How do I fine-tune a Reader model in Haystack?

To fine-tune a Reader model in Haystack, you start by preparing your dataset and configuring the training pipeline. Haystack’s Reader component, typically based on transformer models like BERT or RoBERTa, is designed for extractive question answering. Fine-tuning involves adapting a pre-trained model to your specific domain using a labeled dataset. First, ensure your data is formatted correctly, usually in the SQuAD-style JSON format, which includes context passages, questions, and answer spans. For example, if you’re building a medical QA system, your dataset might contain doctor-patient dialogues as contexts and labeled answers for questions like “What was the diagnosis?”

Next, set up the training pipeline using Haystack’s FarmTrainer class, which leverages the FARM framework for training. Initialize your Reader with a base model (e.g., deepset/roberta-base-squad2) and define training arguments like learning rate, batch size, and epochs. Here’s a simplified code snippet:

from haystack.nodes import FARMReader
from haystack.modeling.trainer import FarmTrainer

reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
trainer = FarmTrainer(reader=reader, data_dir="data/squad", train_filename="train.json", dev_filename="dev.json")
trainer.train(
 learning_rate=3e-5,
 n_epochs=3,
 batch_size=16,
 checkpoint_every=1000,
)

This code loads your dataset from the specified directory, splits it into training and validation sets, and starts training. Adjust hyperparameters based on your dataset size and hardware. For instance, reduce the batch size if you encounter GPU memory issues.

After training, save the model and integrate it into your Haystack pipeline. Use reader.save(directory="my_finetuned_model") to persist the model weights. Then, load the fine-tuned Reader into a Haystack pipeline to test its performance:

pipeline = Pipeline()
pipeline.add_node(component=FARMReader(model_name_or_path="my_finetuned_model"), name="Reader", inputs=["Query"])

Validate the model using a test set or manual queries. For example, if your dataset included legal documents, test questions like “What is the penalty for X?” against new contexts. Fine-tuning improves the model’s ability to locate precise answers in your domain-specific texts, but monitor for overfitting by checking validation accuracy during training. If performance plateaus, consider adjusting the learning rate or adding more diverse training examples.