section3.ipynb

Kernel: Python 3

Finetuner un modèle avec l'API Trainer

Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce notebook.

!pip install datasets transformers[sentencepiece]

from datasets import load_dataset
from transformers import AutoTokenizer, DataCollatorWithPadding

raw_datasets = load_dataset("paws-x", "fr")
checkpoint = "camembert-base"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)


def tokenize_function(example):
    return tokenizer(example["sentence1"], example["sentence2"], truncation=True)


tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

from transformers import TrainingArguments

training_args = TrainingArguments("test-trainer")

from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)

from transformers import Trainer

trainer = Trainer(
    model,
    training_args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["validation"],
    data_collator=data_collator,
    tokenizer=tokenizer,
)

trainer.train() # Attention, une epoch prend 12h !

predictions = trainer.predict(tokenized_datasets["validation"])
print(predictions.predictions.shape, predictions.label_ids.shape)

import numpy as np

preds = np.argmax(predictions.predictions, axis=-1)

from datasets import load_metric

metric = load_metric("glue", "mrpc")
metric.compute(predictions=preds, references=predictions.label_ids)

def compute_metrics(eval_preds):
    metric = load_metric("glue", "mrpc")
    logits, labels = eval_preds
    predictions = np.argmax(logits, axis=-1)
    return metric.compute(predictions=predictions, references=labels)

training_args = TrainingArguments("test-trainer", evaluation_strategy="epoch")
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)

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