fine_tuning.py

from datasets import load_dataset
from transformers import BartTokenizer, BartForSequenceClassification, Trainer, TrainingArguments

import pandas as pd
from datasets import load_dataset, DatasetDict

dataset = load_dataset("csv", data_files="/home/aziz/fine_tuning/FAQ_Appliance_Store_FR.csv")

split_dataset = dataset["train"].train_test_split(test_size=0.2)

dataset = DatasetDict({
    "train": split_dataset["train"],
    "test": split_dataset["test"]
})


# Load pretrained model and tokenizer
model = BartForSequenceClassification.from_pretrained("facebook/bart-large-mnli")
tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-mnli")

# Tokenize the dataset
def preprocess_function(examples):
    return tokenizer(examples['question'], examples['answer'], truncation=True, padding="max_length")

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

# Define training arguments
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    save_strategy="epoch",
    per_device_train_batch_size=8,
    num_train_epochs=3,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["test"],
)

trainer.train()
model.save_pretrained("./my_model")
tokenizer.save_pretrained("./my_model")