410 Open source projects that are alternatives of or similar to keras-bert-ner

Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services

中文NER的那些事儿

🎨 🎨NLP 自然语言处理教程 🎨🎨 https://dataxujing.github.io/NLP-paper/

An easy-to-use named entity recognition (NER) toolkit, implemented the Bi-LSTM+CRF model in tensorflow.

Language Understanding Evaluation benchmark for Chinese: datasets, baselines, pre-trained models,corpus and leaderboard

pytorch实现bert做seq2seq任务，使用unilm方案,现在也可以做自动摘要，文本分类，情感分析，NER，词性标注等任务,支持GPT2进行文章续写。

reference tensorflow code for named entity tagging

Documents, papers and codes related to Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation)，etc. All codes are implemented intensorflow 2.0.

keras attentional bi-LSTM-CRF for Joint NLU (slot-filling and intent detection) with ATIS

从模型训练到部署，实战知识图谱(Knowledge Graph)&自然语言处理(NLP)。涉及 Tensorflow, Bert+Bi-LSTM+CRF,Neo4j等 涵盖 Named Entity Recognition,Text Classify,Information Extraction,Relation Extraction 等任务。

2019达观杯信息提取第5名代码

CLUE baseline pytorch CLUE的pytorch版本基线

A dockerized flask API, serving ALBERT and BERT predictions using TensorFlow 2.0.

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

中文语言理解测评基准 Chinese Language Understanding Evaluation Benchmark: datasets, baselines, pre-trained models, corpus and leaderboard

LSTM-CRF for NER with ConLL-2002 dataset

reference pytorch code for named entity tagging

命名体识别(NER)综述-论文-模型-代码(BiLSTM-CRF/BERT-CRF)-竞赛资源总结-随时更新

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.

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

🇷🇺 Punctuation restoration production-ready model for Russian language 🇷🇺

keras+bi-lstm+crf，中文命名实体识别

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텐서플로2와 머신러닝으로 시작하는 자연어처리 (로지스틱회귀부터 BERT와 GPT3까지) 실습자료

A Dataset of German Legal Documents for Named Entity Recognition

multi-label，classifier，text classification，多标签文本分类，文本分类，BERT，ALBERT，multi-label-classification，seq2seq，attention，beam search

KU_NERDY 이동엽, 임희석 (2017 국어 정보 처리 시스템경진대회 금상) - 한글 및 한국어 정보처리 학술대회

Weakly supervised medical named entity classification

A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS, 海量中文预训练ALBERT模型

Anonymization of legal cases (Fr) based on Flair embeddings

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.

State of the Art Natural Language Processing

自然语言处理工具Macropodus，基于Albert+BiLSTM+CRF深度学习网络架构，中文分词，词性标注，命名实体识别，新词发现，关键词，文本摘要，文本相似度，科学计算器，中文数字阿拉伯数字(罗马数字)转换，中文繁简转换，拼音转换。tookit(tool) of NLP，CWS(chinese word segnment)，POS(Part-Of-Speech Tagging)，NER(name entity recognition)，Find(new words discovery)，Keyword(keyword extraction)，Summarize(text summarization)，Sim(text similarity)，Calculate(scientific calculator)，Chi2num(chinese number to arabic number)

A Natural Language Inference (NLI) model based on Transformers (BERT and ALBERT)

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

An Inplementation of CRF (Conditional Random Fields) in PyTorch 1.0

Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)

datagrand 2019 information extraction competition rank9

Chinese & English Cws Pos Ner Entity Recognition implement using CNN bi-directional lstm and crf model with char embedding.基于字向量的CNN池化双向BiLSTM与CRF模型的网络，可能一体化的完成中文和英文分词，词性标注，实体识别。主要包括原始文本数据，数据转换,训练脚本,预训练模型,可用于序列标注研究.注意：唯一需要实现的逻辑是将用户数据转化为序列模型。分词准确率约为93%，词性标注准确率约为90%，实体标注（在本样本上）约为85%。

面向中文电子病历的命名实体识别

multi-label，classifier，text classification，多标签文本分类，文本分类，BERT，ALBERT，multi-label-classification

离线端阅读理解应用 QA for mobile, Android & iPhone

KoBERT와 CRF로 만든 한국어 개체명인식기 (BERT+CRF based Named Entity Recognition model for Korean)

Pytorch Implementation of ALBERT(A Lite BERT for Self-supervised Learning of Language Representations)

Named Entity Recognition (LSTM + CRF) - Tensorflow

Conversational Question Answering on Clinical Text

Implement Question Generator with SOTA pre-trained Language Models (RoBERTa, BERT, GPT, BART, T5, etc.)

This repo contains a PyTorch implementation of a BiLSTM-CRF model for named entity recognition task.

中文自然语言的实体抽取和意图识别（Natural Language Understanding），可选Bi-LSTM + CRF 或者 IDCNN + CRF

🤗 ParsBERT Persian NER Tasks

基于tensorflow1.x的预训练模型调用，支持单机多卡、梯度累积，XLA加速，混合精度。可灵活训练、验证、预测。

参考NER，基于BERT的电商评论观点挖掘和情感分析

本NER项目包含多个中文数据集，模型采用BiLSTM+CRF、BERT+Softmax、BERT+Cascade、BERT+WOL等，最后用TFServing进行模型部署，线上推理和线下推理。

Bangla-Bert is a pretrained bert model for Bengali language

Multi-class text categorization using state-of-the-art pre-trained contextualized language models, e.g. BERT

一套针对中文实体识别的BLSTM-CRF解决方案

Open-source benchmark datasets and pretrained transformer models in the Filipino language.

CRF, Partial CRF and Marginal CRF in PyTorch

机器翻译子任务-翻译质量评价-在BERT模型后面加上Bi-LSTM进行fine-tuning