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.

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

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.

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

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

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

Official implementation of the papers "GECToR – Grammatical Error Correction: Tag, Not Rewrite" (BEA-20) and "Text Simplification by Tagging" (BEA-21)

Empower Sequence Labeling with Task-Aware Language Model

The BiLSTM-CRF model implementation in Tensorflow, for sequence labeling tasks.

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

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

Learning Named Entity Tagger from Domain-Specific Dictionary

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.

LSTM-CRF in PyTorch

AdaSeq: An All-in-One Library for Developing State-of-the-Art Sequence Understanding Models

Deep neural models for core NLP tasks (Pytorch version)

a Deep Learning Framework for Text https://delft.readthedocs.io/

A Japanese tokenizer based on recurrent neural networks

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

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

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

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）。

中文文本分类、序列标注工具包（pytorch），支持中文长文本、短文本的多类、多标签分类任务，支持中文命名实体识别、词性标注、分词、抽取式文本摘要等序列标注任务。 Chinese text classification and sequence labeling toolkit, supports multi class and multi label classification, text similsrity, text summary and NER.

Macadam是一个以Tensorflow(Keras)和bert4keras为基础，专注于文本分类、序列标注和关系抽取的自然语言处理工具包。支持RANDOM、WORD2VEC、FASTTEXT、BERT、ALBERT、ROBERTA、NEZHA、XLNET、ELECTRA、GPT-2等EMBEDDING嵌入; 支持FineTune、FastText、TextCNN、CharCNN、BiRNN、RCNN、DCNN、CRNN、DeepMoji、SelfAttention、HAN、Capsule等文本分类算法; 支持CRF、Bi-LSTM-CRF、CNN-LSTM、DGCNN、Bi-LSTM-LAN、Lattice-LSTM-Batch、MRC等序列标注算法。

[ACL-IJCNLP 2021] Automated Concatenation of Embeddings for Structured Prediction

LSTM+CRF NER

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

Hierarchically-Refined Label Attention Network for Sequence Labeling