4.6 KiB
Fine-Tuning Question Answering Model on Custom Dataset
This repository provides the code and instructions for fine-tuning a question answering (QA) model such as BERT, RoBERTa, or similar models on a custom dataset. The fine-tuning process aims to adapt the pre-trained QA model to effectively answer questions based on the specific context provided in your dataset.
Table of Contents
Introduction
Question answering models are designed to answer questions based on a given context. Fine-tuning pre-trained models like BERT, RoBERTa, or DistilBERT on a specific QA dataset can significantly improve their performance for targeted applications.
Prerequisites
Ensure you have the following installed:
- Python 3.8 or higher
- PyTorch 1.8.1 or higher
- Transformers library by Hugging Face
Setup
-
Clone this repository:
git clone https://github.com/yourusername/qa-finetune.git cd qa-finetune -
Install the required packages:
pip install -r requirements.txt
Dataset
Prepare your custom QA dataset in JSON format, following the SQuAD (Stanford Question Answering Dataset) format. Place the dataset in the data/ directory:
data/
train.json
val.json
Ensure your JSON files follow this structure:
{
"data": [
{
"title": "Dataset Title",
"paragraphs": [
{
"context": "Context paragraph where the answer is located.",
"qas": [
{
"question": "What is the question?",
"id": "unique-id-1",
"answers": [
{
"text": "Answer to the question",
"answer_start": 34
}
]
}
]
}
]
}
]
}
Fine-Tuning
To fine-tune the QA model on your dataset, execute the following command:
python fine_tune.py --train_file data/train.json --val_file data/val.json --model_name bert-base-uncased --output_dir models/qa-finetuned
Fine-Tuning Parameters
--train_file: Path to the training dataset file.--val_file: Path to the validation dataset file.--model_name: Name or path of the pre-trained model to use (e.g.,bert-base-uncased).--output_dir: Directory where the fine-tuned model will be saved.
Additional training parameters such as batch size, learning rate, and number of epochs can be customized in the fine_tune.py script.
Evaluation
After fine-tuning, evaluate the model to ensure it meets the desired performance metrics. Run the evaluation script as follows:
python evaluate.py --model_dir models/qa-finetuned --test_file data/val.json
This script will generate performance metrics relevant to QA tasks, such as exact match (EM) and F1 score.
Results
The results of the fine-tuning process, including training and evaluation metrics, will be saved in the results/ directory. Key metrics to consider are exact match (EM) and F1 score.
Usage
To use the fine-tuned QA model for inference, load it using the Transformers library:
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("models/qa-finetuned")
model = AutoModelForQuestionAnswering.from_pretrained("models/qa-finetuned")
context = "Context paragraph where the answer is located."
question = "What is the question?"
inputs = tokenizer.encode_plus(question, context, return_tensors="pt")
input_ids = inputs["input_ids"].tolist()[0]
outputs = model(**inputs)
answer_start = torch.argmax(outputs.start_logits)
answer_end = torch.argmax(outputs.end_logits) + 1
answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
print(f"Question: {question}")
print(f"Answer: {answer}")
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
License
Distributed under the MIT License. See LICENSE for more information.
Thank you for your interest in this project! If you have any questions or feedback, please open an issue or contact the repository maintainer.