import gradio as gr import math import spacy from datasets import load_dataset from sentence_transformers import SentenceTransformer from sentence_transformers import InputExample from sentence_transformers import losses from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification from transformers import TrainingArguments, Trainer import torch import torch.nn.functional as F from torch.utils.data import DataLoader import numpy as np import evaluate import nltk from nltk.corpus import stopwords import subprocess import sys # !pip install https://huggingface.co/spacy/en_core_web_sm/resolve/main/en_core_web_sm-any-py3-none-any.whl subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'https://huggingface.co/spacy/en_core_web_sm/resolve/main/en_core_web_sm-any-py3-none-any.whl']) tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") nltk.download('stopwords') nlp = spacy.load("en_core_web_sm") stops = stopwords.words("english") # answer = "Pizza" guesses = [] answer = "Pizza" #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) def normalize(comment, lowercase, remove_stopwords): if lowercase: comment = comment.lower() comment = nlp(comment) lemmatized = list() for word in comment: lemma = word.lemma_.strip() if lemma: if not remove_stopwords or (remove_stopwords and lemma not in stops): lemmatized.append(lemma) return " ".join(lemmatized) def tokenize_function(examples): return tokenizer(examples["text"]) def compute_metrics(eval_pred): logits, labels = eval_pred predictions = np.argmax(logits, axis=-1) metric = evaluate.load("accuracy") return metric.compute(predictions=predictions, references=labels) def training(): dataset_id = "ag_news" dataset = load_dataset(dataset_id) # dataset = dataset["train"] # tokenized_datasets = dataset.map(tokenize_function, batched=True) print(f"- The {dataset_id} dataset has {dataset['train'].num_rows} examples.") print(f"- Each example is a {type(dataset['train'][0])} with a {type(dataset['train'][0]['text'])} as value.") print(f"- Examples look like this: {dataset['train'][0]}") # small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000)) # small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000)) # dataset = dataset["train"].map(tokenize_function, batched=True) # dataset.set_format(type="torch", columns=["input_ids", "token_type_ids", "attention_mask", "label"]) # dataset.format['type'] # print(dataset) train_examples = [] train_data = dataset["train"] # For agility we only 1/2 of our available data n_examples = dataset["train"].num_rows // 2 # n_remaining = dataset["train"].num_rows - n_examples dataset_clean = {} dataset_0 = [] dataset_1 = [] dataset_2 = [] dataset_3 = [] for i in range(n_examples): dataset_clean[i] = {} dataset_clean[i]["text"] = normalize(train_data[i]["text"], lowercase=True, remove_stopwords=True) dataset_clean[i]["label"] = train_data[i]["label"] if train_data[i]["label"] == 0: dataset_0.append(dataset_clean[i]) elif train_data[i]["label"] == 1: dataset_1.append(dataset_clean[i]) elif train_data[i]["label"] == 2: dataset_2.append(dataset_clean[i]) elif train_data[i]["label"] == 3: dataset_3.append(dataset_clean[i]) n_0 = len(dataset_0) // 2 n_1 = len(dataset_1) // 2 n_2 = len(dataset_2) // 2 n_3 = len(dataset_3) // 2 print("Label lengths:", len(dataset_0), len(dataset_1), len(dataset_2), len(dataset_3)) # for i in range(n_examples): # example = dataset_clean[i] # example_opposite = dataset_clean[-(i)] # # print(example["text"]) # train_examples.append(InputExample(texts=[example['text'], example_opposite["text"]])) for i in range(n_0): example = dataset_0[i] # example_opposite = dataset_0[-(i)] # print(example["text"]) train_examples.append(InputExample(texts=example['text'], label=0)) for i in range(n_1): example = dataset_1[i] # example_opposite = dataset_1[-(i)] # print(example["text"]) train_examples.append(InputExample(texts=example['text'], label=1)) for i in range(n_2): example = dataset_2[i] # example_opposite = dataset_2[-(i)] # print(example["text"]) train_examples.append(InputExample(texts=example['text'], label=2)) for i in range(n_3): example = dataset_3[i] # example_opposite = dataset_3[-(i)] # print(example["text"]) train_examples.append(InputExample(texts=example['text'], label=3)) train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=25) # print(train_examples) embeddings = finetune(train_dataloader) return (dataset['train'].num_rows, type(dataset['train'][0]), type(dataset['train'][0]['text']), dataset['train'][0], embeddings) def finetune(train_dataloader): # model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5) model_id = "sentence-transformers/all-MiniLM-L6-v2" model = SentenceTransformer(model_id) # training_args = TrainingArguments(output_dir="test_trainer") # USE THIS LINK # https://huggingface.co/blog/how-to-train-sentence-transformers train_loss = losses.BatchHardTripletLoss(model=model) model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=10) model.save_to_hub( "sentence-transformers/all-MiniLM-L6-v2", organization="smhavens", train_datasets=["ag_news"], ) # accuracy = compute_metrics(eval, metric) # training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch") # trainer = Trainer( # model=model, # args=training_args, # train_dataset=train, # eval_dataset=eval, # compute_metrics=compute_metrics, # ) # trainer.train() sentences = ["This is an example sentence", "Each sentence is converted"] # model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') embeddings = model.encode(sentences) print(embeddings) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub # tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') # model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) return sentence_embeddings def greet(name): return "Hello " + name + "!!" def check_answer(guess:str): global guesses global answer guesses.append(guess) output = "" for guess in guesses: output += ("- " + guess + "\n") output = output[:-1] if guess.lower() == answer.lower(): return "Correct!", output else: return "Try again!", output def main(): word1 = "Black" word2 = "White" word3 = "Sun" global answer answer = "Moon" global guesses num_rows, data_type, value, example, embeddings = training() prompt = f"{word1} is to {word2} as {word3} is to ____" with gr.Blocks() as iface: gr.Markdown(prompt) with gr.Tab("Guess"): text_input = gr.Textbox() text_output = gr.Textbox() text_button = gr.Button("Submit") with gr.Accordion("Open for previous guesses"): text_guesses = gr.Textbox() with gr.Tab("Testing"): gr.Markdown(f"""Number of rows in dataset is {num_rows}, with each having type {data_type} and value {value}. An example is {example}. The Embeddings are {embeddings}.""") text_button.click(check_answer, inputs=[text_input], outputs=[text_output, text_guesses]) # iface = gr.Interface(fn=greet, inputs="text", outputs="text") iface.launch() if __name__ == "__main__": main()