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How to fine-tune on custom dataset Hugging Face

Q: How to fine-tune on custom dataset Hugging Face

Use the transformers and datasets libraries from Hugging Face to load your custom dataset, preprocess it, and fine-tune a pretrained model with Trainer. Prepare your dataset as a Dataset object, tokenize it, then call Trainer.train() to fine-tune on your data.

Quick answer

Use the transformers and datasets libraries from Hugging Face to load your custom dataset, preprocess it, and fine-tune a pretrained model with Trainer. Prepare your dataset as a Dataset object, tokenize it, then call Trainer.train() to fine-tune on your data.

PREREQUISITES

Python 3.8+
pip install transformers datasets
Basic knowledge of Python and PyTorch or TensorFlow

Setup

Install the required libraries and import necessary modules for fine-tuning.

bash

pip install transformers datasets

Step by step fine-tuning

This example shows how to fine-tune a Hugging Face bert-base-uncased model on a custom text classification dataset using the Trainer API.

python

import os
from datasets import Dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments

# Example custom dataset
raw_data = {
    'text': ["I love AI", "I hate bugs", "Python is great", "Debugging is hard"],
    'label': [1, 0, 1, 0]
}

# Convert to Hugging Face Dataset
dataset = Dataset.from_dict(raw_data)

# Load tokenizer and model
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)

# Tokenize function
def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True)

# Tokenize dataset
encoded_dataset = dataset.map(tokenize_function, batched=True)

# Set format for PyTorch
encoded_dataset.set_format(type="torch", columns=["input_ids", "attention_mask", "label"])

# Training arguments
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=2,
    num_train_epochs=3,
    weight_decay=0.01,
    save_total_limit=1
)

# Initialize Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=encoded_dataset,
    eval_dataset=encoded_dataset
)

# Train model
trainer.train()

output

***** Running training *****
  Num examples = 4
  Num Epochs = 3
  Instantaneous batch size per device = 2
  Total train batch size = 2
  Gradient Accumulation steps = 1
  Total optimization steps = 6

[...training logs...]
Training completed. Model saved to ./results

Common variations

Use Trainer with a validation split for better evaluation.
Fine-tune other models like roberta-base or distilbert-base-uncased.
Use mixed precision training with fp16=True in TrainingArguments for faster training on GPUs.
For large datasets, load from CSV or JSON files using datasets.load_dataset.

Troubleshooting

If you get tokenization errors, verify your dataset text fields are strings and not empty.
Out of memory errors? Reduce batch size or enable gradient checkpointing.
Model not improving? Check learning rate and dataset quality.
Ensure transformers and datasets libraries are up to date.

✅

Key Takeaways

Use Hugging Face datasets to load and preprocess your custom data efficiently.
Tokenize your dataset with the model's tokenizer before training.
Use Trainer and TrainingArguments for streamlined fine-tuning.
Adjust batch size, learning rate, and epochs based on your dataset size and hardware.
Troubleshoot common issues by checking data types, memory limits, and library versions.

Verified 2026-04 · bert-base-uncased, roberta-base, distilbert-base-uncased

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