How do you fine-tune a model using the Hugging Face Trainer API?

The Trainer class encapsulates the standard training loop — batching, gradient accumulation, mixed precision, evaluation, checkpointing, logging to TensorBoard/WandB — behind a clean API. Combined with TrainingArguments, it handles most production training concerns so you can focus on data preparation and model selection rather than boilerplate.

from transformers import (
    AutoTokenizer, AutoModelForSequenceClassification,
    TrainingArguments, Trainer, DataCollatorWithPadding
)
from datasets import load_dataset
import evaluate
import numpy as np

model_name = 'distilbert-base-uncased'
tokenizer  = AutoTokenizer.from_pretrained(model_name)
model      = AutoModelForSequenceClassification.from_pretrained(
    model_name, num_labels=2
)

# Tokenise IMDB dataset
ds = load_dataset('imdb')
def tokenize(batch):
    return tokenizer(batch['text'], truncation=True, max_length=512)
tokenized = ds.map(tokenize, batched=True, remove_columns=['text'])

# Metric
accuracy_metric = evaluate.load('accuracy')
def compute_metrics(eval_pred):
    logits, labels = eval_pred
    preds = np.argmax(logits, axis=-1)
    return accuracy_metric.compute(predictions=preds, references=labels)

# Training configuration
args = TrainingArguments(
    output_dir='./distilbert-imdb',
    num_train_epochs=3,
    per_device_train_batch_size=32,
    per_device_eval_batch_size=64,
    learning_rate=2e-5,
    weight_decay=0.01,
    evaluation_strategy='epoch',
    save_strategy='epoch',
    load_best_model_at_end=True,
    fp16=True,             # mixed precision
    logging_steps=50,
    report_to='none',      # or 'wandb' / 'tensorboard'
)

trainer = Trainer(
    model=model,
    args=args,
    train_dataset=tokenized['train'],
    eval_dataset=tokenized['test'],
    tokenizer=tokenizer,
    data_collator=DataCollatorWithPadding(tokenizer),  # dynamic padding
    compute_metrics=compute_metrics,
)

trainer.train()
trainer.save_model('./final-model')