A very simple framework for state-of-the-art Natural Language Processing (NLP)

Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.

NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

中文命名实体识别（包括多种模型：HMM，CRF，BiLSTM，BiLSTM+CRF的具体实现）

Empower Sequence Labeling with Task-Aware Language Model

The classical BiLSTM-CRF model implemented in Tensorflow, for sequence labeling tasks. In Vex version, everything is configurable.

CLUENER2020 中文细粒度命名实体识别 Fine Grained Named Entity Recognition

A TensorFlow implementation of Recurrent Neural Networks for Sequence Classification and Sequence Labeling

A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)

Deep neural models for core NLP tasks (Pytorch version)

word2vec, sentence2vec, machine reading comprehension, dialog system, text classification, pretrained language model (i.e., XLNet, BERT, ELMo, GPT), sequence labeling, information retrieval, information extraction (i.e., entity, relation and event extraction), knowledge graph, text generation, network embedding

序列化标注工具，基于PyTorch实现BLSTM-CNN-CRF模型，CoNLL 2003 English NER测试集F1值为91.10%（word and char feature）。

Learning Named Entity Tagger from Domain-Specific Dictionary

LSTM-CRF in PyTorch

Tensorflow implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling" (https://arxiv.org/abs/1609.01454)

ACL 2018: Hybrid semi-Markov CRF for Neural Sequence Labeling (http://aclweb.org/anthology/P18-2038)

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 versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.

slot filling, intent detection, joint training, ATIS & SNIPS datasets, the Facebook’s multilingual dataset, MIT corpus, E-commerce Shopping Assistant (ECSA) dataset, CoNLL2003 NER, ELMo, BERT, XLNet

a Deep Learning Framework for Text

LSTM+CRF NER

This is the template code to use BERT for sequence lableing and text classification, in order to facilitate BERT for more tasks. Currently, the template code has included conll-2003 named entity identification, Snips Slot Filling and Intent Prediction.

A Japanese tokenizer based on recurrent neural networks

Empower Sequence Labeling with Task-Aware Neural Language Model | a PyTorch Tutorial to Sequence Labeling

Hierarchically-Refined Label Attention Network for Sequence Labeling

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF implement in pyotrch

A TensorFlow implementation of Neural Sequence Labeling model, which is able to tackle sequence labeling tasks such as POS Tagging, Chunking, NER, Punctuation Restoration and etc.

multi_task_NLP is a utility toolkit enabling NLP developers to easily train and infer a single model for multiple tasks.

Official implementation of the paper “GECToR – Grammatical Error Correction: Tag, Not Rewrite” // Published on BEA15 Workshop (co-located with ACL 2020) https://www.aclweb.org/anthology/2020.bea-1.16.pdf