在自然语言处理领域中，预训练语言模型（Pretrained Language Models）已成为非常重要的基础技术，本仓库主要收集目前网上公开的一些高质量中文预训练模型(感谢分享资源的大佬)，并将持续更新......

注: 🤗huggingface模型下载地址: 1. huggingface官方地址

• NLU系列

  • BERT
  • RoBERTa
  • ALBERT
  • NEZHA
  • XLNET
  • MacBERT
  • WoBERT
  • ELECTRA
  • ZEN
  • ERNIE
  • ERNIE3
  • RoFormer
  • StructBERT
  • Lattice-BERT
  • Mengzi-BERT
  • ChineseBERT
  • TaCL
  • MC-BERT
  • 二郎神
  • PERT
  • MobileBERT
  • GAU-α
  • DeBERTa
  • GlyphBERT
  • CKBERT

• NLG系列

  • GPT
  • GPT-3
  • NEZHA-GEN
  • CPM-Generate
  • T5
  • T5-PEGASUS
  • Mengzi-T5
  • 盘古α
  • EVA
  • BART
  • 闻仲
  • 余元
  • RWKV
  • Bloom

• NLU-NLG系列

  • UniLM
  • Simbert
  • RoFormer-sim
  • CPM-2
  • CPT
  • 周文王
  • GLM
  • PLUG

• Multi-Modal

  • WenLan
  • CogView
  • 紫东太初
  • Mengzi-oscar
  • R2D2
  • Chinese-CLIP
  • TaiYi-CLIP

• Table

  • SDCUP

• 更新日志

• 2018 | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | Jacob Devlin, et al. | arXiv | PDF
• 2019 | Pre-Training with Whole Word Masking for Chinese BERT | Yiming Cui, et al. | arXiv | PDF

备注:

wwm全称为**Whole Word Masking **,一个完整的词的部分WordPiece子词被mask，则同属该词的其他部分也会被mask

ext表示在更多数据集下训练

• 2021 | ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information | Zijun Sun, et al. | arXiv | PDF

• 2019 | RoBERTa: A Robustly Optimized BERT Pretraining Approach | Yinhan Liu, et al. | arXiv | PDF

• 2019 | ALBERT: A Lite BERT For Self-Supervised Learning Of Language Representations | Zhenzhong Lan, et al. | arXiv | PDF

• 2019 | NEZHA: Neural Contextualized Representation for Chinese Language Understanding | Junqiu Wei, et al. | arXiv | PDF

• 2020 | Revisiting Pre-Trained Models for Chinese Natural Language Processing | Yiming Cui, et al. | arXiv | PDF

• 2020 | 提速不掉点：基于词颗粒度的中文WoBERT | 苏剑林. | spaces | Blog post

• 2019 | XLNet: Generalized Autoregressive Pretraining for Language Understanding | Zhilin Yang, et al. | arXiv | PDF

• 2020 | ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators | Kevin Clark, et al. | arXiv | PDF

• 2019 | ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations | Shizhe Diao, et al. | arXiv | PDF

• 2019 | ERNIE: Enhanced Representation through Knowledge Integration | Yu Sun, et al. | arXiv | PDF

• 2020 | SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis | Hao Tian, et al. | arXiv | PDF

• 2020 | ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding | Dongling Xiao, et al. | arXiv | PDF

备注:

PaddlePaddle转TensorFlow可参考: tensorflow_ernie

PaddlePaddle转PyTorch可参考: ERNIE-Pytorch

• 2021 | ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation | Yu Sun, et al. | arXiv | PDF

• 2021 | ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation | Shuohuan Wang, et al. | arXiv | PDF

PaddlePaddle转PyTorch可参考: ERNIE-Pytorch

• 2021 | RoFormer: Enhanced Transformer with Rotary Position Embedding | Jianlin Su, et al. | arXiv | PDF

• 2021 | Transformer升级之路：2、博采众长的旋转式位置编码 | 苏剑林. | spaces | Blog post

• 2019 | StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding | Wei Wang, et al. | arXiv | PDF

• 2021 | Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models | Yuxuan Lai, et al. | arXiv | PDF

• 2021 | Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese | Zhuosheng Zhang, et al. | arXiv | PDF

• 2022 | Bloom: BigScience Large Open-science Open-access Multilingual Language Model | huggingface bigscience | - | BLOG

• 2021 | TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning | Yixuan Su, et al. | arXiv | PDF

• 2021 | MC-BERT: Conceptualized Representation Learning for Chinese Biomedical Text Mining | alibaba-research | arXiv | PDF

• 2022 | PERT: Pre-Training BERT with Permuted Language Model | Yiming Cui, et al. | arXiv | PDF

• 2020 | MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices | Zhiqing Sun, et al. | arXiv | PDF

• 2022 | GAU-α: (FLASH) Transformer Quality in Linear Time | Weizhe Hua, et al. | arXiv | PDF | blog

• 2020 | DeBERTa: Decoding-enhanced BERT with Disentangled Attention | Pengcheng He, et al. | arXiv | PDF |

• 2021 | GlyphCRM: Bidirectional Encoder Representation for Chinese Character with its Glyph | Yuxin li, et al. | arXiv | PDF |

• 2022 | Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training | Zhang, Taolin, et al. | arXiv | PDF

• 2019 | Improving Language Understandingby Generative Pre-Training | Alec Radford, et al. | arXiv | PDF

• 2019 | Language Models are Unsupervised Multitask Learners | Alec Radford, et al. | arXiv | PDF

• 2019 | Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context | Zihang Dai, et al. | arXiv | PDF

• 2020 | Language Models are Few-Shot Learners | Tom B. Brown, et al. | arXiv | PDF

• 2019 | NEZHA: Neural Contextualized Representation for Chinese Language Understanding | Junqiu Wei, et al. | arXiv | PDF

• 2019 | Improving Language Understandingby Generative Pre-Training | Alec Radford, et al. | arXiv | PDF

• 2020 | CPM: A Large-scale Generative Chinese Pre-trained Language Model | Zhengyan Zhang, et al. | arXiv | PDF

备注:

PyTorch转TensorFlow可参考: CPM-LM-TF2

PyTorch转PaddlePaddle可参考: CPM-Generate-Paddle

• 2019 | Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer | Colin Raffel, et al. | arXiv | PDF

• 2019 | Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer | Colin Raffel, et al. | arXiv | PDF

• 2019 | PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization | Jingqing Zhang, et al. | arXiv | PDF

• 2021 | T5 PEGASUS：开源一个中文生成式预训练模型 | 苏剑林. | spaces | Blog post

Keras转PyTorch可参考: t5-pegasus-pytorch

• 2021 | Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese | Zhuosheng Zhang, et al. | arXiv | PDF

• 2021 | PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation | Wei Zeng, et al. | arXiv | PDF

• 2021 | EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training | Hao Zhou, et al. | arXiv | PDF

• 2019 | BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension | Mike Lewis, et al. | arxiv | PDF

• 2019 | Unified Language Model Pre-training for Natural Language Understanding and Generation | Li Dong, et al. | arXiv | PDF

• 2020 | 鱼与熊掌兼得：融合检索和生成的SimBERT模型 | 苏剑林. | spaces | Blog post

• 2021 | SimBERTv2来了！融合检索和生成的RoFormer-Sim模型 | 苏剑林. | spaces | Blog post

• 2021 | CPM-2: Large-scale Cost-effective Pre-trained Language Models | Zhengyan Zhang, et al. | arXiv | PDF

• 2021 | CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation | Yunfan Shao, et al. | arxiv | PDF

• 2022 | GLM: General Language Model Pretraining with Autoregressive Blank Infilling | Zhengxiao Du, et al. | arXiv | PDF

• 2019 | StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding | Wei Wang, et al. | arXiv | PDF
• 2020 | PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation | Bin Bi, et al. | ACL| PDF

• 2021 | WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training | Yuqi Huo, et al. | arXiv | PDF

• 2021 | CogView: Mastering Text-to-Image Generation via Transformers | Ming Ding, et al. | arXiv | PDF

• 2021 | Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese | Zhuosheng Zhang, et al. | arXiv | PDF

• 2022 | Zero and R2D2: A Large-scale Chinese Cross-modal Benchmark and A Vision-Language Framework | Chunyu Xie, et al. | arXiv | PDF

• 2021 | Learning Transferable Visual Models From Natural Language Supervision | Alec Radford, et al. | arXiv | PDF

• 2021 | Learning Transferable Visual Models From Natural Language Supervision | Alec Radford, et al. | arXiv | PDF

• 2021 | Improving Text-to-SQL with Schema Dependency Learning | Binyuan Hui, et al. | arXiv | PDF

• 2022.10.14 增加CKBERT，中文知识库增强BERT预训练语言模型。
• 2022.10.01 增加GlyphBERT, GlyphBERT是一个包含了汉字字形特征中文预训练模型。它通过将输入的字符渲染成图像并设计成多通道位置特征图的形式，并设计了一个两层 残差卷积神经网络模块来提取字符的图像特征进行训练。
• 2022.09.30 增加DeBERTa，一个中文版的DeBERTa-v2，我们用悟道语料库(180G版本)进行预训练，在预训练阶段中使用了封神框架。
• 2022.09.30 增加TaiYi-CLIP,首个开源的中文CLIP模型，1.23亿图文对上进行预训练的文本端RoBERTa-large。
• 2022.09.27 增加PLUG,PLUG集语言理解与生成能力于一身，支持文本生成、问答、语义理解等多类下游任务，PLUG开源将助力开发者在语言理解和语言生成上做出更多延拓。
• 2022.09.11 增加bloom-6b4,多语言预训练bloom系列生成模型7b1参数(https://huggingface.co/bigscience/bloom-7b1 )的中文vocab提取，bloom系列另有最大176B模型(https://huggingface.co/bigscience/bloom).
• 2022.09.11 增加GLM-130B,提出了开源的双语预训练生成模型 GLM(General Language Model)。
• 2022.09.11 增加PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation 2.6B和13B 生成模型pytorch版
• 2022.07.10 增加Chinese-CLIP,CLIP模型的中文版本，使用大规模中文数据进行训练（~2亿图文对），旨在帮助用户实现中文领域的跨模态检索、图像表示等.
• 2022.06.29 增加ERNIE 3.0,大规模知识增强预训练语言理解和生成.
• 2022.06.22 增加Zero and R2D2: A Large-scale Chinese Cross-modal Benchmark and A Vision-Language Framework，基于大规模中文跨模态基准数据集Zero，训练视觉语言预训练框架 R2D2，用于大规模跨模态学习。
• 2022.06.15 增加GLM: General Language Model Pretraining with Autoregressive Blank Infilling,提出了一种新的通用语言模型 GLM(General Language Model)。 使用自回归填空目标进行预训练，可以针对各种自然语言理解和生成任务进行微调。
• 2022.05.16 增加GAU-α,主要提出了一个融合了Attention层和FFN层的新设计GAU（Gated Attention Unit，门控注意力单元），它是新模型更快、更省、更好的关键，此外它使得整个模型只有一种层，也显得更为优雅。
• 2022.03.27 增加RoFormer-V2,RoFormer升级版，主要通过结构的简化来提升速度，并通过无监督预训练和有监督预训练的结合来提升效果，从而达到了速度与效果的“双赢”。
• 2022.03.02 增加MobileBERT,MobileBERT是BERT-large模型更“苗条”的版本，使用了瓶颈结构（bottleneck）并且对自注意力和前馈神经网络之间的平衡做了细致的设计。
• 2022.02.24 增加PERT: Pre-Training BERT with Permuted Language Model,一种基于乱序语言模型的预训练模型（PERT），在不引入掩码标记[MASK]的情况下自监督地学习文本语义信息。
• 2021.12.06 增加SDCUP: Improving Text-to-SQL with Schema Dependency Learning,达摩院深度语言模型体系 AliceMind 发布中文社区首个表格预训练模型 SDCUP。
• 2021.11.27 增加RWKV中文预训练生成模型,类似 GPT-2,模型参考地址：RWKV-LM
• 2021.11.27 增加IDEA研究院开源的封神榜系列语言模型，包含二郎神、周文王、闻仲、余元。
• 2021.11.25 增加MC-BERT: Conceptualized Representation Learning for Chinese Biomedical Text Mining, 生物医学领域的中文预训练模型.
• 2021.11.24 增加TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning, Token-aware对比学习预训练模型.
• 2021.10.18 增加Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese,基于语言学信息融入和训练加速等方法研发了 Mengzi 系列模型.
• 2021.10.14 增加中文版BART,训练比较可靠的中文版BART，为中文生成类任务如摘要等提供Baseline.
• 2021.10.14 增加CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation,CPT：兼顾理解和生成的中文预训练模型.
• 2021.10.13 增加紫东太初多模态大模型: 全球首个多模态图文音预训练模型,实现了视觉-文本-语音三模态统一表示，构建了三模态预训练大模型。
• 2021.09.19 增加CogView: Mastering Text-to-Image Generation via Transformers,世界最大的中文多模态生成模型,模型支持文生成图为基础的多领域下游任务.
• 2021.09.10 增加WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training，首个中文通用图文多模态大规模预训练模型。
• 2021.09.10 增加EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training，一个开放领域的中文对话预训练模型。
• 2021.08.19 增加Chinese-Transformer-XL：基于中文预训练语料WuDaoCorpus（290G）训练的GPT-3模型。
• 2021.08.16 增加CPM-2: Large-scale Cost-effective Pre-trained Language Models
• 2021.08.16 增加Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models
• 2021.07.19 增加roformer-sim-v2：利用标注数据增强版本
• 2021.07.15 增加BERT-CCPoem：古典诗歌语料训练的BERT
• 2021.07.06 增加ChineseBERT：Chinese Pretraining Enhanced by Glyph and Pinyin Information
• 2021.06.22 增加StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
• 2021.06.14 增加RoFormer：Enhanced Transformer with Rotary Position Embedding
• 2021.05.25 增加ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding
• 2021.04.28 增加PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
• 2021.03.16 增加T5-PEGASUS: 开源一个中文生成式预训练模型
• 2021.03.09 增加UER系列模型
• 2021.03.04 增加WoBERT: 基于词颗粒度的中文
• 2020.11.11 初始化BERT系列模型BERT