NCRFpp - NCRF++, an Open-source Neural Sequence Labeling Toolkit

•    Python

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Sequence labeling models are quite popular in many NLP tasks, such as Named Entity Recognition (NER), part-of-speech (POS) tagging and word segmentation. State-of-the-art sequence labeling models mostly utilize the CRF structure with input word features. LSTM (or bidirectional LSTM) is a popular deep learning based feature extractor in sequence labeling task. And CNN can also be used due to faster computation. Besides, features within word are also useful to represent word, which can be captured by character LSTM or character CNN structure or human-defined neural features. NCRF++ is a PyTorch based framework with flexiable choices of input features and output structures. The design of neural sequence labeling models with NCRF++ is fully configurable through a configuration file, which does not require any code work. NCRF++ is a neural version of CRF++, which is a famous statistical CRF framework.

pytorch ner sequence-labeling crf lstm-crf char-rnn char-cnn named-entity-recognition part-of-speech-tagger chunking neural-networks nbest lstm cnn batch

universal-data-tool - Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app

•    Javascript

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Try it out at udt.dev, download the desktop app or run on-premise. The Universal Data Tool is a web/desktop app for editing and annotating images, text, audio, documents and to view and edit any data defined in the extensible .udt.json and .udt.csv standard.

machine-learning csv computer-vision deep-learning image-annotation desktop dataset named-entity-recognition classification labeling image-segmentation hacktoberfest semantic-segmentation annotation-tool text-annotation labeling-tool entity-recognition annotate-images image-labeling-tool text-labeling

doccano - Open source annotation tool for machine learning practitioners.

•    Python

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doccano is an open source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequence to sequence tasks. So, you can create labeled data for sentiment analysis, named entity recognition, text summarization and so on. Just create a project, upload data and start annotating. You can build a dataset in hours. You can try the annotation demo.

machine-learning natural-language-processing vuejs vue nuxt dataset datasets nuxtjs annotation-tool text-annotation data-labeling

nlpnet - A neural network architecture for NLP tasks, inspired in the SENNA system

•    Python

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Gitter is chat room for developers. nlpnet is a Python library for Natural Language Processing tasks based on neural networks. Currently, it performs part-of-speech tagging, semantic role labeling and dependency parsing. Most of the architecture is language independent, but some functions were specially tailored for working with Portuguese. This system was inspired by SENNA.

nlp natural-language-processing neural-network pos-tagging semantic-role-labeling parsing

RNNSharp - RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on

•    CSharp

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RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above version. This page introduces what is RNNSharp, how it works and how to use it. To get the demo package, you can access release page.

rnn crf deep-learning machine-learning c-sharp sequence-labeling rnn-model recurrent-neural-networks nlp lstm

lac - Baidu's open-source lexical analysis tool for Chinese, including word segmentation, part-of-speech tagging & named entity recognition

•    C++

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Baidu's open-source lexical analysis tool for Chinese, including word segmentation, part-of-speech tagging & named entity recognition.

lexical-analysis word-segmentation part-of-speech-tagger named-entity-recognition chinese-word-segmentation chinese-nlp

Stanza - A Python NLP Library for Many Human Languages

•    Python

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Stanza is a Python NLP Library for Many Human Languages. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python. A new collection of biomedical and clinical English model packages are now available, offering seamless experience for syntactic analysis and named entity recognition (NER) from biomedical literature text and clinical notes.

nlp machine-learning natural-language-processing deep-learning pytorch artificial-intelligence named-entity-recognition universal-dependencies corenlp

deepnl - Deep Learning for Natural Language Processing

•    Python

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deepnl is a Python library for Natural Language Processing tasks based on a Deep Learning neural network architecture. The library currently provides tools for performing part-of-speech tagging, Named Entity tagging and Semantic Role Labeling.

spacy-streamlit - 👑 spaCy building blocks and visualizers for Streamlit apps

•    Python

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This package contains utilities for visualizing spaCy models and building interactive spaCy-powered apps with Streamlit. It includes various building blocks you can use in your own Streamlit app, like visualizers for syntactic dependencies, named entities, text classification, semantic similarity via word vectors, token attributes, and more. The package includes building blocks that call into Streamlit and set up all the required elements for you. You can either use the individual components directly and combine them with other elements in your app, or call the visualize function to embed the whole visualizer.

nlp machine-learning natural-language-processing text-classification spacy visualizer named-entity-recognition ner dependency-parsing tokenization word-vectors visualizers streamlit part-of-speech-tagging

semantic-segmentation-editor - Web labeling tool for bitmap images and point clouds

•    Javascript

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A web based labeling tool for creating AI training data sets (2D and 3D). The tool has been developed in the context of autonomous driving research. It supports images (.jpg or .png) and point clouds (.pcd). It is a Meteor app developed with React, Paper.js and three.js. (Optional) You can modify settings.json to customize classes data.

machine-learning semantic-segmentation manual-annotations pointcloud pcd image-labeling labeling-tool image-labeling-tool labeling ai machine_learning

Labelbox - The most versatile data labeling platform for training expert AI.

•    TypeScript

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Labelbox is a data labeling tool that's purpose built for machine learning applications. Start labeling data in minutes using pre-made labeling interfaces, or create your own pluggable interface to suit the needs of your data labeling task. Labelbox is lightweight for single users or small teams and scales up to support large teams and massive data sets. Simple image labeling: Labelbox makes it quick and easy to do basic image classification or segmentation tasks. To get started, simply upload your data or a CSV file containing URLs pointing to your data hosted on a server, select a labeling interface, (optional) invite collaborators and start labeling.

image-classification image-segmentation computer-vision tensorflow labeling annotations deep-learning recognition tools image-annotation

PyTorch-NLP - Supporting Rapid Prototyping with a Toolkit (incl. Datasets and Neural Network Layers)

•    Python

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PyTorch-NLP, or torchnlp for short, is a library of neural network layers, text processing modules and datasets designed to accelerate Natural Language Processing (NLP) research. Join our community, add datasets and neural network layers! Chat with us on Gitter and join the Google Group, we're eager to collaborate with you.

pytorch nlp natural-language-processing pytorch-nlp torchnlp data-loader embeddings word-vectors deep-learning dataset metrics neural-network sru machine-learning

decaNLP - The Natural Language Decathlon: A Multitask Challenge for NLP

•    Python

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The Natural Language Decathlon is a multitask challenge that spans ten tasks: question answering (SQuAD), machine translation (IWSLT), summarization (CNN/DM), natural language inference (MNLI), sentiment analysis (SST), semantic role labeling(QA‑SRL), zero-shot relation extraction (QA‑ZRE), goal-oriented dialogue (WOZ, semantic parsing (WikiSQL), and commonsense reasoning (MWSC). Each task is cast as question answering, which makes it possible to use our new Multitask Question Answering Network (MQAN). This model jointly learns all tasks in decaNLP without any task-specific modules or parameters in the multitask setting. For a more thorough introduction to decaNLP and the tasks, see the main website, our blog post, or the paper. While the research direction associated with this repository focused on multitask learning, the framework itself is designed in a way that should make single-task training, transfer learning, and zero-shot evaluation simple. Similarly, the paper focused on multitask learning as a form of question answering, but this framework can be easily adapted for different approached to single-task or multitask learning.

deep-learning natural-language-processing multitask-learning

Information-Extraction-Chinese - Chinese Named Entity Recognition with IDCNN/biLSTM+CRF, and Relation Extraction with biGRU+2ATT 中文实体识别与关系提取

•    Python

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Chinese information extraction, including named entity recognition, relation extraction and more, focused on state-of-art deep learning methods.

nlp chinese-nlp information-extraction relation-extraction named-entity-recognition

deeplab2 - DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks

•    Python

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DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks, including, but not limited to semantic segmentation, instance segmentation, panoptic segmentation, depth estimation, or even video panoptic segmentation. Deep labeling refers to solving computer vision problems by assigning a predicted value for each pixel in an image with a deep neural network. As long as the problem of interest could be formulated in this way, DeepLab2 should serve the purpose. Additionally, this codebase includes our recent and state-of-the-art research models on deep labeling. We hope you will find it useful for your projects.

Awesome-Chinese-NLP - A curated list of resources for Chinese NLP 中文自然语言处理相关资料

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BaiduLac by 百度 Baidu's open-source lexical analysis tool for Chinese, including word segmentation, part-of-speech tagging & named entity recognition.

nlp chinese-nlp

CoreNLP - Stanford CoreNLP: A Java suite of core NLP tools.

•    Java

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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, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, and indicate which noun phrases refer to the same entities. It provides the foundational building blocks for higher level text understanding applications.

natural-language-processing nlp nlp-parsing named-entity-recognition stanford-nlp

RUBRIX - Python framework to explore, label, and monitor data for NLP

•    Python

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Rubrix is a production-ready Python framework for exploring, annotating, and managing data in NLP projects. Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. Once a model goes into production, you want to monitor and analyze its predictions, and collect more data to improve your model over time. Rubrix is designed to close this gap, enabling you to iterate as much as you need.

nlp elasticsearch data-science machine-learning natural-language-processing pytorch artificial-intelligence weak-supervision knowledge-graph developer-tools active-learning annotation-tool weakly-supervised-learning human-in-the-loop mlops text-labeling

spaCy - 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython

•    Python

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spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pre-trained statistical models and word vectors, and currently supports tokenization for 20+ languages. It features the fastest syntactic parser in the world, convolutional neural network models for tagging, parsing and named entity recognition and easy deep learning integration. It's commercial open-source software, released under the MIT license. 💫 Version 2.0 out now! Check out the new features here.

natural-language-processing data-science big-data machine-learning cython nlp artificial-intelligence ai spacy nlp-library neural-network neural-networks deep-learning

treat - Natural language processing framework for Ruby.

•    Ruby

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Treat is a toolkit for natural language processing and computational linguistics in Ruby. The Treat project aims to build a language- and algorithm- agnostic NLP framework for Ruby with support for tasks such as document retrieval, text chunking, segmentation and tokenization, natural language parsing, part-of-speech tagging, keyword extraction and named entity recognition. Learn more by taking a quick tour or by reading the manual. I am actively seeking developers that can help maintain and expand this project. You can find a list of ideas for contributing to the project here.