hugging-face/fine_tune_bert.py

# Convert Reddit data into a format that can be used by the BERT model

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_recall_fscore_support, accuracy_score
import torch
from transformers import (
    DistilBertTokenizerFast,
    DistilBertForSequenceClassification,
    Trainer,
    TrainingArguments,
)

# train test split
def read_reddit_split(reddit_csv):
    df = pd.read_csv(reddit_csv)
    texts = df["body"].tolist()
    labels = df["Class"].tolist()
    # 80% train, 10% test, 10% valid
    train_texts, test_texts, train_labels, test_labels = train_test_split(
        texts, labels, test_size=0.2, stratify=labels
    )
    train_texts, val_texts, train_labels, val_labels = train_test_split(
        train_texts, train_labels, test_size=1.0 / 8.0, stratify=train_labels
    )
    print(f"size of train data is {len(train_texts)}")
    print(f"size of test data is {len(test_texts)}")
    print(f"size of valid data is {len(val_texts)}")
    return train_texts, test_texts, val_texts, train_labels, test_labels, val_labels


# tokenize data
def tokenize_data(train_texts, test_texts, val_texts, tokenizer):
    train_enc = tokenizer(train_texts, truncation=True, padding=True)
    test_enc = tokenizer(test_texts, truncation=True, padding=True)
    valid_enc = tokenizer(val_texts, truncation=True, padding=True)
    return train_enc, test_enc, valid_enc


# convert to Dataset object
class RedditDataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels

    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item["labels"] = torch.tensor(self.labels[idx])
        return item

    def __len__(self):
        return len(self.labels)


def compute_metrics(pred):
    labels = pred.label_ids
    preds = pred.predictions.argmax(-1)
    precision, recall, f1, _ = precision_recall_fscore_support(
        labels, preds, average="binary"
    )
    acc = accuracy_score(labels, preds)
    return {"accuracy": acc, "f1": f1, "precision": precision, "recall": recall}


# run on main
if __name__ == "__main__":
    # tokenizer
    bert_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
    # model
    model = DistilBertForSequenceClassification.from_pretrained(
        "distilbert-base-uncased"
    )
    # read data
    x_train, x_test, x_valid, y_train, y_test, y_valid = read_reddit_split(
        "/workspaces/Michelle_Li_NLP_Project/reddit_data/reddit_annotated.csv"
    )

    # tokenize data
    train_encodings, test_encodings, valid_encodings = tokenize_data(
        x_train, x_test, x_valid, bert_tokenizer
    )

    train_dataset = RedditDataset(train_encodings, y_train)
    test_dataset = RedditDataset(test_encodings, y_test)
    val_dataset = RedditDataset(valid_encodings, y_valid)

    # fine-tune BERT model
    training_args = TrainingArguments(
        output_dir="./results",  # output directory
        num_train_epochs=3,  # total number of training epochs
        per_device_train_batch_size=16,  # batch size per device during training
        per_device_eval_batch_size=64,  # batch size for evaluation
        warmup_steps=500,  # number of warmup steps for learning rate scheduler
        weight_decay=0.01,  # strength of weight decay
        logging_dir="./logs",  # directory for storing logs
        logging_steps=10,
    )

    trainer = Trainer(
        model=model,  # the instantiated 🤗 Transformers model to be trained
        args=training_args,  # training arguments, defined above
        train_dataset=train_dataset,  # training dataset
        eval_dataset=val_dataset,  # evaluation dataset
        compute_metrics=compute_metrics,  # compute metrics
    )

    trainer.train()

    # test model
    trainer.evaluate(test_dataset)

    # save model
    trainer.save_model("./models")
    # save tokenizer
    bert_tokenizer.save_pretrained("./models")