Can I use Haystack with pre-trained language models?

Yes, you can use Haystack with pre-trained language models. Haystack, an open-source framework developed by deepset, is designed to work seamlessly with transformer-based models, including popular pre-trained ones like BERT, RoBERTa, or GPT. The framework provides tools to integrate these models into pipelines for tasks like question answering, semantic search, or document retrieval. By leveraging pre-trained models, developers can build applications that understand natural language queries and extract relevant information without training models from scratch. For example, you can use a pre-trained BERT model to power a question-answering system that scans documents for answers, or employ a sentence-transformers model to encode text for semantic similarity comparisons.

Haystack’s architecture simplifies the process of incorporating pre-trained models. The framework includes components like Retrievers (for fetching documents) and Readers (for extracting answers), which can both utilize pre-trained models. For instance, you might pair a dense retriever like Dense Passage Retrieval (DPR) with a BERT-based reader to create an end-to-end question-answering system. The Retriever uses pre-trained encoders to map queries and documents into vector spaces, enabling efficient similarity searches. The Reader then processes the retrieved documents to find precise answers. Additionally, Haystack supports Hugging Face’s Transformers library, allowing developers to load thousands of pre-trained models directly. For example, you could use text-embedding-3-small from OpenAI for embeddings or all-mpnet-base-v2 from sentence-transformers for semantic search, all within Haystack’s pipelines.

Customization is straightforward. If a pre-trained model doesn’t fully meet your needs, you can fine-tune it on your specific dataset using Haystack’s training utilities. For example, you might fine-tune a RoBERTa model on a custom medical corpus to improve its performance in healthcare applications. Haystack also supports hybrid systems, where pre-trained models work alongside rules-based components like keyword search. This flexibility lets developers balance accuracy and speed—for instance, using a lightweight model for initial document filtering and a larger model for detailed analysis. By abstracting away complexities like pipeline orchestration and model deployment, Haystack lets developers focus on tailoring pre-trained models to their use cases, whether for enterprise search, customer support automation, or research tools.