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: this https URL
Abstract (translated)
基于Transformer的语言模型已经彻底革新了自然语言处理（NLP）领域。然而，使用这些模型通常涉及到在多个框架和工具之间切换，并且需要编写大量的样板代码。这种复杂性可能会让非编程人员和初学者望而却步，甚至会减慢经验丰富的开发者的原型设计过程。为了应对这些挑战，我们推出了Langformers——一个开源的Python库，旨在通过统一、工厂式的界面简化NLP流水线，该界面支持大型语言模型（LLM）和掩码语言模型（MLM）任务。 Langformers将对话AI、MLM预训练、文本分类、句子嵌入/重排序、数据标注、语义搜索以及知识蒸馏等整合到一个连贯的API中，并且它支持流行平台如Hugging Face和Ollama。关键创新包括： 1. 任务特定工厂：抽象了训练、推理和部署的复杂性。 2. 内置记忆与流处理功能，专为对话代理设计。 3. 轻量级模块化设计，优先考虑易用性。 文档：[此处插入具体URL链接]
URL
https://arxiv.org/abs/2504.09170
