Transformers 4.37 中文文档(一百)(4)https://developer.aliyun.com/article/1565788
🤗 Transformers
原文链接:
huggingface.co/docs/transformers/v4.37.2/en/index#contents
PyTorch、TensorFlow和JAX的最先进机器学习。
🤗 Transformers 提供 API 和工具,可轻松下载和训练最先进的预训练模型。使用预训练模型可以减少计算成本、碳足迹,并节省训练模型所需的时间和资源。这些模型支持不同模态的常见任务,如:
📝 自然语言处理:文本分类、命名实体识别、问答、语言建模、摘要、翻译、多项选择和文本生成。
🖼️ 计算机视觉:图像分类、目标检测和分割。
🗣️ 音频:自动语音识别和音频分类。
🐙 多模态:表格问答、光学字符识别、从扫描文档中提取信息、视频分类和视觉问答。
🤗 Transformers 支持 PyTorch、TensorFlow 和 JAX 之间的框架互操作性。这提供了在模型的生命周期的每个阶段使用不同框架的灵活性;在一个框架中用三行代码训练模型,然后在另一个框架中加载进行推断。模型还可以导出到 ONNX 和 TorchScript 等格式,以在生产环境中部署。
如果您正在寻找 Hugging Face 团队的定制支持
目录
文档分为五个部分:
- 开始提供了一个快速浏览库和安装说明,让您快速上手。
- 教程是初学者入门的好地方。本节将帮助您获得开始使用库所需的基本技能。
- 操作指南向您展示如何实现特定目标,比如微调预训练模型用于语言建模,或者如何编写和分享自定义模型。
- 概念指南提供了更多关于模型、任务和🤗 Transformers 设计理念背后的概念和想法的讨论和解释。
- API描述了所有类和函数:
- 主要类详细介绍了配置、模型、分词器和管道等最重要的类。
- 模型详细介绍了库中实现的每个模型相关的类和函数。
- 内部助手详细介绍了内部使用的实用类和函数。
支持的模型和框架
下表表示库中对每个模型的当前支持情况,它们是否有 Python 分词器(称为“slow”)。一个由🤗 Tokenizers 库支持的“fast”分词器,以及它们在 Jax(通过 Flax)、PyTorch 和/或 TensorFlow 中的支持情况。
| 模型 | PyTorch 支持 | TensorFlow 支持 | Flax 支持 |
| ALBERT | ✅ | ✅ | ✅ |
| ALIGN | ✅ | ❌ | ❌ |
| AltCLIP | ✅ | ❌ | ❌ |
| 音频频谱变换器 | ✅ | ❌ | ❌ |
| Autoformer | ✅ | ❌ | ❌ |
| Bark | ✅ | ❌ | ❌ |
| BART | ✅ | ✅ | ✅ |
| BARThez | ✅ | ✅ | ✅ |
| BARTpho | ✅ | ✅ | ✅ |
| BEiT | ✅ | ❌ | ✅ |
| BERT | ✅ | ✅ | ✅ |
| Bert 生成 | ✅ | ❌ | ❌ |
| Bert 日语 | ✅ | ✅ | ✅ |
| BERTweet | ✅ | ✅ | ✅ |
| BigBird | ✅ | ❌ | ✅ |
| BigBird-Pegasus | ✅ | ❌ | ❌ |
| BioGpt | ✅ | ❌ | ❌ |
| BiT | ✅ | ❌ | ❌ |
| Blenderbot | ✅ | ✅ | ✅ |
| BlenderbotSmall | ✅ | ✅ | ✅ |
| BLIP | ✅ | ✅ | ❌ |
| BLIP-2 | ✅ | ❌ | ❌ |
| BLOOM | ✅ | ❌ | ✅ |
| BORT | ✅ | ✅ | ✅ |
| BridgeTower | ✅ | ❌ | ❌ |
| BROS | ✅ | ❌ | ❌ |
| ByT5 | ✅ | ✅ | ✅ |
| CamemBERT | ✅ | ✅ | ❌ |
| CANINE | ✅ | ❌ | ❌ |
| Chinese-CLIP | ✅ | ❌ | ❌ |
| CLAP | ✅ | ❌ | ❌ |
| CLIP | ✅ | ✅ | ✅ |
| CLIPSeg | ✅ | ❌ | ❌ |
| CLVP | ✅ | ❌ | ❌ |
| CodeGen | ✅ | ❌ | ❌ |
| CodeLlama | ✅ | ❌ | ✅ |
| Conditional DETR | ✅ | ❌ | ❌ |
| ConvBERT | ✅ | ✅ | ❌ |
| ConvNeXT | ✅ | ✅ | ❌ |
| ConvNeXTV2 | ✅ | ✅ | ❌ |
| CPM | ✅ | ✅ | ✅ |
| CPM-Ant | ✅ | ❌ | ❌ |
| CTRL | ✅ | ✅ | ❌ |
| CvT | ✅ | ✅ | ❌ |
| Data2VecAudio | ✅ | ❌ | ❌ |
| Data2VecText | ✅ | ❌ | ❌ |
| Data2VecVision | ✅ | ✅ | ❌ |
| DeBERTa | ✅ | ✅ | ❌ |
| DeBERTa-v2 | ✅ | ✅ | ❌ |
| Decision Transformer | ✅ | ❌ | ❌ |
| Deformable DETR | ✅ | ❌ | ❌ |
| DeiT | ✅ | ✅ | ❌ |
| DePlot | ✅ | ❌ | ❌ |
| DETA | ✅ | ❌ | ❌ |
| DETR | ✅ | ❌ | ❌ |
| DialoGPT | ✅ | ✅ | ✅ |
| DiNAT | ✅ | ❌ | ❌ |
| DINOv2 | ✅ | ❌ | ❌ |
| DistilBERT | ✅ | ✅ | ✅ |
| DiT | ✅ | ❌ | ✅ |
| DonutSwin | ✅ | ❌ | ❌ |
| DPR | ✅ | ✅ | ❌ |
| DPT | ✅ | ❌ | ❌ |
| EfficientFormer | ✅ | ✅ | ❌ |
| EfficientNet | ✅ | ❌ | ❌ |
| ELECTRA | ✅ | ✅ | ✅ |
| EnCodec | ✅ | ❌ | ❌ |
| Encoder decoder | ✅ | ✅ | ✅ |
| ERNIE | ✅ | ❌ | ❌ |
| ErnieM | ✅ | ❌ | ❌ |
| ESM | ✅ | ✅ | ❌ |
| FairSeq Machine-Translation | ✅ | ❌ | ❌ |
| Falcon | ✅ | ❌ | ❌ |
| FastSpeech2Conformer | ✅ | ❌ | ❌ |
| FLAN-T5 | ✅ | ✅ | ✅ |
| FLAN-UL2 | ✅ | ✅ | ✅ |
| FlauBERT | ✅ | ✅ | ❌ |
| FLAVA | ✅ | ❌ | ❌ |
| FNet | ✅ | ❌ | ❌ |
| FocalNet | ✅ | ❌ | ❌ |
| Funnel Transformer | ✅ | ✅ | ❌ |
| Fuyu | ✅ | ❌ | ❌ |
| GIT | ✅ | ❌ | ❌ |
| GLPN | ✅ | ❌ | ❌ |
| GPT Neo | ✅ | ❌ | ✅ |
| GPT NeoX | ✅ | ❌ | ❌ |
| GPT NeoX Japanese | ✅ | ❌ | ❌ |
| GPT-J | ✅ | ✅ | ✅ |
| GPT-Sw3 | ✅ | ✅ | ✅ |
| GPTBigCode | ✅ | ❌ | ❌ |
| GPTSAN-japanese | ✅ | ❌ | ❌ |
| Graphormer | ✅ | ❌ | ❌ |
| GroupViT | ✅ | ✅ | ❌ |
| HerBERT | ✅ | ✅ | ✅ |
| Hubert | ✅ | ✅ | ❌ |
| I-BERT | ✅ | ❌ | ❌ |
| IDEFICS | ✅ | ❌ | ❌ |
| ImageGPT | ✅ | ❌ | ❌ |
| Informer | ✅ | ❌ | ❌ |
| InstructBLIP | ✅ | ❌ | ❌ |
| Jukebox | ✅ | ❌ | ❌ |
| KOSMOS-2 | ✅ | ❌ | ❌ |
| LayoutLM | ✅ | ✅ | ❌ |
| LayoutLMv2 | ✅ | ❌ | ❌ |
| LayoutLMv3 | ✅ | ✅ | ❌ |
| LayoutXLM | ✅ | ❌ | ❌ |
| LED | ✅ | ✅ | ❌ |
| LeViT | ✅ | ❌ | ❌ |
| LiLT | ✅ | ❌ | ❌ |
| LLaMA | ✅ | ❌ | ✅ |
| Llama2 | ✅ | ❌ | ✅ |
| LLaVa | ✅ | ❌ | ❌ |
| Longformer | ✅ | ✅ | ❌ |
| LongT5 | ✅ | ❌ | ✅ |
| LUKE | ✅ | ❌ | ❌ |
| LXMERT | ✅ | ✅ | ❌ |
| M-CTC-T | ✅ | ❌ | ❌ |
| M2M100 | ✅ | ❌ | ❌ |
| MADLAD-400 | ✅ | ✅ | ✅ |
| Marian | ✅ | ✅ | ✅ |
| MarkupLM | ✅ | ❌ | ❌ |
| Mask2Former | ✅ | ❌ | ❌ |
| MaskFormer | ✅ | ❌ | ❌ |
| MatCha | ✅ | ❌ | ❌ |
| mBART | ✅ | ✅ | ✅ |
| mBART-50 | ✅ | ✅ | ✅ |
| MEGA | ✅ | ❌ | ❌ |
| Megatron-BERT | ✅ | ❌ | ❌ |
| Megatron-GPT2 | ✅ | ✅ | ✅ |
| MGP-STR | ✅ | ❌ | ❌ |
| Mistral | ✅ | ❌ | ❌ |
| Mixtral | ✅ | ❌ | ❌ |
| mLUKE | ✅ | ❌ | ❌ |
| MMS | ✅ | ✅ | ✅ |
| MobileBERT | ✅ | ✅ | ❌ |
| MobileNetV1 | ✅ | ❌ | ❌ |
| MobileNetV2 | ✅ | ❌ | ❌ |
| MobileViT | ✅ | ✅ | ❌ |
| MobileViTV2 | ✅ | ❌ | ❌ |
| MPNet | ✅ | ✅ | ❌ |
| MPT | ✅ | ❌ | ❌ |
| MRA | ✅ | ❌ | ❌ |
| MT5 | ✅ | ✅ | ✅ |
| MusicGen | ✅ | ❌ | ❌ |
| MVP | ✅ | ❌ | ❌ |
| NAT | ✅ | ❌ | ❌ |
| Nezha | ✅ | ❌ | ❌ |
| NLLB | ✅ | ❌ | ❌ |
| NLLB-MOE | ✅ | ❌ | ❌ |
| Nougat | ✅ | ✅ | ✅ |
| Nyströmformer | ✅ | ❌ | ❌ |
| OneFormer | ✅ | ❌ | ❌ |
| OpenAI GPT | ✅ | ✅ | ❌ |
| OpenAI GPT-2 | ✅ | ✅ | ✅ |
| OpenLlama | ✅ | ❌ | ❌ |
| OPT | ✅ | ✅ | ✅ |
| OWL-ViT | ✅ | ❌ | ❌ |
| OWLv2 | ✅ | ❌ | ❌ |
| PatchTSMixer | ✅ | ❌ | ❌ |
| PatchTST | ✅ | ❌ | ❌ |
| Pegasus | ✅ | ✅ | ✅ |
| PEGASUS-X | ✅ | ❌ | ❌ |
| Perceiver | ✅ | ❌ | ❌ |
| Persimmon | ✅ | ❌ | ❌ |
| Phi | ✅ | ❌ | ❌ |
| PhoBERT | ✅ | ✅ | ✅ |
| Pix2Struct | ✅ | ❌ | ❌ |
| PLBart | ✅ | ❌ | ❌ |
| PoolFormer | ✅ | ❌ | ❌ |
| Pop2Piano | ✅ | ❌ | ❌ |
| ProphetNet | ✅ | ❌ | ❌ |
| PVT | ✅ | ❌ | ❌ |
| QDQBert | ✅ | ❌ | ❌ |
| Qwen2 | ✅ | ❌ | ❌ |
| RAG | ✅ | ✅ | ❌ |
| REALM | ✅ | ❌ | ❌ |
| Reformer | ✅ | ❌ | ❌ |
| RegNet | ✅ | ✅ | ✅ |
| RemBERT | ✅ | ✅ | ❌ |
| ResNet | ✅ | ✅ | ✅ |
| RetriBERT | ✅ | ❌ | ❌ |
| RoBERTa | ✅ | ✅ | ✅ |
| RoBERTa-PreLayerNorm | ✅ | ✅ | ✅ |
| RoCBert | ✅ | ❌ | ❌ |
| RoFormer | ✅ | ✅ | ✅ |
| RWKV | ✅ | ❌ | ❌ |
| SAM | ✅ | ✅ | ❌ |
| SeamlessM4T | ✅ | ❌ | ❌ |
| SeamlessM4Tv2 | ✅ | ❌ | ❌ |
| SegFormer | ✅ | ✅ | ❌ |
| SEW | ✅ | ❌ | ❌ |
| SEW-D | ✅ | ❌ | ❌ |
| SigLIP | ✅ | ❌ | ❌ |
| Speech Encoder decoder | ✅ | ❌ | ✅ |
| Speech2Text | ✅ | ✅ | ❌ |
| SpeechT5 | ✅ | ❌ | ❌ |
| Splinter | ✅ | ❌ | ❌ |
| SqueezeBERT | ✅ | ❌ | ❌ |
| SwiftFormer | ✅ | ❌ | ❌ |
| Swin Transformer | ✅ | ✅ | ❌ |
| Swin Transformer V2 | ✅ | ❌ | ❌ |
| Swin2SR | ✅ | ❌ | ❌ |
| SwitchTransformers | ✅ | ❌ | ❌ |
| T5 | ✅ | ✅ | ✅ |
| T5v1.1 | ✅ | ✅ | ✅ |
| Table Transformer | ✅ | ❌ | ❌ |
| TAPAS | ✅ | ✅ | ❌ |
| TAPEX | ✅ | ✅ | ✅ |
| Time Series Transformer | ✅ | ❌ | ❌ |
| TimeSformer | ✅ | ❌ | ❌ |
| Trajectory Transformer | ✅ | ❌ | ❌ |
| Transformer-XL | ✅ | ✅ | ❌ |
| TrOCR | ✅ | ❌ | ❌ |
| TVLT | ✅ | ❌ | ❌ |
| TVP | ✅ | ❌ | ❌ |
| UL2 | ✅ | ✅ | ✅ |
| UMT5 | ✅ | ❌ | ❌ |
| UniSpeech | ✅ | ❌ | ❌ |
| UniSpeechSat | ✅ | ❌ | ❌ |
| UnivNet | ✅ | ❌ | ❌ |
| UPerNet | ✅ | ❌ | ❌ |
| VAN | ✅ | ❌ | ❌ |
| VideoMAE | ✅ | ❌ | ❌ |
| ViLT | ✅ | ❌ | ❌ |
| VipLlava | ✅ | ❌ | ❌ |
| Vision Encoder decoder | ✅ | ✅ | ✅ |
| VisionTextDualEncoder | ✅ | ✅ | ✅ |
| VisualBERT | ✅ | ❌ | ❌ |
| ViT | ✅ | ✅ | ✅ |
| ViT Hybrid | ✅ | ❌ | ❌ |
| VitDet | ✅ | ❌ | ❌ |
| ViTMAE | ✅ | ✅ | ❌ |
| ViTMatte | ✅ | ❌ | ❌ |
| ViTMSN | ✅ | ❌ | ❌ |
| VITS | ✅ | ❌ | ❌ |
| ViViT | ✅ | ❌ | ❌ |
| Wav2Vec2 | ✅ | ✅ | ✅ |
| Wav2Vec2-BERT | ✅ | ❌ | ❌ |
| Wav2Vec2-Conformer | ✅ | ❌ | ❌ |
| Wav2Vec2Phoneme | ✅ | ✅ | ✅ |
| WavLM | ✅ | ❌ | ❌ |
| Whisper | ✅ | ✅ | ✅ |
| X-CLIP | ✅ | ❌ | ❌ |
| X-MOD | ✅ | ❌ | ❌ |
| XGLM | ✅ | ✅ | ✅ |
| XLM | ✅ | ✅ | ❌ |
| XLM-ProphetNet | ✅ | ❌ | ❌ |
| XLM-RoBERTa | ✅ | ✅ | ✅ |
| XLM-RoBERTa-XL | ✅ | ❌ | ❌ |
| XLM-V | ✅ | ✅ | ✅ |
| XLNet | ✅ | ✅ | ❌ |
| XLS-R | ✅ | ✅ | ✅ |
| XLSR-Wav2Vec2 | ✅ | ✅ | ✅ |
| YOLOS | ✅ | ❌ | ❌ |
| YOSO | ✅ | ❌ | ❌ |
Transformers 4.37 中文文档(一百)(6)https://developer.aliyun.com/article/1565790