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| 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 | |
| from transformers import DataCollatorWithPadding | |
| # !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('sentence-transformers/all-MiniLM-L6-v2') | |
| # data_collator = DataCollatorWithPadding(tokenizer=tokenizer) | |
| # 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"], truncation=True) | |
| 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" | |
| print("GETTING DATASET") | |
| 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'] | |
| # tokenized_news = dataset.map(tokenize_function, batched=True) | |
| # model = AutoModelForSequenceClassification.from_pretrained("sentence-transformers/all-MiniLM-L6-v2", num_labels=2) | |
| # 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 | |
| for i in range(n_examples): | |
| example = train_data[i] | |
| # example_opposite = dataset_clean[-(i)] | |
| # print(example["text"]) | |
| train_examples.append(InputExample(texts=[example['text']], label=example['label'])) | |
| train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=25) | |
| print("END DATALOADER") | |
| # 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) | |
| device = torch.device('cuda:0') | |
| model = model.to(device) | |
| # training_args = TrainingArguments(output_dir="test_trainer") | |
| # USE THIS LINK | |
| # https://huggingface.co/blog/how-to-train-sentence-transformers | |
| train_loss = losses.BatchHardSoftMarginTripletLoss(model=model) | |
| print("BEGIN FIT") | |
| model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=10) | |
| model.save("ag_news_model") | |
| model.save_to_hub("smhavens/all-MiniLM-agNews") | |
| # 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 0 | |
| 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(): | |
| print("BEGIN") | |
| 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() |