suolyer

suolyer's Stars

• huggingface/transformers

  🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

  Language:Python137k1.1k16.4k27.4k

• google-research/bert

  TensorFlow code and pre-trained models for BERT

  Language:Python38.4k1k1.1k9.6k

• bojone/bert4keras

  keras implement of transformers for humans

  Language:Python5.4k72490927

• macanv/BERT-BiLSTM-CRF-NER

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

  Language:Python4.7k884311.3k

• ownthink/Jiagu

  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

  Language:Python3.3k8771614

• ymcui/Chinese-XLNet

  Pre-Trained Chinese XLNet（中文XLNet预训练模型）

  Language:Python1.7k3269280

• yuanxiaosc/Entity-Relation-Extraction

  Entity and Relation Extraction Based on TensorFlow and BERT. 基于TensorFlow和BERT的管道式实体及关系抽取，2019语言与智能技术竞赛信息抽取任务解决方案。Schema based Knowledge Extraction, SKE 2019

  Language:Python1.2k2579273

• BDBC-KG-NLP/IE-Survey

  北京航空航天大学大数据高精尖中心自然语言处理研究团队对信息抽取领域的调研。包括实体识别，关系抽取，属性抽取等子任务，每类子任务分别对学术界和工业界进行调研。

  46413269

• xuanzebi/BERT-CH-NER

  基于BERT的中文命名实体识别

  Language:Python3931013101

• Wangpeiyi9979/IE-Bert-CNN

  一个关于百度2019语言与智能技术竞赛信息抽取 (http://lic2019.ccf.org.cn/kg) 模型, 模型采用BERT+CNN。DEMO地址 https://github.com/Wangpeiyi9979/InformationExtractionDemo

  Language:Python18751740

• lvjianxin/Relationship-extraction

  中文关系抽取

  Language:Python1368836

• bowang-lab/joint-ner-and-re

  This repository contains the corpora and supplementary data, along with instructions for recreating the experiments, for our paper: "End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models".

  Language:Jupyter Notebook9111514

• wgwang/ccks2020-baseline

  CCKS 2020: 基于本体的金融知识图谱自动化构建技术评测

  Language:Jupyter Notebook892520

• Hanlard/Electra_CRF_NER

  We start a company-name recognition task with a small scale and low quality training data, then using skills to enhanced model training speed and predicting performance with least artificial participation. The methods we use involve lite pre-training models such as Albert-small or Electra-small with financial corpus, knowledge of distillation and multi-stage learning. The result is that we improve the recall rate of company names recognition task from 0.73 to 0.92 and get 4 times as fast as BERT-Bilstm-CRF model.

  Language:Python805314

• percent4/cnews_text_classification

  利用kashgari轻松搭建文本分类模型。

  Language:Python25208

• dmhaomoon/ner-three

  该项目是常用的命名实体识别，包括seqlabel-crf，bilstm-crf，bert-bilstm-crf

  Language:Python1101