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attention-is-all-you-need-pytorch

A PyTorch implementation of the Transformer model in "Attention is All You Need".

awesome-pretrained-chinese-nlp-models

Awesome Pretrained Chinese NLP Models，高质量中文预训练模型集合

bert-pytorch

Google AI 2018 BERT pytorch implementation

bert_finetune

中文语料 Bert finetune（Fine-tune Chinese for BERT）

chinese-llama-alpaca

中文LLaMA&Alpaca大语言模型+本地CPU/GPU部署 (Chinese LLaMA & Alpaca LLMs)

enterprise-registration-data-of-chinese-mainland

**大陆 31 个省份1978 年至 2019 年一千多万工商企业注册信息，包含企业名称、注册地址、统一社会信用代码、地区、注册日期、经营范围、法人代表、注册资金、企业类型等详细资料。This repository is an dataset of over 10,000,000 enterprise registration data of 31 provinces in Chinese mainland from 1978 to 2019.【工商大数据】、【企业信息】、【enterprise registration data】。

fastchat

An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and FastChat-T5.

glados-checkin

自动签到，自动续期，高速富强

glm

GLM (General Language Model)

gpt-neo

An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.

gpt2-chinese

Chinese version of GPT2 training code, using BERT tokenizer.

graphembedding

Implementation and experiments of graph embedding algorithms.deep walk,LINE(Large-scale Information Network Embedding),node2vec,SDNE(Structural Deep Network Embedding),struc2vec

kdd2019_hetgnn

code of HetGNN

kms-activate

Microsoft Windows/Office 一键激活工具，基于kms.jm33.me的KMS服务器

knowledge_representation_pytorch

Several knowledge graph representation algorithms implemented with pytorch

lawdocsearch

第一版本，只是完成了首页展示等效果

learn-nlp-with-transformers

we want to create a repo to illustrate usage of transformers in chinese

llama

Inference code for LLaMA models

megatron-lm

Ongoing research training transformer models at scale

metaseq

Repo for external large-scale work

net2vec

nlp_ability

总结梳理自然语言处理工程师(NLP)需要积累的各方面知识，包括面试题，各种基础知识，工程能力等等，提升核心竞争力

nlp_chinese_corpus

大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP

openkp

Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural model with key phrase extraction on open domains we have created OpenKP: a dataset of over 150,000 documents with the most relevant keyphrases generated by expert annotation.

paragraph-vectors

:page_facing_up: A PyTorch implementation of Paragraph Vectors (doc2vec)

pytorch-lightning-template

An easy/swift-to-adapt PyTorch-Lighting template. 套壳模板，简单易用，稍改原来Pytorch代码，即可适配Lightning。You can translate your previous Pytorch code much easier using this template, and keep your freedom to edit all the functions as well. Big-project-friendly as well.

pytorch-pretrained-bert

A PyTorch implementation of Google AI's BERT model provided with Google's pre-trained models, examples and utilities.

read_bert_code

Bert源码阅读与讲解(Pytorch版本)-以BERT文本分类代码为例子

roberta_zh

RoBERTa中文预训练模型: RoBERTa for Chinese