Open Source Python Natural Language Processing (NLP) Tools - Page 5
Sort By:
Python Natural Language Processing (NLP) Tools
View 188 business solutionsBrowse free open source Python Natural Language Processing (NLP) Tools and projects below. Use the toggles on the left to filter open source Python Natural Language Processing (NLP) Tools by OS, license, language, programming language, and project status.
-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
Forever Free Full-Stack Observability | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
-
1
TEES
Turku Event Extraction System
Turku Event Extraction System (TEES) is a free and open source natural language processing system developed for the extraction of events and relations from biomedical text. It is written mostly in Python, and should work in generic Unix/Linux environments. Currently, the TEES source code repository still remains on GitHub at http://jbjorne.github.com/TEES/ where there is also a wiki with more information. -
2
Texar
Toolkit for Machine Learning, Natural Language Processing
Texar is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks. Texar provides a library of easy-to-use ML modules and functionalities for composing whatever models and algorithms. The tool is designed for both researchers and practitioners for fast prototyping and experimentation. Texar was originally developed and is actively contributed by Petuum and CMU in collaboration with other institutes. A mirror of this repository is maintained by Petuum Open Source. Two Versions, (Mostly) Same Interfaces. Texar-TensorFlow (this repo) and Texar-PyTorch have mostly the same interfaces. Both further combine the best design of TF and PyTorch. Rich Pre-trained Models, Rich Usage with Uniform Interfaces. BERT, GPT2, XLNet, etc, for encoding, classification, generation, and composing complex models with other Texar components! -
3
Text Generation Inference
Large Language Model Text Generation Inference
Text Generation Inference is a high-performance inference server for text generation models, optimized for Hugging Face's Transformers. It is designed to serve large language models efficiently with optimizations for performance and scalability. -
4
TextBlob
TextBlob is a Python library for processing textual data
Simple, Pythonic, text processing, Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. TextBlob stands on the giant shoulders of NLTK and pattern, and plays nicely with both. Supports word inflection (pluralization and singularization) and lemmatization, as well as spelling correction. Add new models or languages through extensions. Also, it comes with a WordNet integration. If you only intend to use TextBlob’s default models (no model overrides), you can pass the lite argument. This downloads only those corpora needed for basic functionality. TextBlob is also available as a conda package. -
Try Google Cloud Risk-Free With $300 in CreditUse your credit across every product. Compute, storage, AI, analytics. When it runs out, 20+ products stay free. You only pay when you choose to.
-
5
TextBrewer
A PyTorch-based knowledge distillation toolkit
TextBrewer is a PyTorch-based model distillation toolkit for natural language processing. It includes various distillation techniques from both NLP and CV field and provides an easy-to-use distillation framework, which allows users to quickly experiment with the state-of-the-art distillation methods to compress the model with a relatively small sacrifice in the performance, increasing the inference speed and reducing the memory usage. -
6
TextRank
TextRank implementation for Python 3
TextRank is an implementation of the TextRank algorithm for extractive text summarization and keyword extraction, inspired by Google’s PageRank. -
7
TorchDistill
A coding-free framework built on PyTorch
torchdistill (formerly kdkit) offers various state-of-the-art knowledge distillation methods and enables you to design (new) experiments simply by editing a declarative yaml config file instead of Python code. Even when you need to extract intermediate representations in teacher/student models, you will NOT need to reimplement the models, which often change the interface of the forward, but instead specify the module path(s) in the yaml file. In addition to knowledge distillation, this framework helps you design and perform general deep learning experiments (WITHOUT coding) for reproducible deep learning studies. i.e., it enables you to train models without teachers simply by excluding teacher entries from a declarative yaml config file. -
8
Transformers-Interpret
Model explainability that works seamlessly with Hugging Face
Transformers-Interpret is an interpretability tool for Transformer-based NLP models, providing insights into attention mechanisms and feature importance. -
9
Transformers4Rec
Transformers4Rec is a flexible and efficient library
Transformers4Rec is an advanced recommendation system library that leverages Transformer models for sequential and session-based recommendations. The library works as a bridge between natural language processing (NLP) and recommender systems (RecSys) by integrating with one of the most popular NLP frameworks, Hugging Face Transformers (HF). Transformers4Rec makes state-of-the-art transformer architectures available for RecSys researchers and industry practitioners. Traditional recommendation algorithms usually ignore the temporal dynamics and the sequence of interactions when trying to model user behavior. Generally, the next user interaction is related to the sequence of the user's previous choices. In some cases, it might be a repeated purchase or song play. User interests can also suffer from interest drift because preferences can change over time. Those challenges are addressed by the sequential recommendation task. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
-
10
UniEM
Unified embedding model
UniEM is a unified embedding model designed to create high-quality text embeddings for various natural language processing tasks. -
11
WikiChat
WikiChat is an improved RAG
WikiChat is a chatbot framework designed to interactively retrieve and summarize Wikipedia information, allowing users to ask questions and get context-aware responses? -
12
XLM (Cross-lingual Language Model)
PyTorch original implementation of Cross-lingual Language Model
XLM (Cross-lingual Language Model) is a family of multilingual pretraining methods that align representations across languages to enable strong zero-shot transfer. It popularized objectives like Masked Language Modeling (MLM) across many languages and Translation Language Modeling (TLM) that jointly trains on parallel sentence pairs to tighten cross-lingual alignment. Using a shared subword vocabulary, XLM learns language-agnostic features that work well for classification and sequence labeling tasks such as XNLI, NER, and POS without target-language supervision. The repository provides preprocessing pipelines, training code, and fine-tuning scripts so you can reproduce benchmark results or adapt models to your own multilingual corpora. Pretrained checkpoints cover dozens of languages and multiple model sizes, balancing quality and compute needs. -
13
aseryla
Aseryla code repositories
This project describes a model of how the semantic human memory represents the information relevant to the objects of the world in text format. It provides a system and a GUI application capable of extracting and managing concepts and relations from English texts. https://aseryla2.sourceforge.io/ -
14
cocoNLP
A Chinese information extraction tool
cocoNLP is a lightweight natural-language processing toolkit geared toward practical information extraction from raw text, especially for Chinese and mixed Chinese–English content. Instead of requiring a heavy pipeline, it focuses on quick wins such as extracting names, places, organizations, emails, phone numbers, and dates directly from unstructured sentences. The project blends pattern-based methods with NLP heuristics, giving developers dependable results for real-world texts like chats, comments, and user-generated content. Its API is intentionally simple, so you can drop it into scripts, ETL jobs, or dashboards without deep ML expertise. Because it aims at utility over complexity, it’s useful for prototyping data products or building lightweight text analytics where large models would be overkill. The repository also includes examples and test snippets to help you understand expected inputs and typical outputs, which shortens the learning curve for newcomers. -
15
deepdoctection
A Repo For Document AI
DeepDoctection is a document AI framework that applies deep learning techniques to analyze and extract structured data from scanned documents, PDFs, and images. deepdoctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated frameworks for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. -
16
fastNLP
fastNLP: A Modularized and Extensible NLP Framework
fastNLP is a lightweight framework for natural language processing (NLP), the goal is to quickly implement NLP tasks and build complex models. A unified Tabular data container simplifies the data preprocessing process. Built-in Loader and Pipe for multiple datasets, eliminating the need for preprocessing code. Various convenient NLP tools, such as Embedding loading (including ELMo and BERT), intermediate data cache, etc.. Provide a variety of neural network components and recurrence models (covering tasks such as Chinese word segmentation, named entity recognition, syntactic analysis, text classification, text matching, metaphor resolution, summarization, etc.). Trainer provides a variety of built-in Callback functions to facilitate experiment recording, exception capture, etc. Automatic download of some datasets and pre-trained models. -
17
find-similar
User-friendly library to find similar objects
The mission of the FindSimilar project is to provide a powerful and versatile open source library that empowers developers to efficiently find similar objects and perform comparisons across a variety of data types. Whether dealing with texts, images, audio, or more, our project aims to simplify the process of identifying similarities and enhancing decision-making. https://github.com/findsimilar/find-similar - GitHub repo http://demo.findsimilar.org/ - Demo project and tutorial https://docs.findsimilar.org/ - Documentation -
18
funNLP
Resources, corpora, and tools for Chinese natural language processing
FunNLP is a large, curated collection of resources, corpora, and tools for Chinese natural language processing (NLP). It aggregates datasets, lexicons, wordlists, sentiment dictionaries, knowledge graphs, and pretrained model references, serving as a one-stop resource hub for Chinese NLP practitioners. The repository is organized into categories such as sentiment analysis, text classification, named entity recognition, knowledge graphs, and various lexicons (e.g. sensitive words, emotion dictionaries, stopwords). It also includes links to academic papers, open-source model implementations, and practical utilities like word segmentation or text cleaning scripts. The project is highly community-oriented, frequently updated with contributions and new resources, and it’s widely used in both academic and applied NLP research. Its value lies in providing not just tools but also curated, domain-specific data, which can be hard to find elsewhere. -
19
jiant
jiant is an nlp toolkit
Jiant is a multitask NLP framework for fine-tuning transformer-based models on multiple natural language understanding (NLU) tasks. -
20
lazynlp
Library to scrape and clean web pages to create massive datasets
LazyNLP is a lightweight tool for collecting and curating large-scale text datasets for machine learning and NLP applications with minimal manual effort. -
21
textacy
NLP, before and after spaCy
textacy is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. With the fundamentals, tokenization, part-of-speech tagging, dependency parsing, etc., delegated to another library, textacy focuses primarily on the tasks that come before and follow after. -
22
tnets
Theano-based (Deep) Neural Networks for Speech Research
Alpha tool build on the top of Theano and delibaretly tuned for ASR/NLP tasks. -
23
torchtext
Data loaders and abstractions for text and NLP
We recommend Anaconda as a Python package management system. Please refer to pytorch.org for the details of PyTorch installation. LTS versions are distributed through a different channel than the other versioned releases. Alternatively, you might want to use the Moses tokenizer port in SacreMoses (split from NLTK). You have to install SacreMoses. To build torchtext from source, you need git, CMake and C++11 compiler such as g++. When building from source, make sure that you have the same C++ compiler as the one used to build PyTorch. A simple way is to build PyTorch from source and use the same environment to build torchtext. If you are using the nightly build of PyTorch, check out the environment it was built with conda (here) and pip (here). Text classification: SST2, AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, YelpReviewFull, YahooAnswers, AmazonReviewPolarity, AmazonReviewFull, IMDB, etc. -
24
txtai
Build AI-powered semantic search applications
txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Traditional search systems use keywords to find data. Semantic search applications have an understanding of natural language and identify results that have the same meaning, not necessarily the same keywords. Backed by state-of-the-art machine learning models, data is transformed into vector representations for search (also known as embeddings). Innovation is happening at a rapid pace, models can understand concepts in documents, audio, images and more. Machine-learning pipelines to run extractive question-answering, zero-shot labeling, transcription, translation, summarization and text extraction. Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes). Applications range from similarity search to complex NLP-driven data extractions to generate structured databases. The following applications are powered by txtai. -
25
webXcreta users natural language processing to create grammatical averages of textual communication and then generate original content based on these statistics.
