Delete after_model_fitting.py

after_model_fitting.py

@@ -1,324 +0,0 @@
-# -*- coding: utf-8 -*-
-"""After model-fitting
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/#fileId=https%3A//storage.googleapis.com/kaggle-colab-exported-notebooks/after-model-fitting-b220d687-d8e5-4eb5-aafd-6a7e94d72073.ipynb%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com/20240128/auto/storage/goog4_request%26X-Goog-Date%3D20240128T102031Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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
-"""
-
-# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES
-# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,
-# THEN FEEL FREE TO DELETE THIS CELL.
-# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON
-# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR
-# NOTEBOOK.
-
-import os
-import sys
-from tempfile import NamedTemporaryFile
-from urllib.request import urlopen
-from urllib.parse import unquote, urlparse
-from urllib.error import HTTPError
-from zipfile import ZipFile
-import tarfile
-import shutil
-
-CHUNK_SIZE = 40960
-DATA_SOURCE_MAPPING = 'llm-detect-ai-generated-text:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-competitions-data%2Fkaggle-v2%2F61542%2F7516023%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240128%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240128T102030Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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,argugpt:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F3946973%2F6867914%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240128%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240128T102030Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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,daigt-proper-train-dataset:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F3942644%2F6890527%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240128%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240128T102031Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'
-
-KAGGLE_INPUT_PATH='/kaggle/input'
-KAGGLE_WORKING_PATH='/kaggle/working'
-KAGGLE_SYMLINK='kaggle'
-
-!umount /kaggle/input/ 2> /dev/null
-shutil.rmtree('/kaggle/input', ignore_errors=True)
-os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)
-os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)
-
-try:
-  os.symlink(KAGGLE_INPUT_PATH, os.path.join("..", 'input'), target_is_directory=True)
-except FileExistsError:
-  pass
-try:
-  os.symlink(KAGGLE_WORKING_PATH, os.path.join("..", 'working'), target_is_directory=True)
-except FileExistsError:
-  pass
-
-for data_source_mapping in DATA_SOURCE_MAPPING.split(','):
-    directory, download_url_encoded = data_source_mapping.split(':')
-    download_url = unquote(download_url_encoded)
-    filename = urlparse(download_url).path
-    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)
-    try:
-        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:
-            total_length = fileres.headers['content-length']
-            print(f'Downloading {directory}, {total_length} bytes compressed')
-            dl = 0
-            data = fileres.read(CHUNK_SIZE)
-            while len(data) > 0:
-                dl += len(data)
-                tfile.write(data)
-                done = int(50 * dl / int(total_length))
-                sys.stdout.write(f"\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded")
-                sys.stdout.flush()
-                data = fileres.read(CHUNK_SIZE)
-            if filename.endswith('.zip'):
-              with ZipFile(tfile) as zfile:
-                zfile.extractall(destination_path)
-            else:
-              with tarfile.open(tfile.name) as tarfile:
-                tarfile.extractall(destination_path)
-            print(f'\nDownloaded and uncompressed: {directory}')
-    except HTTPError as e:
-        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')
-        continue
-    except OSError as e:
-        print(f'Failed to load {download_url} to path {destination_path}')
-        continue
-
-print('Data source import complete.')
-
-# This Python 3 environment comes with many helpful analytics libraries installed
-# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
-# For example, here's several helpful packages to load
-
-import numpy as np # linear algebra
-import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
-
-# Input data files are available in the read-only "../input/" directory
-# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
-
-import os
-for dirname, _, filenames in os.walk('/kaggle/input'):
-    for filename in filenames:
-        print(os.path.join(dirname, filename))
-
-# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
-# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
-
-!git clone https://huggingface.co/spaces/kaitehtzeng/primary_app
-
-"""## Import Necessary Library"""
-
-import torch.nn.functional as F
-from transformers import AutoModel
-from transformers import AutoTokenizer
-from tokenizers import Tokenizer, trainers, pre_tokenizers, models
-from transformers import DebertaTokenizer
-from sklearn.model_selection import train_test_split
-import torch
-import torch.nn as nn
-import numpy as np
-import pandas as pd
-from tqdm.notebook import tqdm
-import matplotlib.pyplot as plt
-import nltk
-from nltk.corpus import stopwords
-from nltk.tokenize import word_tokenize
-from nltk.tokenize.treebank import TreebankWordDetokenizer
-from collections import Counter
-#import spacy
-import re
-import gc
-# ----------
-import os
-
-config = {
-    'model': '/kaggle/input/transformers-model-downloader-pytorch-tf2-0/microsoft/deberta-v3-base',
-    'dropout': 0.2,
-    'max_length': 512,
-    'batch_size':3,
-    'epochs': 1,
-    'lr': 1e-5,
-    'device': 'cuda' if torch.cuda.is_available() else 'cpu',
-    'scheduler': 'CosineAnnealingWarmRestarts'
-}
-
-"""### Preparation
-Comparing two essays. <br>
-One predicted written by students, one predicted written by LLM
-"""
-
-train_essays = pd.read_csv("/kaggle/input/llm-detect-ai-generated-text/train_essays.csv")
-external = pd.read_csv("/kaggle/input/daigt-proper-train-dataset/train_drcat_04.csv")
-
-df = pd.concat([
-    external[external.source=="persuade_corpus"].sample(10000,random_state=101),
-    external[external.source!='persuade_corpus']
-])
-df = df.reset_index()
-
-df['stratify'] = df.label.astype(str)+df.source.astype(str)
-train_df,val_df = train_test_split(df,test_size=0.2,random_state = 101,stratify=df['stratify'])
-train_df, val_df = train_df.reset_index(), val_df.reset_index()
-
-import transformers
-print('transformers version:', transformers.__version__)
-
-#train_df,val_df = train_test_split(train_essays,test_size=0.2,random_state = 101)
-#train_df, val_df = train_df.reset_index(), val_df.reset_index()
-#print('dataframe shapes:',train_df.shape, val_df.shape)
-
-tokenizer = AutoTokenizer.from_pretrained(config['model'])
-tokenizer.train_new_from_iterator(train_essays['text'], 52000)
-
-"""### Building Training Dataset and Loader"""
-
-class EssayDataset:
-    def __init__(self, df, config,tokenizer, is_test = False):
-        self.df = df
-        self.tokenizer = tokenizer
-        self.is_test = is_test
-        self.config = config
-
-    def token_start(self, idx):
-        sample_text = self.df.loc[idx,'text']
-
-        tokenized = tokenizer.encode_plus(sample_text,
-                                          None,
-                                          add_special_tokens=True,
-                                          max_length= self.config['max_length'],
-                                          truncation=True,
-                                          padding="max_length"
-                                         )
-
-        inputs = {
-           "input_ids": torch.tensor(tokenized['input_ids'],dtype=torch.long),
-            "token_type_ids": torch.tensor(tokenized['token_type_ids'],dtype=torch.long),
-            "attention_mask": torch.tensor(tokenized['attention_mask'],dtype = torch.long)
-        }
-
-        return inputs
-
-
-    def __getitem__(self,idx):
-
-        input_text = self.token_start(idx)
-
-        if self.is_test:
-            return input_text
-
-        else:
-            labels = self.df.loc[idx,'label']
-            targets = {'labels' : torch.tensor(labels,dtype = torch.float32)}
-
-            return input_text,targets
-
-    def __len__(self):
-        return len(self.df)
-
-eval_ds = EssayDataset(val_df,config,tokenizer = tokenizer,is_test=True)
-
-eval_loader = torch.utils.data.DataLoader(eval_ds,
-                          batch_size= config['batch_size'])
-
-"""Build the Model"""
-
-class mymodel(nn.Module):
-
-    def __init__(self,config):
-        super(mymodel,self).__init__()
-
-        self.model_name = config['model']
-        self.deberta = AutoModel.from_pretrained(self.model_name)
-#12801 = len(tokenizer)
-        self.deberta.resize_token_embeddings(128001)
-        self.dropout = nn.Dropout(config['dropout'])
-        self.fn0 = nn.Linear(self.deberta.config.hidden_size,256)
-        self.fn2 = nn.Linear(256,1)
-        self.pooling = MeanPooling()
-
-    def forward(self, input):
-        output = self.deberta(**input,return_dict = True)
-        output = self.pooling(output['last_hidden_state'],input['attention_mask'])
-        output = self.dropout(output)
-        output = self.fn0(output)
-        output = self.dropout(output)
-        output = self.fn2(output)
-        output = torch.sigmoid(output)
-        return output
-
-import torch.nn as nn
-class MeanPooling(nn.Module):
-    def __init__(self):
-        super(MeanPooling,self).__init__()
-
-
-    def forward(self,last_hidden_state, attention_mask):
-        new_weight = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float()
-        final = torch.sum(new_weight*last_hidden_state,1)
-        total_weight = new_weight.sum(1)
-        total_weight = torch.clamp(total_weight, min = 1e-9)
-        mean_embedding = final/total_weight
-
-        return mean_embedding
-
-model = mymodel(config).to(device=config['device'])
-model.load_state_dict(torch.load('/kaggle/input/fine-tune-model/my_model.pth'))
-model.eval()
-
-#preds = []
-#for (inputs) in eval_loader:
-#        inputs = {k:inputs[k].to(device=config['device']) for k in inputs.keys()}
-#
-#        outputs = model(inputs)
-#        preds.append(outputs.detach().cpu())
-
-#preds = torch.concat(preds)
-
-#val_df['preds'] = preds.numpy()
-#val_df['AI'] = val_df['preds']>0.5
-
-#sample_predict_AI = val_df.loc[val_df['AI'] == True].iloc[0]['text']
-#sample_predict_student = val_df.loc[val_df['AI'] == False].iloc[0]['text']
-
-#sample_predict_AI
-
-#sample_predict_student
-
-def trial(text):
-
-    tokenized = tokenizer.encode_plus(text,
-                                          None,
-                                          add_special_tokens=True,
-                                          max_length= config['max_length'],
-                                          truncation=True,
-                                          padding="max_length"
-                                         )
-    inputs = {
-           "input_ids": torch.tensor(tokenized['input_ids'],dtype=torch.long),
-            "token_type_ids": torch.tensor(tokenized['token_type_ids'],dtype=torch.long),
-            "attention_mask": torch.tensor(tokenized['attention_mask'],dtype = torch.long)
-        }
-    inputs = {k:inputs[k].unsqueeze(0).to(device=config['device']) for k in inputs.keys()}
-
-    if model(inputs).item()>=0.5:
-        return "AI"
-    else:
-        return "Student"
-
-!pip install -q gradio==3.45.0
-
-import gradio as gr
-
-trial('hello fuck you')
-
-
-
-demo = gr.Interface(
-    fn=trial,
-    inputs=gr.Textbox(placeholder="..."),
-    outputs="textbox"
-    )
-
-demo.launch(share=True)
-
-"""### Model
-Fine tuning the deberta-v3-base model with new-added layers
-
-The model is later used to participate the Kaggle Competition:LLM - Detect AI Generated Text.
-The Auc of the model is 0.75
-"""
-
-!git push