Top 53 sequence-labeling open source projects

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

Hierarchically-Refined Label Attention Network for Sequence Labeling

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

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.

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.

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。

Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling

Helsinki Prosody Corpus and A System for Predicting Prosodic Prominence from Text

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.

A (CNN+)RNN(LSTM/BiLSTM)+CRF model for sequence labelling.😏

Aspect extraction from product reviews - window-CNN+maxpool+CRF, BiLSTM+CRF, MLP+CRF

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

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

Inference with state-of-the-art models (pre-trained by LD-Net / AutoNER / VanillaNER / ...)

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

reference pytorch code for named entity tagging

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

Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings

Empower Sequence Labeling with Task-Aware Language Model

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

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

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

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

Deep neural models for core NLP tasks (Pytorch version)

LSTM-CRF in PyTorch

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

Learning Named Entity Tagger from Domain-Specific Dictionary

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）。

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

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 Deep Learning Framework for Text

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

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

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

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.

A Japanese tokenizer based on recurrent neural networks

LSTM+CRF NER

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

A neural network layer that enables training of deep neural networks directly from crowdsourced labels (e.g. from Amazon Mechanical Turk) or, more generally, labels from multiple annotators with different biases and levels of expertise.

Fast + Non-Autoregressive Grammatical Error Correction using BERT. Code and Pre-trained models for paper "Parallel Iterative Edit Models for Local Sequence Transduction": www.aclweb.org/anthology/D19-1435.pdf (EMNLP-IJCNLP 2019)

a Fairseq fork for sequence tagging/labeling tasks

AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging (ACL 2019 Demo)

自然语言处理常见任务（主要包括文本分类，序列标注，自动问答等）解决方案试验田

Pytorch-NLU，一个中文文本分类、序列标注工具包，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、分词等序列标注任务。 Ptorch NLU, a Chinese text classification and sequence annotation toolkit, supports multi class and multi label classification tasks of Chinese long text and short text, and supports sequence annotation tasks such as Chinese named entity recognition, part of speech ta…

Benchmark Arabic text diacritization dataset

BERT-BiLSTM-CRF的Keras版实现

Chinese word segmentation in tensorflow 2.x

CrossNER: Evaluating Cross-Domain Named Entity Recognition (AAAI-2021)

Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning (EMNLP'19)

Sequence Labeling in Tensorflow

A HMM-like linear-chain CRF, used Tensorflow API. 🐣

WRENCH: Weak supeRvision bENCHmark

🌈 Implementation of Neural Network based Named Entity Recognizer (Lample+, 2016) using Chainer.