+ if subdir.name[5:].isdigit() and (
+ self.state.best_model_checkpoint is None or subdir != Path(self.state.best_model_checkpoint)
+ ):
+ old_checkpoints.append({"step": int(subdir.name[5:]), "path": str(subdir)})
+
+ if len(old_checkpoints) > checkpoint_save_total_limit - 1:
+ old_checkpoints = sorted(old_checkpoints, key=lambda x: x["step"])
+ shutil.rmtree(old_checkpoints[0]["path"])
+
+ checkpoint_file_path = str(Path(checkpoint_path) / f"step_{step}")
+ self.model.save_pretrained(checkpoint_file_path)
+ return checkpoint_file_path
+
+ def train_classifier(
+ self, x_train: List[str], y_train: Union[List[int], List[List[int]]], args: Optional[TrainingArguments] = None
+ ) -> None:
+ """
+ Method to perform the classifier phase: fitting a classifier head.
+
+ Args:
+ x_train (`List[str]`): A list of training sentences.
+ y_train (`Union[List[int], List[List[int]]]`): A list of labels corresponding to the training sentences.
+ args (`TrainingArguments`, *optional*):
+ Temporarily change the training arguments for this training call.
+ """
+ args = args or self.args or TrainingArguments()
+
+ self.model.fit(
+ x_train,
+ y_train,
+ num_epochs=args.classifier_num_epochs,
+ batch_size=args.classifier_batch_size,
+ body_learning_rate=args.body_classifier_learning_rate,
+ head_learning_rate=args.head_learning_rate,
+ l2_weight=args.l2_weight,
+ max_length=args.max_length,
+ show_progress_bar=args.show_progress_bar,
+ end_to_end=args.end_to_end,
+ )
+
+ def evaluate(self, dataset: Optional[Dataset] = None, metric_key_prefix: str = "test") -> Dict[str, float]:
+ """
+ Computes the metrics for a given classifier.
+
+ Args:
+ dataset (`Dataset`, *optional*):
+ The dataset to compute the metrics on. If not provided, will use the evaluation dataset passed via
+ the `eval_dataset` argument at `Trainer` initialization.
+
+ Returns:
+ `Dict[str, float]`: The evaluation metrics.
+ """
+
+ if dataset is not None:
+ self._validate_column_mapping(dataset)
+ if self.column_mapping is not None:
+ logger.info("Applying column mapping to the evaluation dataset")
+ eval_dataset = self._apply_column_mapping(dataset, self.column_mapping)
+ else:
+ eval_dataset = dataset
+ else:
+ eval_dataset = self.eval_dataset
+
+ if eval_dataset is None:
+ raise ValueError("No evaluation dataset provided to `Trainer.evaluate` nor the `Trainer` initialzation.")
+
+ x_test = eval_dataset["text"]
+ y_test = eval_dataset["label"]
+
+ logger.info("***** Running evaluation *****")
+ y_pred = self.model.predict(x_test, use_labels=False)
+ #
+ #
+ #
+ #
+ #
+ if isinstance(y_pred, torch.Tensor):
+ y_pred = y_pred.cpu()
+ #
+ #
+ #
+ #
+ #
+
+ # Normalize string outputs
+ if y_test and isinstance(y_test[0], str):
+ encoder = LabelEncoder()
+ encoder.fit(list(y_test) + list(y_pred))
+ y_test = encoder.transform(y_test)
+ y_pred = encoder.transform(y_pred)
+
+ metric_kwargs = self.metric_kwargs or {}
+ if isinstance(self.metric, str):
+ metric_config = "multilabel" if self.model.multi_target_strategy is not None else None
+ metric_fn = evaluate.load(self.metric, config_name=metric_config)
+
+ results = metric_fn.compute(predictions=y_pred, references=y_test, **metric_kwargs)
+
+ elif callable(self.metric):
+ results = self.metric(y_pred, y_test, **metric_kwargs)
+
+ else:
+ raise ValueError("metric must be a string or a callable")
+
+ if not isinstance(results, dict):
+ results = {"metric": results}
+ self.model.model_card_data.post_training_eval_results(
+ {f"{metric_key_prefix}_{key}": value for key, value in results.items()}
+ )
+ return results
+
+ def hyperparameter_search(
+ self,
+ hp_space: Optional[Callable[["optuna.Trial"], Dict[str, float]]] = None,
+ compute_objective: Optional[Callable[[Dict[str, float]], float]] = None,
+ n_trials: int = 10,
+ direction: str = "maximize",
+ backend: Optional[Union["str", HPSearchBackend]] = None,
+ hp_name: Optional[Callable[["optuna.Trial"], str]] = None,
+ **kwargs,
+ ) -> BestRun:
+ """
+ Launch a hyperparameter search using `optuna`. The optimized quantity is determined
+ by `compute_objective`, which defaults to a function returning the evaluation loss when no metric is provided,
+ the sum of all metrics otherwise.
+
+
+
+ To use this method, you need to have provided a `model_init` when initializing your [`Trainer`]: we need to
+ reinitialize the model at each new run.
+
+
+
+ Args:
+ hp_space (`Callable[["optuna.Trial"], Dict[str, float]]`, *optional*):
+ A function that defines the hyperparameter search space. Will default to
+ [`~transformers.trainer_utils.default_hp_space_optuna`].
+ compute_objective (`Callable[[Dict[str, float]], float]`, *optional*):
+ A function computing the objective to minimize or maximize from the metrics returned by the `evaluate`
+ method. Will default to [`~transformers.trainer_utils.default_compute_objective`] which uses the sum of metrics.
+ n_trials (`int`, *optional*, defaults to 100):
+ The number of trial runs to test.
+ direction (`str`, *optional*, defaults to `"maximize"`):
+ Whether to optimize greater or lower objects. Can be `"minimize"` or `"maximize"`, you should pick
+ `"minimize"` when optimizing the validation loss, `"maximize"` when optimizing one or several metrics.
+ backend (`str` or [`~transformers.training_utils.HPSearchBackend`], *optional*):
+ The backend to use for hyperparameter search. Only optuna is supported for now.
+ TODO: add support for ray and sigopt.
+ hp_name (`Callable[["optuna.Trial"], str]]`, *optional*):
+ A function that defines the trial/run name. Will default to None.
+ kwargs (`Dict[str, Any]`, *optional*):
+ Additional keyword arguments passed along to `optuna.create_study`. For more
+ information see:
+
+ - the documentation of
+ [optuna.create_study](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html)
+
+ Returns:
+ [`trainer_utils.BestRun`]: All the information about the best run.
+ """
+ if backend is None:
+ backend = default_hp_search_backend()
+ if backend is None:
+ raise RuntimeError("optuna should be installed. To install optuna run `pip install optuna`.")
+ backend = HPSearchBackend(backend)
+ if backend == HPSearchBackend.OPTUNA and not is_optuna_available():
+ raise RuntimeError("You picked the optuna backend, but it is not installed. Use `pip install optuna`.")
+ elif backend != HPSearchBackend.OPTUNA:
+ raise RuntimeError("Only optuna backend is supported for hyperparameter search.")
+ self.hp_search_backend = backend
+ if self.model_init is None:
+ raise RuntimeError(
+ "To use hyperparameter search, you need to pass your model through a model_init function."
+ )
+
+ self.hp_space = default_hp_space_optuna if hp_space is None else hp_space
+ self.hp_name = hp_name
+ self.compute_objective = default_compute_objective if compute_objective is None else compute_objective
+
+ backend_dict = {
+ HPSearchBackend.OPTUNA: run_hp_search_optuna,
+ }
+ best_run = backend_dict[backend](self, n_trials, direction, **kwargs)
+
+ self.hp_search_backend = None
+ return best_run
+
+ def push_to_hub(self, repo_id: str, **kwargs) -> str:
+ """Upload model checkpoint to the Hub using `huggingface_hub`.
+
+ See the full list of parameters for your `huggingface_hub` version in the\
+ [huggingface_hub documentation](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.ModelHubMixin.push_to_hub).
+
+ Args:
+ repo_id (`str`):
+ The full repository ID to push to, e.g. `"tomaarsen/setfit-sst2"`.
+ config (`dict`, *optional*):
+ Configuration object to be saved alongside the model weights.
+ commit_message (`str`, *optional*):
+ Message to commit while pushing.
+ private (`bool`, *optional*, defaults to `False`):
+ Whether the repository created should be private.
+ api_endpoint (`str`, *optional*):
+ The API endpoint to use when pushing the model to the hub.
+ token (`str`, *optional*):
+ The token to use as HTTP bearer authorization for remote files.
+ If not set, will use the token set when logging in with
+ `transformers-cli login` (stored in `~/.huggingface`).
+ branch (`str`, *optional*):
+ The git branch on which to push the model. This defaults to
+ the default branch as specified in your repository, which
+ defaults to `"main"`.
+ create_pr (`boolean`, *optional*):
+ Whether or not to create a Pull Request from `branch` with that commit.
+ Defaults to `False`.
+ allow_patterns (`List[str]` or `str`, *optional*):
+ If provided, only files matching at least one pattern are pushed.
+ ignore_patterns (`List[str]` or `str`, *optional*):
+ If provided, files matching any of the patterns are not pushed.
+
+ Returns:
+ str: The url of the commit of your model in the given repository.
+ """
+ if "/" not in repo_id:
+ raise ValueError(
+ '`repo_id` must be a full repository ID, including organisation, e.g. "tomaarsen/setfit-sst2".'
+ )
+ commit_message = kwargs.pop("commit_message", "Add SetFit model")
+ return self.model.push_to_hub(repo_id, commit_message=commit_message, **kwargs)
+
+
+class SetFitTrainer(Trainer):
+ """
+ `SetFitTrainer` has been deprecated and will be removed in v2.0.0 of SetFit.
+ Please use `Trainer` instead.
+ """
+
+ def __init__(
+ self,
+ model: Optional["SetFitModel"] = None,
+ train_dataset: Optional["Dataset"] = None,
+ eval_dataset: Optional["Dataset"] = None,
+ model_init: Optional[Callable[[], "SetFitModel"]] = None,
+ metric: Union[str, Callable[["Dataset", "Dataset"], Dict[str, float]]] = "accuracy",
+ metric_kwargs: Optional[Dict[str, Any]] = None,
+ loss_class=losses.CosineSimilarityLoss,
+ num_iterations: int = 20,
+ num_epochs: int = 1,
+ learning_rate: float = 2e-5,
+ batch_size: int = 16,
+ seed: int = 42,
+ column_mapping: Optional[Dict[str, str]] = None,
+ use_amp: bool = False,
+ warmup_proportion: float = 0.1,
+ distance_metric: Callable = BatchHardTripletLossDistanceFunction.cosine_distance,
+ margin: float = 0.25,
+ samples_per_label: int = 2,
+ ):
+ warnings.warn(
+ "`SetFitTrainer` has been deprecated and will be removed in v2.0.0 of SetFit. "
+ "Please use `Trainer` instead.",
+ DeprecationWarning,
+ stacklevel=2,
+ )
+ args = TrainingArguments(
+ num_iterations=num_iterations,
+ num_epochs=num_epochs,
+ body_learning_rate=learning_rate,
+ head_learning_rate=learning_rate,
+ batch_size=batch_size,
+ seed=seed,
+ use_amp=use_amp,
+ warmup_proportion=warmup_proportion,
+ distance_metric=distance_metric,
+ margin=margin,
+ samples_per_label=samples_per_label,
+ loss=loss_class,
+ )
+ super().__init__(
+ model=model,
+ args=args,
+ train_dataset=train_dataset,
+ eval_dataset=eval_dataset,
+ model_init=model_init,
+ metric=metric,
+ metric_kwargs=metric_kwargs,
+ column_mapping=column_mapping,
+ )
diff --git a/test_models/setfit/trainer_distillation.py b/test_models/setfit/trainer_distillation.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f09c15e3ec79f76ef9773ceef1770af999c0bc1
--- /dev/null
+++ b/test_models/setfit/trainer_distillation.py
@@ -0,0 +1,166 @@
+import warnings
+from typing import TYPE_CHECKING, Callable, Dict, Iterable, List, Optional, Tuple, Union
+
+import torch
+from datasets import Dataset
+from sentence_transformers import InputExample, losses, util
+from torch import nn
+from torch.utils.data import DataLoader
+
+from . import logging
+from .sampler import ContrastiveDistillationDataset
+from .trainer import Trainer
+from .training_args import TrainingArguments
+
+
+if TYPE_CHECKING:
+ from .modeling import SetFitModel
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+class DistillationTrainer(Trainer):
+ """Trainer to compress a SetFit model with knowledge distillation.
+
+ Args:
+ teacher_model (`SetFitModel`):
+ The teacher model to mimic.
+ student_model (`SetFitModel`, *optional*):
+ The model to train. If not provided, a `model_init` must be passed.
+ args (`TrainingArguments`, *optional*):
+ The training arguments to use.
+ train_dataset (`Dataset`):
+ The training dataset.
+ eval_dataset (`Dataset`, *optional*):
+ The evaluation dataset.
+ model_init (`Callable[[], SetFitModel]`, *optional*):
+ A function that instantiates the model to be used. If provided, each call to
+ [`~DistillationTrainer.train`] will start from a new instance of the model as given by this
+ function when a `trial` is passed.
+ metric (`str` or `Callable`, *optional*, defaults to `"accuracy"`):
+ The metric to use for evaluation. If a string is provided, we treat it as the metric
+ name and load it with default settings.
+ If a callable is provided, it must take two arguments (`y_pred`, `y_test`).
+ column_mapping (`Dict[str, str]`, *optional*):
+ A mapping from the column names in the dataset to the column names expected by the model.
+ The expected format is a dictionary with the following format:
+ `{"text_column_name": "text", "label_column_name: "label"}`.
+ """
+
+ _REQUIRED_COLUMNS = {"text"}
+
+ def __init__(
+ self,
+ teacher_model: "SetFitModel",
+ student_model: Optional["SetFitModel"] = None,
+ args: TrainingArguments = None,
+ train_dataset: Optional["Dataset"] = None,
+ eval_dataset: Optional["Dataset"] = None,
+ model_init: Optional[Callable[[], "SetFitModel"]] = None,
+ metric: Union[str, Callable[["Dataset", "Dataset"], Dict[str, float]]] = "accuracy",
+ column_mapping: Optional[Dict[str, str]] = None,
+ ) -> None:
+ super().__init__(
+ model=student_model,
+ args=args,
+ train_dataset=train_dataset,
+ eval_dataset=eval_dataset,
+ model_init=model_init,
+ metric=metric,
+ column_mapping=column_mapping,
+ )
+
+ self.teacher_model = teacher_model
+ self.student_model = self.model
+
+ def dataset_to_parameters(self, dataset: Dataset) -> List[Iterable]:
+ return [dataset["text"]]
+
+ def get_dataloader(
+ self,
+ x: List[str],
+ y: Optional[Union[List[int], List[List[int]]]],
+ args: TrainingArguments,
+ max_pairs: int = -1,
+ ) -> Tuple[DataLoader, nn.Module, int]:
+ x_embd_student = self.teacher_model.model_body.encode(
+ x, convert_to_tensor=self.teacher_model.has_differentiable_head
+ )
+ cos_sim_matrix = util.cos_sim(x_embd_student, x_embd_student)
+
+ input_data = [InputExample(texts=[text]) for text in x]
+ data_sampler = ContrastiveDistillationDataset(
+ input_data, cos_sim_matrix, args.num_iterations, args.sampling_strategy, max_pairs=max_pairs
+ )
+ # shuffle_sampler = True can be dropped in for further 'randomising'
+ shuffle_sampler = True if args.sampling_strategy == "unique" else False
+ batch_size = min(args.embedding_batch_size, len(data_sampler))
+ dataloader = DataLoader(data_sampler, batch_size=batch_size, shuffle=shuffle_sampler, drop_last=False)
+ loss = args.loss(self.model.model_body)
+ return dataloader, loss, batch_size
+
+ def train_classifier(self, x_train: List[str], args: Optional[TrainingArguments] = None) -> None:
+ """
+ Method to perform the classifier phase: fitting the student classifier head.
+
+ Args:
+ x_train (`List[str]`): A list of training sentences.
+ args (`TrainingArguments`, *optional*):
+ Temporarily change the training arguments for this training call.
+ """
+ y_train = self.teacher_model.predict(x_train, as_numpy=not self.student_model.has_differentiable_head)
+ return super().train_classifier(x_train, y_train, args)
+
+
+class DistillationSetFitTrainer(DistillationTrainer):
+ """
+ `DistillationSetFitTrainer` has been deprecated and will be removed in v2.0.0 of SetFit.
+ Please use `DistillationTrainer` instead.
+ """
+
+ def __init__(
+ self,
+ teacher_model: "SetFitModel",
+ student_model: Optional["SetFitModel"] = None,
+ train_dataset: Optional["Dataset"] = None,
+ eval_dataset: Optional["Dataset"] = None,
+ model_init: Optional[Callable[[], "SetFitModel"]] = None,
+ metric: Union[str, Callable[["Dataset", "Dataset"], Dict[str, float]]] = "accuracy",
+ loss_class: torch.nn.Module = losses.CosineSimilarityLoss,
+ num_iterations: int = 20,
+ num_epochs: int = 1,
+ learning_rate: float = 2e-5,
+ batch_size: int = 16,
+ seed: int = 42,
+ column_mapping: Optional[Dict[str, str]] = None,
+ use_amp: bool = False,
+ warmup_proportion: float = 0.1,
+ ) -> None:
+ warnings.warn(
+ "`DistillationSetFitTrainer` has been deprecated and will be removed in v2.0.0 of SetFit. "
+ "Please use `DistillationTrainer` instead.",
+ DeprecationWarning,
+ stacklevel=2,
+ )
+ args = TrainingArguments(
+ num_iterations=num_iterations,
+ num_epochs=num_epochs,
+ body_learning_rate=learning_rate,
+ head_learning_rate=learning_rate,
+ batch_size=batch_size,
+ seed=seed,
+ use_amp=use_amp,
+ warmup_proportion=warmup_proportion,
+ loss=loss_class,
+ )
+ super().__init__(
+ teacher_model=teacher_model,
+ student_model=student_model,
+ args=args,
+ train_dataset=train_dataset,
+ eval_dataset=eval_dataset,
+ model_init=model_init,
+ metric=metric,
+ column_mapping=column_mapping,
+ )
diff --git a/test_models/setfit/training_args.py b/test_models/setfit/training_args.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec687653acd5ff2090208af8ba57f0d5bcda2e95
--- /dev/null
+++ b/test_models/setfit/training_args.py
@@ -0,0 +1,355 @@
+from __future__ import annotations
+
+import inspect
+import json
+from copy import copy
+from dataclasses import dataclass, field, fields
+from typing import Any, Callable, Dict, Optional, Tuple, Union
+
+import torch
+from sentence_transformers import losses
+from transformers import IntervalStrategy
+from transformers.integrations import get_available_reporting_integrations
+from transformers.training_args import default_logdir
+from transformers.utils import is_torch_available
+
+from . import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+class TrainingArguments:
+ """
+ TrainingArguments is the subset of the arguments which relate to the training loop itself.
+ Note that training with SetFit consists of two phases behind the scenes: **finetuning embeddings** and
+ **training a classification head**. As a result, some of the training arguments can be tuples,
+ where the two values are used for each of the two phases, respectively. The second value is often only
+ used when training the model was loaded using `use_differentiable_head=True`.
+
+ Parameters:
+ output_dir (`str`, defaults to `"checkpoints"`):
+ The output directory where the model predictions and checkpoints will be written.
+ batch_size (`Union[int, Tuple[int, int]]`, defaults to `(16, 2)`):
+ Set the batch sizes for the embedding and classifier training phases respectively,
+ or set both if an integer is provided.
+ Note that the batch size for the classifier is only used with a differentiable PyTorch head.
+ num_epochs (`Union[int, Tuple[int, int]]`, defaults to `(1, 16)`):
+ Set the number of epochs the embedding and classifier training phases respectively,
+ or set both if an integer is provided.
+ Note that the number of epochs for the classifier is only used with a differentiable PyTorch head.
+ max_steps (`int`, defaults to `-1`):
+ If set to a positive number, the total number of training steps to perform. Overrides `num_epochs`.
+ The training may stop before reaching the set number of steps when all data is exhausted.
+ sampling_strategy (`str`, defaults to `"oversampling"`):
+ The sampling strategy of how to draw pairs in training. Possible values are:
+
+ - `"oversampling"`: Draws even number of positive/ negative sentence pairs until every
+ sentence pair has been drawn.
+ - `"undersampling"`: Draws the minimum number of positive/ negative sentence pairs until
+ every sentence pair in the minority class has been drawn.
+ - `"unique"`: Draws every sentence pair combination (likely resulting in unbalanced
+ number of positive/ negative sentence pairs).
+
+ The default is set to `"oversampling"`, ensuring all sentence pairs are drawn at least once.
+ Alternatively, setting `num_iterations` will override this argument and determine the number
+ of generated sentence pairs.
+ num_iterations (`int`, *optional*):
+ If not set the `sampling_strategy` will determine the number of sentence pairs to generate.
+ This argument sets the number of iterations to generate sentence pairs for
+ and provides compatability with Setfit = 1.6.0
+ warmup_proportion (`float`, defaults to `0.1`):
+ Proportion of the warmup in the total training steps.
+ Must be greater than or equal to 0.0 and less than or equal to 1.0.
+ l2_weight (`float`, *optional*):
+ Optional l2 weight for both the model body and head, passed to the `AdamW` optimizer in the
+ classifier training phase if a differentiable PyTorch head is used.
+ max_length (`int`, *optional*):
+ The maximum token length a tokenizer can generate. If not provided, the maximum length for
+ the `SentenceTransformer` body is used.
+ samples_per_label (`int`, defaults to `2`): Number of consecutive, random and unique samples drawn per label.
+ This is only relevant for triplet loss and ignored for `CosineSimilarityLoss`.
+ Batch size should be a multiple of samples_per_label.
+ show_progress_bar (`bool`, defaults to `True`):
+ Whether to display a progress bar for the training epochs and iterations.
+ seed (`int`, defaults to `42`):
+ Random seed that will be set at the beginning of training. To ensure reproducibility across
+ runs, use the `model_init` argument to [`Trainer`] to instantiate the model if it has some
+ randomly initialized parameters.
+ report_to (`str` or `List[str]`, *optional*, defaults to `"all"`):
+ The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`,
+ `"comet_ml"`, `"mlflow"`, `"neptune"`, `"tensorboard"`,`"clearml"` and `"wandb"`. Use `"all"` to report to
+ all integrations installed, `"none"` for no integrations.
+ run_name (`str`, *optional*):
+ A descriptor for the run. Typically used for [wandb](https://www.wandb.com/) and
+ [mlflow](https://www.mlflow.org/) logging.
+ logging_dir (`str`, *optional*):
+ [TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to
+ *runs/**CURRENT_DATETIME_HOSTNAME***.
+ logging_strategy (`str` or [`~transformers.trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
+ The logging strategy to adopt during training. Possible values are:
+
+ - `"no"`: No logging is done during training.
+ - `"epoch"`: Logging is done at the end of each epoch.
+ - `"steps"`: Logging is done every `logging_steps`.
+
+ logging_first_step (`bool`, *optional*, defaults to `False`):
+ Whether to log and evaluate the first `global_step` or not.
+ logging_steps (`int`, defaults to 50):
+ Number of update steps between two logs if `logging_strategy="steps"`.
+ evaluation_strategy (`str` or [`~transformers.trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`):
+ The evaluation strategy to adopt during training. Possible values are:
+
+ - `"no"`: No evaluation is done during training.
+ - `"steps"`: Evaluation is done (and logged) every `eval_steps`.
+ - `"epoch"`: Evaluation is done at the end of each epoch.
+
+ eval_steps (`int`, *optional*):
+ Number of update steps between two evaluations if `evaluation_strategy="steps"`. Will default to the same
+ value as `logging_steps` if not set.
+ eval_delay (`float`, *optional*):
+ Number of epochs or steps to wait for before the first evaluation can be performed, depending on the
+ evaluation_strategy.
+ eval_max_steps (`int`, defaults to `-1`):
+ If set to a positive number, the total number of evaluation steps to perform. The evaluation may stop
+ before reaching the set number of steps when all data is exhausted.
+
+ save_strategy (`str` or [`~transformers.trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
+ The checkpoint save strategy to adopt during training. Possible values are:
+
+ - `"no"`: No save is done during training.
+ - `"epoch"`: Save is done at the end of each epoch.
+ - `"steps"`: Save is done every `save_steps`.
+ save_steps (`int`, *optional*, defaults to 500):
+ Number of updates steps before two checkpoint saves if `save_strategy="steps"`.
+ save_total_limit (`int`, *optional*, defaults to `1`):
+ If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
+ `output_dir`. Note, the best model is always preserved if the `evaluation_strategy` is not `"no"`.
+ load_best_model_at_end (`bool`, *optional*, defaults to `False`):
+ Whether or not to load the best model found during training at the end of training.
+
+
+
+ When set to `True`, the parameters `save_strategy` needs to be the same as `evaluation_strategy`, and in
+ the case it is "steps", `save_steps` must be a round multiple of `eval_steps`.
+
+
+ """
+
+ output_dir: str = "checkpoints"
+
+ # batch_size is only used to conveniently set `embedding_batch_size` and `classifier_batch_size`
+ # which are used in practice
+ batch_size: Union[int, Tuple[int, int]] = field(default=(16, 2), repr=False)
+
+ # num_epochs is only used to conveniently set `embedding_num_epochs` and `classifier_num_epochs`
+ # which are used in practice
+ num_epochs: Union[int, Tuple[int, int]] = field(default=(1, 16), repr=False)
+
+ max_steps: int = -1
+
+ sampling_strategy: str = "oversampling"
+ num_iterations: Optional[int] = None
+
+ # As with batch_size and num_epochs, the first value in the tuple is the learning rate
+ # for the embeddings step, while the second value is the learning rate for the classifier step.
+ body_learning_rate: Union[float, Tuple[float, float]] = field(default=(2e-5, 1e-5), repr=False)
+ head_learning_rate: float = 1e-2
+
+ # Loss-related arguments
+ loss: Callable = losses.CosineSimilarityLoss
+ distance_metric: Callable = losses.BatchHardTripletLossDistanceFunction.cosine_distance
+ margin: float = 0.25
+
+ end_to_end: bool = field(default=False)
+
+ use_amp: bool = False
+ warmup_proportion: float = 0.1
+ l2_weight: Optional[float] = None
+ max_length: Optional[int] = None
+ samples_per_label: int = 2
+
+ # Arguments that do not affect performance
+ show_progress_bar: bool = True
+ seed: int = 42
+
+ # Logging & callbacks
+ report_to: str = "all"
+ run_name: Optional[str] = None
+ logging_dir: Optional[str] = None
+ logging_strategy: str = "steps"
+ logging_first_step: bool = True
+ logging_steps: int = 50
+
+ evaluation_strategy: str = "no"
+ eval_steps: Optional[int] = None
+ eval_delay: int = 0
+ eval_max_steps: int = -1
+
+ save_strategy: str = "steps"
+ save_steps: int = 500
+ save_total_limit: Optional[int] = 1
+
+ load_best_model_at_end: bool = False
+ metric_for_best_model: str = field(default="embedding_loss", repr=False)
+ greater_is_better: bool = field(default=False, repr=False)
+
+ def __post_init__(self) -> None:
+ # Set `self.embedding_batch_size` and `self.classifier_batch_size` using values from `self.batch_size`
+ if isinstance(self.batch_size, int):
+ self.batch_size = (self.batch_size, self.batch_size)
+
+ # Set `self.embedding_num_epochs` and `self.classifier_num_epochs` using values from `self.num_epochs`
+ if isinstance(self.num_epochs, int):
+ self.num_epochs = (self.num_epochs, self.num_epochs)
+
+ # Set `self.body_embedding_learning_rate` and `self.body_classifier_learning_rate` using
+ # values from `self.body_learning_rate`
+ if isinstance(self.body_learning_rate, float):
+ self.body_learning_rate = (self.body_learning_rate, self.body_learning_rate)
+
+ if self.warmup_proportion < 0.0 or self.warmup_proportion > 1.0:
+ raise ValueError(
+ f"warmup_proportion must be greater than or equal to 0.0 and less than or equal to 1.0! But it was: {self.warmup_proportion}"
+ )
+
+ if self.report_to in (None, "all", ["all"]):
+ self.report_to = get_available_reporting_integrations()
+ elif self.report_to in ("none", ["none"]):
+ self.report_to = []
+ elif not isinstance(self.report_to, list):
+ self.report_to = [self.report_to]
+
+ if self.logging_dir is None:
+ self.logging_dir = default_logdir()
+
+ self.logging_strategy = IntervalStrategy(self.logging_strategy)
+ self.evaluation_strategy = IntervalStrategy(self.evaluation_strategy)
+
+ if self.eval_steps is not None and self.evaluation_strategy == IntervalStrategy.NO:
+ logger.info('Using `evaluation_strategy="steps"` as `eval_steps` is defined.')
+ self.evaluation_strategy = IntervalStrategy.STEPS
+
+ # eval_steps has to be defined and non-zero, fallbacks to logging_steps if the latter is non-zero
+ if self.evaluation_strategy == IntervalStrategy.STEPS and (self.eval_steps is None or self.eval_steps == 0):
+ if self.logging_steps > 0:
+ self.eval_steps = self.logging_steps
+ else:
+ raise ValueError(
+ f"evaluation strategy {self.evaluation_strategy} requires either non-zero `eval_steps` or"
+ " `logging_steps`"
+ )
+
+ # Sanity checks for load_best_model_at_end: we require save and eval strategies to be compatible.
+ if self.load_best_model_at_end:
+ if self.evaluation_strategy != self.save_strategy:
+ raise ValueError(
+ "`load_best_model_at_end` requires the save and eval strategy to match, but found\n- Evaluation "
+ f"strategy: {self.evaluation_strategy}\n- Save strategy: {self.save_strategy}"
+ )
+ if self.evaluation_strategy == IntervalStrategy.STEPS and self.save_steps % self.eval_steps != 0:
+ raise ValueError(
+ "`load_best_model_at_end` requires the saving steps to be a round multiple of the evaluation "
+ f"steps, but found {self.save_steps}, which is not a round multiple of {self.eval_steps}."
+ )
+
+ # logging_steps must be non-zero for logging_strategy that is other than 'no'
+ if self.logging_strategy == IntervalStrategy.STEPS and self.logging_steps == 0:
+ raise ValueError(f"Logging strategy {self.logging_strategy} requires non-zero `logging_steps`")
+
+ @property
+ def embedding_batch_size(self) -> int:
+ return self.batch_size[0]
+
+ @property
+ def classifier_batch_size(self) -> int:
+ return self.batch_size[1]
+
+ @property
+ def embedding_num_epochs(self) -> int:
+ return self.num_epochs[0]
+
+ @property
+ def classifier_num_epochs(self) -> int:
+ return self.num_epochs[1]
+
+ @property
+ def body_embedding_learning_rate(self) -> float:
+ return self.body_learning_rate[0]
+
+ @property
+ def body_classifier_learning_rate(self) -> float:
+ return self.body_learning_rate[1]
+
+ def to_dict(self) -> Dict[str, Any]:
+ """Convert this instance to a dictionary.
+
+ Returns:
+ `Dict[str, Any]`: The dictionary variant of this dataclass.
+ """
+ return {field.name: getattr(self, field.name) for field in fields(self) if field.init}
+
+ @classmethod
+ def from_dict(cls, arguments: Dict[str, Any], ignore_extra: bool = False) -> TrainingArguments:
+ """Initialize a TrainingArguments instance from a dictionary.
+
+ Args:
+ arguments (`Dict[str, Any]`): A dictionary of arguments.
+ ignore_extra (`bool`, *optional*): Whether to ignore arguments that do not occur in the
+ TrainingArguments __init__ signature. Defaults to False.
+
+ Returns:
+ `TrainingArguments`: The instantiated TrainingArguments instance.
+ """
+ if ignore_extra:
+ return cls(**{key: value for key, value in arguments.items() if key in inspect.signature(cls).parameters})
+ return cls(**arguments)
+
+ def copy(self) -> TrainingArguments:
+ """Create a shallow copy of this TrainingArguments instance."""
+ return copy(self)
+
+ def update(self, arguments: Dict[str, Any], ignore_extra: bool = False) -> TrainingArguments:
+ return TrainingArguments.from_dict({**self.to_dict(), **arguments}, ignore_extra=ignore_extra)
+
+ def to_json_string(self):
+ # Serializes this instance to a JSON string.
+ return json.dumps({key: str(value) for key, value in self.to_dict().items()}, indent=2)
+
+ def to_sanitized_dict(self) -> Dict[str, Any]:
+ # Sanitized serialization to use with TensorBoardâs hparams
+ d = self.to_dict()
+ d = {**d, **{"train_batch_size": self.embedding_batch_size, "eval_batch_size": self.embedding_batch_size}}
+
+ valid_types = [bool, int, float, str]
+ if is_torch_available():
+ valid_types.append(torch.Tensor)
+
+ return {k: v if type(v) in valid_types else str(v) for k, v in d.items()}
diff --git a/test_models/setfit/utils.py b/test_models/setfit/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..261ade5c184ec79690bf6abd8bbec96dfec38ebf
--- /dev/null
+++ b/test_models/setfit/utils.py
@@ -0,0 +1,162 @@
+import types
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from time import monotonic_ns
+from typing import Any, Dict, List, NamedTuple, Optional, Tuple
+
+from datasets import Dataset, DatasetDict, load_dataset
+from sentence_transformers import losses
+from transformers.utils import copy_func
+
+from .data import create_fewshot_splits, create_fewshot_splits_multilabel
+from .losses import SupConLoss
+
+
+SEC_TO_NS_SCALE = 1000000000
+
+
+DEV_DATASET_TO_METRIC = {
+ "sst2": "accuracy",
+ "imdb": "accuracy",
+ "subj": "accuracy",
+ "bbc-news": "accuracy",
+ "enron_spam": "accuracy",
+ "student-question-categories": "accuracy",
+ "TREC-QC": "accuracy",
+ "toxic_conversations": "matthews_correlation",
+}
+
+TEST_DATASET_TO_METRIC = {
+ "emotion": "accuracy",
+ "SentEval-CR": "accuracy",
+ "sst5": "accuracy",
+ "ag_news": "accuracy",
+ "enron_spam": "accuracy",
+ "amazon_counterfactual_en": "matthews_correlation",
+}
+
+MULTILINGUAL_DATASET_TO_METRIC = {
+ f"amazon_reviews_multi_{lang}": "mae" for lang in ["en", "de", "es", "fr", "ja", "zh"]
+}
+
+LOSS_NAME_TO_CLASS = {
+ "CosineSimilarityLoss": losses.CosineSimilarityLoss,
+ "ContrastiveLoss": losses.ContrastiveLoss,
+ "OnlineContrastiveLoss": losses.OnlineContrastiveLoss,
+ "BatchSemiHardTripletLoss": losses.BatchSemiHardTripletLoss,
+ "BatchAllTripletLoss": losses.BatchAllTripletLoss,
+ "BatchHardTripletLoss": losses.BatchHardTripletLoss,
+ "BatchHardSoftMarginTripletLoss": losses.BatchHardSoftMarginTripletLoss,
+ "SupConLoss": SupConLoss,
+}
+
+
+def default_hp_space_optuna(trial) -> Dict[str, Any]:
+ from transformers.integrations import is_optuna_available
+
+ assert is_optuna_available(), "This function needs Optuna installed: `pip install optuna`"
+ return {
+ "learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-4, log=True),
+ "num_epochs": trial.suggest_int("num_epochs", 1, 5),
+ "num_iterations": trial.suggest_categorical("num_iterations", [5, 10, 20]),
+ "seed": trial.suggest_int("seed", 1, 40),
+ "batch_size": trial.suggest_categorical("batch_size", [4, 8, 16, 32, 64]),
+ }
+
+
+def load_data_splits(
+ dataset: str, sample_sizes: List[int], add_data_augmentation: bool = False
+) -> Tuple[DatasetDict, Dataset]:
+ """Loads a dataset from the Hugging Face Hub and returns the test split and few-shot training splits."""
+ print(f"\n\n\n============== {dataset} ============")
+ # Load one of the SetFit training sets from the Hugging Face Hub
+ train_split = load_dataset(f"SetFit/{dataset}", split="train")
+ train_splits = create_fewshot_splits(train_split, sample_sizes, add_data_augmentation, f"SetFit/{dataset}")
+ test_split = load_dataset(f"SetFit/{dataset}", split="test")
+ print(f"Test set: {len(test_split)}")
+ return train_splits, test_split
+
+
+def load_data_splits_multilabel(dataset: str, sample_sizes: List[int]) -> Tuple[DatasetDict, Dataset]:
+ """Loads a dataset from the Hugging Face Hub and returns the test split and few-shot training splits."""
+ print(f"\n\n\n============== {dataset} ============")
+ # Load one of the SetFit training sets from the Hugging Face Hub
+ train_split = load_dataset(f"SetFit/{dataset}", "multilabel", split="train")
+ train_splits = create_fewshot_splits_multilabel(train_split, sample_sizes)
+ test_split = load_dataset(f"SetFit/{dataset}", "multilabel", split="test")
+ print(f"Test set: {len(test_split)}")
+ return train_splits, test_split
+
+
+@dataclass
+class Benchmark:
+ """
+ Performs simple benchmarks of code portions (measures elapsed time).
+
+ Typical usage example:
+
+ bench = Benchmark()
+ with bench.track("Foo function"):
+ foo()
+ with bench.track("Bar function"):
+ bar()
+ bench.summary()
+ """
+
+ out_path: Optional[str] = None
+ summary_msg: str = field(default_factory=str)
+
+ def print(self, msg: str) -> None:
+ """
+ Prints to system out and optionally to specified out_path.
+ """
+ print(msg)
+
+ if self.out_path is not None:
+ with open(self.out_path, "a+") as f:
+ f.write(msg + "\n")
+
+ @contextmanager
+ def track(self, step):
+ """
+ Computes the elapsed time for given code context.
+ """
+ start = monotonic_ns()
+ yield
+ ns = monotonic_ns() - start
+ msg = f"\n{'*' * 70}\n'{step}' took {ns / SEC_TO_NS_SCALE:.3f}s ({ns:,}ns)\n{'*' * 70}\n"
+ print(msg)
+ self.summary_msg += msg + "\n"
+
+ def summary(self) -> None:
+ """
+ Prints summary of all benchmarks performed.
+ """
+ self.print(f"\n{'#' * 30}\nBenchmark Summary:\n{'#' * 30}\n\n{self.summary_msg}")
+
+
+class BestRun(NamedTuple):
+ """
+ The best run found by a hyperparameter search (see [`~Trainer.hyperparameter_search`]).
+
+ Parameters:
+ run_id (`str`):
+ The id of the best run.
+ objective (`float`):
+ The objective that was obtained for this run.
+ hyperparameters (`Dict[str, Any]`):
+ The hyperparameters picked to get this run.
+ backend (`Any`):
+ The relevant internal object used for optimization. For optuna this is the `study` object.
+ """
+
+ run_id: str
+ objective: float
+ hyperparameters: Dict[str, Any]
+ backend: Any = None
+
+
+def set_docstring(method, docstring, cls=None):
+ copied_function = copy_func(method)
+ copied_function.__doc__ = docstring
+ return types.MethodType(copied_function, cls or method.__self__)
diff --git a/test_models/setfit_model_finetune.py b/test_models/setfit_model_finetune.py
new file mode 100644
index 0000000000000000000000000000000000000000..9af180f4e39c29127b68c88bb9394488dbc1561e
--- /dev/null
+++ b/test_models/setfit_model_finetune.py
@@ -0,0 +1,150 @@
+from torch import nn
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
+
+def get_eval_metric(y_pred, y_test):
+ return {
+ 'accuracy': accuracy_score(y_test, y_pred),
+ 'precision': precision_score(y_test, y_pred, average='weighted'),
+ 'recall': recall_score(y_test, y_pred, average='weighted'),
+ 'f1': f1_score(y_test, y_pred, average='weighted'),
+ 'confusion_mat': confusion_matrix(y_test, y_pred, normalize='true'),
+ }
+
+class MLP(nn.Module):
+ def __init__(self, input_size=768, hidden_size=256, output_size=3, dropout_rate=.2, class_weights=None):
+ super(MLP, self).__init__()
+ self.class_weights = class_weights
+
+ self.activation = nn.ReLU()
+ # self.activation = nn.Tanh()
+ # self.activation = nn.LeakyReLU()
+ # self.activation = nn.Sigmoid()
+ self.bn1 = nn.BatchNorm1d(hidden_size)
+ self.dropout = nn.Dropout(dropout_rate)
+
+ self.fc1 = nn.Linear(input_size, hidden_size)
+ self.fc2 = nn.Linear(hidden_size, output_size)
+
+ # nn.init.kaiming_normal_(self.fc1.weight, nonlinearity='relu')
+ # nn.init.kaiming_normal_(self.fc2.weight)
+
+ def forward(self, x):
+ input_is_dict = False
+ if isinstance(x, dict):
+ assert "sentence_embedding" in x
+ input_is_dict = True
+ x = x['sentence_embedding']
+ # print(x)
+ x = self.fc1(x)
+ x = self.bn1(x)
+ x = self.activation(x)
+ x = self.dropout(x)
+
+ x = self.fc2(x)
+
+ if input_is_dict:
+ return {'logits': x}
+ return x
+
+ def predict(self, x):
+ _, predicted = torch.max(self.forward(x), 1)
+ print('I am predict')
+ return predicted
+
+ def predict_proba(self, x):
+ print('I am predict_proba')
+ return self.forward(x)
+
+ def get_loss_fn(self):
+ return nn.CrossEntropyLoss(weight=self.class_weights, reduction='mean')
+
+if __name__ == '__main__':
+ from setfit.__init__ import SetFitModel, Trainer, TrainingArguments
+ from datasets import Dataset, load_dataset
+ from sentence_transformers import SentenceTransformer, models, util
+ from sentence_transformers.losses import BatchAllTripletLoss, BatchHardSoftMarginTripletLoss, BatchHardTripletLoss, BatchSemiHardTripletLoss
+ from sklearn.linear_model import LogisticRegression
+ import sys
+ import os
+ import warnings
+ import torch
+ import torch.nn as nn
+ import torch.nn.functional as F
+ from datetime import datetime
+ import torch.optim as optim
+ from pprint import pprint
+ from torch.utils.data import DataLoader, TensorDataset
+ from safetensors.torch import load_model, save_model
+ from itertools import chain
+ from time import perf_counter
+ from tqdm import trange
+ from collections import Counter
+ from sklearn.utils.class_weight import compute_class_weight
+
+ warnings.filterwarnings("ignore")
+
+ SEED = 1003200212 + 1
+ DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+ start = perf_counter()
+
+ dataset = load_dataset("CabraVC/vector_dataset_stratified_ttv_split_2023-12-05_21-07")
+ class_weights_vect = compute_class_weight('balanced', classes=[0, 1, 2], y=dataset['train']['labels'])
+ class_weights = torch.tensor(compute_class_weight('balanced', classes=[0, 1, 2], y=dataset['train']['labels']), dtype=torch.float).to(DEVICE) ** .5
+
+ model_body = SentenceTransformer('sentence-transformers/all-distilroberta-v1')
+ model_head = MLP(hidden_size=256, class_weights=class_weights) # 128 82%acc
+ model = SetFitModel(model_body=model_body,
+ model_head=model_head,
+ labels=dataset['train'].features['labels'].names).to(DEVICE)
+
+
+ train_ds = dataset['train']
+ val_ds = dataset['val'].select(range(128))
+ test_ds = dataset['test'].select(range(128))
+
+
+ train_args = TrainingArguments(
+ seed=SEED,
+ batch_size=(16, 24),
+ num_epochs=(15, 16), # 15 best
+ margin=.5, # .5, 1, .8 1.1 good, .5 best, .4 BEST
+ loss=BatchSemiHardTripletLoss,
+ use_amp=True,
+ body_learning_rate=(3e-6, 4e-5), # 5e-5 for smaller margin=.3, (2e-6, 2-3 e-5) best
+ l2_weight=7e-3,
+ evaluation_strategy='epoch',
+ end_to_end=True,
+ samples_per_label=4,
+ max_length=model.model_body.get_max_seq_length()
+ )
+
+ trainer = Trainer(
+ model=model,
+ args=train_args,
+ train_dataset=train_ds,
+ eval_dataset=val_ds,
+ metric=get_eval_metric,
+ column_mapping={'texts': 'text', 'labels': 'label'},
+ )
+
+ print('Test unseen data')
+ metrics = trainer.evaluate(test_ds)
+ pprint(metrics)
+
+ trainer.train()
+
+ print('Test on train data')
+ metrics = trainer.evaluate(train_ds)
+ pprint(metrics)
+
+ print('Test unseen data')
+ metrics = trainer.evaluate(test_ds)
+ pprint(metrics)
+
+
+ trainer.push_to_hub('CabraVC/emb_classifier_model',
+ private=True)
+
+ print('-' * 50)
+ print('Successfully trained the model.')
+ print(f'It took me: {(perf_counter() - start) // 60:.0f} mins {(perf_counter() - start) % 60:.0f} secs')
\ No newline at end of file
diff --git a/test_models/test.py b/test_models/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..a725e31faba8137367046b878f862ea9cd6ed411
--- /dev/null
+++ b/test_models/test.py
@@ -0,0 +1,9 @@
+from multiprocessing import Pool, freeze_support, cpu_count
+
+def f(x, y):
+ return x*x + y
+
+if __name__ == '__main__':
+ with Pool(5) as p:
+ print(p.starmap(f, [[1, 1], [2, 1],[3, 1]]))
+ print(cpu_count())
\ No newline at end of file
diff --git a/test_models/test_embeddings.ipynb b/test_models/test_embeddings.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6963977a466f36fee9003997a39103eda92a365e
--- /dev/null
+++ b/test_models/test_embeddings.ipynb
@@ -0,0 +1,267 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "da5094d4-73fa-4e6c-89a1-0639709d9bc0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sentence: This framework generates embeddings for each input sentence\n",
+ "Embedding: 384\n",
+ "\n",
+ "Sentence: Sentences are passed as a list of string.\n",
+ "Embedding: 384\n",
+ "\n",
+ "Sentence: The quick brown fox jumps over the lazy dog.\n",
+ "Embedding: 384\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sentence_transformers import SentenceTransformer\n",
+ "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
+ "\n",
+ "#Our sentences we like to encode\n",
+ "sentences = ['This framework generates embeddings for each input sentence',\n",
+ " 'Sentences are passed as a list of string.',\n",
+ " 'The quick brown fox jumps over the lazy dog.']\n",
+ "\n",
+ "#Sentences are encoded by calling model.encode()\n",
+ "embeddings = model.encode(sentences)\n",
+ "\n",
+ "#Print the embeddings\n",
+ "for sentence, embedding in zip(sentences, embeddings):\n",
+ " print(\"Sentence:\", sentence)\n",
+ " print(\"Embedding:\", type(embedding), embedding.size)\n",
+ " print(\"\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "9f519c4d-1a1a-4f74-801d-2bb9e4e14e3a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cosine-Similarity: tensor([[0.6153]])\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "from sentence_transformers import SentenceTransformer, util\n",
+ "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
+ "\n",
+ "#Sentences are encoded by calling model.encode()\n",
+ "emb1 = model.encode(\"This is a red cat with a hat.\")\n",
+ "emb2 = model.encode(\"Have you seen my red cat?\")\n",
+ "\n",
+ "cos_sim = util.cos_sim(emb1, emb2)\n",
+ "print(\"Cosine-Similarity:\", cos_sim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "74e2bf51-6e6d-4d80-8449-6c7d168d561a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Top-5 most similar pairs:\n",
+ "A man is eating food. \t A man is eating a piece of bread. \t 0.7553\n",
+ "A man is riding a horse. \t A man is riding a white horse on an enclosed ground. \t 0.7369\n",
+ "A monkey is playing drums. \t Someone in a gorilla costume is playing a set of drums. \t 0.6433\n",
+ "A woman is playing violin. \t Someone in a gorilla costume is playing a set of drums. \t 0.2564\n",
+ "A man is eating food. \t A man is riding a horse. \t 0.2474\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sentence_transformers import SentenceTransformer, util\n",
+ "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
+ "\n",
+ "sentences = ['A man is eating food.',\n",
+ " 'A man is eating a piece of bread.',\n",
+ " 'The girl is carrying a baby.',\n",
+ " 'A man is riding a horse.',\n",
+ " 'A woman is playing violin.',\n",
+ " 'Two men pushed carts through the woods.',\n",
+ " 'A man is riding a white horse on an enclosed ground.',\n",
+ " 'A monkey is playing drums.',\n",
+ " 'Someone in a gorilla costume is playing a set of drums.'\n",
+ " ]\n",
+ "\n",
+ "#Encode all sentences\n",
+ "embeddings = model.encode(sentences)\n",
+ "\n",
+ "#Compute cosine similarity between all pairs\n",
+ "cos_sim = util.cos_sim(embeddings, embeddings)\n",
+ "\n",
+ "#Add all pairs to a list with their cosine similarity score\n",
+ "all_sentence_combinations = []\n",
+ "for i in range(len(cos_sim)-1):\n",
+ " for j in range(i+1, len(cos_sim)):\n",
+ " all_sentence_combinations.append([cos_sim[i][j], i, j])\n",
+ "\n",
+ "#Sort list by the highest cosine similarity score\n",
+ "all_sentence_combinations = sorted(all_sentence_combinations, key=lambda x: x[0], reverse=True)\n",
+ "\n",
+ "print(\"Top-5 most similar pairs:\")\n",
+ "for score, i, j in all_sentence_combinations[0:5]:\n",
+ " print(\"{} \\t {} \\t {:.4f}\".format(sentences[i], sentences[j], cos_sim[i][j]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a1ae46dd-1c19-4385-85b3-ec8f13dc6fe5",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2ae4f9f4-b9dd-440e-86ec-7ec1ba7166e7",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a00a4b61-3e9e-4e92-aa4e-c972b78bfcb8",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "07a67248-1f90-4163-98e5-3daf612686d1",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "989ebf4f-1078-4431-b7d7-95d0470b86b0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sentence_transformers import SentenceTransformer\n",
+ "model = SentenceTransformer('all-distilroberta-v1')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "77ddfd4f-cdf9-4193-a479-d2d2ef86d780",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sentence: This framework generates embeddings for each input sentence\n",
+ "Embedding: 768\n",
+ "\n",
+ "Sentence: Sentences are passed as a list of string.\n",
+ "Embedding: 768\n",
+ "\n",
+ "Sentence: The quick brown fox jumps over the lazy dog.\n",
+ "Embedding: 768\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "sentences = ['This framework generates embeddings for each input sentence',\n",
+ " 'Sentences are passed as a list of string.',\n",
+ " 'The quick brown fox jumps over the lazy dog.']\n",
+ "\n",
+ "embeddings = model.encode(sentences)\n",
+ "\n",
+ "for sentence, embedding in zip(sentences, embeddings):\n",
+ " print(\"Sentence:\", sentence)\n",
+ " print(\"Embedding:\", type(embedding), embedding.size)\n",
+ " print(\"\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ff56d32d-9046-41d6-bb92-ac08a176faf2",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8de0b905-99ad-4b12-8aa8-76cd2cad8252",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "27a8ed1b-47e0-4de1-b9fb-8e939efff368",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3948830e-5ce7-4d97-9f26-eec9904671e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sentence_transformers import SentenceTransformer, models\n",
+ "\n",
+ "word_embedding_model = models.Transformer('distilroberta-base')\n",
+ "\n",
+ "## Step 2: use a pool function over the token embeddings\n",
+ "pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())\n",
+ "\n",
+ "## Join steps 1 and 2 using the modules argument\n",
+ "model = SentenceTransformer(modules=[word_embedding_model, pooling_model])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/test_models/test_model.py b/test_models/test_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..af711a818468a3a11c7fc86e50908bf77df34ab4
--- /dev/null
+++ b/test_models/test_model.py
@@ -0,0 +1,75 @@
+from create_setfit_model import model
+from time import perf_counter
+import os
+import sys
+from statistics import mean
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import torch
+from collections import Counter
+from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score, confusion_matrix
+import matplotlib.pyplot as plt
+import seaborn as sns
+from tqdm import tqdm
+
+start = perf_counter()
+
+
+dataset_dir = os.path.abspath(os.path.join(os.getcwd(), '..', '..', 'financial_dataset'))
+sys.path.append(dataset_dir)
+from load_test_data import get_labels_df, get_texts
+
+labels_dir = dataset_dir + '/csvs/'
+df = get_labels_df(labels_dir)
+texts_dir = dataset_dir + '/txts/'
+texts = get_texts(texts_dir)
+df = df.iloc[[0, 13, 113], :]
+print(df.loc[:, 'Label'])
+texts = [texts[0]] + [texts[13]] + [texts[113]]
+print(len(df), len(texts))
+print(mean(list(map(len, texts))))
+
+
+text_splitter = RecursiveCharacterTextSplitter(
+ chunk_size=3200, chunk_overlap=200,
+ length_function = len, separators=[" ", ",", "\n"]
+ )
+
+labels = []
+pred_labels = []
+
+for text, (idx, (year, label, company)) in tqdm(zip(texts, df.iterrows())):
+ documents = text_splitter.create_documents([text])
+ texts = [document.page_content for document in documents]
+
+ with torch.no_grad():
+ model.model_head.eval()
+ text_pred_labels = model(texts)
+
+ pred_labels_counter = Counter(text_pred_labels)
+ pred_label = pred_labels_counter.most_common(1)[0][0]
+
+ labels.append(label)
+ pred_labels.append(pred_label)
+
+
+accuracy = accuracy_score(labels, pred_labels)
+precision = precision_score(labels, pred_labels, average='weighted')
+recall = recall_score(labels, pred_labels, average='weighted')
+f1 = f1_score(labels, pred_labels, average='weighted')
+
+confusion_mat = confusion_matrix(labels, pred_labels, normalize='true')
+
+print("Accuracy:", accuracy)
+print("Precision:", precision)
+print("Recall:", recall)
+print("F1 Score:", f1)
+
+labels = ['hold', 'buy', 'sell']
+plt.figure(figsize=(8, 6))
+sns.heatmap(confusion_mat, annot=True, fmt='.2%', cmap='Blues', xticklabels=labels, yticklabels=labels)
+plt.xlabel('Predicted labels')
+plt.ylabel('True labels')
+plt.title('Confusion Matrix')
+plt.show()
+
+print(f'It took me: {(perf_counter() - start) // 60:.0f} mins {(perf_counter() - start) % 60:.0f} secs')
\ No newline at end of file
diff --git a/test_models/train_classificator.py b/test_models/train_classificator.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5715432e8bf6845808a0d56b37e82d6ec167192
--- /dev/null
+++ b/test_models/train_classificator.py
@@ -0,0 +1,331 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
+import numpy as np
+# import torch.nn as nn
+torch.set_printoptions(sci_mode=False)
+# labels = ['buy', 'hold', 'sell']
+
+class MLP(nn.Module):
+ def __init__(self, input_size=768, hidden_size=256, output_size=3, dropout_rate=.2, class_weights=None):
+ super(MLP, self).__init__()
+ self.class_weights = class_weights
+
+ self.activation = nn.ReLU()
+ # self.activation = nn.Tanh()
+ # self.activation = nn.LeakyReLU()
+ # self.activation = nn.Sigmoid()
+ self.bn1 = nn.BatchNorm1d(hidden_size)
+ self.dropout = nn.Dropout(dropout_rate)
+
+ self.fc1 = nn.Linear(input_size, hidden_size)
+ self.fc2 = nn.Linear(hidden_size, output_size)
+
+ # nn.init.kaiming_normal_(self.fc1.weight, nonlinearity='relu')
+ # nn.init.kaiming_normal_(self.fc2.weight)
+
+ def forward(self, x):
+ input_is_dict = False
+ if isinstance(x, dict):
+ assert "sentence_embedding" in x
+ input_is_dict = True
+ x = x['sentence_embedding']
+ # print(x)
+ x = self.fc1(x)
+ x = self.bn1(x)
+ x = self.activation(x)
+ x = self.dropout(x)
+
+ x = self.fc2(x)
+
+ if input_is_dict:
+ return {'logits': x}
+ return x
+
+ def predict(self, x):
+ _, predicted = torch.max(self.forward(x), 1)
+ print('I am predict')
+ return predicted
+
+ def predict_proba(self, x):
+ print('I am predict_proba')
+ return self.forward(x)
+
+ def get_loss_fn(self):
+ return nn.CrossEntropyLoss(weight=self.class_weights, reduction='mean')
+
+
+def split_text(text, chunk_size=1200, chunk_overlap=200):
+ text_splitter = RecursiveCharacterTextSplitter(
+ chunk_size=chunk_size, chunk_overlap=chunk_overlap,
+ length_function = len, separators=[" ", ",", "\n"]
+ )
+
+ text_chunks = text_splitter.create_documents([text])
+ return text_chunks
+
+
+def plot_labels_distribution(dataset, save_as_filename=None):
+ plt.figure(figsize = (10, 6))
+
+ freqs, bins, _ = plt.hist([
+ dataset['train']['labels'],
+ dataset['val']['labels'],
+ dataset['test']['labels']
+ ], label=['80% - train', '10% - val', '10% - test'], bins=[-.25, .25, .75, 1.25, 1.75, 2.25])
+ plt.legend(loc='upper left')
+ plt.xticks([bin - .25 for bin in bins], ['', 'Buy', '', 'Hold', '', 'Sell'], fontsize=16)
+
+ bin_centers = np.diff(bins) * .5 + bins[:-1]
+ for offset, freq in zip([-.135, 0, .135], freqs):
+ for fr, x in zip(freq, bin_centers):
+ height = int(fr)
+ if height:
+ plt.annotate("{}".format(height),
+ xy = (x + offset, height),
+ xytext = (0, .2),
+ textcoords = "offset points",
+ ha = 'center', va = 'bottom'
+ )
+
+ plt.title('Labels distribution')
+ if save_as_filename:
+ plt.savefig(save_as_filename)
+ plt.show()
+
+
+def plot_training_metrics(losses, accuracies, show=False, save_as_filename=None):
+ plt.figure(figsize=(10, 5))
+ plt.subplot(1, 2, 1)
+ plt.plot(losses['train'], label='Training Loss')
+ plt.plot(losses['val'], label='Validation Loss')
+ plt.xlabel('Epoch')
+ plt.ylabel('Loss')
+ plt.title('Loss over Epochs')
+ plt.legend()
+
+ plt.subplot(1, 2, 2)
+ plt.plot(accuracies['train'], label='Training Accuracy')
+ plt.plot(accuracies['val'], label='Validation Accuracy')
+ plt.xlabel('Epoch')
+ plt.ylabel('Accuracy')
+ plt.title('Accuracy over Epochs')
+ plt.legend()
+ plt.tight_layout()
+
+ if save_as_filename:
+ plt.savefig(save_as_filename)
+
+ if show:
+ plt.show()
+
+
+def train_model(model, criterion, optimizer, lr_scheduler, train_loader, val_loader, train_data, val_data, epochs):
+ print_param = epochs // 8
+ losses = {
+ 'train': [],
+ 'val': []
+ }
+ accuracies = {
+ 'train': [],
+ 'val': []
+ }
+
+ for epoch in range(epochs):
+ model.train()
+ total_loss = 0.0
+ correct_predictions = 0
+
+ for inputs, labels in train_loader:
+ optimizer.zero_grad()
+ outputs = model(inputs)
+ loss = criterion(outputs, labels)
+ loss.backward()
+ optimizer.step()
+ total_loss += loss.item()
+
+ _, predicted = torch.max(outputs, 1)
+ correct_predictions += (predicted == labels).sum().item()
+
+ losses['train'].append(total_loss / len(train_loader))
+ accuracies['train'].append(correct_predictions / len(train_data))
+
+ if epoch % print_param == 0:
+ print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss / len(train_loader)}, Accuracy: {correct_predictions / len(train_data)}")
+
+
+ model.eval()
+ total_loss = 0.0
+ correct_predictions = 0
+
+ for inputs, labels in val_loader:
+ outputs = model(inputs)
+ loss = criterion(outputs, labels)
+ total_loss += loss.item()
+
+ _, predicted = torch.max(outputs, 1)
+ correct_predictions += (predicted == labels).sum().item()
+
+ losses['val'].append(total_loss / len(val_loader))
+ accuracies['val'].append(correct_predictions / len(val_data))
+
+ if epoch % print_param == 0:
+ print(f"Validation Loss: {total_loss / len(val_loader)}, Accuracy: {correct_predictions / len(val_data)}")
+
+ lr_scheduler.step(total_loss / len(val_loader))
+
+ return losses, accuracies
+
+
+def eval_model(model, criterion, test_loader, test_data, show=False, save_as_filename=None):
+ total_loss = 0.0
+ correct_predictions = 0
+ all_labels = []
+ all_predictions = []
+
+ with torch.no_grad():
+ model.eval()
+
+ for inputs, labels in test_loader:
+ outputs = model(inputs)
+ loss = criterion(outputs, labels)
+ total_loss += loss.item()
+
+ _, predicted = torch.max(outputs, 1)
+ correct_predictions += (predicted == labels).sum().item()
+
+ probabilities = F.softmax(outputs, dim=1)
+ predicted_labels = torch.argmax(probabilities, dim=1).tolist()
+
+ all_labels.extend(labels)
+ all_predictions.extend(predicted_labels)
+
+ loss, accuracy = total_loss / len(test_loader), correct_predictions / len(test_data)
+ print(f'Model test loss: {loss:2f}, test accurracy: {accuracy * 100:1f}')
+
+ accuracy = accuracy_score(all_labels, all_predictions)
+ precision = precision_score(all_labels, all_predictions, average='weighted')
+ recall = recall_score(all_labels, all_predictions, average='weighted')
+ f1 = f1_score(all_labels, all_predictions, average='weighted')
+
+ confusion_mat = confusion_matrix(all_labels, all_predictions, normalize='true')
+
+ print("Accuracy:", accuracy)
+ print("Precision:", precision)
+ print("Recall:", recall)
+ print("F1 Score:", f1)
+
+ labels = ['hold', 'buy', 'sell']
+ if show:
+ plt.figure(figsize=(8, 6))
+ sns.heatmap(confusion_mat, annot=True, fmt='.2%', cmap='Blues', xticklabels=labels, yticklabels=labels)
+ plt.xlabel('Predicted labels')
+ plt.ylabel('True labels')
+ plt.title('Confusion Matrix')
+ if save_as_filename:
+ plt.savefig(save_as_filename)
+
+ if show:
+ plt.show()
+
+ return loss, accuracy
+
+
+
+
+
+
+
+
+
+
+if __name__ == '__main__':
+ from datasets import load_dataset
+ from sentence_transformers import SentenceTransformer
+ import sys
+ from datetime import datetime
+ from collections import Counter
+ from langchain.text_splitter import RecursiveCharacterTextSplitter
+ import torch
+ import torch.nn.functional as F
+ import torch.optim as optim
+ from torch.utils.data import DataLoader, TensorDataset
+ from safetensors.torch import load_model, save_model
+
+ from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
+ from sklearn.utils.class_weight import compute_class_weight
+ import warnings
+
+ warnings.filterwarnings("ignore")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ model_name = 'all-distilroberta-v1'
+ # model_name = 'all-MiniLM-L6-v2'
+ model = SentenceTransformer(model_name)
+ dataset = load_dataset("CabraVC/vector_dataset_stratified_ttv_split_2023-12-05_21-07")
+ # plot_labels_distribution(dataset
+ # # , save_as_filename=f'plots/labels_distribution_{datetime.now().strftime("%Y-%m-%d_%H-%M")}.png'
+ # )
+
+ input_size = len(dataset['train']['embeddings'][0])
+ hidden_size = 256
+ dropout_rate = 0.2
+ learning_rate = 2 * 1e-4
+ batch_size = 256
+ epochs = 100
+
+
+ class_weights = torch.tensor(compute_class_weight('balanced', classes=[0, 1, 2], y=dataset['train']['labels']), dtype=torch.float) ** .5
+ model = MLP(input_size=input_size, hidden_size=hidden_size, dropout_rate=dropout_rate, class_weights=class_weights)
+
+
+ criterion = model.get_loss_fn()
+ # print(class_weights)
+
+ # criterion = nn.CrossEntropyLoss()
+
+ optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=8 * 1e-2)
+ lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=.25, patience=10, threshold=5 * 1e-5, min_lr=1e-7, verbose=True)
+
+
+ train_data = TensorDataset(torch.tensor(dataset['train']['embeddings']), torch.tensor(dataset['train']['labels']))
+ train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
+
+ val_data = TensorDataset(torch.tensor(dataset['val']['embeddings']), torch.tensor(dataset['val']['labels']))
+ val_loader = DataLoader(val_data, batch_size=batch_size, shuffle=True)
+
+
+
+ losses, accuracies = train_model(model, criterion, optimizer, lr_scheduler, train_loader, val_loader, train_data, val_data, epochs)
+
+ plot_training_metrics(losses, accuracies
+ # , save_as_filename=f'plots/training_metrics_plot_{datetime.now().strftime("%Y-%m-%d_%H-%M")}.png'
+ )
+
+ test_data = TensorDataset(torch.tensor(dataset['test']['embeddings']), torch.tensor(dataset['test']['labels']))
+ test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)
+ loss, accuracy = eval_model(model, criterion, test_loader, test_data,
+ # save_as_filename=f'plots/confusion_matrix_{datetime.now().strftime("%Y-%m-%d_%H-%M")}.png'
+ )
+
+ # torch.save(model.state_dict(), f'models/head_{datetime.now().strftime("%Y-%m-%d_%H-%M")}.pth')
+ # save_model(model, f'models/head_{datetime.now().strftime("%Y-%m-%d_%H-%M")}.safetensors')
+ # load_model(model, f'models/head_{datetime.now().strftime("%Y-%m-%d_%H-%M")}.safetensors')
+ # print(model)
+ # dataset.push_to_hub(f'CabraVC/vector_dataset_stratified_ttv_split_{datetime.now().strftime("%Y-%m-%d_%H-%M")}', private=True)
+
\ No newline at end of file
diff --git a/test_models/train_head.py b/test_models/train_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2944aa58348e719a72eb42da4f6f1f40a276dae
--- /dev/null
+++ b/test_models/train_head.py
@@ -0,0 +1,126 @@
+import torch
+from torch import nn
+import matplotlib.pyplot as plt
+import numpy as np
+# import torch.nn as nn
+torch.set_printoptions(sci_mode=False)
+
+
+class MLP(nn.Module):
+ def __init__(self, input_size=768, output_size=3, dropout_rate=.2, class_weights=None):
+ super(MLP, self).__init__()
+ self.class_weights = class_weights
+
+ # self.bn1 = nn.BatchNorm1d(hidden_size)
+ self.dropout = nn.Dropout(dropout_rate)
+
+ self.linear = nn.Linear(input_size, output_size)
+
+ # nn.init.kaiming_normal_(self.fc1.weight, nonlinearity='relu')
+ # nn.init.kaiming_normal_(self.fc2.weight)
+
+ def forward(self, x):
+ # return self.linear(self.dropout(x))
+ return self.dropout(self.linear(x))
+
+ def predict(self, x):
+ _, predicted = torch.max(self.forward(x), 1)
+ print('I am predict')
+ return predicted
+
+ def predict_proba(self, x):
+ print('I am predict_proba')
+ return self.forward(x)
+
+ def get_loss_fn(self):
+ return nn.CrossEntropyLoss(weight=self.class_weights, reduction='mean')
+
+
+
+
+
+
+
+if __name__ == '__main__':
+ from datasets import load_dataset
+ from sentence_transformers import SentenceTransformer
+ import sys
+ # from datetime import datetime
+ # from collections import Counter
+ import torch
+ import torch.optim as optim
+ from torch.utils.data import DataLoader, TensorDataset
+ from safetensors.torch import load_model, save_model
+
+ from sklearn.utils.class_weight import compute_class_weight
+ import warnings
+
+ from train_classificator import (
+ # MLP,
+ plot_labels_distribution,
+ plot_training_metrics,
+ train_model,
+ eval_model
+ )
+
+ warnings.filterwarnings("ignore")
+
+ SEED = 1003200212 + 1
+ DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
+
+
+ dataset = load_dataset("CabraVC/vector_dataset_roberta-fine-tuned")
+ # plot_labels_distribution(dataset
+ # # , save_as_filename=f'plots/labels_distribution_{datetime.now().strftime("%Y-%m-%d_%H-%M")}.png'
+ # )
+
+ input_size = len(dataset['train']['embeddings'][0])
+ learning_rate = 5e-4
+ weight_decay = 0
+ batch_size = 128
+ epochs = 40
+
+
+ class_weights = torch.tensor(compute_class_weight('balanced', classes=[0, 1, 2], y=dataset['train']['labels']), dtype=torch.float) ** .5
+ model = MLP(input_size=input_size, dropout_rate=.2, class_weights=class_weights)
+
+
+ criterion = model.get_loss_fn()
+ test_data = TensorDataset(torch.tensor(dataset['test']['embeddings']), torch.tensor(dataset['test']['labels']))
+ test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)
+ loss, accuracy = eval_model(model, criterion, test_loader, test_data, show=False,
+ # save_as_filename=f'plots/confusion_matrix_{datetime.now().strftime("%Y-%m-%d_%H-%M")}.png'
+ )
+
+
+ optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
+ lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=.2, patience=5, threshold=1e-4, min_lr=1e-7, verbose=True)
+
+
+ train_data = TensorDataset(torch.tensor(dataset['train']['embeddings']), torch.tensor(dataset['train']['labels']))
+ train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
+
+ val_data = TensorDataset(torch.tensor(dataset['val']['embeddings']), torch.tensor(dataset['val']['labels']))
+ val_loader = DataLoader(val_data, batch_size=batch_size, shuffle=True)
+
+
+
+ losses, accuracies = train_model(model, criterion, optimizer, lr_scheduler, train_loader, val_loader, train_data, val_data, epochs)
+
+ plot_training_metrics(losses, accuracies
+ # , save_as_filename=f'plots/training_metrics_plot_{datetime.now().strftime("%Y-%m-%d_%H-%M")}.png'
+ )
+
+ test_data = TensorDataset(torch.tensor(dataset['test']['embeddings']), torch.tensor(dataset['test']['labels']))
+ test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)
+ loss, accuracy = eval_model(model, criterion, test_loader, test_data, show=True
+ # save_as_filename=f'plots/confusion_matrix_{datetime.now().strftime("%Y-%m-%d_%H-%M")}.png'
+ )
+
+ # torch.save(model.state_dict(), f'models/linear_head.pth')
+ # save_model(model, f'models/linear_head.safetensors')
+ # load_model(model, f'models/head_{datetime.now().strftime("%Y-%m-%d_%H-%M")}.safetensors')
+ # print(model)
+ # dataset.push_to_hub(f'CabraVC/vector_dataset_stratified_ttv_split_{datetime.now().strftime("%Y-%m-%d_%H-%M")}', private=True)
+
\ No newline at end of file
diff --git a/utils/agent.ipynb b/utils/agent.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..186ea419018119f726c3ae7522529f7edcd673fe
--- /dev/null
+++ b/utils/agent.ipynb
@@ -0,0 +1,287 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "2ea22fb4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\r\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -qU google-api-python-client"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5d8d76d9",
+ "metadata": {},
+ "source": [
+ "# Conversation buffer memory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "0d9e8e8f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from dotenv import load_dotenv\n",
+ "from langchain.agents import AgentExecutor, Tool, ZeroShotAgent\n",
+ "from langchain.chains import LLMChain\n",
+ "from langchain.llms import OpenAI\n",
+ "from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory\n",
+ "from langchain.prompts import PromptTemplate\n",
+ "from langchain.utilities import GoogleSearchAPIWrapper\n",
+ "\n",
+ "llm = OpenAI(temperature=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "96286dd4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "template = \"\"\"This is a piece of financial report, namely Form 10-K, section 7:\n",
+ "\n",
+ "{chat_history}\n",
+ "\n",
+ "Summarize this text into 2-3 sentences as best as you can.\n",
+ "\"\"\"\n",
+ "\n",
+ "prompt = PromptTemplate(input_variables=[\"chat_history\"], template=template)\n",
+ "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
+ "readonlymemory = ReadOnlySharedMemory(memory=memory)\n",
+ "summary_chain = LLMChain(\n",
+ " llm=OpenAI(),\n",
+ " prompt=prompt,\n",
+ " verbose=True,\n",
+ " memory=readonlymemory,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "d76360b0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "search = GoogleSearchAPIWrapper()\n",
+ "tools = [\n",
+ " Tool(\n",
+ " name=\"Search\",\n",
+ " func=search.run,\n",
+ " description=\"useful for when you need to answer questions about current events or find some relevant information on the internet.\",\n",
+ " ),\n",
+ " Tool(\n",
+ " name=\"Summary\",\n",
+ " func=summary_chain.run,\n",
+ " description=\"useful for when you need to summarize a piece of financial report text. The input to this tool should be a string.\",\n",
+ " ),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5c6a7dc8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prefix = \"\"\"\n",
+ "You are the best broker in the world. You are asked to read the financial report for some company.\n",
+ "Then you should suggest what is the best action: sell, buy or hold. You need to return only of those three options.\n",
+ "\"\"\"\n",
+ "suffix = \"\"\"Begin!\n",
+ "\n",
+ "{chat_history}\n",
+ "Question: {input}\n",
+ "{agent_scratchpad}\"\"\"\n",
+ "\n",
+ "prompt = ZeroShotAgent.create_prompt(\n",
+ " tools,\n",
+ " prefix=prefix,\n",
+ " suffix=suffix,\n",
+ " input_variables=[\"text_chunk\", \"chat_history\", \"agent_scratchpad\"],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3bd67b6d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
+ "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)\n",
+ "agent_chain = AgentExecutor.from_agent_and_tools(\n",
+ " agent=agent, tools=tools, verbose=True, memory=memory\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "925c632e",
+ "metadata": {},
+ "source": [
+ "# Conversation summarization memory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6c72c255",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8af6c4ab",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9885e240",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1c7b7e74",
+ "metadata": {},
+ "source": [
+ "# Entity memory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "e3423486",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langchain.llms import OpenAI\n",
+ "from langchain.memory import ConversationEntityMemory\n",
+ "llm = OpenAI(temperature=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "d36440a7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "memory = ConversationEntityMemory(llm=llm)\n",
+ "_input = {\"input\": \"Deven & Sam are working on a hackathon project\"}\n",
+ "memory.load_memory_variables(_input)\n",
+ "memory.save_context(\n",
+ " _input,\n",
+ " {\"output\": \" That sounds like a great project! What kind of project are they working on?\"}\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "95b32eb8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'history': 'Human: Deven & Sam are working on a hackathon project\\nAI: That sounds like a great project! What kind of project are they working on?',\n",
+ " 'entities': {'Sam': 'Sam is working on a hackathon project with Deven.'}}"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "memory.load_memory_variables({\"input\": 'who is Sam'})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "8b763a07",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "memory = ConversationEntityMemory(llm=llm, return_messages=True)\n",
+ "_input = {\"input\": \"Deven & Sam are working on a hackathon project\"}\n",
+ "memory.load_memory_variables(_input)\n",
+ "memory.save_context(\n",
+ " _input,\n",
+ " {\"output\": \" That sounds like a great project! What kind of project are they working on?\"}\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "a95a2393",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'history': [HumanMessage(content='Deven & Sam are working on a hackathon project'),\n",
+ " AIMessage(content=' That sounds like a great project! What kind of project are they working on?')],\n",
+ " 'entities': {'Sam': 'Sam is working on a hackathon project with Deven.'}}"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "memory.load_memory_variables({\"input\": 'who is Sam'})"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/utils/compare_summarizators.ipynb b/utils/compare_summarizators.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..de14fad189651fd0a0b699e40fb8c3bc7d55a020
--- /dev/null
+++ b/utils/compare_summarizators.ipynb
@@ -0,0 +1,1350 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "1d3464a9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# !pip install rouge"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "033234c2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from rouge import Rouge"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "bed31d7f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reference = \"\"\"\n",
+ "2005\n",
+ "\n",
+ "Item 7. Managementâs Discussion and Analysis of Financial Condition and Results of Operations.\n",
+ " \n",
+ "OVERVIEW\n",
+ "3M is a diversified global manufacturer, technology innovator and marketer of a wide variety of products. In 2005, 3M managed its operations in seven operating business segments: Health Care; Industrial; Display and Graphics; Consumer and Office; Electro and Communications; Safety, Security and Protection Services; and Transportation. Refer to the Performance by Business Segment section for discussion of segment changes effective in the first quarter of 2006.\n",
+ " \n",
+ "3Mâs 2005 performance demonstrated the operational strength of 3M and the value of the diversification of the 3M business portfolio. 3Mâs sourcing organization and the businesses worked together to maintain customer service, while successfully managing the business to avoid supply disruptions in the face of hurricanes and shortages of key raw materials. 3M increased its dividend 16.7%, the 47th consecutive year of 3M dividend increases, and repurchased $2.3 billion of stock under its stock repurchase authorization. The combination of dividends and stock buy-backs returned a total of $3.6 billion to shareholders during 2005. 3M also acquired CUNO, a liquid filtration company.\n",
+ " \n",
+ "In 2005, 3M reported record net sales of $21.167 billion and record net income of $3.199 billion, or $4.12 per diluted share, compared with net sales of $20.011 billion and net income of $2.990 billion, or $3.75 per diluted share, in 2004. The combination of a 5.8% increase in net sales, including core local-currency sales growth of 4.1% (which excludes the impact of businesses acquired in the last 12 months), and declining manufacturing costs as a percent of sales, resulted in a 23.7% operating income profit margin.\n",
+ " \n",
+ "In 2005, income before cumulative effect of accounting change totaled $3.234 billion, or $4.16 per diluted share. As of December 31, 2005, 3M adopted Financial Accounting Standards Board Interpretation (FASB) No. 47, âAccounting for Conditional Asset Retirement Obligationsâ (FIN 47). The adoption of FIN 47 resulted in an after tax charge of $35 million, which is reflected as a cumulative change in accounting principle (refer to Note 1 to the Consolidated Financial Statements for more detail). In addition, during 2005, 3M completed its evaluation of the repatriation provision of the American Jobs Creation Act of 2004 and repatriated approximately $1.8 billion of foreign earnings into the U.S. pursuant to its provisions. As a consequence, in the second quarter of 2005, 3M recorded a tax expense of $75 million, net of available foreign tax credits. Combined, these two items reduced net income by $110 million in 2005.\n",
+ "\n",
+ "3Mâs performance in 2005 was broad-based, with all seven business segments contributing to positive local-currency sales growth. Sales growth in Health Care was led by 3Mâs core medical and dental businesses and strong growth in health information systems, which helped overcome the growth challenges of the pharmaceuticals and personal care businesses. Sales growth in the Industrial segment was led by industrial adhesives and tapes, as well as the abrasives businesses. The CUNO acquisition added 5.1% to Industrial sales growth. Display and Graphics sales growth in display enhancement films used in flat-panel devices was partially offset by the continued decline in lens systems for the CRT rear projection television market along with the phase out of the commercial videotape business. Sales growth in the Consumer and Office segment was broad-based across the many channels 3M serves, most notably in the mass-market consumer and home improvement retail channels. For the Electro and Communications segment, sales growth was led by demand for 3M electronic products for the semiconductor manufacturers, along with continued strong growth in electrical products for insulating, testing and sensing. Sales growth in the Safety, Security and Protection Services segment was driven by continued strong demand for personal protection products and solutions, particularly respiratory protection products, along with strong demand for cleaning and protection products for commercial buildings. Sales growth in the Transportation segment was led by both the automotive OEM and repair markets. Refer to the Performance by Business Segment section for a more detailed discussion of the results of the respective segments.\n",
+ " \n",
+ "Geographically, U.S. sales revenue increased 4.9%, Asia Pacific local-currency sales (which exclude translation impacts) increased 10.6%, European local-currency sales increased 0.9%, and the combined Latin America and Canada area local-currency sales increased 1.3%. Refer to the Performance by Geographic Area section for a more detailed discussion of the results for the respective areas.\n",
+ " \n",
+ "Operating income in 2005 increased by 9.4% versus 2004, as all seven business segments posted increases. The combination of solid sales growth and positive benefits from corporate initiatives helped drive the increase in operating income. The Company estimates that cost reduction projects related to initiatives provided a combined incremental benefit to operating income of approximately $400 million in 2005. These initiatives contributed more than $400 million to operating income in both 2004 and 2003.\n",
+ " \n",
+ "3M generated $4.258 billion of operating cash flows in 2005, essentially flat when compared to 2004, and ended the year with $1.072 billion of cash and cash equivalents. In 2005, the Company utilized approximately $3.6 billion of cash to repurchase 3M common stock under its share repurchase authorization and to pay dividends, and contributed $788 million to its pension and postretirement plans. 3Mâs debt to total capital ratio (total capital defined as debt plus equity) as of December 31, 2005, was approximately 19%. 3M has an AA credit rating from Standard & Poorâs and an Aa1 credit rating from Moodyâs Investors Service.\n",
+ " \n",
+ "The Company experienced both price increases and supply limitations affecting several oil-derived raw materials in 2005, which is expected to carry forward into 2006, but to date the Company is receiving sufficient quantities of such materials to meet its reasonably foreseeable production requirements. It is impossible to predict future shortages of raw materials or the impact any such shortages would have. Hurricanes Katrina and Rita resulted in tight supply conditions and significant increases in energy costs, fuel surcharges and prices for certain natural gas and petroleum-related raw materials and their derivatives. 3M has avoided disruption to its manufacturing operations through careful management of existing raw material inventories and development and qualification of additional supply sources. 3M manages commodity price risks through negotiated supply contracts, price protection agreements and forward physical contracts. Fluctuations in foreign currency exchange rates also impact results, although the Company minimizes this effect through hedging about half of this impact. 3M will also continue, as it has for many years, to incur expenses (insured and uninsured) in managing its litigation and environmental contingencies.\n",
+ " \n",
+ "In 2006, 3M expects to drive profitable growth by investing in its most promising commercialization, geographic and technology opportunities, with part of this investment coming from savings generated through continuous operational improvements. The Company expects solid sales growth across the majority of its business portfolio. The Companyâs long history and unique ability to match technological solutions with the needs of its customers has resulted in a steady flow of new products and solutions, with this trend expected to continue in 2006. In addition, the Companyâs increasing focus on products and solutions for emerging economies, such as Asia and Eastern Europe, is expected to foster significant growth in 2006. The Company expects to increase both research and development and capital expenditures in 2006, led primarily by growth programs. Research, development and related expenses totaled $1.242 billion in 2005, or 5.9% of sales. The Company expects 2006 capital expenditures to total approximately $1.1 billion, compared with $943 million in 2005, providing the capacity to meet expected growth.\n",
+ " \n",
+ "While 3M anticipates solid sales growth across the majority of its businesses, sales are expected to decline in a few of its businesses. In Health Care, 3M expects continued solid growth in its core medical and dental businesses as 3M continues to invest in fast growth areas such as the alternate care segment in medical, digital dentistry, and emerging markets. However, 3M expects declines in its personal care business (which will become part of the combined Industrial and Transportation segment in 2006) and in its branded pharmaceuticals business (Health Care) to persist throughout 2006. 3M experienced a sales decline in the fourth quarter of 2005 for Metrogel-Vaginal, a womenâs health care product, due to a competitive product. 3M now expects that there will be a generic substitute approved for Metrogel-Vaginal, which accounts for approximately 2% of total Health Care sales, in mid 2006. Health Care sales for 3Mâs Aldara⢠(imiquimod) pharmaceutical product for the actinic keratosis (a pre-cancerous skin condition) indication has and is expected to continue to fall short of expectations 3M had at the time of FDA approval and 3M is currently reassessing Aldaraâs total market potential. In Display and Graphics, 3M expects the continued negative impact from the CRT rear projection lens business to continue into the first half of 2006, with sales in the second half of 2006 expected to be comparable to the second half of 2005. However, in Display and Graphics, 3M expects this decline in CRT rear projection lens sales to be more than offset by strong sales growth in display enhancement films used in flat-panel devices, such as LCD televisions.\n",
+ " \n",
+ "The preceding forward-looking statements involve risks and uncertainties that could cause results to differ materially from those projected (refer to the forward-looking statements section in Item 7 and the risk factors provided in Item 1A for discussion of these risks and uncertainties).\n",
+ "\n",
+ "RESULTS OF OPERATIONS\n",
+ " \n",
+ "In 2005, local-currency sales growth was broad based, with selling prices increasing 0.6%. Along with the benefits provided by 3Mâs sourcing initiative, 3Mâs pricing strategy has been key to maintaining margins in the face of significant raw material price pressure. 3Mâs pricing strategy resulted in U.S. price growth of 2.5% in 2005. Internationally, selling prices declined 0.7% in 2005. Adjusting for the price decreases in consumer electronics related businesses (LCD films and flex circuits), international pricing would have increased 0.3% in 2005. Acquisitions increased 2005 sales by 1.0%, driven by the 2005 acquisition of CUNO. Refer to both the âPerformance by Business Segmentâ and âPerformance by Geographic Areaâ sections for additional discussion of sales change.\n",
+ " \n",
+ "In 2004, core volume growth (which excludes the impact of businesses acquired in the last 12 months) was broad-based, with all seven businesses posting worldwide local-currency sales growth. Local-currency growth was led by Display and Graphics; Industrial; Consumer and Office; Safety, Security and Protection Services; and the Transportation businesses. Health Care local-currency sales increased 1.7%, as results were negatively impacted by 2003 sales from pharmaceutical and drug delivery agreements that did not repeat in 2004. Electro and Communications local-currency sales increased 2.7%, the first year of positive local-currency sales growth since 2000. Acquisitions increased 2004 sales by 0.5%, driven by the 2004 acquisitions of HighJump Software, Inc. and Hornell Holding AB. Internationally, selling prices declined 1.1%, with most of the decline coming in certain businesses that serve the electronics industry, where it is important to look at the combined impact of volume and price. On a geographic basis, local-currency sales growth in 2004 was led by the Asia Pacific area.\n",
+ " \n",
+ "Operating Expenses:\n",
+ " \n",
+ "Cost of Sales:\n",
+ "Cost of sales decreased 0.8 percentage points in 2005. Cost of sales as a percent of net sales benefited from the combination of improved selling prices, favorable product mix, productivity gains, factory efficiency and sourcing, which helped offset the impact of higher raw material prices. Raw material costs increased approximately 6.0% for 2005 when compared to 2004, with this impact mitigated through commodity hedging programs and negotiated supply contracts. Cost of sales includes manufacturing, engineering and freight costs.\n",
+ " \n",
+ "The 2004 decrease as a percent of net sales was driven by a combination of higher volumes, productivity gains, ongoing benefits of corporate initiatives and positive currency impacts (including hedging impacts). While 3M raw material costs increased during the year, 3Mâs global sourcing initiative was important in enabling 3M to minimize raw material cost increases during a period of commodity price inflation.\n",
+ " \n",
+ "Research, Development and Related Expenses:\n",
+ "Research, development and related expenses as a percent of sales were flat when comparing 2005 to 2004. However, spending in dollars increased approximately 4%, reflecting 3Mâs continuing commitment to fund future growth for the Company.\n",
+ " \n",
+ "Selling, General and Administrative Expenses:\n",
+ "Selling, general and administrative (SG&A) expenses as a percent of net sales were flat when comparing 2005 to 2004. 3M continues to invest in growth programs and brand building throughout the portfolio as a means of stimulating growth. SG&A in the fourth quarter of 2005 was impacted by a pre-tax charge of approximately $30 million in connection with settlement agreements of one pending LePageâs follow-on class actions and of two individual follow-on actions, all involving direct purchasers of transparent tape. For more detail, refer to the discussion in Note 11 to the Consolidated Financial Statements.\n",
+ " \n",
+ "Selling, general and administrative expenses improved by 0.5 percentage points in 2004 compared to 2003. The improvement in 2004 as a percent of net sales was helped by leverage related to 3Mâs strong growth in the Asia Pacific area. SG&A expenses in U.S. dollars increased in 2004, negatively impacted by currency translation and increased advertising and merchandising spending to support 3Mâs strong brand portfolio. On an ongoing basis, the Company is shifting SG&A dollars toward faster-growth businesses and geographic areas.\n",
+ " \n",
+ "Other Expense:\n",
+ "In 2003, 3M recorded pre-tax charges of $93 million ($58 million after-tax) related to an adverse ruling in a lawsuit filed against 3M in 1997 by LePageâs Inc. The pre-tax charge of $93 million is classified as âOther expenseâ within operating income.\n",
+ "\n",
+ "Operating Income:\n",
+ "3M uses operating income as one of its primary business segment performance measurement tools. Operating income in 2005 was 23.7% of sales, up from 22.9% of sales in 2004 and 20.4% of sales in 2003. Operating income in 2005 grew by $431 million, or 9.4 percent, following 2004 operating income growth of $865 million, or 23.3 percent. The LePageâs Inc. lawsuit negatively impacted operating income in 2003 by $93 million, or 0.5% of sales.\n",
+ " \n",
+ "Interest Expense and Income:\n",
+ " \n",
+ "Interest Expense: Interest expense increased in 2005 compared to 2004, primarily due to higher interest rates. The decrease in 2004 interest expense was primarily the result of lower average debt balances, partially offset by higher interest rates in the United States.\n",
+ "Interest Income: Interest income was higher in 2005, benefiting primarily from higher interest rates. Interest income increased in 2004 due to substantially higher cash balances.\n",
+ " \n",
+ "Provision for Income Taxes:\n",
+ " \n",
+ "The tax rate for 2005 was 34.0%, compared with 33.0% in 2004. During 2005, 3M completed its evaluation of the repatriation provision of the American Jobs Creation Act of 2004 (Jobs Act) and repatriated approximately $1.8 billion of foreign earnings into the U.S. pursuant to its provisions. The Jobs Act provides 3M the opportunity to tax effectively repatriate foreign earnings for U.S. qualifying investments specified by 3Mâs domestic reinvestment plan. As a consequence, in the second quarter of 2005, 3M recorded a tax expense of $75 million, net of available foreign tax credits, which negatively impacted the 2005 effective worldwide tax rate by 1.5%. A half-point tax rate reduction compared to the same periods last year is primarily attributable to the combination of the effects of the Medicare Modernization Act and the domestic manufacturerâs deduction, which was a part of the Jobs Act.\n",
+ " \n",
+ "The tax rate of 33.0% for 2004 was comparable to the 2003 rate of 32.9%. Income taxes associated with repatriating certain cash from outside the United States negatively impacted the 2004 and 2003 income tax rates.\n",
+ " \n",
+ "Minority Interest:\n",
+ " \n",
+ "Minority interest expense eliminates the income or loss attributable to non-3M ownership interests in 3M consolidated entities. 3Mâs most significant consolidated entity with non-3M ownership interests is Sumitomo 3M Limited in Japan (3M owns 75% of Sumitomo 3M Limited). The decrease in 2005 related primarily to lower net income in Sumitomo 3M, while the increase in 2004 related primarily to higher net income in Sumitomo 3M.\n",
+ " \n",
+ "Cumulative Effect of Accounting Change:\n",
+ "As of December 31, 2005, the Company adopted FASB Interpretation No. 47, âAccounting for Conditional Asset Retirement Obligationsâ (FIN 47). This accounting standard applies to the fair value of a liability for an asset retirement obligation associated with the retirement of tangible long-lived assets and where the liability can be reasonably estimated. Conditional asset retirement obligations exist for certain of the Companyâs long-term assets. The fair value of these obligations is recorded as liabilities on a discounted basis. Over time the liabilities are accreted for the change in the present value and the initial capitalized costs are depreciated over the useful lives of the related assets. The adoption of FIN 47 resulted in the recognition of an asset retirement obligation liability of $59 million and an after tax charge of $35 million, which is reflected as a cumulative change in accounting principle in the Consolidated Statement of Income. The pro forma effect of applying this guidance in all prior periods presented was determined not to be material.\n",
+ "\n",
+ "Currency Effects:\n",
+ "3M estimates that year-on-year currency effects, including hedging impacts, increased net income by approximately $115 million in 2005, $181 million in 2004 and $73 million in 2003. This estimate includes the effect of translating profits from local currencies into U.S. dollars; the impact of currency fluctuations on the transfer of goods between 3M operations in the United States and abroad; and transaction gains and losses, including derivative instruments designed to reduce foreign currency exchange rate risks. 3M estimates that year-on-year derivative and other transaction gains and losses increased net income by approximately $50 million for 2005 and $48 million in 2004. 3M estimates that year-on-year derivative and other transaction gains and losses decreased net income by $73 million in 2003.\n",
+ " \n",
+ "PERFORMANCE BY BUSINESS SEGMENT\n",
+ "Effective January 1, 2006, 3M combined its Industrial and Transportation business segments. This new segment will leverage common markets, sales channels and customers, technologies, manufacturing facilities and selling processes. This combination will provide additional efficiencies that will be reinvested in growth. The results for the new Industrial and Transportation segment can be approximated by combining the existing Industrial and Transportation segments. In addition, during the first quarter of 2006, the Personal Care Division (2005 annual sales of approximately $600 million) within the Health Care segment transferred to the combined Industrial and Transportation segment. Segment information for all periods presented will be reclassified in 2006 to reflect the combined Industrial and Transportation segment in addition to the transfer of the Personal Care Division.\n",
+ " \n",
+ "Disclosures relating to 3Mâs business segments are provided in Item 1, Business Segments. Financial information and other disclosures are provided in the Notes to the Consolidated Financial Statements. In 2005, 3M managed its operations in seven operating business segments: Health Care; Industrial; Display and Graphics; Consumer and Office; Electro and Communications; Safety, Security and Protection Services; and Transportation. Information related to 3Mâs business segments is presented in the tables that follow. Local-currency sales (which include both core and acquisition volume impacts, plus price impacts) are provided for each segment. The translation impact and total sales change are also provided for each segment.\n",
+ " \n",
+ "Health Care Business (20.7% of consolidated sales):\n",
+ " \n",
+ "The Health Care segment serves markets that include medical, surgical, pharmaceutical, dental and orthodontic, health information systems and personal care. Products provided to these markets include medical and surgical supplies, skin health and infection prevention products, pharmaceuticals, drug delivery systems, dental and orthodontic products, health information systems, microbiology products, and closures for disposable diapers.\n",
+ " \n",
+ "In 2005, Health Care reported local-currency sales growth of 2.9%. 3Mâs core medical and dental businesses and health information systems businesses experienced local-currency sales growth of approximately 6%. The strength of these businesses helped overcome the sales growth challenges of the pharmaceutical and personal care businesses. Personal care, which is 3Mâs diaper tape business, has experienced significant raw material price increases in some product lines over the past year, and 3M has elected to drive profits at the expense of volume in this business, which has the lowest margins in the Health Care segment. Sales of certain products within 3Mâs pharmaceuticals business, primarily comprised of prescription drugs in inhalation, womenâs health, and cardiovascular, are declining due to price pressure in Europe and decreased demand for some of these older products. 3M continues to generate growth in its Aldara⢠pharmaceutical product, which accounts for approximately 6% of total Health Care sales. Aldara sales grew nearly 10% in 2005, and growth outside the U.S. was particularly strong. However, fourth quarter 2005 year-over-year local-currency sales declined for the first time since the product was launched in 1997. Health Care sales for 3Mâs Aldara pharmaceutical product for the actinic keratois (a pre-cancerous skin condition) indication has and is expected to continue to fall short of expectations 3M had at the time of FDA approval and 3M is currently reassessing Aldaraâs total market potential. Health Care continued to focus on operational efficiency, which helped drive an 8.2% increase in operating income in 2005. The Companyâs agreement with Takeda Pharmaceutical Co., Ltd., announced in early 2005 and described further below, is currently the focus of the Companyâs efforts to develop its immune response modifier technology while the Company reviews its other development efforts.\n",
+ " \n",
+ "3M received U.S. Food and Drug Administration (FDA) approval for Aldara⢠(imiquimod) Cream, 5%, in 1996 for the treatment of external genital warts, in March 2004 for the treatment of certain types of actinic keratosis (a pre-cancerous skin condition), and in July 2004 for the treatment of superficial basal cell carcinoma (a common form of non-melanoma skin cancer). The patent and related rights for the imiquimod molecule are important to the Health Care Business. The original patent on the imiquimod molecule expired in August 2004, but the patent term extension runs through August 2009, with an anticipated pediatric exclusivity extension of a further six months to February 2010.\n",
+ " \n",
+ "In the first quarter of 2005, 3M and Takeda Pharmaceutical Co. Ltd. entered into an agreement to collaborate on a potential breakthrough treatment utilizing an immune response modifier for cervical high-risk human papilloma virus (HPV) infection and cervical dysplasia, which are known risk factors for cervical cancer. This immune response modifier currently is in early stage clinical trials, and 3M and Takeda will share further development costs. Upon successful clinical development and regulatory approvals, the parties will commercialize jointly in the United States and Europe. Takeda will hold commercial rights in certain countries in Asia, while 3M will retain the rights in other parts of the world.\n",
+ " \n",
+ "In October 2003, IVAX Corporation agreed to assume exclusive rights to 3Mâs branded health care respiratory products, together with related marketing and sales personnel, in nine European countries. The agreement covered QVAR⢠(beclomethasone dipropionate HFA) Inhalation Aerosol, a âmaintenanceâ medication used to prevent asthma attacks, and also covered Airomir⢠(albuterol sulfate) Inhaler, a ârescueâ medication used to relieve acute asthma symptoms. 3M will continue to manufacture and supply these products to IVAX. The total consideration due under the agreement, including minimum annual royalty payments, was $77 million, of which $24 million was paid in 2005, $24 million was paid in 2004 and $26 million was paid in 2003. 3M expects to receive $3 million in 2006. 3M may also receive additional royalty payments in 2010 (up to a maximum of approximately $7 million in total) if IVAX achieves certain annual sales levels. The Company recognizes the royalty revenue related to the IVAX agreement ratably over the term of the licensing arrangement.\n",
+ " \n",
+ "In 2004, local-currency sales in Health Care increased 1.7%, with 2004 negatively impacted by 2003 pharmaceutical and drug delivery agreements that did not repeat. Fourth quarter 2004 local-currency sales grew 5.0%, as year-on-year comparisons became more favorable. Operating income increased 9.3% to $1.123 billion in 2004.\n",
+ " \n",
+ "Industrial Business (18.0% of consolidated sales):\n",
+ " \n",
+ "The Industrial segment serves a broad range of industrial markets, from appliance and electronics to paper and packaging and food and beverage. Products include tapes, a wide variety of coated and nonwoven abrasives, adhesives, specialty materials and supply chain execution software solutions. The August 2005 CUNO acquisition adds a comprehensive line of filtration products for the separation, clarification and purification of fluids and gases.\n",
+ " \n",
+ "In 2005, Industrial local-currency sales grew 9.3%. The August 2005 CUNO acquisition, whose results are included in Industrial, added 5.1% of growth in 2005. In addition to CUNO, growth was led by industrial adhesives and tapes, as well as the abrasives businesses. 3M continues to selectively raise selling prices to offset commodity raw material price pressures. Industrial continues to demonstrate strong operational discipline, as operating income grew 20.5% in 2005.\n",
+ " \n",
+ "In 2004, Industrial local-currency sales growth of 8.2% for the year was broad-based across major geographic areas and Industrial businesses. Acquisitions increased sales by 1.4%, driven by the February 2004 acquisition of HighJump Software, Inc., a provider of supply chain execution software. Strong local-currency sales growth helped leverage operating income growth. Operating income increased 43.7% to $610 million in 2004.\n",
+ " \n",
+ "Display and Graphics Business (16.8% of consolidated sales):\n",
+ " \n",
+ "The Display and Graphics segment serves markets that include electronic display, touch screen, traffic safety and commercial graphics. This segment includes optical film and lens solutions for electronic displays; touch screens and touch monitors; reflective sheeting for transportation safety; and commercial graphics systems. The optical film business provides films that serve numerous market segments of the display lighting industry. 3M provides distinct products for five market segments, including products for: 1) LCD computer monitors 2) LCD televisions 3) handheld devices such as cellular phones 4) notebook PCs and 5) automotive displays. The optical business includes a number of different products that are protected by various patents and groups of patents. The remaining lifetimes of such patents, as well as patents protecting future products, range from less than a few years to greater than 15 years. These patents provide varying measures of exclusivity to 3M for a number of such products. 3Mâs proprietary manufacturing technology and know-how also provide a competitive advantage to 3M independent of such patents.\n",
+ " \n",
+ "In 2005, Display and Graphics local-currency sales grew 4.0%, impacted by many factors. The first half of 2005 was tempered by tough year-on-year optical film comparisons, while 3Mâs traffic safety systems business awaited a new highway funding bill in the U.S. and the sluggish economies in Western Europe and Japan held back growth in the commercial graphics business. Growth rebounded in the second half of 2005 as a new U.S. highway funding bill was passed in July, the economies in Western Europe and Japan started to experience some moderate growth and, as expected, 3M saw acceleration in demand for consumer electronics, especially flat-panel LCD televisions and a more normal LCD component inventory situation. This growth in the second half of 2005 drove record sales of 3Mâs proprietary optical films and components despite growing pricing pressure. Display and Graphics sales growth was negatively impacted by approximately 4% in 2005 due to the continued decline in lens systems for the CRT rear projection television market along with the phase out of the commercial videotape business announced in the fourth quarter of 2004. Operating income increased 2.3% in 2005. Operating income was impacted by the commercial videotape business phase out and decline in lens systems for the CRT rear projection television market, which negatively impacted 2005 operating income by approximately 8%.\n",
+ " \n",
+ "In 2004, Display and Graphicsâ local-currency sales growth was 10.5%. Strong demand for 3M films that brighten the displays on electronic products, such as flat-panel computer monitors, cellular phones, notebook PCs and LCD televisions, continued to drive results in 2004. Year-on-year local-currency sales growth in the Optical Systems business was slower in the last half of 2004, primarily due to inventory channel adjustments in the LCD market. This resulted in reduced demand for 3Mâs proprietary optical films and components. While this business is subject to periodic customer inventory fluctuations, 3M believes that this business will continue to be a significant growth engine for 3M. In the fourth quarter of 2004, 3M announced the phase out of its commercial videotape business, and this action, combined with a continuing decline in lens systems for the CRT rear-projection television market, negatively impacted sales and operating income. Operating income increased 27.9% to $1.133 billion in 2004.\n",
+ "\n",
+ "Consumer and Office Business (14.1% of consolidated sales):\n",
+ " \n",
+ "The Consumer and Office segment serves markets that include consumer retail, office retail, education, home improvement, building maintenance and other markets. Products in this segment include office supply products, stationery products, construction and home improvement products, home care products, protective material products (including consumer health care products such as bandages), and visual systems products.\n",
+ " \n",
+ "In 2005, Consumer and Office local-currency sales increased 3.4%, with broad-based growth across the many channels 3M serves. Consumer and Office experienced solid local-currency sales growth in construction and home improvement, home care and in its protective materials businesses. In the fourth quarter of 2005, sales were up slightly in local-currency terms as compared to an exceptional fourth quarter of 2004, when sales increased 8.3%, as several large retailers apparently increased their purchase rate level to meet their objectives. The continuing decline in 3Mâs Visual Systems business impacted sales by approximately 2% for the year. Consumer and Office continues to drive success by combining unique functionality along with customer inspired design into new and mature products such as ScotchÂŽ Tape, Post-ItÂŽ Notes, Filtrete⢠Filters and O-Cel-O⢠sponges. Operating income increased 6.3% in 2005.\n",
+ " \n",
+ "In 2004, local-currency sales growth in Consumer and Office was 6.9%. Sales growth was fairly broad-based across the many retail channels 3M serves, most notably in mass-market consumer retail and home improvement. This included strong sales growth in construction and home improvement/home care products, office supply products and stationery products. Sales increased 8.3% in the fourth quarter of 2004 as several large retailers apparently increased their purchase rate level to meet their objectives. Geographic area local-currency growth was led by the United States. Operating income increased 17.9% to $542 million in 2004.\n",
+ " \n",
+ "Electro and Communications Business (11.0% of consolidated sales):\n",
+ " \n",
+ "The Electro and Communications segment serves the electrical, electronics and communications industries, including electrical utilities; electrical construction, maintenance and repair; OEM electrical and electronics; computers and peripherals; consumer electronics; telecommunications central office, outside plant and enterprise; as well as aerospace, military, automotive and medical markets; with products that enable the efficient transmission of electrical power and speed the delivery of information and ideas. Products include electronic and interconnect solutions, microinterconnect systems, high-performance fluids, high-temperature and display tapes, telecommunications products and electrical products.\n",
+ "\n",
+ "In 2005, local-currency sales in Electro and Communications increased 4.2%, with improving end market conditions and success driving existing products into new applications helping the business post its best local-currency growth since 2000. Local-currency growth accelerated in the second half of 2005, with local-currency growth in the fourth quarter of 2005 up 10.9%. Local-currency growth was led by demand for 3M electronic products for semiconductor manufacturers, along with continued strong growth in electrical products used for insulating, testing and sensing. This strong sales growth helped offset weakness in the electronic solutions and communications markets. Operating margins were 19.8% in 2005, with operating income increasing 35.4% in 2005.\n",
+ " \n",
+ "In 2004, local-currency sales in Electro and Communications increased 2.7%, led by electronic materials, along with electrical products for insulating, testing and sensing. Sales in the electronic solutions and telecommunications segments were negatively impacted by the general slowdown in the semiconductor industry and continued softness in the hard-line infrastructure segment of the telecommunications market. Geographically, local-currency growth in this business for 2004 was led by the Latin America and Canada area along with the Asia Pacific area. Operating income was up 18.8% to $342 million in 2004.\n",
+ " \n",
+ "Safety, Security and Protection Services Business (10.8% of consolidated sales):\n",
+ " \n",
+ "The Safety, Security and Protection Services segment serves a broad range of markets that strive to increase the safety, security and productivity of workers, facilities and systems. Major product offerings include personal protection products, safety and security products, energy control products, cleaning and protection products for commercial establishments, and roofing granules for asphalt shingles.\n",
+ " \n",
+ "In 2005, Safety, Security and Protection Services local-currency sales growth was 6.9%, driven by broad-based growth across the business portfolio and geographies. The continued global threat of events such as terrorism, natural disasters, SARS and Avian flu helped raise the awareness in the general public about the importance of personal protective equipment, especially respiratory protection for overall health. Sales growth was driven by continued strong global demand for personal protection products and solutions, particularly respiratory protection products, along with strong demand for cleaning and protection products for commercial buildings. Roofing granules for asphalt shingles also experienced solid sales growth. Operating income improved 12.6% in 2005.\n",
+ " \n",
+ "In 2004, Safety, Security and Protection Services local-currency sales growth was 6.6%. Local-currency growth was driven by strong global demand for personal protective products and solutions, along with cleaning and protective products for commercial buildings. 3Mâs acquisition of Hornell Holding AB, a European-based global supplier of personal safety equipment, added 2.3 percentage points of growth in 2004. Operating income increased 12.3% to $491 million in 2004.\n",
+ "\n",
+ "Transportation Business (8.4% of consolidated sales): \n",
+ " \n",
+ "The Transportation segment serves markets that include automotive, automotive aftermarket, marine, aerospace and specialty vehicle markets. This segment provides components and products that are used in the manufacture, repair and maintenance of automotive, marine, aircraft and specialty vehicles.\n",
+ " \n",
+ "In 2005, Transportation local-currency sales growth was 5.0%, with benefits from customer-focused new products and productivity solutions driving results. One of these new products is 3M⢠Paint Replacement Film, which is an alternative to using paint around car and truck windows. In the automotive aftermarket area, the 3M⢠Paint Preparation System shortens paint changeover and clean-up time while also reducing the use of solvents for cleaning paint guns. Sales growth was broad based in Transportation, led by businesses that serve the automotive OEMs and auto body repair shops, despite challenges in the U.S. OEM Big-3 automotive market along with lower levels of distribution buy-in due to cash flow trade-offs by customers in the automotive aftermarket business. Operating income increased 8.1% in 2005.\n",
+ " \n",
+ "In March 2005, 3Mâs automotive business completed the purchase from TI&M Beteiligungsgesellschaft mbH of 19 percent of I&T Innovation Technology (I&T), which was founded in Austria in 1999. 3M and I&T will collaborate to deliver flat flexible wiring systems for automotive interior applications to the global automotive market. The purchase price of approximately $55 million is reported as âInvestmentsâ in the Consolidated Balance Sheet and as âPurchases of Investmentsâ in the Consolidated Statement of Cash Flows. Due to its distribution involvement and voting rights, the Company is using equity method accounting for its investment in I&T. The Company has a purchase option to buy an additional 31% investment of I&T after certain conditions have been met. This purchase option expires December 31, 2008. The Company also has a put option, which provides the Company the right to sell back its entire ownership interest in I&T, exercisable between January 1, 2007 and March 31, 2009, unless the Company exercises its purchase option before then.\n",
+ " \n",
+ "In 2004, local-currency sales growth was 5.1% in Transportation. Top-line growth in this business continued to benefit from new products and solutions for customers, along with a strategy of replicating successful 3M solutions across several distinct segments of the transportation industry. Operating income increased 9.8% to $426 million in 2004.\n",
+ " \n",
+ "PERFORMANCE BY GEOGRAPHIC AREA\n",
+ "Financial information related to 3M operations in various geographic areas is provided in Note 16 to the Consolidated Financial Statements. A summary of key information and discussion related to 3Mâs geographic areas follow:\n",
+ "\n",
+ "While 3M manages its businesses globally and believes its business segment results are the most relevant measure of performance, the Company also utilizes geographic area data as a secondary performance measure. Export sales are reported within the geographic area where the final sales to 3M customers are made. A portion of the products or components sold by 3Mâs operations to its customers are exported by these customers to different geographic areas. As customers move their operations from one geographic area to another, 3Mâs results will follow. Thus, net sales in a particular geography is not indicative of end-user consumption in that geography.\n",
+ " \n",
+ "U.S. sales revenue increased 4.9%, with growth led by Industrial; Consumer and Office; and Safety, Security and Protection Services. Asia Pacific local-currency sales (which exclude translation impacts) increased 10.6%. All seven business segments contributed to this increase in the Asia Pacific area, with optical film being the largest growth component. Japan sales totaled approximately $2.1 billion, with local-currency sales up 3.6% from 2004. European local-currency sales increased 0.9%, with good growth in Industrial; Safety, Security and Protection Services; and Transportation. In the combined Latin America and Canada area, local-currency sales increases of 1.3% were led by Consumer and Office; Industrial; Safety, Security and Protection Services; and Transportation. Growth in Latin America was impacted by the continued decline of 3Mâs CRT rear projection lens business in Mexico and the move of a flex circuits customer from Puerto Rico to Singapore. Foreign currency translation positively impacted the combined Latin America and Canada area sales by 7.4%, and the Asia Pacific area sales by 0.5%, as the U.S. dollar weakened against these currencies. Foreign currency translation had a minimal impact on European sales. For 2005, international operations represented approximately 61% of 3Mâs sales.\n",
+ " \n",
+ "Geographic Area Supplemental Information\n",
+ " \n",
+ "Employment:\n",
+ "Employment increased by 2,244 people since year-end 2004. The CUNO acquisition in August 2005 added approximately 2,300 employees. The Company continues to increase headcount in faster-growing areas of the world, such as Asia Pacific, primarily to support increasing local sales. Excluding the impact of CUNO, employment has been decreasing in the United States and combined Europe, Middle East and Africa area. Sales per employee in local currencies increased approximately 4% in 2005, approximately 7% in 2004 and 8.5% in 2003.\n",
+ " \n",
+ "Capital Spending/Net Property, Plant and Equipment:\n",
+ "The bulk of 3M capital spending historically has been in the United States, resulting in higher net property, plant and equipment balances in the U.S. The Company is striving to more closely align its manufacturing and sourcing with geographic market sales, and because approximately 61% of sales are outside the United States, this would increase production outside the United States, helping to improve customer service and reduce working capital requirements. The 2005 decrease in net property, plant and equipment in the Europe, Middle East and Africa area was primarily due to currency translation (due to the stronger U.S dollar at December 31, 2005 when compared to December 31, 2004). Capital spending in Asia has more than doubled since 2003 as we continue to grow our presence in this region.\n",
+ " \n",
+ "CRITICAL ACCOUNTING ESTIMATES\n",
+ "Information regarding significant accounting policies is included in Note 1 to the Consolidated Financial Statements. As stated in Note 1, the preparation of financial statements requires management to make estimates and assumptions that affect the reported amounts of assets, liabilities, revenue and expenses, and related disclosure of contingent assets and liabilities. Management bases its estimates on historical experience and on various other assumptions that are believed to be reasonable under the circumstances, the results of which form the basis for making judgments about the carrying values of assets and liabilities that are not readily apparent from other sources. Actual results may differ from these estimates.\n",
+ "\n",
+ "The Company believes its most critical accounting estimates relate to legal proceedings, the Companyâs pension and postretirement obligations, and potential asset impairment issues. Senior management has discussed the development, selection and disclosure of its critical accounting estimates with the Audit Committee of 3Mâs Board of Directors.\n",
+ " \n",
+ "Legal Proceedings:\n",
+ "The categories of claims for which the Company has estimated its probable liability, the amount of its liability accruals, and the estimates of its related insurance receivables are critical accounting estimates related to legal proceedings. Please refer to the section entitled âAccrued Liabilities and Insurance Receivables Related to Legal Proceedingsâ (contained in âLegal Proceedingsâ in Note 11 to the Consolidated Financial Statements) for additional information about such estimates.\n",
+ " \n",
+ "Pension and Postretirement Obligations:\n",
+ "3M has various company-sponsored retirement plans covering substantially all U.S. employees and many employees outside the United States. The Company accounts for its defined benefit pension and postretirement health care and life insurance benefit plans in accordance with Statement of Financial Accounting Standards (SFAS) No. 87, âEmployersâ Accounting for Pensionsâ and SFAS No. 106, âEmployerâs Accounting for Postretirement Benefits Other than Pensionsâ, which require that amounts recognized in financial statements be determined on an actuarial basis. Pension benefits associated with these plans are generally based primarily on each participantâs years of service, compensation, and age at retirement or termination. Two critical assumptions, the discount rate and the expected return on plan assets, are important elements of expense and liability measurement. The assumed health care trend rate is the most significant postretirement health care assumption. See Note 10 to the Consolidated Financial Statements for additional discussion of actuarial assumptions used in determining pension and postretirement health care liabilities and expenses.\n",
+ " \n",
+ "The Company determines the discount rate used to measure plan liabilities as of the December 31 measurement date for the U.S. pension and postretirement benefit plans. The discount rate reflects the current rate at which the associated liabilities could be effectively settled at the end of the year. In estimating this rate, the Company looks at rates of return on fixed-income investments of similar duration to the liabilities in the plan that receive high, investment grade ratings by recognized ratings agencies. Using these methodologies, the Company determined a discount rate of 5.50% to be appropriate as of December 31, 2005, which is a reduction of 0.25 percentage points from the rate used as of December 31, 2004.\n",
+ " \n",
+ "As of December 31, 2005, the Company converted to the RP (Retirement Plans) 2000 Mortality Table for calculating the year-end 2005 U.S. pension and postretirement obligations and 2006 expense. The impact of this change increased the year-end 2005 U.S. Projected Benefit Obligations for pension by $385 million, the year-end 2005 U.S. Accumulated Benefit Obligations for pension by $349 million and the 2005 U.S. Accumulated Postretirement Benefit Obligation by $93 million. This change will also increase pension expenses for 2006 by $64 million and postretirement expenses by $17 million.\n",
+ " \n",
+ "A significant element in determining the Companyâs pension expense in accordance with SFAS No. 87 is the expected return on plan assets, which is based on historical results for similar allocations among asset classes. For the U.S. pension plan, the Companyâs assumption for the expected return on plan assets was 8.75% for 2005 and will remain at 8.75% for 2006. Refer to Note 10 to the Consolidated Financial Statements for information on how this rate is determined.\n",
+ " \n",
+ "The difference between the expected return and the actual return on plan assets is deferred and, under certain circumstances, amortized over future years of service. Therefore, the net deferral of past asset gains (losses) ultimately affects future pension expense. This is also true of changes to actuarial assumptions. As of December 31, 2005, the Company had net unrecognized pension actuarial losses of $2.676 billion and $954 million for the U.S. and International pension benefit plans, respectively, and $1.005 billion for the postretirement health care and life insurance benefit plan. These amounts represent potential future pension and postretirement expenses that would be amortized over average future service periods. The average remaining service periods for U.S. and International pension plans and the postretirement plans are 10.2 years, 14.1 years and 10.0 years, respectively.\n",
+ " \n",
+ "For the year ended December 31, 2005, the Company recognized total consolidated pre-tax pension expense (after settlements, curtailments and special termination benefits) of $331 million, up from $325 million in 2004. Pension expense (before settlements, curtailments and special termination benefits) is anticipated to decrease to approximately $300 million in 2006. For the pension plans, holding all other factors constant, an increase/decrease in the expected long-term rate of return on plan assets by 0.25 percentage points would decrease/increase U.S. 2006 pension expense by approximately $22 million for U.S. pension plans and approximately $7 million for international pension plans. Also, holding all other factors constant, an increase/decrease in the discount rate used to measure plan liabilities by 0.25 percentage points would decrease/increase 2006 pension expense by approximately $31 million for U.S. pension plans and approximately $7 million for international pension plans. See Note 10 to the Consolidated Financial Statements for details of the impact of a one percentage point change in assumed health care trend rates on the postretirement health care benefit expense and obligation.\n",
+ " \n",
+ "Potential Asset Impairment Issues:\n",
+ "3M net property, plant and equipment totaled approximately $5.6 billion at December 31, 2005. Management makes estimates and assumptions in preparing the consolidated financial statements for which actual results will emerge over long periods of time. This includes the recoverability of long-lived assets employed in the business, including assets of acquired businesses. These estimates and assumptions are closely monitored by management and periodically adjusted as circumstances warrant. For instance, expected asset lives may be shortened or an impairment recorded based on a change in the expected use of the asset or performance of the related business reporting unit. \n",
+ " \n",
+ "3M goodwill totaled approximately $3.5 billion at December 31, 2005, which, based on impairment testing, is not impaired. Impairment testing for goodwill is done at a reporting unit level. Reporting units are one level below the business segment level, but can be combined when reporting units within the same segment have similar economic characteristics. 3M had 18 reporting units at December 31, 2005. The majority of goodwill relates to and is assigned directly to a specific reporting unit. An impairment loss generally would be recognized when the carrying amount of the reporting unitâs net assets exceeds the estimated fair value of the reporting unit. The estimated fair value of a reporting unit is determined using earnings for the reporting unit multiplied by a price/earnings ratio for comparable industry groups, or by using a discounted cash flow analysis.\n",
+ " \n",
+ "NEW ACCOUNTING PRONOUNCEMENTS\n",
+ "As of December 31, 2005, the Company adopted FASB Interpretation No. 47, âAccounting for Conditional Asset Retirement Obligationsâ (FIN 47). This accounting standard applies to the fair value of a liability for an asset retirement obligation associated with the retirement of tangible long-lived assets and where the liability can be reasonably estimated. Conditional asset retirement obligations exist for certain of the Companyâs long-term assets. The fair value of these obligations is recorded as liabilities on a discounted basis. Over time the liabilities are accreted for the change in the present value and the initial capitalized costs are depreciated over the useful lives of the related assets. The adoption of FIN 47 resulted in the recognition of an asset retirement obligation liability of $59 million and an after tax charge of $35 million, which is reflected as a cumulative change in accounting principle in the Consolidated Statement of Income. The pro forma effect of applying this guidance in all prior periods presented was determined not to be material.\n",
+ " \n",
+ "Effective January 1, 2006, 3M adopted SFAS No. 123 (revised 2004), âShare-Based Paymentâ, which requires 3M to expense stock-based compensation expense. The Company is adopting SFAS No. 123R using the modified retrospective method. All prior periods will be adjusted to give effect to the fair-value-based method of accounting for awards granted in fiscal years beginning on or after January 1, 1995. Stock-based compensation disclosures in Note 1 reflect pro forma expense of $.14 cents per diluted share in 2005. The 2006 impact of adopting SFAS No. 123R is estimated to be approximately $.16 per diluted share with an estimated $0.02 per diluted share cost in the first quarter, an estimated $0.08 per diluted share cost in the second quarter, and an estimated $0.03 per diluted share cost in both the third and fourth quarters. The pro forma impact of stock-based compensation on net income and earnings per share provided in Note 1 for the years ended December 31, 2005, 2004 and 2003, was recognized over the nominal vesting period, whereby if an employee retired before the end of the vesting period, the Company would recognize any remaining unrecognized compensation cost at the date of retirement. SFAS No. 123R requires recognition under a non-substantive vesting period approach, requiring compensation expense recognition when an employee is eligible to retire. 3M employees in the U.S. are eligible to retire beginning at age 55 and after having completed five years of service. Approximately 25 to 30% of the number of stock-based compensation awards are made to this population. The Company will change to the non-substantive vesting period approach for new stock compensation grants made after the Companyâs adoption of SFAS No. 123R on January 1, 2006. Therefore, primarily beginning in May 2006 with the annual Management Stock Ownership Program grant, immediate expensing of those stock-based compensation awards granted to employees eligible to retire will result in a higher compensation expense than historically recognized in comparable prior periods. The total expense in 2006 and beyond will depend on several variables, including the number of share-based awards granted, the fair value of those awards, and the period the vesting of those awards is recognized over; therefore the actual expense may be different from this estimate.\n",
+ " \n",
+ "Additional information regarding these and other accounting pronouncements is included in Note 1 to the Consolidated Financial Statements.\n",
+ "\n",
+ "FINANCIAL CONDITION AND LIQUIDITY\n",
+ "The Company generates significant ongoing cash flow. Net debt decreased significantly in 2004, but increased in 2005, primarily related to the $1.36 billion CUNO acquisition.\n",
+ " \n",
+ "3M believes its ongoing cash flows provide ample cash to fund expected investments and capital expenditures. The Company has an AA credit rating from Standard & Poorâs and an Aa1 credit rating from Moodyâs Investors Service. The Company has sufficient access to capital markets to meet currently anticipated growth and acquisition investment funding needs. The Company does not utilize derivative instruments linked to the Companyâs stock. However, the Company does have contingently convertible debt that, if conditions for conversion are met, is convertible into shares of 3M common stock (refer to Note 8 in this document).\n",
+ " \n",
+ "The Companyâs financial condition and liquidity at December 31, 2005, remained strong. Various assets and liabilities, including cash and short-term debt, can fluctuate significantly from month-to-month depending on short-term liquidity needs. Working capital (defined as current assets minus current liabilities) totaled $1.877 billion at December 31, 2005, compared with $2.649 billion at December 31, 2004. This decrease was primarily related to a decrease in cash and cash equivalents ($1.685 billion) partially offset by a decrease in debt classified as short-term borrowings and current portion of long-term debt ($1.022 billion). The cash and cash equivalents balance was impacted by the acquisition of CUNO and repayment of debt.\n",
+ " \n",
+ "The Company uses various working capital measures that place emphasis and focus on certain working capital assets and liabilities. These measures are not defined under U.S. generally accepted accounting principles and may not be computed the same as similarly titled measures used by other companies. One of the primary working capital measures 3M uses is a combined index, which includes accounts receivables, inventory and accounts payable. This combined index (defined as quarterly net sales â fourth quarter at year-end â multiplied by four, divided by ending net accounts receivable plus inventory less accounts payable) was 5.7 at December 31, 2005, down from 5.8 at December 31, 2004. Excluding CUNO, net working capital turns at December 31, 2005, were 5.8, the same as at December 31, 2004. Receivables increased $46 million, or 1.6%, compared with December 31, 2004. At December 31, 2005, the CUNO acquisition increased accounts receivable by $88 million. Currency translation (due to the stronger U.S dollar) reduced accounts receivable by $231 million year-on-year. Inventories increased $265 million, or 14.0%, compared with December 31, 2004. At December 31, 2005, the CUNO acquisition increased inventories by $56 million. Currency translation reduced inventories by $89 million year-on-year. Accounts payable increased $88 million compared with December 31, 2004, with CUNO accounting for $18 million of this increase.\n",
+ " \n",
+ "Cash flows from operating, investing and financing activities are provided in the tables that follow. Individual amounts in the Consolidated Statement of Cash Flows exclude the effects of acquisitions, divestitures and exchange rate impacts, which are presented separately in the cash flows. Thus, the amounts presented in the following operating, investing and financing activities tables reflect changes in balances from period to period adjusted for these effects.\n",
+ "\n",
+ "Cash Flows from Operating Activities:\n",
+ " \n",
+ "Cash flows from operating activities can fluctuate significantly from period to period, as pension funding decisions, tax timing differences and other items can significantly impact cash flows. In 2005, cash flow was essentially flat when compared to 2004. Higher net income, higher accounts payable and increased insurance receivable collections were offset by accounts receivable increases, inventory increases and other items. Product and other insurance receivables and claims increased cash flow by $122 million in 2005, benefiting from the $148 million in insurance recoveries for the breast implant matter in 2005. For a more detailed discussion of these and other legal proceedings, refer to Note 11 in the Consolidated Financial Statements of this Annual Report on Form 10-K. The category âOther â netâ in the preceding table reflects changes in other asset and liability accounts. For example, in 2005, this category includes the non-cash impact of adopting FIN 47 ($35 million cumulative effect of accounting change), increases in accrued liabilities (such as the $30 million increase in liability related to legal settlement agreements), and other items.\n",
+ " \n",
+ "In 2005, the Company made discretionary contributions totaling $500 million to its U.S. qualified pension plan, with $200 million contributed in the fourth quarter of 2005, and $300 million contributed in the third quarter of 2005. In the third quarter of 2004, the Company made a special pension contribution to 3Mâs Japanese pension plan of $155 million and a discretionary contribution of $300 million to its U.S. qualified pension plan. In the third quarter of 2003, 3M made a discretionary contribution of $600 million to its U.S. qualified pension plan. Future contributions will depend on market conditions, interest rates and other factors. 3M believes its strong cash flow and balance sheet will allow it to fund future pension needs without compromising growth opportunities.\n",
+ " \n",
+ "In 2004, cash flow improvements were primarily driven by higher net income. In all periods presented, significant Company pension contributions negatively impacted cash flows. In all years, with a larger amount in 2003, a portion of the tax timing benefit relates to the tax benefit received from Company pension contributions.\n",
+ " \n",
+ "Cash Flows from Investing Activities: \n",
+ " \n",
+ "Investments in property, plant and equipment are enabling growth in diverse markets, helping to meet product demand and increasing manufacturing efficiency. These investments will continue to be primarily capacity and growth focused. For example, in December 2005, 3M announced its intention to build an LCD optical film manufacturing facility in Poland to support the fast-growing LCD-TV market in Europe and to better serve its customers. The Company expects 2006 capital expenditures to total approximately $1.1 billion, compared with $943 million in 2005.\n",
+ " \n",
+ "In the third quarter of 2005, 3M completed the acquisition of CUNO. 3M acquired CUNO for approximately $1.36 billion, including assumption of debt. This $1.36 billion included $1.27 billion of cash paid (net of cash acquired) and the assumption of $80 million of debt, most of which has been repaid. In 2005, the Company also entered into two additional business combinations for a total purchase price of $27 million. Refer to Note 2 to the Consolidated Financial Statements for more information on these 2005 business combinations, and for information concerning 2004 and 2003 business combinations.\n",
+ " \n",
+ "Purchases of investments in 2005 include the purchase from TI&M Beteiligungsgesellschaft mbH of 19 percent of I&T Innovation Technology (discussed previously under the Transportation business segment). The purchase price of approximately $55 million is reported as âInvestmentsâ in the Consolidated Balance Sheet and as âPurchases of Investmentsâ in the Consolidated Statement of Cash Flows. Other âPurchases of Investmentsâ and âProceeds from Sale of Investmentsâ in 2005 are primarily attributable to auction rate securities, which are classified as available-for-sale. Prior to 2005, purchases of and proceeds from the sale of auction rate securities were classified as Cash and Cash Equivalents. At December 31, 2004, the amount of such securities taken as a whole was immaterial to Cash and Cash Equivalents, and accordingly were not reclassified for 2004 and prior.Proceeds from the sale of investments in 2003 include $26 million of cash received related to the sale of 3Mâs 50% ownership in Durel Corporation to Rogers Corporation. Additional purchases of investments totaled $5 million in 2005, $10 million in 2004 and $16 million in 2003. These purchases include additional survivor benefit insurance and equity investments.\n",
+ " \n",
+ "The Company is actively considering additional acquisitions, investments and strategic alliances.\n",
+ " \n",
+ "Cash Flows from Financing Activities: \n",
+ " \n",
+ "Total debt at December 31, 2005, was $2.381 billion, down from $2.821 billion at year-end 2004, with the decrease primarily attributable to the retirement of $400 million in medium-term notes. There were no new long-term debt issuances in 2005. In 2005, the cash flow decrease in net short-term debt of $258 million includes the portion of short-term debt with original maturities of 90 days or less. The repayment of debt of $656 million primarily related to the retirement of $400 million in medium-term notes and commercial paper retirements. Proceeds from debt of $429 million primarily related to commercial paper issuances. Total debt was 19% of total capital (total capital is defined as debt plus equity), compared with 21% at year-end 2004.\n",
+ " \n",
+ "Debt securities, including the Companyâs shelf registration, its medium-term notes program, dealer remarketable securities and Convertible Note, are all discussed in more detail in Note 8 to the Consolidated Financial Statements. 3M has a shelf registration and medium-term notes program through which $1.5 billion of medium-term notes may be offered. In 2004, the Company issued approximately $62 million in debt securities under its medium-term notes program. No debt was issued under this program in 2005. The medium-term notes program and shelf registration have remaining capacity of approximately $1.438 billion. The Companyâs $350 million of dealer remarketable securities (classified as current portion of long-term debt) were remarketed for one year in December 2005. In addition, the Company has Convertible Notes with a book value of $539 million at December 31, 2005.\n",
+ "\n",
+ "The next put option date for these Convertible Notes is November 2007, thus at year-end 2005 this debt is classified as long-term debt. At December 31, 2005, the dealer remarketable securities and $62 million of medium-term notes are classified as current portion of long-term debt as the result of put provisions associated with these debt instruments. For a discussion of accounting pronouncements that will affect accounting treatment for the Convertible Note, refer to Note 1 to the Consolidated Financial Statements for discussion of EITF Issue No. 04-08, âThe Effect of Contingently Convertible Debt on Diluted Earnings per Shareâ and proposed SFAS No. 128R, âEarnings per Shareâ.\n",
+ " \n",
+ "Repurchases of common stock are made to support the Companyâs stock-based employee compensation plans and for other corporate purposes. On November 8, 2004, the Board of Directors authorized the purchase of $2.0 billion of the Companyâs common stock between January 1, 2005 and January 31, 2006. In October 2005, 3Mâs Board of Directors authorized the repurchase of an additional $300 million of the Companyâs stock through January 31, 2006. This increased the total repurchase authorization to $2.3 billion for the period through January 31, 2006. As of December 31, 2005, substantially all of this repurchase authorization had been utilized. Refer to the table captioned âIssuer Purchases of Equity Securitiesâ in Part II, Item 5, for more information.\n",
+ " \n",
+ "Cash dividends paid to stockholders totaled $1.286 billion ($1.68 per share) in 2005, $1.125 billion ($1.44 per share) in 2004 and $1.034 billion ($1.32 per share) in 2003. 3M has paid dividends since 1916. Other cash flows from financing activities include distributions to minority interests, changes in cash overdraft balances, and principal payments for capital leases.\n",
+ " \n",
+ "Liquidity:\n",
+ "The Companyâs liquidity remains strong. Primary short-term liquidity needs are provided through U.S. commercial paper and euro commercial paper issuances. As of December 31, 2005, outstanding total commercial paper issued totaled $514 million and averaged approximately $823 million during 2005. Medium-term note shelf borrowing capacity totaled $1.438 billion as of December 31, 2005. Credit support for outstanding commercial paper is provided by a $565 million credit agreement among a group of primary relationship banks. In March 2005, the Company replaced its 364-day credit agreement with a five-year credit agreement with similar terms. This $565 million credit facility provides up to $115 million in letters of credit ($97 million of which was utilized at December 31, 2005), with provisions for increasing this limit up to $150 million. Committed credit facilities of $53 million are in place across several international subsidiary locations.\n",
+ " \n",
+ "The Company believes it is unlikely that its access to the commercial paper market will be restricted. Cash and cash equivalents and certain other current assets could provide additional liquidity to meet near term obligations, if necessary. At year-end 2005, certain debt agreements ($350 million of dealer remarketable securities and $165 million of ESOP debt) had ratings triggers (BBB-/Baa3 or lower) that would require repayment of debt. The Company currently has AA/Aa1 debt ratings. In addition, the $565 million, five-year credit agreement requires 3M to maintain a capitalization ratio at no more than 0.60 to 1 at the end of each quarter. This ratio is calculated as funded debt (including all borrowed money and letters of credit utilized) to the sum of funded debt and equity. At December 31, 2005, this ratio was approximately 0.20 to 1.\n",
+ " \n",
+ "3Mâs cash balance at December 31, 2005 totaled $1.072 billion. 3Mâs strong balance sheet and liquidity provide the Company with significant flexibility to take advantage of numerous opportunities going forward. The Company will continue to invest in its operations to drive growth, including continual review of acquisition opportunities. 3M paid dividends of $1.286 billion in 2005, and has a long history of dividend increases. In February 2006, the Board of Directors increased the quarterly dividend on 3M common stock by 9.5% to 46 cents per share, equivalent to an annual dividend of $1.84 per share. In February 2006, 3Mâs Board of Directors also authorized the purchase of up to $2.0 billion of the Companyâs common stock between February 13, 2006 and February 28, 2007. The Company may also make additional contributions to its pension plan in the future, but exact amounts are uncertain and will depend on market conditions.\n",
+ " \n",
+ "Off-Balance Sheet Arrangements and Contractual Obligations:\n",
+ "As of December 31, 2005, the Company had not utilized special purpose entities to facilitate off-balance sheet financing arrangements. 3Mâs accrued product warranty liabilities, recorded on the Consolidated Balance Sheet as part of current and long-term liabilities, are estimated at approximately $22 million. 3M does not consider this amount to be material. The fair value of 3M guarantees of loans with third parties and other guarantee arrangements are not material.\n",
+ " \n",
+ "In addition to guarantees, 3M, in the normal course of business, periodically enters into agreements that require 3M to indemnify either major customers or suppliers for specific risks, such as claims for injury or property damage arising out of 3M products or the negligence of 3M personnel, or claims alleging that 3M products infringe third party patents or other intellectual property. While 3Mâs maximum exposure under these indemnification provisions cannot be estimated, these indemnifications are not expected to have a material impact on the Companyâs consolidated financial position or results of operations.\n",
+ " \n",
+ "Contractual Obligations\n",
+ " \n",
+ "Long-term debt payments due in 2006 include $350 million of dealer remarketable securities (final maturity 2010) and $62 million of medium-term notes (final maturity 2044). These securities are classified as current portion of long-term debt as the result of put provisions associated with these debt instruments. The next date on which investors can require repurchase of the Convertible Notes is 2007, thus in the above schedule this is considered due in 2007 (final maturity 2032).\n",
+ " \n",
+ "Unconditional purchase obligations are defined as an agreement to purchase goods or services that is enforceable and legally binding on the Company. Included in the unconditional purchase obligations category above are certain obligations related to take or pay contracts, capital commitments, service agreements and utilities. These estimates include both unconditional purchase obligations with terms in excess of one year and normal ongoing purchase obligations with terms of less than one year. Many of these commitments relate to take or pay contracts, in which 3M guarantees payment to ensure availability of products or services that are sold to customers. The Company expects to receive consideration (products or services) for these unconditional purchase obligations. The purchase obligation amounts do not represent the entire anticipated purchases in the future, but represent only those items for which the Company is contractually obligated. The majority of 3Mâs products and services are purchased as needed, with no unconditional commitment. For this reason, these amounts will not provide a reliable indicator of the Companyâs expected future cash outflows on a stand-alone basis.\n",
+ " \n",
+ "As discussed in Note 10 to the Consolidated Financial Statements, the Company does not have a required minimum pension contribution obligation for its U.S. plans in 2006. Thus, Company contributions to its U.S. and international pension plans are expected to be largely discretionary in 2006 and future years. Contractual capital commitments are also included in the preceding table, but these commitments represent a small part of the Companyâs expected capital spending in 2006 and beyond.\n",
+ " \n",
+ "FINANCIAL INSTRUMENTS\n",
+ "The Company enters into contractual derivative arrangements in the ordinary course of business to manage foreign currency exposure, interest rate risks and commodity price risks. A financial risk management committee, composed of senior management, provides oversight for risk management and derivative activities. This committee determines the Companyâs financial risk policies and objectives, and provides guidelines for derivative instrument utilization. This committee also establishes procedures for control and valuation, risk analysis, counterparty credit approval, and ongoing monitoring and reporting.\n",
+ " \n",
+ "The Company enters into foreign exchange forward contracts, options and swaps to hedge against the effect of exchange rate fluctuations on cash flows denominated in foreign currencies and certain intercompany financing transactions. In 2001, the Company increased the amount and duration of its foreign currency hedges to help lessen year-over-year impacts and to improve the predictability of future earnings. However, this hedging program does not make 3M immune to currency impacts.\n",
+ " \n",
+ "The Company manages interest rate risks using a mix of fixed and floating rate debt. To help manage borrowing costs, the Company may enter into interest rate swaps. Under these arrangements, the Company agrees to exchange, at specified intervals, the difference between fixed and floating interest amounts calculated by reference to an agreed-upon notional principal amount. The Company manages commodity price risks through negotiated supply contracts, price protection agreements and forward physical contracts.\n",
+ " \n",
+ "A variance/co-variance statistical modeling technique was used to test the Companyâs exposure to changes in currency and interest rates and assess the risk of loss in after-tax earnings of financial instruments, derivatives and underlying exposures outstanding at December 31, 2005. The model (third-party bank dataset) used a 95% confidence level over a 12-month time horizon. Based on this analysis of the Companyâs interest rate risks, possible increases in interest rates would not have a material adverse effect on after-tax earnings ($2 million at December 31, 2005 and $5 million at December 31, 2004). A decrease in interest rates would have increased after-tax earnings by $2 million at December 31, 2005. Based on this analysis of the primary foreign exchange risks, possible changes in foreign exchange rates would have adversely impacted after-tax earnings by $69 million at December 31, 2005 ($61 million at December 31, 2004). A positive change in exchange rates would have benefited after-tax earnings by $66 million at December 31, 2005. When including certain commodity risks, possible changes in commodity rates would have adversely impacted after-tax earnings by an additional $4 million at December 31, 2005 (an additional $10 million at December 31, 2004). A positive change in commodity rates would not have materially impacted after-tax earnings at December 31, 2005. The model used analyzed over 20 different currencies and five commodities, but does not purport to represent what actually will be experienced by the Company. This model does not include certain hedge transactions, because the Company believes their inclusion would not materially impact the results.\n",
+ " \n",
+ "The global exposures related to purchased components and materials are such that a one percent price change would result in a pre-tax cost or savings of approximately $45 million per year. The global energy exposure is such that a 10% price change would result in a pre-tax cost or savings of approximately $35 million per year. Derivative instruments are used to hedge less than two percent of the purchased components and materials exposure and are used to hedge approximately 10% of this energy exposure.\n",
+ " \n",
+ "FORWARD-LOOKING STATEMENTS\n",
+ "This Annual Report on Form 10-K, including âManagementâs Discussion and Analysis of Financial Condition and Results of Operationsâ in Item 7, contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995.The Company may also make forward-looking statements in other reports filed with the Securities and Exchange Commission, in materials delivered to stockholders and in press releases. In addition, the Companyâs representatives may from time to time make oral forward-looking statements.\n",
+ " \n",
+ "Forward-looking statements relate to future events and typically address the Companyâs expected future business and financial performance. Words such as âplan,â âexpect,â âaim,â âbelieve,â âproject,â âtarget,â âanticipate,â âintend,â âestimate,â âwill,â âshould,â âcouldâ and other words and terms of similar meaning, typically identify such forward-looking statements. In particular, these include statements about the Companyâs strategy for growth, product development, market position, future performance or results of current or anticipated products, interest rates, foreign exchange rates, financial results, and the outcome of contingencies, such as legal proceedings. The Company assumes no obligation to update or revise any forward-looking statements.\n",
+ " \n",
+ "Forward-looking statements are based on certain assumptions and expectations of future events and trends that are subject to risks and uncertainties. Actual future results and trends may differ materially from historical results or those reflected in any such forward-looking statements depending on a variety of factors. Discussion of these factors is incorporated by reference from Part I, Item 1A, âRisk Factorsâ, of this document, and should be considered an integral part of Part II, Item 7, âManagementâs Discussion and Analysis of Financial Condition and Results of Operationsâ.\n",
+ "\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "f152e6ff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "large_summary = \"\"\"\n",
+ "0 Record net sales of $21.167 billion, record net income of $3.199 billion. 1\n",
+ "Record net sales, including core local-currency sales growth of 4.1%, result in\n",
+ "23.7% operating income profit margin. 2 3Mâs performance in 2005 was broad-based,\n",
+ "with all seven business segments contributing to positive local-currency sales\n",
+ "growth. 3 Sales growth in 2005 was led by Safety, Security and Protection\n",
+ "Services, Transportation segments. 4 3M generated operating cash flows of $4.258\n",
+ "billion in 2005. 3M generated $4.258 billion of operating cash flows in 2005,\n",
+ "essentially flat when compared to 2004. 5 3M expects commodity prices to remain\n",
+ "volatile in 2006. 6 3M expects solid sales growth across the majority of its\n",
+ "businesses. 7 3M expects generic substitute approved for Metrogel-Vaginal, which\n",
+ "accounts for approximately 2% of total Health Care sales. 8 Price growth was key\n",
+ "to maintaining margins in 2005. Acquisitions increased 2005 sales by 1.0% 9\n",
+ "Acquisitions drove positive local-currency sales growth in 2005. 10 Raw material\n",
+ "costs increased during the year, 3Mâs global sourcing initiative was important.\n",
+ "11 SG&A expenses in U.S. dollars increased in 2005 compared to 2004, negatively\n",
+ "impacted by currency translation. 12 Lower average debt balances, partially\n",
+ "offset by higher interest rates. 3M repatriated $1.8 billion of foreign earnings\n",
+ "into the U.S. 13 Income taxes associated with repatriating certain cash from\n",
+ "outside the United States negatively impacted the 2004 and 2003 income tax rates.\n",
+ "14 Changes to accounting principles reflect adoption of FIN 47. Currency Effects:\n",
+ "3M estimates that year-on-year currency effects increased net income by\n",
+ "approximately $115 million in 2005. 15 New Industrial and Transportation segment\n",
+ "will leverage common markets, sales channels, customers, technologies,\n",
+ "manufacturing facilities and selling processes. 16 Health Care segment reported\n",
+ "local-currency sales growth of 2.9% in 2005. 17 3Mâs Health Care segment has the\n",
+ "lowest margins in the segment. 18 3M and Takeda are collaborating on an immune\n",
+ "response treatment for cervical cancer. 19 3M has agreed to sell its QVAR and\n",
+ "Airomir respiratory products to Takeda. 20 Local-currency sales growth was broad-\n",
+ "based across major geographic areas. 21 Acquisitions drove sales growth of 1.4%\n",
+ "in 2004. 22 3Mâs patents cover a range of technologies, including: 23 Local-\n",
+ "currency sales growth in Display and Graphics was 10.5% in 2004. 24 Consumer and\n",
+ "Office segment grew 3.4% in 2005. 25 Consumer and Office segment serves mass-\n",
+ "market consumer, office and stationery markets. 26 Local-currency sales in\n",
+ "Electro and Communications rose 4.2% in 2005. 27 Local-currency sales growth was\n",
+ "led by the Latin America and Canada area. 28 Local-currency Safety, Security and\n",
+ "Protection Services sales growth was 6.6% in 2005. 29 Sales growth was broad\n",
+ "based in Transportation, led by businesses that serve OEMs. 30 3M has a purchase\n",
+ "option to buy an additional 31% investment of I&T. The Company also has a put\n",
+ "option, which provides the Company the right to sell back its entire ownership\n",
+ "interest in I&T, exercisable between January 1, 2007 and March 31, 2009. 31\n",
+ "Local-currency sales growth was driven by Asia Pacific, Europe, and Latin America.\n",
+ "32 Foreign currency translation positively impacted combined Latin America and\n",
+ "Canada area sales by 7.4%. 33 Significant accounting policies are included in\n",
+ "Note 1 to the Consolidated Financial Statements. 34 Legal proceedings, pension\n",
+ "and postretirement obligations, asset impairment issues. 35 Company uses\n",
+ "actuarial assumptions to determine liability and expense measurement. 36 The\n",
+ "Company used discount rates of 5.50% for U.S. pension plan and 5.50% for\n",
+ "postretirement expense. 37 unrecognized unrecognized pension losses for the U.S.,\n",
+ "International and postretirement plans. 38 Impact of one percentage point change\n",
+ "in assumed health care rates on postretirement expense and obligation. 39 At\n",
+ "December 31, 2005, 3M had 18 reporting units. 40 3M to adopt SFAS No. 123R on\n",
+ "share-based compensation expense 41 Pro forma impact of adopting SFAS No. 123R is\n",
+ "estimated to be approximately $.16 per diluted share. 42 New non-substantive\n",
+ "vesting period approach for new stock compensation grants made after the Companyâs\n",
+ "adoption of SFAS No. 123R. 43 DropCatch DropCatch DropCatch DropCatch DropCatch\n",
+ "DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch\n",
+ "DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch\n",
+ "DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch\n",
+ "DropCatch 888-349-8884 888-349-8884 888-349-8884 888-349-8884 888-349-8884\n",
+ "888-349-8884 888-349-8884 888-349-8884 44 Working capital, cash flows from\n",
+ "operating, investing and financing activities. 45 Cash flows from operating\n",
+ "activities can fluctuate significantly from period to period. 46 Significant\n",
+ "Company pension contributions negatively impacted cash flows. 47 Cash flows from\n",
+ "Investing Activities: Investments in property, plant and equipment are enabling\n",
+ "growth in diverse markets. 48 Purchases of investments in 2005 included the\n",
+ "purchase from TI&M Beteiligungsgesellschaft mbH of 19 percent of I&T Innovation\n",
+ "Technology. 49 Cash flow from financing activities decreased in 2005 due to\n",
+ "retirement of $400 million in medium-term notes. 50 $350 million of dealer\n",
+ "remarketable securities (classified as current portion of long-term debt) were\n",
+ "remarketed for one year. 51 Cash dividends paid to stockholders in 2005 were\n",
+ "$1.286 billion. Cash flows from financing activities include distributions to\n",
+ "minority interests, changes in cash overdraft balances, and principal payments for\n",
+ "capital leases 52 Credit support provided by $565 million credit agreement. 53\n",
+ "3Mâs cash balance at December 31, 2005 was $1.072 billion. 54 Current and long-\n",
+ "term liabilities include: 55 U.S. debt is classified as current portion of long-\n",
+ "term debt as the result of put provisions. 56 Contractual capital commitments are\n",
+ "included in the preceding table, but these commitments represent a small part of\n",
+ "the Companyâs expected capital spending. 57 Study assesses 3Mâs exposure to\n",
+ "changes in currency, interest rates and commodity prices. 58 After-tax earnings\n",
+ "would have increased by $2 million if interest rates had increased. 59 Annual\n",
+ "Report on Form 10-K does not include certain hedge transactions. 60 Forward-\n",
+ "Looking Statements: This document may contain forward-looking statements.\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "d390a60a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "medium_summary = \"\"\"\n",
+ "0 In 2005, 3M managed its operations in seven operating business segments: Health\n",
+ "Care; Industrial; Display and Graphics; Consumer and Office; Electro and\n",
+ "Communications; Safety, Security and Protection Services; and Transportation.Refer\n",
+ "to the Performance by Business Segment section for discussion of segment changes\n",
+ "effective in the first quarter of 2006.The combination of a 5.8% increase in net\n",
+ "sales, including core local-currency sales growth of 4.1% (which excludes the\n",
+ "impact of businesses acquired in the last 12 months), and declining manufacturing\n",
+ "costs as a percent of sales, resulted in a 23.7% operating income profit margin 1\n",
+ "Act of 2004 and repatriated approximately $1.8 billion of foreign earnings into\n",
+ "the U.S. pursuant to its provisions.As a consequence, in the second quarter of\n",
+ "2005, 3M recorded a tax expense of $75 million, net of available foreign tax\n",
+ "credits.Combined, these two items reduced net income by $110 million in\n",
+ "2005.apologetically=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-3M 3M\n",
+ "s performance in 2005 was broad-based, with all seven\n",
+ "business segments contributing to positive local-currency sales growth.Sales\n",
+ "growth in Health Care was led by 3M 2 The combination of solid sales growth and\n",
+ "positive benefits from corporate initiatives helped drive the increase in\n",
+ "operating income.results for the respective areas. Operating income in\n",
+ "2005 increased by 9.4% versus 2004, as all seven business segments posted\n",
+ "increases.The Company estimates that cost reduction projects related to\n",
+ "initiatives provided a combined incremental benefit to operating income of\n",
+ "approximately $400 million in 2005.These initiatives contributed more than $400\n",
+ "million to operating income in both 2004 and 2003. 3M\n",
+ "Generated 4 258 billion of operating cash flows in\n",
+ "2005 3 The Company expects solid sales growth across the majority of its\n",
+ "business portfolio.The Companyâs long history and unique ability to match\n",
+ "technological solutions with the needs of its customers has resulted in a steady\n",
+ "flow of new products and solutions, with this trend expected to continue in\n",
+ "2006.In addition, the Company expects to increase both research and development\n",
+ "and capital expenditures in 2006, led primarily by growth programs.Research,\n",
+ "development and related expenses totaled $1.242 billion in 2005, or 5.9% of\n",
+ "sales.3M now expects that there will be a generic substitute approved for\n",
+ "Metrogel-Vaginal, which 4 However, in Display and Graphics, 3M expects this\n",
+ "decline in CRT rear projection lens sales to be more than offset by strong sales\n",
+ "growth in display enhancement films used in flat-panel devices, such as LCD\n",
+ "televisions.dispensational The previous forward looking\n",
+ "statements are subject to risk and uncertainties that\n",
+ "could be affected by the future performance of\n",
+ "these businesses in 2005, which will have a\n",
+ "significant impact on thecompany 5 that serve the electronics\n",
+ "industry, where it is important to look at the combined impact of volume and\n",
+ "price.On a geographic basis, local-currency sales growth in 2004 was led by the\n",
+ "Asia Pacific area. Operating Expenses:\n",
+ "Cost of sales increased by 0.8 percent in 2005\n",
+ "( 3.0 million ) (2004: 1.9 million) (2005:\n",
+ "2.1 million)* (note 11 to the Consolidated Financial Statements),\n",
+ "reflect the 6 The LePageâs Inc. lawsuit negatively impacted operating income in\n",
+ "2003 by $93 million, or 0.5% of sales.The pre-tax charge of $93 is classified as\n",
+ "Other expense within operating income.The LePage lawsuit adversely impacted\n",
+ "operating income in 2004, negatively impacted by currency translation and\n",
+ "increased advertising and merchandising spending to support 3Mâs strong brand\n",
+ "portfolio.On an ongoing basis, the Company is shifting SG&A dollars toward faster-\n",
+ "growth businesses and geographic areas.The Jobs Act provides 3M the opportunity to\n",
+ "tax effectively repatriate foreign earnings for U. 7 of the Jobs Act.\n",
+ "The tax rate of 33.0% for 2004 was comparable to the 2003 rate of 32.9%.Income\n",
+ "taxes associated with repatriating certain cash from outside the United States\n",
+ "negatively impacted the 2004 and 2003 income tax rates.čŚé Minority\n",
+ "Interest: shapeshifterMinority interest expense eliminates the income or\n",
+ "loss attributable to non-3M ownership interests in 3M consolidated entities.The\n",
+ "pro forma effect of applying this guidance in all prior periods presented was\n",
+ "determined not to be material.The decrease in 2005 related primarily to lower net\n",
+ "income in Sumitomo 3M, 8 This new segment will leverage common markets, sales\n",
+ "channels and customers, technologies, manufacturing facilities and selling\n",
+ "processes.This combination will provide additional efficiencies that will be\n",
+ "reinvested in growth.In addition, during the first quarter of 2006, the Personal\n",
+ "Care Division (2005 annual sales of approximately $600 million) within the Health\n",
+ "Care segment transferred to the combined Industrial and Transportation segment. 9\n",
+ "Care reported local-currency sales growth of 2.9%.3Mâs core medical and dental\n",
+ "businesses and health information systems businesses experienced local-urrency\n",
+ "sales growth of approximately 6%.The strength of these businesses helped overcome\n",
+ "the sales growth challenges of the pharmaceutical and personal care\n",
+ "businesses.Personal care, which is 3M s diaper tape business, has experienced\n",
+ "significant raw material price increases in some product lines over the past year,\n",
+ "and 3M has elected to drive profits at the expense of volume in this business,\n",
+ "which has the lowest margins in the Health Care segment.Sales of certain products\n",
+ "within 3Mďż˝ 10 on the imiquimod molecule expired in August 2004, but the patent\n",
+ "term extension runs through August 2009, with an anticipated pediatric exclusivity\n",
+ "extension of a further six months to February 2010.\n",
+ "In the first quarter of 2005, 3M and Takeda Pharmaceutical Co. Ltd. entered into\n",
+ "an agreement to collaborate on a potential breakthrough treatment utilizing an\n",
+ "immune response modifier for cervical high-risk human papilloma virus (HPV)\n",
+ "infection and cervical dysplasia, which are known risk factors for cervical\n",
+ "cancer. This immune relationship modifier currently is in early stage clinical\n",
+ "trials, and 3M 11 range of industrial markets, from appliance and electronics to\n",
+ "paper and packaging and food and beverage.Products include tapes, a wide variety\n",
+ "of coated and nonwoven abrasives, adhesives, specialty materials and supply chain\n",
+ "execution software solutions.The August 2005 CUNO acquisition adds a comprehensive\n",
+ "line of filtration products for the separation, clarification and purification of\n",
+ "fluids and gases. In 2005 3 MunO\n",
+ "Acquisition of CUNO PLC for $1 million (2005: $0.3\n",
+ "million), significantly 12 In 2005, Display and Graphics local-currency\n",
+ "sales grew 4.0%, impacted by many factors.The first half of 2005 was tempered by\n",
+ "tough year-on-year optical film comparisons, while 3Mâs traffic safety systems\n",
+ "business awaited a new highway funding bill in the U.S. and the sluggish economies\n",
+ "in Western Europe and Japan held back growth in the commercial graphics\n",
+ "business.Growth rebounded in the second half of 2007 as the economies in Europe\n",
+ "and Asia started to experience moderate growth and, as expected, 3M saw\n",
+ "acceleration in demand for consumer electronics, especially flat-panel LCD\n",
+ "televisions and 13 Products in this segment include office supply products,\n",
+ "stationery products, construction and home improvement products, home care\n",
+ "products, protective material products (including consumer health care products\n",
+ "such as bandages), and visual systems products.The continuing decline in 3Mâs\n",
+ "Visual Systems business impacted sales by approximately 2% for the year.Consumer\n",
+ "and Office continues to drive success by combining unique functionality along with\n",
+ "customer inspired design into new and mature Products such as ScotchÂŽ Tape, Post-\n",
+ "ItŽ Notes, Filtrete⢠Filters and O-Cel-O⢠sponges.Operating income increased 6.3%\n",
+ "in 2005 14 construction, maintenance and repair; OEM electrical and electronics;\n",
+ "computers and peripherals; consumer electronics; telecommunications central\n",
+ "office, outside plant and enterprise; as well as aerospace, military, automotive\n",
+ "and medical markets; with products that enable the efficient transmission of\n",
+ "electrical power and speed the delivery of information and ideas.Products include\n",
+ "electronic and interconnect solutions, microinterconnect systems, high-performance\n",
+ "fluids, high,temperature and display tapes, telecommunications products and\n",
+ "electrical products.apologetically=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n",
+ "=-=-=-=-=-=-=-=-=-=-In 2005, local-currency sales in Electro and Communications\n",
+ "increased 4.2%, with improving end market conditions and 15 and roofing granules\n",
+ "for asphalt shingles.The continued global threat of events such as terrorism,\n",
+ "natural disasters, SARS and Avian flu helped raise the awareness in the general\n",
+ "public about the importance of personal protective equipment, especially\n",
+ "respiratory protection for overall health.Sales growth was driven by continued\n",
+ "strong\n",
+ "12:44\n",
+ "global demand for personal protection products and solutions, particularly\n",
+ "respiratory protection products, along with strong demand for cleaning and\n",
+ "protection products for commercial buildings.Roofing granule for asphalt Shingles\n",
+ "also experienced solid sales growth.Operating income improved 12.6% in 2005.\n",
+ "In 2005, Safety, Security 16 aftermarket business.Operating income increased\n",
+ "8.1% in 2005. In March 2005, 3Mâs automotive business\n",
+ "completed the purchase from TI&M Beteiligungsgesellschaft mbH of 19 percent of I&T\n",
+ "Innovation Technology (I&T), which was founded in Austria in 1999. 3M and I&Ts\n",
+ "will collaborate to deliver flat flexible wiring systems for automotive interior\n",
+ "applications to the global automotive market.The purchase price of approximately\n",
+ "$55 million is reported as âInvestmentsâ in the Consolidated Balance Sheet and as\n",
+ "17 revenue increased 4.9%, with growth led by Industrial; Consumer and Office;\n",
+ "and Safety, Security and Protection Services.Asia Pacific local-currency sales\n",
+ "(which exclude translation impacts) increased 10.6%.All seven business segments\n",
+ "contributed to this increase in the Asia Pacific area, with optical film being the\n",
+ "largest growth component.Japan sales totaled approximately $2.1 billion, with\n",
+ "local-urrency sales up 3.6% from 2004.European national-currency revenue increased\n",
+ "0.7%, with good growth in Industrial; Safety, Security and Protection Services;\n",
+ "and Transportation.In the combined Latin America and Canada area 18\n",
+ "requirements. The 2005 decrease in net property, plant and equipment in the\n",
+ "Europe, Middle East and Africa area was primarily due to currency translation (due\n",
+ "to the stronger U.S dollar at December 31, 2005 when compared to December 31.\n",
+ "2004).Capital spending in Asia has more than doubled since 2003 as we continue to\n",
+ "grow our presence in this region. CRITICAL ACCOUNTING\n",
+ "ESTIMATES Information regarding significant accounting policies is\n",
+ "included in Note 1 to the Consolidated Financial Statements.As stated in Note 1,\n",
+ "the preparation of financial statements requires management to make estimates and\n",
+ "19 The impact of this change increased the year-end 2005 U.S. pension and\n",
+ "postretirement obligations and 2006 expense.Projected Benefit Obligations for\n",
+ "pension by $385 million, the Year ended 31 December 2005 (2004: $349 million)\n",
+ "Accumulated Postretirement benefit Obligation by $93 million and the 2005 US\n",
+ "pension plan contribution by $34 million Calculated Return on Capital Employed\n",
+ "(ROCE) is defined as the return on capital employed (ROA) as defined in the\n",
+ "Financial Statements, as adjusted for the impact of amortisation of acquired\n",
+ "intang 20 The average remaining service periods for U.S. and International\n",
+ "pension plans and the postretirement plans are 10.2 years, 14.1 years and 10.0\n",
+ "years, respectively. 21 31, 2005.Management makes estimates and assumptions in\n",
+ "preparing the consolidated financial statements for which actual results will\n",
+ "emerge over long periods of time.This includes the recoverability of long-lived\n",
+ "assets employed in the business, including assets of acquired businesses.For\n",
+ "instance, expected asset lives may be shortened or an impairment recorded based on\n",
+ "a change in the expected use of the asset or performance of the related business\n",
+ "reporting unit. 3M goodwill total goodwill per\n",
+ "share at 31 December 2005 $3.5 billion (2004: $3.4\n",
+ "billion) 22 in accounting principle in the Consolidated\n",
+ "Statement of Income.The pro forma effect of applying this guidance in all prior\n",
+ "periods presented was determined not to be material.The Company is adopting SFAS\n",
+ "No.123R using the modified retrospective method.All prior periods will be adjusted\n",
+ "to give effect to the fair-value-based method of accounting for awards granted in\n",
+ "fiscal years beginning on or after January 1, 1995.Stock-based compensation\n",
+ "disclosures in Note 1 reflect pro formal expense of $.14 cents per diluted share\n",
+ "in 2005. 3M Annual Report and Accounts\n",
+ "23 those awards, and the period the vesting of those awards is recognized over;\n",
+ "therefore the actual expense may be different from this estimate.refer to Note 8\n",
+ "in this document). 24 by four, divided by ending net accounts receivable plus\n",
+ "inventory less accounts payable) was 5.7 at December 31, 2005, down from 5.8 at\n",
+ "December 31 December 2004.Excluding CUNO, net working capital turns at December\n",
+ "1, 2005 were 6.8, the same as at December 31 2004.Receivables increased $46\n",
+ "million, or 1.6%, compared with December 31.2004.Inventories increased $265\n",
+ "million, 14.0% compared with December 31 Dec 2004.At December 31 , 2005, the\n",
+ "CunO acquisition 25 cumulative effect of accounting change), increases in\n",
+ "accrued liabilities (such as the $30 million increase in liability related to\n",
+ "legal settlement agreements), and other items. In 2005, the Company\n",
+ "made discretionary contributions totaling $500 million to its U.S. qualified\n",
+ "pension plan, with $200 million contributed in the fourth quarter of 2005, and\n",
+ "$300 million contributed in the third quarter of 2005\n",
+ "to the qualified pension plan for the future of 3M\n",
+ ", which will be a key factor in the 26 on these\n",
+ "2005 business combinations, and for information concerning 2004 and 2003 business\n",
+ "combinations.The purchase price of approximately $55 million is reported as\n",
+ "Investments in the Consolidated Balance Sheet and as Purchases of Investmentsâ in\n",
+ "the Consolidated Statement of Cash Flows.Other purchases of investments totaled\n",
+ "$5 million in 2005, $10 million in 2004 and $16 million in 2003.These purchases\n",
+ "include additional survivor benefit insurance and equity investments.The Company\n",
+ "is actively considering additional acquisitions, investments and strategic\n",
+ "alliances. 27 in more detail in Note 8 to the Consolidated Financial\n",
+ "Statements.3M has a shelf registration and medium-term notes program through which\n",
+ "$1.5 billion of medium -term notes may be offered.In 2004, the Company issued\n",
+ "approximately $62 million in debt securities under its medium- term notes\n",
+ "program.No debt was issued under this program in 2005.The medium-terms notes\n",
+ "program and shelf registration have remaining capacity of approximately $1._438\n",
+ "billion.The Companyâs $350 million of dealer remarketable securities (classified\n",
+ "as current portion of long-term debt) were remarketed for one 28 activities\n",
+ "include distributions to minority interests, changes in cash overdraft balances,\n",
+ "and principal payments for capital leases. Liquidity: The Companyâs\n",
+ "liquidity remains strong.Primary short-term liquidity needs are provided through\n",
+ "U.S. commercial paper and euro commercial paper issuances.The Company currently\n",
+ "has AA/Aa1 debt ratings.In addition, the $565 million, five-year credit agreement\n",
+ "requires 3M to maintain a capitalization ratio at no more than 0.60 to 1 at the\n",
+ "end of each quarter.This ratio is calculated as funded debt (including all\n",
+ "borrowed money and letters of credit 29 2006, the Board of Directors increased\n",
+ "the quarterly dividend on 3M common stock by 9.5% to 46 cents per share,\n",
+ "equivalent to an annual dividend of $1.84 per share.The Company may also make\n",
+ "additional contributions to its pension plan in the future, but exact amounts are\n",
+ "uncertain and will depend on market conditions.The fair value of 3M guarantees of\n",
+ "loans with third parties and other guarantee arrangements are not material.The\n",
+ "next date on which investors can require repurchase of the Convertible Notes is\n",
+ "2007, thus in the above schedule this is considered due in 2007 (final maturity\n",
+ "2032). 30 related to take or pay contracts, capital commitments, service\n",
+ "agreements and utilities.These estimates include both unconditional purchase\n",
+ "obligations with terms in excess of one year and normal ongoing purchase\n",
+ "obligations with terms of less than one year.The majority of 3Ms products and\n",
+ "services are purchased as needed, with no unconditional commitment.For this\n",
+ "reason, these amounts will not provide a reliable indicator of the Companyâs\n",
+ "expected future cash outflows on a stand-alone basis.The purchase obligation\n",
+ "amounts do not represent the entire anticipated purchases in the future, but\n",
+ "represent only those items for which the Company is contractually obligated.A\n",
+ "financial risk management 31 currency hedges to help lessen year-over-year\n",
+ "impacts and to improve the predictability of future earnings.However, this hedging\n",
+ "program does not make 3M immune to currency impacts.The Company manages commodity\n",
+ "price risks through negotiated supply contracts, price protection agreements and\n",
+ "forward physical contracts.The model (third-party bank dataset) used a 95%\n",
+ "confidence level over a 12-month time horizon.Based on this analysis of the\n",
+ "Companyâs interest rate risks, possible increases in interest rates would not have\n",
+ "a material adverse effect on after-tax earnings ($2 million at December 31, 2005\n",
+ "and $5 million 32 global exposures related to purchased components and materials\n",
+ "are such that a one percent price change would result in a pre-tax cost or savings\n",
+ "of approximately $45 million per year.The global energy exposure is such that a\n",
+ "10% price change would result in a cost or savings of $35 million per\n",
+ "year.Derivative instruments are used to hedge less than two percent of the\n",
+ "purchased components and materials exposure and are used to hedge approximately\n",
+ "10% of this energy exposure.The Company may also make forward-looking statements\n",
+ "in other reports filed with the Securities and Exchange Commission, in materials\n",
+ "delivered\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "8d0a03f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "small_summary = \"\"\"\n",
+ "0 OVERVIEW 3M is a diversified global manufacturer, technology innovator and\n",
+ "marketer of a wide variety of products . in 2005, 3M managed its operations in\n",
+ "seven operating business segments: Health Care; Industrial; Display and Graphics;\n",
+ "Consumer and Office; Electro and Communications; Safety, Security and Protection\n",
+ "Services; and Transportation . 1 the adoption of FIN 47 resulted in an after tax\n",
+ "charge of $35 million . in 2005, 3M repatriated approximately $1.8 billion of\n",
+ "foreign earnings into the united states . sales growth in health care was led by\n",
+ "industrial adhesives and tapes . 2 sales growth in the Safety, Security and\n",
+ "Protection Services segment was driven by continued strong demand for personal\n",
+ "protection products and solutions, particularly respiratory protection products .\n",
+ "sales growth was led by both the automotive OEM and repair markets .\n",
+ "Geographically, U.S. sales revenue increased 4.9%, Asia Pacific local-currency\n",
+ "sales increased 10.6%, European local .currencies sales increased 0.9%, and the\n",
+ "combined Latin America and Canada area local - . . 3 3Mâs debt to total capital\n",
+ "ratio (total capital defined as debt plus equity) as of December 31, 2005, was\n",
+ "approximately 19% . the Company experienced both price increases and supply\n",
+ "limitations affecting several oil-derived raw materials in 2005, which is expected\n",
+ "to carry forward into 2006 . 4 the Company expects to increase both research and\n",
+ "development and capital expenditures in 2006, led primarily by growth programs .\n",
+ "research, development and related expenses totaled $1.242 billion in 2005, or 5.9%\n",
+ "of sales . 3M expects continued solid growth across the majority of its businesses\n",
+ ". 5 3M expects the continued negative impact from the CRT rear projection lens\n",
+ "business to continue into the first half of 2006 . however, in Display and\n",
+ "Graphics, this decline in sales will be more than offset by strong sales growth in\n",
+ "display enhancement films used in flat-panel devices . 6 health care local-\n",
+ "currency sales increased 1.7%, the first year of positive sales growth since 2000\n",
+ ". acquisitions increased 2004 sales by 0.5%, driven by the 2004 acquisitions of\n",
+ "highJump Software, Inc. and Hornell Holding AB . internationally, selling prices\n",
+ "declined 1.1%, with most of the decline coming in certain businesses that serve\n",
+ "the electronics industry . 7 expenses as a percent of net sales were flat when\n",
+ "comparing 2005 to 2004 . however, spending in dollars increased approximately 4%,\n",
+ "reflecting 3Mâs commitment to fund future growth for the Company . SG&A in the\n",
+ "fourth quarter of 2005 was impacted by a pre-tax charge of approximately $30\n",
+ "million in connection with settlement agreements of one pending lePageâs follow-on\n",
+ "class actions . 8 the lePageâs Inc. lawsuit negatively impacted operating income\n",
+ "in 2003 by $93 million, or 0.5% of sales . Interest income: Interest income was\n",
+ "higher in 2005, benefiting primarily from higher interest rates . 9 interest\n",
+ "expense eliminates income or loss attributable to non-3M ownership interests in 3M\n",
+ "consolidated entities . the decrease in 2005 related primarily to lower net income\n",
+ "in Sumitomo 3M, while the increase in 2004 related mainly to higher net income .\n",
+ "10 3M estimates year-on-year derivative and other transaction gains and losses\n",
+ "increased net income by approximately $50 million for 2005 and $48 million in 2004\n",
+ ". the results for the new Industrial and Transportation segment can be\n",
+ "approximated by combining the existing industrial and transportation segments .\n",
+ "this combination will leverage common markets, sales channels and customers,\n",
+ "technologies, manufacturing facilities and selling processes . 11 health care\n",
+ "segment serves markets that include medical, surgical, pharmaceutical, dental and\n",
+ "orthodontic, health information systems and personal care . in 2005, health care\n",
+ "reported local-currency sales growth of 2.9% . 3M continues to generate growth in\n",
+ "its aldaraTM pharmaceutical product, which accounts for approximately 6% of total\n",
+ "health care sales . 12 health care sales for 3Mâs aldara pharmaceutical product\n",
+ "for the actinic keratois (a pre-cancerous skin condition) indication has and is\n",
+ "expected to continue to fall short of expectations 3M had at the time of FDA\n",
+ "approval . health Care continued to focus on operational efficiency, which helped\n",
+ "drive an 8.2% increase in operating income in 2005 . 13 takeda will hold\n",
+ "commercial rights in certain countries in Asia, while 3M will retain the rights in\n",
+ "other parts of the world . the agreement covered QVARTM (beclomethasone\n",
+ "dipropionate HFA) inhalation Aerosol, a âmaintenanceâ medication used to prevent\n",
+ "asthma attacks . 3M expects to receive $3 million in 2006 . 14 the August 2005\n",
+ "CUNO acquisition adds a comprehensive line of filtration products for the\n",
+ "separation, clarification and purification of fluids and gases . in 2005,\n",
+ "Industrial local-currency sales grew 9.3% . the abrasives businesses continued to\n",
+ "raise selling prices to offset commodity raw material price pressures . 15 the\n",
+ "remaining lifetimes of such patents range from less than a few years to greater\n",
+ "than 15 years . these patents provide varying measures of exclusivity to 3M for a\n",
+ "number of such products . in 2005, Display and Graphics local-currency sales grew\n",
+ "4.0%, impacted by many factors . 16 year-on-year local-currency sales growth in\n",
+ "the Optical Systems business was slower in the last half of 2004 . this resulted\n",
+ "in reduced demand for 3Mâs proprietary optical films and components . in the\n",
+ "fourth quarter of 2004, 3M announced the phase out of its commercial videotape\n",
+ "business . 17 continuing decline in 3Mâs Visual Systems business impacted sales\n",
+ "by approximately 2% for the year . consumer and office continues to drive success\n",
+ "by combining unique functionality along with customer inspired design into new and\n",
+ "mature products . 18 local-currency growth accelerated in the second half of 2005\n",
+ ". local growth was driven by demand for 3M electronic products for semiconductor\n",
+ "manufacturers . strong sales growth helped offset weakness in electronic solutions\n",
+ "and communications markets . 19 sales growth driven by strong global demand for\n",
+ "personal protective products and solutions . Roofing granules for asphalt shingles\n",
+ "also experienced solid sales growth . operating income increased 12.3% to $491\n",
+ "million in 2004 . 20 sales growth was broad based in Transportation, led by\n",
+ "businesses that serve the automotive OEMs and auto body repair shops . operating\n",
+ "income increased 8.1% in 2005 . 3M and I&T will collaborate to deliver flat\n",
+ "flexible wiring systems for automotive interior applications to the global\n",
+ "automotive market . 21 information related to 3M operations in various geographic\n",
+ "areas is provided in Note 16 to the Consolidated Financial Statements . the\n",
+ "Company believes its business segment results are the most relevant measure of\n",
+ "performance . a portion of the products or components sold by 3Mâs operations to\n",
+ "its customers are exported to different geographic areas . 22 for 2005,\n",
+ "international operations represented approximately 61% of 3Mâs sales . employment\n",
+ "increased by 2,244 people since year-end 2004 . CUNO acquisition in august 2005\n",
+ "added approximately 2,300 employees . 23 the Company believes its most critical\n",
+ "accounting estimates relate to legal proceedings, the Companyâs pension and\n",
+ "postretirement obligations, and potential asset impairment issues . the categories\n",
+ "of claims for which the Company has estimated probable liability, the amount of\n",
+ "its liability accruals, and the estimates of its related insurance receivables are\n",
+ "critical accounting estimations related to legal proceeding . 24 the assumed\n",
+ "health care trend rate is the most significant postretirement health care\n",
+ "assumption . the Company determined a discount rate of 5.50% to be appropriate as\n",
+ "of December 31, 2005, which is a reduction of 0.25 percentage points from the rate\n",
+ "used in December 31, 2004 . 25 postretirement Benefit Obligation will increase\n",
+ "pension expenses for 2006 by $64 million . postretiment expenses will decrease to\n",
+ "approximately $300 million in 2006 . a significant factor in determining the\n",
+ "Companyâs pension expense is the expected return on plan assets . 26 an\n",
+ "increase/decrease in the expected long-term rate of return on plan assets by 0.25\n",
+ "percentage points would decrease/increase 2006 pension expense by approximately\n",
+ "$22 million for U.S. pension plans and approximately $7 million for international\n",
+ "12:54\n",
+ "pension plans . the majority of goodwill totaled approximately $3.5 billion at\n",
+ "December 31, 2005 . 27 the majority of goodwill relates to and is assigned\n",
+ "directly to a specific reporting unit . the estimated fair value of a reporting\n",
+ "unit is determined by earnings for the reporting unit multiplied by a\n",
+ "price/earnings ratio for comparable industry groups, or by using a discounted cash\n",
+ "flow analysis . 28 stock-based compensation disclosures in Note 1 reflect pro\n",
+ "forma expense of $.14 cents per diluted share in 2005 . the 2006 impact of\n",
+ "adopting SFAS No. 123R is estimated to be approximately $.16 per share . if an\n",
+ "employee retired before the end of the vesting period, the Company would recognize\n",
+ "any remaining unrecognized compensation cost at the date of retirement. 29\n",
+ "FINANCIAL CONDITION AND LIQUIDITY The Company generates significant ongoing cash\n",
+ "flow . net debt decreased significantly in 2004 but increased in 2005, primarily\n",
+ "related to the $1.36 billion CUNO acquisition . 3M believes its ongoing cash flows\n",
+ "provide ample cash to fund expected investments and capital expenditures . 30 the\n",
+ "Company uses various working capital measures that place emphasis on certain\n",
+ "working capital assets and liabilities . these measures are not defined under U.S.\n",
+ "generally accepted accounting principles and may not be computed the same as\n",
+ "similarly titled measures used by other companies . 31 in 2005, cash flow was\n",
+ "essentially flat when compared to 2004 . product and other insurance receivables\n",
+ "and claims increased cash flow by $122 million in 2005 . the category âOther â\n",
+ "netâ in the preceding table reflects changes in other asset and liability accounts\n",
+ ". 32 a portion of the tax timing benefit relates to the tax benefit received from\n",
+ "Company pension contributions . the Company expects 2006 capital expenditures to\n",
+ "total approximately $1.1 billion, compared with $943 million in 2005 . 33 of\n",
+ "approximately $55 million is reported as âInvestmentsâ in the Consolidated Balance\n",
+ "Sheet . other âPurchases of Investmentsâ are primarily attributable to auction\n",
+ "rate securities, which are classified as available-for-sale . prior to 2005,\n",
+ "purchases of and proceeds from the sale were classified as Cash and Cash\n",
+ "Equivalents, and accordingly were not reclassified for 2004 and prior . 34 debt\n",
+ "securities, including the Companyâs shelf registration, its medium-term notes\n",
+ "program, dealer remarketable securities and Convertible Note, are all discussed in\n",
+ "more detail in Note 8 to the Consolidated Financial Statements . in 2004, the\n",
+ "Company issued approximately $62 million in debt securities . no debt was issued\n",
+ "under this program in 2005 . 35 cash dividends paid to stockholders totaled\n",
+ "$1.286 billion in 2005, $1.125 billion in 2004 and $1.034 billion in 2003 . other\n",
+ "cash flows from financing activities include distributions to minority interests,\n",
+ "changes in cash overdraft balances and principal payments for capital leases . 36\n",
+ "at year-end 2005, certain debt agreements had ratings triggers (BBB-/Baa3 or\n",
+ "lower) that would require repayment of debt . the $565 million, five-year credit\n",
+ "agreement requires 3M to maintain a capitalization ratio at no more than 0.60 to 1\n",
+ "at the end of each quarter . 37 warranty liabilities, recorded on the\n",
+ "Consolidated Balance Sheet, are estimated at approximately $22 million . the fair\n",
+ "value of 3M guarantees of loans with third parties and other guarantee\n",
+ "arrangements are not material . 3M periodically enter into agreements that require\n",
+ "3M to indemnify major customers or suppliers for specific risks . 38 the majority\n",
+ "of 3Mâs products and services are purchased as needed, with no unconditional\n",
+ "commitment . these amounts will not provide a reliable indicator of the Companyâs\n",
+ "expected future cash outflows on a stand-alone basis . the Company does not have a\n",
+ "required minimum pension contribution obligation for its U.S. plans in 2006 and\n",
+ "future years . 39 in 2001, the Company increased the amount and duration of its\n",
+ "foreign currency hedges to help lessen year-over-year impacts and improve the\n",
+ "predictability of future earnings . however, this hedging program does not make 3M\n",
+ "immune to currency impacts . 40 a positive change in commodity rates would have\n",
+ "adversely impacted after-tax earnings by an additional $4 million at December 31,\n",
+ "2005 . the model used analyzed over 20 different currencies and five commodities,\n",
+ "but does not purport to represent what actually will be experienced by the Company\n",
+ ". 41 forward-looking statements are based on assumptions and expectations of\n",
+ "future events and trends that are subject to risks and uncertainties . actual\n",
+ "future results and trends may differ materially from historical results or those\n",
+ "reflected in such forward looking statements depending on a variety of factors .\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "dbe278b5",
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+ "outputs": [],
+ "source": [
+ "gpt_summary = \"\"\"\n",
+ "0 In 2005, 3M achieved record net sales of $21.167 billion and record net income\n",
+ "of $3.199 billion. They demonstrated operational strength and value through their\n",
+ "diversified business portfolio. They increased their dividend for the 47th\n",
+ "consecutive year, repurchased $2.3 billion of stock, and returned a total of $3.6\n",
+ "billion to shareholders. 1 In 2005, 3M adopted a new accounting standard which\n",
+ "resulted in a charge of $35 million and repatriated $1.8 billion of foreign\n",
+ "earnings into the US, resulting in a tax expense of $75 million. Despite these\n",
+ "factors, 3M saw positive sales growth in all seven of its business segments, with\n",
+ "particular strength in the health care, industrial, and consumer and office\n",
+ "segments. 2 In 2005, the company experienced sales growth in various segments,\n",
+ "including semiconductor manufacturing, safety and protection services, and\n",
+ "transportation. Geographically, sales revenue increased in the U.S., Asia Pacific,\n",
+ "and Latin America/Canada regions. The companyâs operating income also increased,\n",
+ "driven by solid sales growth and cost reduction initiatives. Additionally, the\n",
+ "company generated operating cash flows of $4.258 billion, repurchased common\n",
+ "stock, paid dividends, and contributed to pension and postretirement plans. 3 In\n",
+ "2005, 3M contributed $30 million to its pension and postretirement plans. The\n",
+ "companyâs debt to total capital ratio was approximately 19%, with a credit rating\n",
+ "of AA from Standard & Poorâs and Aa1 from Moodyâs Investors Service. Despite\n",
+ "challenges related to raw material shortages and price increases, 3M managed to\n",
+ "mitigate disruptions through careful inventory management and development of\n",
+ "additional supply sources. Looking ahead to 2006, the company plans to drive\n",
+ "profitable growth by investing in commercialization, geographic expansion, and\n",
+ "technology opportunities, while also focusing on continuous operational\n",
+ "improvements. 4 The company has a strong track record of developing and\n",
+ "delivering innovative technological solutions for its customers, with a focus on\n",
+ "emerging economies. While most of its businesses are expected to experience solid\n",
+ "sales growth, there may be declines in a few areas such as personal care and\n",
+ "branded pharmaceuticals. The company plans to increase its research and\n",
+ "development and capital expenditures to support its growth programs. 5 In 2005,\n",
+ "3M experienced broad-based sales growth with selling prices increasing by 0.6%.\n",
+ "The companyâs pricing strategy and sourcing initiative helped maintain margins\n",
+ "despite raw material price pressure. International pricing declined by 0.7%, but\n",
+ "would have increased by 0.3% if not for price decreases in consumer electronics.\n",
+ "Acquisitions contributed to a 1.0% increase in sales. 6 In 2004, the company saw\n",
+ "growth in its Health Care and Electro and Communications businesses, driven by\n",
+ "acquisitions and positive local-currency sales growth. Internationally, selling\n",
+ "prices declined, particularly in businesses serving the electronics industry.\n",
+ "Operating expenses, including cost of sales and research and development expenses,\n",
+ "were managed through improved selling prices, productivity gains, and sourcing\n",
+ "initiatives. 7 In comparing 2005 to 2004, the companyâs expenses as a percent of\n",
+ "sales remained consistent, but there was an increase of approximately 4% in\n",
+ "spending in dollars. This increase reflects the companyâs dedication to funding\n",
+ "future growth. Additionally, the company has made efforts to shift its selling,\n",
+ "general, and administrative expenses towards faster-growth businesses and\n",
+ "geographic areas. 8 In 2005, the companyâs operating income increased by $431\n",
+ "million, or 9.4%, following a growth of $865 million, or 23.3%, in 2004. The\n",
+ "LePageâs Inc. lawsuit had a negative impact on operating income in 2003. Interest\n",
+ "expense increased in 2005 due to higher interest rates, while interest income\n",
+ "benefited from higher rates. The tax rate for 2005 was 34.0%, compared to 33.0% in\n",
+ "2004, and the company repatriated foreign earnings in compliance with the American\n",
+ "Jobs Creation Act of 2004. The tax rate reduction in 2004 was due to the Medicare\n",
+ "Modernization Act and the domestic manufacturerâs deduction. Minority interest\n",
+ "expense eliminates income or loss attributable to non-3M ownership interests. 9\n",
+ "The companyâs interest expense is primarily affected by the performance of\n",
+ "Sumitomo 3M Limited, in which 3M owns a 75% stake. The decrease in interest\n",
+ "expense in 2005 was due to lower net income in Sumitomo 3M, while the increase in\n",
+ "2004 was due to higher net income in Sumitomo 3M. The company also adopted a new\n",
+ "accounting standard related to asset retirement obligations, resulting in the\n",
+ "recognition of a liability and a charge of $35 million. Currency effects,\n",
+ "including hedging impacts, increased net income by approximately $115 million in\n",
+ "2005, $181 million in 2004, and $73 million in 2003. 10 3M reported that\n",
+ "derivative and other transaction gains and losses had a significant impact on its\n",
+ "net income in the years 2003-2005, with gains increasing net income in 2005 and\n",
+ "2004 and losses decreasing net income in 2003. Additionally, 3M combined its\n",
+ "Industrial and Transportation business segments in 2006 to leverage common\n",
+ "resources and drive growth, with the Personal Care Division transferring to this\n",
+ "combined segment. Further details on 3Mâs business segments can be found in Item 1\n",
+ "and the Notes to the Consolidated Financial Statements. 11 The Health Care\n",
+ "segment of the company serves various markets including medical, surgical,\n",
+ "pharmaceutical, dental, and personal care. In 2005, the segment reported a local-\n",
+ "currency sales growth of 2.9%, driven by the core medical and dental businesses\n",
+ "and health information systems. However, sales in the pharmaceutical and personal\n",
+ "care businesses faced challenges due to price pressure in Europe and raw material\n",
+ "price increases. Sales of the Aldara pharmaceutical product, which accounts for\n",
+ "approximately 6% of total Health Care sales, saw growth outside the U.S. but\n",
+ "declined in the fourth quarter of 2005. 12 3Mâs Aldara pharmaceutical product for\n",
+ "the treatment of actinic keratosis has not met sales expectations since its FDA\n",
+ "approval in 1997, leading 3M to reassess its total market potential. However, the\n",
+ "company has seen an increase in operating income in 2005 due to a focus on\n",
+ "operational efficiency. Additionally, 3M has entered into an agreement with Takeda\n",
+ "Pharmaceutical to develop a potential breakthrough treatment for cervical high-\n",
+ "risk HPV infection and cervical dysplasia. 13 In 2003, IVAX Corporation acquired\n",
+ "exclusive rights to 3Mâs branded respiratory products in nine European countries,\n",
+ "including QVAR Inhalation Aerosol and Airomir Inhaler. 3M continued to manufacture\n",
+ "and supply these products to IVAX, receiving a total consideration of $77 million.\n",
+ "In 2004, 3Mâs Health Care segment saw a 1.7% increase in sales, with operating\n",
+ "income reaching $1.123 billion. The Industrial segment serves various markets and\n",
+ "offers products such as tapes, abrasives, adhesives, and specialty materials. 14\n",
+ "In 2005, the Industrial segment of the company experienced strong growth,\n",
+ "primarily driven by the acquisition of CUNO and the success of their industrial\n",
+ "adhesives and tapes and abrasives businesses. The segment also demonstrated\n",
+ "operational discipline with a significant increase in operating income. In 2004,\n",
+ "the Industrial segment also experienced solid growth, with acquisitions\n",
+ "contributing to sales and strong local-currency sales growth driving operating\n",
+ "income growth. The Display and Graphics segment serves various markets, including\n",
+ "electronic display, touch screen, traffic safety, and commercial graphics, with a\n",
+ "focus on providing optical film and lens solutions for electronic displays. 15 In\n",
+ "2005, 3Mâs Display and Graphics segment experienced growth in the second half of\n",
+ "the year due to factors such as the passing of a new U.S. highway funding bill,\n",
+ "moderate growth in Western Europe and Japan, and increased demand for consumer\n",
+ "electronics. Despite pricing pressure, sales of 3Mâs proprietary optical films and\n",
+ "components reached a record high. However, the decline in lens systems for CRT\n",
+ "rear projection televisions and the phase out of the commercial videotape business\n",
+ "had a negative impact on sales growth and operating income. 16 Consumer and\n",
+ "Office segment of the company saw a local-currency sales increase of 3.4% in 2005,\n",
+ "driven by growth in construction and home improvement, home care, and protective\n",
+ "materials businesses. Sales in the fourth quarter of 2005 were slightly up\n",
+ "compared to the previous year, when there was a significant increase due to higher\n",
+ "purchase rates by large retailers. 17 In 2005, 3Mâs Visual Systems business\n",
+ "decline impacted sales by approximately 2%, but its Consumer and Office segment\n",
+ "drove success with unique products like Scotch Tape and Post-It Notes. Operating\n",
+ "income increased by 6.3% in 2005. In 2004, the Consumer and Office segment saw\n",
+ "strong sales growth across\n",
+ "1:05\n",
+ "various retail channels, particularly in mass-market\n",
+ "consumer retail and home improvement. Operating income increased by 17.9% to $542\n",
+ "million in 2004. The Electro and Communications segment serves various industries\n",
+ "with products that enable the efficient transmission of electrical power and\n",
+ "information. In 2005, sales in this segment increased by 4.2% due to improving end\n",
+ "market conditions. 18 In 2005, the Electro and Communications segment of the\n",
+ "company experienced a 4.2% increase in sales, driven by improving end market\n",
+ "conditions and success in new applications. The strong sales growth was led by\n",
+ "demand for electronic products for semiconductor manufacturers and electrical\n",
+ "products for insulation, testing, and sensing. Operating margins were 19.8%, with\n",
+ "a 35.4% increase in operating income. In contrast, the Safety, Security and\n",
+ "Protection Services segment saw a 6.9% growth in sales, driven by growth across\n",
+ "its product portfolio. 19 In 2005, the company experienced strong sales growth of\n",
+ "6.9%, driven by increased demand for personal protective equipment, particularly\n",
+ "respiratory protection products, as well as cleaning and protection products for\n",
+ "commercial buildings. Operating income also improved by 12.6% in the same year.\n",
+ "Additionally, the Transportation segment saw sales growth of 5.0% in 2005,\n",
+ "attributed to new customer-focused products and productivity solutions. 20 In\n",
+ "2005, the company saw sales growth in its Transportation segment despite\n",
+ "challenges in the US automotive market. Operating income increased by 8.1%.\n",
+ "Additionally, the company completed the purchase of a 19% investment in I&T\n",
+ "Innovation Technology, with plans to collaborate on delivering flat flexible\n",
+ "wiring systems for automotive interior applications. 21 3Mâs geographic area data\n",
+ "is provided as a secondary performance measure, with net sales not necessarily\n",
+ "indicative of end-user consumption. U.S. sales revenue increased, with growth led\n",
+ "by Industrial, Consumer and Office, and Safety, Security and Protection Services.\n",
+ "Asia Pacific and Japan also saw sales growth, while European sales increased\n",
+ "slightly. Latin America and Canada saw sales increases, but were impacted by\n",
+ "business declines in Mexico and customer moves from Puerto Rico to Singapore. 22\n",
+ "In 2005, 3M experienced growth in sales in Latin America, Canada, and the Asia\n",
+ "Pacific region, largely due to the weakening of the US dollar against these\n",
+ "currencies. International operations accounted for approximately 61% of the\n",
+ "companyâs sales. The company has been increasing employment in faster-growing\n",
+ "areas like Asia Pacific to support local sales, while sales per employee have been\n",
+ "increasing in local currencies. 3M is also working towards aligning manufacturing\n",
+ "and sourcing with geographic market sales, with a focus on increasing production\n",
+ "outside the US to improve customer service and reduce working capital\n",
+ "requirements. Overall, the companyâs international operations and strategic\n",
+ "measures are contributing to its growth and financial performance. 23 Management\n",
+ "of the company is responsible for making estimates and assumptions that impact the\n",
+ "financial statements and disclosure of contingent assets and liabilities. The\n",
+ "companyâs critical accounting estimates are related to legal proceedings, pension\n",
+ "and postretirement obligations, and potential asset impairment issues. These\n",
+ "estimates have been discussed with the Audit Committee of the companyâs Board of\n",
+ "Directors. 24 The Company determines the discount rate used to measure plan\n",
+ "liabilities for pension and postretirement benefit plans based on rates of return\n",
+ "on fixed-income investments with high ratings. The discount rate for December 31,\n",
+ "2005, was determined to be 5.50%, a reduction from the previous year.\n",
+ "Additionally, the Company adopted the RP 2000 Mortality Table, resulting in an\n",
+ "increase in pension and postretirement obligations. 25 In 2006, there will be an\n",
+ "increase in pension expenses by $64 million and postretirement expenses by $17\n",
+ "million due to a change in the postretirement benefit obligation. The Company has\n",
+ "significant unrecognized pension actuarial losses that would potentially result in\n",
+ "future expenses amortized over average future service periods. 26 The expected\n",
+ "long-term rate of return on plan assets and the discount rate used to measure plan\n",
+ "liabilities have a significant impact on pension expenses for both U.S. and\n",
+ "international pension plans. A 0.25 percentage point increase or decrease in these\n",
+ "rates would result in a corresponding increase or decrease in expenses.\n",
+ "Additionally, management closely monitors and adjusts estimates and assumptions\n",
+ "regarding the recoverability of long-lived assets and goodwill, with impairment\n",
+ "testing conducted at the reporting unit level. 27 3M had 18 reporting units at\n",
+ "the end of 2005, with most of its goodwill directly assigned to specific units.\n",
+ "Impairment losses are recognized when a reporting unitâs net assets exceed its\n",
+ "estimated fair value, which is determined using earnings and industry-specific\n",
+ "ratios or discounted cash flow analysis. The company adopted new accounting\n",
+ "standards in 2005 and 2006 related to asset retirement obligations and stock-based\n",
+ "compensation expense. These changes resulted in the recognition of a liability for\n",
+ "asset retirement obligations and the expensing of stock-based compensation. 28\n",
+ "The company will be adjusting its stock-based compensation disclosures to reflect\n",
+ "the adoption of SFAS No. 123R, which requires recognition of compensation expense\n",
+ "when an employee is eligible to retire. This change will result in a higher\n",
+ "compensation expense for stock-based awards granted to employees eligible to\n",
+ "retire, beginning in May 2006 with the annual Management Stock Ownership Program\n",
+ "grant. The total expense will depend on various factors such as the number of\n",
+ "share-based awards granted and their fair value. 29 The companyâs financial\n",
+ "condition and liquidity remained strong at the end of 2005, with significant\n",
+ "ongoing cash flow and access to capital markets. Despite a decrease in working\n",
+ "capital compared to the previous year, the company believes it has ample cash to\n",
+ "fund investments and capital expenditures. However, the actual expense of share-\n",
+ "based awards may differ from estimates due to factors such as vesting periods and\n",
+ "fair value fluctuations. 30 activities for the company increased by $XX million\n",
+ "in the year ending December 31, 2005, compared to the previous year. The company\n",
+ "uses a combined index to measure working capital, which decreased slightly from\n",
+ "5.8 to 5.7. Accounts receivable increased by $46 million, while inventories\n",
+ "increased by $265 million. Accounts payable also increased by $88 million, with\n",
+ "the CUNO acquisition accounting for $18 million of this increase. 31 In 2005, the\n",
+ "company experienced fluctuations in cash flows from operating activities due to\n",
+ "various factors such as pension funding decisions, tax timing differences, and\n",
+ "legal proceedings. The company made discretionary contributions of $500 million to\n",
+ "its pension plans in 2005, and future contributions will depend on market\n",
+ "conditions and other factors. Despite these fluctuations, the company believes it\n",
+ "has a strong cash flow and balance sheet to fund future pension needs. 32 The\n",
+ "company has a strong cash flow and balance sheet which will allow it to fund\n",
+ "future pension needs without affecting growth opportunities. They have made\n",
+ "investments in property, plant, and equipment to support growth in diverse markets\n",
+ "and meet product demand. They have also completed acquisitions and made purchases\n",
+ "of investments to expand their business. 33 The company reported approximately\n",
+ "$55 million in investments in its balance sheet and cash flow statement. They are\n",
+ "actively considering additional acquisitions, investments, and strategic\n",
+ "alliances. Total debt decreased from $2.821 billion in 2004 to $2.381 billion in\n",
+ "2005, primarily due to the retirement of $400 million in medium-term notes. Total\n",
+ "debt was 19% of total capital, compared to 21% in 2004. 34 At year-end 2004, the\n",
+ "company had a debt securities program with remaining capacity of $1.438 billion,\n",
+ "issued $62 million in debt securities in 2004, and had $539 million in Convertible\n",
+ "Notes. The company also authorized the repurchase of $2.0 billion of its common\n",
+ "stock between January 1, 2005 and January 31, 2006, and an additional $300 million\n",
+ "in October 2005. 35 3Mâs Board of Directors has authorized the repurchase of an\n",
+ "additional $300 million of its stock, increasing the total repurchase\n",
+ "authorization to $2.3 billion. The company has a strong liquidity position, with\n",
+ "short-term liquidity needs being met through commercial paper issuances and a\n",
+ "credit agreement with primary relationship banks. 36 The company has a strong\n",
+ "balance sheet and liquidity, with a cash balance of $1.072 billion at the end of\n",
+ "2005. They have the ability to meet near-term obligations and take advantage of\n",
+ "opportunities. The company paid significant dividends in 2005 and has a history of\n",
+ "dividend increases. They also have the option to repurchase up to $2.0 billion of\n",
+ "their common stock. The company has not utilized special purpose entities for off-\n",
+ "balance sheet financing. 37 The company has warranty liabilities of approximately\n",
+ "$22 million, which they do not consider to be material. The fair value of\n",
+ "guarantees and indemnifications made by the company are also not expected to have\n",
+ "a material impact on their financial position or results of operations. The\n",
+ "company has long-term debt payments due in 2006 and unconditional purchase\n",
+ "obligations, including take or pay contracts and capital commitments. 38 The\n",
+ "company has short-term obligations related to take or pay contracts, where they\n",
+ "guarantee payment to ensure availability of products or services. These\n",
+ "obligations do not represent the entirety of future purchases, but only those\n",
+ "items for which the company is contractually obligated. The company also engages\n",
+ "in financial risk management through derivative arrangements to manage foreign\n",
+ "currency exposure, interest rate risks, and commodity price risks. 39 The company\n",
+ "uses foreign exchange forward contracts, options, and swaps to hedge against\n",
+ "exchange rate fluctuations on cash flows in foreign currencies. They also manage\n",
+ "interest rate risks using a mix of fixed and floating rate debt and may enter into\n",
+ "interest rate swaps. The company uses various strategies to manage commodity price\n",
+ "risks. Based on their analysis, possible changes in foreign exchange rates would\n",
+ "have negatively impacted earnings, while changes in interest rates would have had\n",
+ "a minimal effect. 40 According to the Form 10-K, the companyâs after-tax earnings\n",
+ "were positively impacted by a change in exchange rates and negatively impacted by\n",
+ "changes in commodity rates. The company uses derivative instruments to hedge a\n",
+ "small portion of their exposure to purchased components and materials and energy.\n",
+ "The report also mentions that forward-looking statements have been made regarding\n",
+ "the companyâs expected future business and financial performance. 41 The\n",
+ "Companyâs forward-looking statements, outlined in Part II, Item 7, provide insight\n",
+ "into their expected future business and financial performance. These statements\n",
+ "involve key factors such as growth strategy, product development, market position,\n",
+ "financial results, and legal contingencies. However, it should be noted that\n",
+ "actual results may differ from these statements due to various factors outlined in\n",
+ "Part I, Item 1A, âRisk Factorsâ.\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "e7c78e41",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(6510, 19148, 13247, 20555)"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(large_summary), len(medium_summary), len(small_summary), len(gpt_summary)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9bde201e",
+ "metadata": {},
+ "source": [
+ "# Large"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "4a1caf52",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ROUGE = Rouge()\n",
+ "large_scores = ROUGE.get_scores(large_summary, reference)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "0de9e725",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "rouge-1 0.2989130406405069\n",
+ "rouge-2 0.12511915933677617\n",
+ "rouge-l 0.29823369281441997\n"
+ ]
+ }
+ ],
+ "source": [
+ "for rouge, scores in large_scores[-1].items():\n",
+ " print(rouge, scores['f'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "679e4169",
+ "metadata": {},
+ "source": [
+ "# Medium"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "1b3d0ba1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ROUGE = Rouge()\n",
+ "medium_scores = ROUGE.get_scores(medium_summary, reference)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "f19887b3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "rouge-1 0.5896279551184656\n",
+ "rouge-2 0.4226710846913604\n",
+ "rouge-l 0.5890642572605175\n"
+ ]
+ }
+ ],
+ "source": [
+ "for rouge, scores in medium_scores[-1].items():\n",
+ " print(rouge, scores['f'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "32107337",
+ "metadata": {},
+ "source": [
+ "# Small"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "d568d1c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ROUGE = Rouge()\n",
+ "small_scores = ROUGE.get_scores(small_summary, reference)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "aff7933c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "rouge-1 0.4743155914568653\n",
+ "rouge-2 0.3090517211303139\n",
+ "rouge-l 0.4743155914568653\n"
+ ]
+ }
+ ],
+ "source": [
+ "for rouge, scores in small_scores[-1].items():\n",
+ " print(rouge, scores['f'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c26584a",
+ "metadata": {},
+ "source": [
+ "# Chat GPT"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "d9a4dffc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ROUGE = Rouge()\n",
+ "gpt_scores = ROUGE.get_scores(gpt_summary, reference)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "f6b7b976",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "rouge-1 0.4937481784891012\n",
+ "rouge-2 0.2425886809128456\n",
+ "rouge-l 0.47862750387438757\n"
+ ]
+ }
+ ],
+ "source": [
+ "for rouge, scores in gpt_scores[-1].items():\n",
+ " print(rouge, scores['f'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3b63a636",
+ "metadata": {},
+ "source": [
+ "# Sanity check"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "7098c9c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ROUGE = Rouge()\n",
+ "scores = ROUGE.get_scores(reference, reference)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "3b4f154c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "rouge-1 0.999999995\n",
+ "rouge-2 0.999999995\n",
+ "rouge-l 0.999999995\n"
+ ]
+ }
+ ],
+ "source": [
+ "for rouge, score in scores[-1].items():\n",
+ " print(rouge, score['f'])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/utils/large_summarizator_finetuning.ipynb b/utils/large_summarizator_finetuning.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1ad86bc34b113047b0b9538d7cc92c556811061d
--- /dev/null
+++ b/utils/large_summarizator_finetuning.ipynb
@@ -0,0 +1,219 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "e3000a69",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of PegasusForConditionalGeneration were not initialized from the model checkpoint at human-centered-summarization/financial-summarization-pegasus and are newly initialized: ['model.decoder.embed_positions.weight', 'model.encoder.embed_positions.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration\n",
+ "from rouge import Rouge\n",
+ "\n",
+ "# Let's load the model and the tokenizer \n",
+ "model_name = \"human-centered-summarization/financial-summarization-pegasus\"\n",
+ "tokenizer = PegasusTokenizer.from_pretrained(model_name, local_files_only=True)\n",
+ "model = PegasusForConditionalGeneration.from_pretrained(model_name, local_files_only=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "6832cc0c",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2 8\n",
+ "0.09230769142721895\n",
+ "0.02312138672190853\n",
+ "0.09230769142721895\n",
+ "----------------------------------------------------------------------\n",
+ "2 32\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "2 64\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "2 128\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "2 256\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "\n",
+ "5 8\n",
+ "0.09230769142721895\n",
+ "0.02312138672190853\n",
+ "0.09230769142721895\n",
+ "----------------------------------------------------------------------\n",
+ "5 32\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "5 64\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "5 128\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "5 256\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "\n",
+ "8 8\n",
+ "0.09230769142721895\n",
+ "0.02312138672190853\n",
+ "0.09230769142721895\n",
+ "----------------------------------------------------------------------\n",
+ "8 32\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "8 64\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "8 128\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "8 256\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "\n",
+ "12 8\n",
+ "0.09230769142721895\n",
+ "0.02312138672190853\n",
+ "0.09230769142721895\n",
+ "----------------------------------------------------------------------\n",
+ "12 32\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "12 64\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "12 128\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "12 256\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "\n",
+ "20 8\n",
+ "0.09230769142721895\n",
+ "0.02312138672190853\n",
+ "0.09230769142721895\n",
+ "----------------------------------------------------------------------\n",
+ "20 32\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "20 64\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "20 128\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "20 256\n",
+ "0.28767123031713265\n",
+ "0.11578947163656512\n",
+ "0.2465753399061738\n",
+ "----------------------------------------------------------------------\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "reference = \"National Commercial Bank (NCB), Saudi Arabiaâs largest lender by assets, agreed to buy rival Samba Financial Group for $15 billion in the biggest banking takeover this year.NCB will pay 28.45 riyals ($7.58) for each Samba share, according to a statement on Sunday, valuing it at about 55.7 billion riyals. NCB will offer 0.739 new shares for each Samba share, at the lower end of the 0.736-0.787 ratio the banks set when they signed an initial framework agreement in June.The offer is a 3.5% premium to Sambaâs Oct. 8 closing price of 27.50 riyals and about 24% higher than the level the shares traded at before the talks were made public. Bloomberg News first reported the merger discussions.The new bank will have total assets of more than $220 billion, creating the Gulf regionâs third-largest lender. The entityâs $46 billion market capitalization nearly matches that of Qatar National Bank QPSC, which is still the Middle Eastâs biggest lender with about $268 billion of assets.\"\n",
+ "for num_beams in [2, 5, 8, 12, 20]:\n",
+ " for max_length in [8, 32, 64, 128, 256]:\n",
+ " print(num_beams, max_length)\n",
+ " input_ids = tokenizer(reference, return_tensors=\"pt\").input_ids\n",
+ "\n",
+ " # Generate the output (Here, we use beam search but you can also use any other strategy you like)\n",
+ " output = model.generate(\n",
+ " input_ids, \n",
+ " max_length=max_length, \n",
+ " num_beams=5, \n",
+ " early_stopping=True\n",
+ " )\n",
+ "\n",
+ " summary = tokenizer.decode(output[0], skip_special_tokens=True)\n",
+ " ROUGE = Rouge()\n",
+ " scores = ROUGE.get_scores(summary, reference)\n",
+ " for rouge, score in scores[-1].items():\n",
+ " print(score['f'])\n",
+ " print('-' * 70)\n",
+ " print()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/utils/test.py b/utils/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..3bdf098394b65665d35247a7dd832772fcd54ad1
--- /dev/null
+++ b/utils/test.py
@@ -0,0 +1,11 @@
+from random import randint
+
+
+lst = [1, True, {1: ['a', 'zz'], 'z': 't'}]
+
+
+def appender(lst):
+ lst.append('TARAS')
+
+appender(lst)
+print(lst)
\ No newline at end of file
diff --git a/utils/test_inference_api.ipynb b/utils/test_inference_api.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a8014077b427d5dbbf788f7291cf9fb32a549236
--- /dev/null
+++ b/utils/test_inference_api.ipynb
@@ -0,0 +1,84 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "cba1cb3c-c494-4d82-a5ef-1aa92422bc0e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import requests\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "c598629b-d066-419d-86d6-184f612c3672",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "API_URL = \"https://api-inference.huggingface.co/models/lxyuan/distilgpt2-finetuned-finance\"\n",
+ "headers = {\"Authorization\": f\"Bearer {os.environ['HG_api_key']}\"}\n",
+ "\n",
+ "def query(payload):\n",
+ "\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
+ "\treturn response.json()\n",
+ "\t\n",
+ "output = query({\n",
+ "\t\"inputs\": \"Can you please let us know more details about your \",\n",
+ "})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "de6cac32-cbd2-4372-a642-99841876b2db",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'generated_text': 'Can you please let us know more details about your private investment experience or any other strategies you utilize! \\n\\nThank you in advance for your participation! \\n\\nIf I had been able to give in to this community, I would have'}]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8a7a4452-0f9a-4b1e-9efb-98c3d07de77a",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/utils/test_peft_model.ipynb b/utils/test_peft_model.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2fda0b430237444a778d1090ae95a94594dd504a
--- /dev/null
+++ b/utils/test_peft_model.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "7170d278-696d-4c14-815b-8c83b3bcba46",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from peft import PeftModel, PeftConfig\n",
+ "from transformers import AutoModelForCausalLM"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "998ead23-4b30-4607-980c-05bcafb53aad",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "PEFT_MODEL = \"dylanalloy/falcon-ehc-contrived-financial-7b\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "6d503b4e-88fd-4b42-8c82-eeb72f60fc23",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "9679f49862c84c0fa828522922405b95",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "adapter_config.json: 0%| | 0.00/419 [00:00 str:\n",
+ " prompt = f\"\"\"QUESTION: {question}\n",
+ " CONTEXT:\n",
+ " {context}\n",
+ " FOLLOWUP:\n",
+ " \"\"\".strip()\n",
+ " encoding = tokenizer(prompt, return_tensors='pt').to(DEVICE)\n",
+ " with torch.inference_mode():\n",
+ " outputs = model.generate(\n",
+ " input_ids=encoding.input_ids\n",
+ " , attention_mask=encoding.attention_mask\n",
+ " , generation_config=generation_config\n",
+ " )\n",
+ " return tokenizer.decode(outputs[0], skip_special_tokens=True).split(\"FOLLOWUP: \")[1]\n",
+ "\n",
+ "# starting the engineer off with a real bit of context from an SEC filing with a naive question posed. \n",
+ "# the same question was used to retrieve the context from a vector database initially\n",
+ "answer = generate_response(\n",
+ " \"\"\"What are the potential risks for Bank of America?\"\"\"\n",
+ " , \"\"\"We believe that these factors include, but are not limited to, the following: Insurance Risk • the cyclical nature of the insurance and reinsurance business leading to periods with excess underwriting capacity and unfavorable premium rates; • the occurrence and magnitude of natural and man-made disasters, including the potential increase of our exposure to natural catastrophe losses due to climate change and the potential for inherently unpredictable losses from man-made catastrophes, such as cyber-attacks.; • the effects of emerging claims, systemic risks, and coverage and regulatory issues, including increasing litigation and uncertainty related to coverage definitions, limits, terms and conditions; • actual claims exceeding reserves for losses and loss expenses; • the adverse impact of inflation; • the failure of any of the loss limitation methods we employ; • the failure of our cedants to adequately evaluate risks; Strategic Risk • losses from war including losses related to the Russian invasion of Ukraine, terrorism and political unrest, or other unanticipated losses; • changes in the political environment of certain countries in which we operate or underwrite business, including the United Kingdom's withdrawal from the European Union; • the loss of business provided to us by major brokers; • a decline in our ratings with rating agencies; • the loss of one or more of our key executives; • difficulties with technology and/or data security; • increasing scrutiny and evolving expectations from investors, customers, regulators, policymakers and other stakeholders regarding environmental, social and governance matters; COVID-19 • the adverse impact of the ongoing COVID-19 pandemic on our business, results of operations, financial condition, and liquidity; Credit and Market Risk • the inability to purchase reinsurance or collect amounts due to us from reinsurance we have purchased; • the failure of our policyholders or intermediaries to pay premiums; • general economic, capital and credit market conditions, including banking sector instability, financial market illiquidity and fluctuations in interest rates, credit spreads, equity securities' prices, and/or foreign currency exchange rates; • breaches by third parties in our program business of their obligations to us; Liquidity Risk • the inability to access sufficient cash to meet our obligations when they are due; Operational Risk • changes in accounting policies or practices; • the use of industry models and changes to these models; • difficulties with technology and/or data security; Regulatory Risk • changes in governmental regulations and potential government intervention in our industry; • inadvertent failure to comply with certain laws and regulations relating to sanctions and foreign corrupt practices; data protection and privacy; and Risks Related to Taxation • changes in tax laws; <|endoftext|>\"\"\"\n",
+ ")\n",
+ "\n",
+ "## your to-do:\n",
+ "## process & chunk the responses from your source of context (usually a vector db) & loop into generating longer pieces until the '[ANSWER]:' is created by this adapter model\n",
+ "## without your intervention, [FOLLOWUP]: and [CONTEXT]: will be hallucinated and will be derived from mostly undesirable model knowledge\n",
+ "\n",
+ "## this will not do you much good because it will use base model knowledge to continue its own research\n",
+ "# print(\"FOLLOWUP: \"+answer)\n",
+ "## but this will get you started with a context flow where you can inject information and generate further until an answer is found\n",
+ "print(\"[FOLLOWUP]: \"+answer.split('CONTEXT:')[0])\n",
+ ">> [FOLLOWUP]: What steps has Bank of America taken to mitigate these risks?\n",
+ "print(answer)\n",
+ ">> [QUESTION]: What steps has Bank of America taken to mitigate these risks?\n",
+ "[CONTEXT]: We believe that these factors include, but are not limited to, the following: Insurance Risk • the cyclical nature of the insurance and reinsurance business leading to periods with excess underwriting capacity and unfavorable premium rates; • the occurrence and magnitude of natural and man-made disasters, including the potential increase of our exposure to natural catastrophe losses due to climate change and the potential for inherently unpredictable losses from man-made catastrophes, such as cyber-attacks.; • the effects of emerging claims, systemic risks, and coverage and regulatory issues, including increasing litigation and uncertainty related to coverage definitions, limits, terms and conditions; • actual claims exceeding reserves for losses and loss expenses; • the adverse impact of inflation; • the failure of any of the loss limitation methods we employ; • the failure of our cedants to adequately evaluate risks; Strategic Risk • losses from war including losses related to the Russian invasion of Ukraine, terrorism and political unrest, or other unanticipated losses; • changes in the political environment of certain countries in which we operate or underwrite business, including the United Kingdom's withdrawal from the European Union; • the loss of business provided to us by major brokers; • a decline in our ratings with rating agencies; • the loss of one or more of our key executives; • difficulties with technology and/or data security; • increasing scrutiny and evolving expectations from investors, customers, regulators, policymakers and other stakeholders regarding environmental, social and governance matters; COVID-19 • the adverse impact of the ongoing COVID-19 pandemic on our business, results of operations, financial condition, and liquidity; Credit and Market Risk • the inability to purchase reinsurance or collect amounts due to us from reinsurance we have purchased; • the failure of our policyholders or intermediaries to pay premiums; • general economic, capital and credit market conditions, including banking sector instability, financial market illiquidity and fluctuations in interest rates, credit spreads, equity securities' prices, and/or foreign currency exchange rates; • breaches by third parties in our program business of their obligations to us; Liquidity Risk • the inability to access sufficient cash to meet our obligations when they are due; Operational Risk • changes in accounting policies or practices; • the use of industry models and changes to these models; • difficulties with technology and/or data security; Regulatory Risk • changes in governmental regulations and potential government intervention in our industry; • inadvertent failure to comply with certain laws and regulations relating to sanctions and foreign corrupt practices; data protection and privacy; and Risks Related to Taxation • changes in tax laws; \n",
+ "[FOLLOWUP]: What steps has Bank of America taken to address these factors?\n",
+ "[CONTEXT]: Bank of America has implemented various measures to address these factors. For example: • We have implemented a comprehensive risk management framework that includes risk identification risk assessment risk mitigation and risk monitoring. • We have implemented advanced data analytics and predictive modeling techniques to better understand and anticipate potential risks. • We have enhanced our risk management processes to ensure timely identification and mitigation of risks. • We have implemented a robust risk management structure that includes regular risk assessments and monitoring of key risk indicators. • We have established a dedicated risk management team to oversee the implementation of risk mitigation strategies. • We have implemented a comprehensive cyber security program to protect against potential cyber threats. • We have implemented a comprehensive environmental risk management program to address environmental risks. • We have implemented a comprehensive risk management program to address operational risks. • We have implemented a comprehensive risk management program to address liquidity risks. • We have implemented a comprehensive risk management program to address regulatory risks. • We have implemented a comprehensive risk management program to address tax-related risks. [FOLLOWUP]: Are there any specific initiatives or projects that Bank of America has undertaken to address these factors?\n",
+ "[CONTEXT]: Yes Bank of America has undertaken several initiatives and projects to address these factors. For example: • We have implemented a comprehensive risk management program that includes risk assessments and mitigation strategies. • We have implemented a comprehensive cyber security program to protect against potential cyber threats. • We have implemented a comprehensive environmental risk management program to address environmental risks. • We have implemented a comprehensive risk management program to address operational risks. • We have implemented a comprehensive risk management program to address liquidity risks. • We have implemented a comprehensive risk management program to address regulatory risks. [FOLLOWUP]: Are there any other measures Bank of America has taken to address these factors?\n",
+ "[CONTEXT]: Yes Bank of America has taken additional measures to address these factors. For example: • We have implemented a comprehensive risk management program th\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/utils/testing_loading_models.ipynb b/utils/testing_loading_models.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..50452e6d8e71d79ab4d4bb5ac63740a7816ba895
--- /dev/null
+++ b/utils/testing_loading_models.ipynb
@@ -0,0 +1,421 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "23879688",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+ "To disable this warning, you can either:\n",
+ "\t- Avoid using `tokenizers` before the fork if possible\n",
+ "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a7147b7318214d9da894766e55a4a895",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading (âŚ)okenizer_config.json: 0%| | 0.00/226 [00:00', 'pad_token': '', 'additional_special_tokens': ['', '', '', '']}, clean_up_tokenization_spaces=True), added_tokens_decoder={\n",
+ "\t0: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t1: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t2: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t3: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t4: AddedToken(\"[START_REF]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t5: AddedToken(\"[END_REF]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t6: AddedToken(\"[IMAGE]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t7: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t8: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t9: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t10: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t11: AddedToken(\"[START_SUP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t12: AddedToken(\"[END_SUP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t13: AddedToken(\"[START_SUB]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t14: AddedToken(\"[END_SUB]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t15: AddedToken(\"[START_DNA]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t16: AddedToken(\"[END_DNA]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t17: AddedToken(\"[START_AMINO]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t18: AddedToken(\"[END_AMINO]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t19: AddedToken(\"[START_SMILES]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t20: AddedToken(\"[END_SMILES]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t21: AddedToken(\"[START_I_SMILES]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t22: AddedToken(\"[END_I_SMILES]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t50000: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t50001: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t50002: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t50003: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "}"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "PreTrainedTokenizerFast(name_or_path='theblackcat102/galactica-1.3b-v2', vocab_size=50000, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'eos_token': '', 'pad_token': '', 'additional_special_tokens': ['', '', '', '']}, clean_up_tokenization_spaces=True), added_tokens_decoder={\n",
+ "\t0: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t1: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t2: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t3: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t4: AddedToken(\"[START_REF]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t5: AddedToken(\"[END_REF]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t6: AddedToken(\"[IMAGE]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t7: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t8: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t9: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t10: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t11: AddedToken(\"[START_SUP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t12: AddedToken(\"[END_SUP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t13: AddedToken(\"[START_SUB]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t14: AddedToken(\"[END_SUB]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t15: AddedToken(\"[START_DNA]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t16: AddedToken(\"[END_DNA]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t17: AddedToken(\"[START_AMINO]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t18: AddedToken(\"[END_AMINO]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t19: AddedToken(\"[START_SMILES]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t20: AddedToken(\"[END_SMILES]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t21: AddedToken(\"[START_I_SMILES]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t22: AddedToken(\"[END_I_SMILES]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t50000: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t50001: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t50002: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "\t50003: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+ "}"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokenizer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "695b08bf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "text = 'We connect the four chains using SequentialChain. The output of one chain becomes the input to the next chain.'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "976a0ffc",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'input_ids': [1246, 12775, 286, 1715, 7067, 672, 22319, 40118, 36, 381, 2380, 299, 717, 2764, 3778, 286, 1964, 321, 286, 2857, 2764, 36], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "{'input_ids': [1246, 12775, 286, 1715, 7067, 672, 22319, 40118, 36, 381, 2380, 299, 717, 2764, 3778, 286, 1964, 321, 286, 2857, 2764, 36], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokenized = tokenizer(text)\n",
+ "tokenized"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "id": "5004a189",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# dir(tokenized)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "id": "e80ede4a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tokens = tokenizer.encode(text, return_tensors='pt')\n",
+ "# print(\"These are tokens!\", tokens)\n",
+ "# tokens, tokenized['input_ids']\n",
+ "# for token in tokens[0]:\n",
+ "# print(\"This are decoded tokens!\", tokenizer.decode([token]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "0fcbc2ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
+ "# print(model.embeddings.word_embeddings(tokens))\n",
+ "# for e in model.embeddings.word_embeddings(tokens)[0]:\n",
+ "# print(\"This is an embedding!\", e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "a6c31367",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# dir(model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "id": "f6be2cdf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([ 0.0139, -0.0367, 0.0186, -0.0564, -0.0004, -0.0255, -0.0011, -0.0179,\n",
+ " -0.0128, -0.0046, -0.0361, -0.0222, 0.0443, 0.0058, -0.0008, 0.0186,\n",
+ " -0.0252, -0.0082, 0.0186, -0.0195, 0.0058, -0.0128],\n",
+ " grad_fn=)"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "tensor([ 0.0139, -0.0367, 0.0186, -0.0564, -0.0004, -0.0255, -0.0011, -0.0179,\n",
+ " -0.0128, -0.0046, -0.0361, -0.0222, 0.0443, 0.0058, -0.0008, 0.0186,\n",
+ " -0.0252, -0.0082, 0.0186, -0.0195, 0.0058, -0.0128],\n",
+ " grad_fn=)"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "embs = [emb for emb in model.get_input_embeddings().parameters()]\n",
+ "embs[0][tokenized['input_ids'], 0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b4be6e5c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "511fb260",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "705ff113",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+ "To disable this warning, you can either:\n",
+ "\t- Avoid using `tokenizers` before the fork if possible\n",
+ "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "df88b11fada24110bdaca104aecc5a3f",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Loading checkpoint shards: 0%| | 0/2 [00:00 20 {gsub(/[^[:alnum:]]/, " "); for (i=1; i<=NF; i++) if (length($i) > 20) print FILENAME ":", $i}' {} \;
+To print files and words, which are longer than 20 letters
+
+find ./txts -type f -name "*.txt" -exec awk 'length($0) > 20 {gsub(/[^[:alnum:]]/, " "); for (i=1; i<=NF; i++) if (length($i) > 20) count++} END {if (count > 0) print FILENAME ":", count}' {} \;
+To print how many broken words there are in each file.
\ No newline at end of file