Transformers 简介:Transformers是TensorFlow 2.0和PyTorch的最新自然语言处理库

transformers

作者|huggingface
编译|VK
来源|Github

Transformers是TensorFlow 2.0和PyTorch的最新自然语言处理库

Transformers(以前称为pytorch-transformers和pytorch-pretrained-bert)提供用于自然语言理解(NLU)和自然语言生成(NLG)的最先进的模型(BERTGPT-2,RoBERTaXLMDistilBertXLNet,CTRL …) ,拥有超过32种预训练模型,支持100多种语言,并且在TensorFlow 2.0和PyTorch之间具有深厚的互操作性。

特性

  • 与pytorch-transformers一样易于使用
  • 像Keras一样强大而简洁
  • 在NLU和NLG任务上具有高性能
  • 教育者和从业者进入的门槛低

面向所有人的最新NLP架构
– 深度学习研究人员
– 练习实践学习人员
– AI/ML/NLP教师和教育者

降低计算成本
– 研究人员可以共享训练好的模型,而不必总是再训练
– 从业人员可以减少计算时间和生产成本
– 具有30多种预训练模型的10种架构,其中一些采用100多种语言

为模型生命周期的每个部分选择合适的框架
– 3行代码训练最先进的模型
– TensorFlow 2.0和PyTorch模型之间的深层互操作性
– 在TF2.0/PyTorch框架之间随意迁移模型
– 无缝选择合适的框架进行训练,评估和生产

目录

该库当前包含以下模型的PyTorch和Tensorflow实现,预训练模型权重,用法脚本和一些转换实用程序:

  1. BERT (来自Google) 源自论文BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding 》,作者:Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.

  2. GPT (来自OpenAI) 源自论文《Improving Language Understanding by Generative Pre-Training 》,作者:Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.

  3. GPT-2 (来自OpenAI) 源自论文《Language Models are Unsupervised Multitask Learners 》,作者:Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.

  4. Transformer-XL (来自Google/CMU) 源自论文《Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context 》,作者:Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.

  5. XLNet (来自Google/CMU) 源自论文《​XLNet: Generalized Autoregressive Pretraining for Language Understanding 》,作者:Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.

  6. XLM (来自Facebook) 源自论文《Cross-lingual Language Model Pretraining 》,作者:Guillaume Lample and Alexis Conneau.

  7. RoBERTa (来自Facebook), 源自论文《a Robustly Optimized BERT Pretraining Approach 》,作者:Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.

  8. DistilBERT (来自HuggingFace) 源自论文《DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter 》,作者:Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into DistilGPT2.

  9. CTRL (来自Salesforce), 源自论文《CTRL: A Conditional Transformer Language Model for Controllable Generation 》,作者:Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong and Richard Socher.

  10. CamemBERT (来自FAIR, Inria, Sorbonne Université) 源自论文《CamemBERT: a Tasty French Language Model 》,作者:Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la Clergerie, Djame Seddah, and Benoît Sagot.

  11. ALBERT (来自Google Research), 源自论文《a ALBERT: A Lite BERT for Self-supervised Learning of Language Representations 》,作者:Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.

  12. XLM-RoBERTa (来自Facebook AI), 源自论文《Unsupervised Cross-lingual Representation Learning at Scale 》,作者:Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.

  13. FlauBERT (来自CNRS) 源自论文《FlauBERT: Unsupervised Language Model Pre-training for French 》,作者:Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.

目录

  • 安装
    • pip安装
    • 从源安装
    • 测试
    • OpenAI GPT原始标记分析工作流
    • 模型下载注意事项(持续集成或大规模部署)
    • 您想在移动设备上运行Transformer模型吗?
  • 快速入门
    • 理念
    • 主要概念
    • 快速入门:使用
  • 术语表
    • 输入id
    • 注意力掩码
    • 词语类型id
    • 位置id
  • 预训练模型
  • 模型上传与共享
  • 例子
    • TensorFlow 2.0 Bert 在GLUE上的测评
    • 语言模型训练
    • 语言生成
    • GLUE
    • 选择题
    • SQuAD
    • XNLI
    • MM-IMDb
    • 模型性能的对抗评估
  • Jupyter Notebooks
  • 加载谷歌AI或OpenAI预训练权重或PyTorch转储
    • from_pretrained()方法
    • 缓存目录
  • 序列化实践
  • 转换Tensorflow检查点
    • BERT
    • OpenAI GPT
    • OpenAI GPT-2
    • Transformer-XL
    • XLNet
    • XLM
  • 从pytorch-pretrained-bert迁移
    • 模型总是输出元组
    • 序列化
    • 优化器:BertAdam和OpenAIAdam现在改成AdamW,schedules是标准的PyTorch schedules
  • BERTology
  • TorchScript
    • 隐式
    • 在Python中使用TorchScript
  • 多语言模型
    • BERT
    • 伯特
  • 基准
    • 对所有模型进行推理基准测试
    • 带混合精度, XLA,分布的TF2(@tlkh)

主要的类

  • Configuration
    • PretrainedConfig
  • Models
    • PreTrainedModel
    • TFPreTrainedModel
  • Tokenizer
    • PreTrainedTokenizer
  • Optimizer
    • AdamW
    • AdamWeightDecay
  • Schedules
    • Learning Rate Schedules
    • Warmup
  • Gradient Strategies
    • GradientAccumulator
  • Processors
    • Processors
    • GLUE
    • XNLI
    • SQuAD

包引用

  • AutoModels
    • AutoConfig
    • AutoTokenizer
    • AutoModel
    • AutoModelForPreTraining
    • AutoModelWithLMHead
    • AutoModelForSequenceClassification
    • AutoModelForQuestionAnswering
    • AutoModelForTokenClassification
  • BERT
    • Overview
    • BertConfig
    • BertTokenizer
    • BertModel
    • BertForPreTraining
    • BertForMaskedLM
    • BertForNextSentencePrediction
    • BertForSequenceClassification
    • BertForMultipleChoice
    • BertForTokenClassification
    • BertForQuestionAnswering
    • TFBertModel
    • TFBertForPreTraining
    • TFBertForMaskedLM
    • TFBertForNextSentencePrediction
    • TFBertForSequenceClassification
    • TFBertForMultipleChoice
    • TFBertForTokenClassification
    • TFBertForQuestionAnswering
  • OpenAI GPT
    • Overview
    • OpenAIGPTConfig
    • OpenAIGPTTokenizer
    • OpenAIGPTModel
    • OpenAIGPTLMHeadModel
    • OpenAIGPTDoubleHeadsModel
    • TFOpenAIGPTModel
    • TFOpenAIGPTLMHeadModel
    • TFOpenAIGPTDoubleHeadsModel
  • Transformer XL
    • Overview
    • TransfoXLConfig
    • TransfoXLTokenizer
    • TransfoXLModel
    • TransfoXLLMHeadModel
    • TFTransfoXLModel
    • TFTransfoXLLMHeadModel
  • OpenAI GPT2
    • Overview
    • GPT2Config
    • GPT2Tokenizer
    • GPT2Model
    • GPT2LMHeadModel
    • GPT2DoubleHeadsModel
    • TFGPT2Model
    • TFGPT2LMHeadModel
    • TFGPT2DoubleHeadsModel
  • XLM
    • Overview
    • XLMConfig
    • XLMTokenizer
    • XLMModel
    • XLMWithLMHeadModel
    • XLMForSequenceClassification
    • XLMForQuestionAnsweringSimple
    • XLMForQuestionAnswering
    • TFXLMModel
    • TFXLMWithLMHeadModel
    • TFXLMForSequenceClassification
    • TFXLMForQuestionAnsweringSimple
  • XLNet
    • Overview
    • XLNetConfig
    • XLNetTokenizer
    • XLNetModel
    • XLNetLMHeadModel
    • XLNetForSequenceClassification
    • XLNetForTokenClassification
    • XLNetForMultipleChoice
    • XLNetForQuestionAnsweringSimple
    • XLNetForQuestionAnswering
    • TFXLNetModel
    • TFXLNetLMHeadModel
    • TFXLNetForSequenceClassification
    • TFXLNetForQuestionAnsweringSimple
  • RoBERTa
    • RobertaConfig
    • RobertaTokenizer
    • RobertaModel
    • RobertaForMaskedLM
    • RobertaForSequenceClassification
    • RobertaForTokenClassification
    • TFRobertaModel
    • TFRobertaForMaskedLM
    • TFRobertaForSequenceClassification
    • TFRobertaForTokenClassification
  • DistilBERT
    • DistilBertConfig
    • DistilBertTokenizer
    • DistilBertModel
    • DistilBertForMaskedLM
    • DistilBertForSequenceClassification
    • DistilBertForQuestionAnswering
    • TFDistilBertModel
    • TFDistilBertForMaskedLM
    • TFDistilBertForSequenceClassification
    • TFDistilBertForQuestionAnswering
  • CTRL
    • CTRLConfig
    • CTRLTokenizer
    • CTRLModel
    • CTRLLMHeadModel
    • TFCTRLModel
    • TFCTRLLMHeadModel
  • CamemBERT
    • CamembertConfig
    • CamembertTokenizer
    • CamembertModel
    • CamembertForMaskedLM
    • CamembertForSequenceClassification
    • CamembertForMultipleChoice
    • CamembertForTokenClassification
    • TFCamembertModel
    • TFCamembertForMaskedLM
    • TFCamembertForSequenceClassification
    • TFCamembertForTokenClassification
  • ALBERT
    • Overview
    • AlbertConfig
    • AlbertTokenizer
    • AlbertModel
    • AlbertForMaskedLM
    • AlbertForSequenceClassification
    • AlbertForQuestionAnswering
    • TFAlbertModel
    • TFAlbertForMaskedLM
    • TFAlbertForSequenceClassification
  • XLM-RoBERTa
    • XLMRobertaConfig
    • XLMRobertaTokenizer
    • XLMRobertaModel
    • XLMRobertaForMaskedLM
    • XLMRobertaForSequenceClassification
    • XLMRobertaForMultipleChoice
    • XLMRobertaForTokenClassification
    • TFXLMRobertaModel
    • TFXLMRobertaForMaskedLM
    • TFXLMRobertaForSequenceClassification
    • TFXLMRobertaForTokenClassification
  • FlauBERT
    • FlaubertConfig
    • FlaubertTokenizer
    • FlaubertModel
    • FlaubertWithLMHeadModel
    • FlaubertForSequenceClassification
    • FlaubertForQuestionAnsweringSimple
    • FlaubertForQuestionAnswering
  • Bart
    • BartModel
    • BartForMaskedLM
    • BartForSequenceClassification
    • BartConfig
    • Automatic Creation of Decoder Inputs

原创文章,作者:pytorch,如若转载,请注明出处:http://pytorchchina.com/2020/02/29/transformers-%e7%ae%80%e4%bb%8b%ef%bc%9atransformers%e6%98%aftensorflow-2-0%e5%92%8cpytorch%e7%9a%84%e6%9c%80%e6%96%b0%e8%87%aa%e7%84%b6%e8%af%ad%e8%a8%80%e5%a4%84%e7%90%86%e5%ba%93/

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