Popular GitHub Repositories for Flat Ner

CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.

Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

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

搜索所有中文NLP数据集，附常用英文NLP数据集

Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI

[EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction

Snips Python library to extract meaning from text

中文 NLP 预处理、解析工具包，准确、高效、易用 A Chinese NLP Preprocessing & Parsing Package www.jionlp.com

Jiagu深度学习自然语言处理工具 知识图谱关系抽取 中文分词 词性标注 命名实体识别 情感分析 新词发现 关键词 文本摘要 文本聚类

Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo

Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)

该仓库主要记录 NLP 算法工程师相关的面试题

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.

Utilizing Spacy and Tensorflow to train custom Named Entity Recognizers.

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

Named Entity Recognition (LSTM + CRF) - Tensorflow

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.

:house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.

Microsoft.Recognizers.Text provides recognition and resolution of numbers, units, date/time, etc. in multiple languages (ZH, EN, FR, ES, PT, DE, IT, TR, HI, NL. Partial support for JA, KO, AR, SV). Packages available at: https://www.nuget.org/profiles/Recognizers.Text, https://www.npmjs.com/~recognizers.text

自然语言处理、知识图谱、对话系统三大技术研究与应用。