Résumé (PyTorch)¶

!pip install datasets transformers[sentencepiece]
!pip install accelerate
# Pour exécuter l'entraînement sur TPU, vous devez décommenter la ligne suivante :
# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl
!apt install git-lfs

!git config --global user.email "you@example.com"
!git config --global user.name "Your Name"

from huggingface_hub import notebook_login

notebook_login()

from datasets import load_dataset

french_dataset = load_dataset("amazon_reviews_multi", "fr")
english_dataset = load_dataset("amazon_reviews_multi", "en")
french_dataset

def show_samples(dataset, num_samples=3, seed=42):
    sample = dataset["train"].shuffle(seed=seed).select(range(num_samples))
    for example in sample:
        print(f"\n'>> Title: {example['review_title']}'")
        print(f"'>> Review: {example['review_body']}'")

show_samples(french_dataset)

french_dataset.set_format("pandas")
french_df = french_dataset["train"][:]
# Afficher les comptes des 20 premiers produits
french_df["product_category"].value_counts()[:20]

def filter_books(example):
    return (
        example["product_category"] == "book"
        or example["product_category"] == "digital_ebook_purchase"
    )

french_dataset.reset_format()

french_books = french_dataset.filter(filter_books)
english_books = english_dataset.filter(filter_books)
show_samples(french_dataset)

from datasets import concatenate_datasets, DatasetDict

books_dataset = DatasetDict()

for split in english_books.keys():
    books_dataset[split] = concatenate_datasets(
        [english_books[split], french_books[split]]
    )
    books_dataset[split] = books_dataset[split].shuffle(seed=42)

# Quelques exemples
show_samples(books_dataset)

books_dataset = books_dataset.filter(lambda x: len(x["review_title"].split()) > 2)

from transformers import AutoTokenizer

model_checkpoint = "google/mt5-small"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

inputs = tokenizer("J'ai adoré lire les Hunger Games !")
inputs

tokenizer.convert_ids_to_tokens(inputs.input_ids)

max_input_length = 512
max_target_length = 30


def preprocess_function(examples):
    model_inputs = tokenizer(
        examples["review_body"], max_length=max_input_length, truncation=True
    )
    # Configurer le tokenizer pour les cibles
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(
            examples["review_title"], max_length=max_target_length, truncation=True
        )

    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

tokenized_datasets = books_dataset.map(preprocess_function, batched=True)

generated_summary = "J'ai absolument adoré lire les Hunger Games"
reference_summary = "J'ai adoré lire les Hunger Games"

!pip install rouge_score

from datasets import load_metric

rouge_score = load_metric("rouge")

scores = rouge_score.compute(
    predictions=[generated_summary], references=[reference_summary]
)
scores

scores["rouge1"].mid

!pip install nltk

import nltk

nltk.download("punkt")

from nltk.tokenize import sent_tokenize


def three_sentence_summary(text):
    return "\n".join(sent_tokenize(text)[:3])


print(three_sentence_summary(books_dataset["train"][1]["review_body"]))

def evaluate_baseline(dataset, metric):
    summaries = [three_sentence_summary(text) for text in dataset["review_body"]]
    return metric.compute(predictions=summaries, references=dataset["review_title"])

import pandas as pd

score = evaluate_baseline(books_dataset["validation"], rouge_score)
rouge_names = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
rouge_dict = dict((rn, round(score[rn].mid.fmeasure * 100, 2)) for rn in rouge_names)
rouge_dict

from transformers import AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)

from transformers import Seq2SeqTrainingArguments

batch_size = 8
num_train_epochs = 8
# Montre la perte d'entraînement à chaque époque
logging_steps = len(tokenized_datasets["train"]) // batch_size
model_name = model_checkpoint.split("/")[-1]

args = Seq2SeqTrainingArguments(
    output_dir=f"{model_name}-finetuned-amazon-en-fr",
    evaluation_strategy="epoch",
    learning_rate=5.6e-5,
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    weight_decay=0.01,
    save_total_limit=3,
    num_train_epochs=num_train_epochs,
    predict_with_generate=True,
    logging_steps=logging_steps,
    push_to_hub=True,
)

import numpy as np


def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    # Décoder les résumés générés en texte
    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
    # Remplacer -100 dans les étiquettes car nous ne pouvons pas les décoder
    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    # Décoder les résumés de référence en texte
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
    # ROUGE attend une nouvelle ligne après chaque phrase
    decoded_preds = ["\n".join(sent_tokenize(pred.strip())) for pred in decoded_preds]
    decoded_labels = ["\n".join(sent_tokenize(label.strip())) for label in decoded_labels]
    # Calculer les scores ROUGE
    result = rouge_score.compute(
        predictions=decoded_preds, references=decoded_labels, use_stemmer=True
    )
    # Extraire les scores médians
    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
    return {k: round(v, 4) for k, v in result.items()}

from transformers import DataCollatorForSeq2Seq

data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)

tokenized_datasets = tokenized_datasets.remove_columns(
    books_dataset["train"].column_names
)

features = [tokenized_datasets["train"][i] for i in range(2)]
data_collator(features)

from transformers import Seq2SeqTrainer

trainer = Seq2SeqTrainer(
    model,
    args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["validation"],
    data_collator=data_collator,
    tokenizer=tokenizer,
    compute_metrics=compute_metrics,
)

trainer.train()

trainer.evaluate()

tokenized_datasets.set_format("torch")

model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)

from torch.utils.data import DataLoader

batch_size = 8
train_dataloader = DataLoader(
    tokenized_datasets["train"],
    shuffle=True,
    collate_fn=data_collator,
    batch_size=batch_size,
)
eval_dataloader = DataLoader(
    tokenized_datasets["validation"], collate_fn=data_collator, batch_size=batch_size
)

from torch.optim import AdamW

optimizer = AdamW(model.parameters(), lr=2e-5)

from accelerate import Accelerator

accelerator = Accelerator()
model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

from transformers import get_scheduler

num_train_epochs = 10
num_update_steps_per_epoch = len(train_dataloader)
num_training_steps = num_train_epochs * num_update_steps_per_epoch

lr_scheduler = get_scheduler(
    "linear",
    optimizer=optimizer,
    num_warmup_steps=0,
    num_training_steps=num_training_steps,
)

def postprocess_text(preds, labels):
    preds = [pred.strip() for pred in preds]
    labels = [label.strip() for label in labels]

    # ROUGE attend une nouvelle ligne après chaque phrase
    preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
    labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]

    return preds, labels

from huggingface_hub import get_full_repo_name

model_name = "test-bert-finetuned-squad-accelerate"
repo_name = get_full_repo_name(model_name)
repo_name

from huggingface_hub import Repository

output_dir = "results-mt5-finetuned-squad-accelerate"
repo = Repository(output_dir, clone_from=repo_name)

from tqdm.auto import tqdm
import torch
import numpy as np

progress_bar = tqdm(range(num_training_steps))

for epoch in range(num_train_epochs):
    # Entraînement
    model.train()
    for step, batch in enumerate(train_dataloader):
        outputs = model(**batch)
        loss = outputs.loss
        accelerator.backward(loss)

        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
        progress_bar.update(1)

    # Evaluation
    model.eval()
    for step, batch in enumerate(eval_dataloader):
        with torch.no_grad():
            generated_tokens = accelerator.unwrap_model(model).generate(
                batch["input_ids"],
                attention_mask=batch["attention_mask"],
            )

            generated_tokens = accelerator.pad_across_processes(
                generated_tokens, dim=1, pad_index=tokenizer.pad_token_id
            )
            labels = batch["labels"]

            # Si nous n'avons pas rempli la longueur maximale, nous devons également remplir les étiquettes
            labels = accelerator.pad_across_processes(
                batch["labels"], dim=1, pad_index=tokenizer.pad_token_id
            )

            generated_tokens = accelerator.gather(generated_tokens).cpu().numpy()
            labels = accelerator.gather(labels).cpu().numpy()

            # Remplacer -100 dans les étiquettes car nous ne pouvons pas les décoder
            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
            if isinstance(generated_tokens, tuple):
                generated_tokens = generated_tokens[0]
            decoded_preds = tokenizer.batch_decode(
                generated_tokens, skip_special_tokens=True
            )
            decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

            decoded_preds, decoded_labels = postprocess_text(
                decoded_preds, decoded_labels
            )

            rouge_score.add_batch(predictions=decoded_preds, references=decoded_labels)

    # Calculer les métriques
    result = rouge_score.compute()
    # Extraire les scores médians de ROUGE
    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
    result = {k: round(v, 4) for k, v in result.items()}
    print(f"Epoch {epoch}:", result)

    # Sauvegarder et télécharger
    accelerator.wait_for_everyone()
    unwrapped_model = accelerator.unwrap_model(model)
    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)
    if accelerator.is_main_process:
        tokenizer.save_pretrained(output_dir)
        repo.push_to_hub(
            commit_message=f"Training in progress epoch {epoch}", blocking=False
        )

from transformers import pipeline

hub_model_id = "huggingface-course/mt5-small-finetuned-amazon-en-fr"
summarizer = pipeline("summarization", model=hub_model_id)

def print_summary(idx):
    review = books_dataset["test"][idx]["review_body"]
    title = books_dataset["test"][idx]["review_title"]
    summary = summarizer(books_dataset["test"][idx]["review_body"])[0]["summary_text"]
    print(f"'>>> Review: {review}'")
    print(f"\n'>>> Title: {title}'")
    print(f"\n'>>> Summary: {summary}'")

print_summary(100)

print_summary(0)