Transformershuggingface/transformers
A deep learning library containing thousands of pre-trained models on different tasks. The goto place for anything related to Large Language Models.
144 libraries, 49 updated this year
Libraries for working with text. Hugging Face Transformers covers modern model-based NLP, spaCy is the production toolkit for tokenization, tagging, parsing and named entities, and NLTK remains the teaching standard with corpora and classic algorithms. gensim does topic modelling and word vectors, and SentencePiece and Hugging Face tokenizers split text into subwords for model training.
144 libraries
A deep learning library containing thousands of pre-trained models on different tasks. The goto place for anything related to Large Language Models.
A framework for building industrial-strength applications with Transformer models and LLMs.
Track, log, visualize and evaluate your LLM prompts and prompt chains.
Weaviate is an open source vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale.
Reference implementations of ML models written in numpy
A leading platform for building Python programs to work with human language data.
A C++ library for unsupervised text tokenization and detokenization, widely used in modern NLP models.
Fast State-of-the-Art Tokenizers optimized for Research and Production
AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.
Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words.
Providing a consistent API for diving into common natural language processing (NLP) tasks. Stands on the giant shoulders of NLTK and Pattern, and plays nicely with both.
A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
Conversational AI library with many pre-trained Russian NLP models.
An NLP library built in node over Natural, with entity extraction, sentiment analysis, automatic language identify, and so more.
Python-free Rust inference server for NLP models with OpenAI API compatibility and hot model swapping.
A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
Natural language processing library built on top of Apache Spark ML to provide simple, performant, and accurate NLP annotations for machine learning pipelines, that scale easily in a distributed environment.
Natural Language Understanding library for intent classification and entity extraction
ScalaNLP is a suite of machine learning and numerical computing libraries.
Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
C, C++, and Python tools for named entity recognition and relation extraction
A python library for doing approximate and phonetic matching of strings.
Self-contained Machine Learning and Natural Language Processing library in Go.
Toolbox of models, callbacks, and datasets for AI/ML researchers.
Named-entity recognition using neural networks providing state-of-the-art-results
The simplest way to run an LLM locally. Supports tool calling and grammar constrained sampling.
A machine learning based toolkit for the processing of natural language text.
Text Retrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby.
A python framework to transform natural language questions to queries in a database query language.
Machine Learning framework for rapid development of Machine Learning and Statistical applications.