Langformers: Unified NLP Pipelines for Language Models
Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera
TL;DR
Langformers tackles the fragmentation of NLP tooling by delivering a unified, factory-based API that covers LLM and MLM workflows from pretraining to deployment. The library combines generators, labellers, classifiers, embedders, and distillation into cohesive pipelines, with built-in memory for conversational AI and streaming capabilities. Its modular design and compatibility with Hugging Face and Ollama aim to accelerate prototyping and reduce boilerplate, enabling practical applications in chat, semantic search, and domain-specific modeling. This work presents a pragmatic bridge between cutting-edge NLP research and real-world deployment, with significant implications for privacy-preserving AI, scalable content generation, and efficient edge deployment.
Abstract
Transformer-based language models have revolutionized the field of natural language processing (NLP). However, using these models often involves navigating multiple frameworks and tools, as well as writing repetitive boilerplate code. This complexity can discourage non-programmers and beginners, and even slow down prototyping for experienced developers. To address these challenges, we introduce Langformers, an open-source Python library designed to streamline NLP pipelines through a unified, factory-based interface for large language model (LLM) and masked language model (MLM) tasks. Langformers integrates conversational AI, MLM pretraining, text classification, sentence embedding/reranking, data labelling, semantic search, and knowledge distillation into a cohesive API, supporting popular platforms such as Hugging Face and Ollama. Key innovations include: (1) task-specific factories that abstract training, inference, and deployment complexities; (2) built-in memory and streaming for conversational agents; and (3) lightweight, modular design that prioritizes ease of use. Documentation: https://langformers.com