322 lines
40 KiB
Markdown
322 lines
40 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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# 🤗 Transformers简介
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为 [PyTorch](https://pytorch.org/)、[TensorFlow](https://www.tensorflow.org/) 和 [JAX](https://jax.readthedocs.io/en/latest/) 打造的先进的机器学习工具.
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🤗 Transformers 提供了可以轻松地下载并且训练先进的预训练模型的 API 和工具。使用预训练模型可以减少计算消耗和碳排放，并且节省从头训练所需要的时间和资源。这些模型支持不同模态中的常见任务，比如：
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📝 **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。<br>
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🖼️ **机器视觉**：图像分类、目标检测和语义分割。<br>
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🗣️ **音频**：自动语音识别和音频分类。<br>
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🐙 **多模态**：表格问答、光学字符识别、从扫描文档提取信息、视频分类和视觉问答。
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🤗 Transformers 支持在 PyTorch、TensorFlow 和 JAX 上的互操作性. 这给在模型的每个阶段使用不同的框架带来了灵活性；在一个框架中使用几行代码训练一个模型，然后在另一个框架中加载它并进行推理。模型也可以被导出为 ONNX 和 TorchScript 格式，用于在生产环境中部署。
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马上加入在 [Hub](https://huggingface.co/models)、[论坛](https://discuss.huggingface.co/) 或者 [Discord](https://discord.com/invite/JfAtkvEtRb) 上正在快速发展的社区吧！
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## 如果你需要来自 Hugging Face 团队的个性化支持
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<a target="_blank" href="https://huggingface.co/support">
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<img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="width: 100%; max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
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</a>
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## 目录
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这篇文档由以下 5 个章节组成：
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- **开始使用** 包含了库的快速上手和安装说明，便于配置和运行。
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- **教程** 是一个初学者开始的好地方。本章节将帮助你获得你会用到的使用这个库的基本技能。
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- **操作指南** 向你展示如何实现一个特定目标，比如为语言建模微调一个预训练模型或者如何创造并分享个性化模型。
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- **概念指南** 对 🤗 Transformers 的模型，任务和设计理念背后的基本概念和思想做了更多的讨论和解释。
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- **API 介绍** 描述了所有的类和函数：
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- **主要类别** 详述了配置（configuration）、模型（model）、分词器（tokenizer）和流水线（pipeline）这几个最重要的类。
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- **模型** 详述了在这个库中和每个模型实现有关的类和函数。
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- **内部帮助** 详述了内部使用的工具类和函数。
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### 支持的模型和框架
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下表展示了库中对每个模型的支持情况，如是否具有 Python 分词器（表中的“Tokenizer slow”）、是否具有由 🤗 Tokenizers 库支持的快速分词器（表中的“Tokenizer fast”）、是否支持 Jax（通过 Flax）、PyTorch 与 TensorFlow。
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<!--This table is updated automatically from the auto modules with _make fix-copies_. Do not update manually!-->
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| 模型 | PyTorch 支持 | TensorFlow 支持 | Flax 支持 |
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|:------------------------------------------------------------------------:|:---------------:|:------------------:|:------------:|
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| [ALBERT](../en/model_doc/albert.md) | ✅ | ✅ | ✅ |
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| [ALIGN](../en/model_doc/align.md) | ✅ | ❌ | ❌ |
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| [AltCLIP](../en/model_doc/altclip) | ✅ | ❌ | ❌ |
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| [Audio Spectrogram Transformer](../en/model_doc/audio-spectrogram-transformer) | ✅ | ❌ | ❌ |
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| [Autoformer](../en/model_doc/autoformer) | ✅ | ❌ | ❌ |
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| [Bark](../en/model_doc/bark) | ✅ | ❌ | ❌ |
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| [BART](../en/model_doc/bart) | ✅ | ✅ | ✅ |
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| [BARThez](../en/model_doc/barthez) | ✅ | ✅ | ✅ |
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| [BARTpho](../en/model_doc/bartpho) | ✅ | ✅ | ✅ |
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| [BEiT](../en/model_doc/beit) | ✅ | ❌ | ✅ |
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| [BERT](../en/model_doc/bert) | ✅ | ✅ | ✅ |
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| [Bert Generation](../en/model_doc/bert-generation) | ✅ | ❌ | ❌ |
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| [BertJapanese](../en/model_doc/bert-japanese) | ✅ | ✅ | ✅ |
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| [BERTweet](../en/model_doc/bertweet) | ✅ | ✅ | ✅ |
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| [BigBird](../en/model_doc/big_bird) | ✅ | ❌ | ✅ |
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| [BigBird-Pegasus](../en/model_doc/bigbird_pegasus) | ✅ | ❌ | ❌ |
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| [BioGpt](../en/model_doc/biogpt) | ✅ | ❌ | ❌ |
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| [BiT](../en/model_doc/bit) | ✅ | ❌ | ❌ |
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| [Blenderbot](../en/model_doc/blenderbot) | ✅ | ✅ | ✅ |
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| [BlenderbotSmall](../en/model_doc/blenderbot-small) | ✅ | ✅ | ✅ |
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| [BLIP](../en/model_doc/blip) | ✅ | ✅ | ❌ |
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| [BLIP-2](../en/model_doc/blip-2) | ✅ | ❌ | ❌ |
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| [BLOOM](../en/model_doc/bloom) | ✅ | ❌ | ✅ |
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| [BORT](../en/model_doc/bort) | ✅ | ✅ | ✅ |
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| [BridgeTower](../en/model_doc/bridgetower) | ✅ | ❌ | ❌ |
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| [BROS](../en/model_doc/bros) | ✅ | ❌ | ❌ |
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| [ByT5](../en/model_doc/byt5) | ✅ | ✅ | ✅ |
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| [CamemBERT](../en/model_doc/camembert) | ✅ | ✅ | ❌ |
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| [CANINE](../en/model_doc/canine) | ✅ | ❌ | ❌ |
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| [Chinese-CLIP](../en/model_doc/chinese_clip) | ✅ | ❌ | ❌ |
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| [CLAP](../en/model_doc/clap) | ✅ | ❌ | ❌ |
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| [CLIP](../en/model_doc/clip) | ✅ | ✅ | ✅ |
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| [CLIPSeg](../en/model_doc/clipseg) | ✅ | ❌ | ❌ |
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| [CLVP](../en/model_doc/clvp) | ✅ | ❌ | ❌ |
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| [CodeGen](../en/model_doc/codegen) | ✅ | ❌ | ❌ |
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| [CodeLlama](../en/model_doc/code_llama) | ✅ | ❌ | ✅ |
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| [Conditional DETR](../en/model_doc/conditional_detr) | ✅ | ❌ | ❌ |
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| [ConvBERT](../en/model_doc/convbert) | ✅ | ✅ | ❌ |
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| [ConvNeXT](../en/model_doc/convnext) | ✅ | ✅ | ❌ |
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| [ConvNeXTV2](../en/model_doc/convnextv2) | ✅ | ✅ | ❌ |
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| [CPM](../en/model_doc/cpm) | ✅ | ✅ | ✅ |
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| [CPM-Ant](../en/model_doc/cpmant) | ✅ | ❌ | ❌ |
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| [CTRL](../en/model_doc/ctrl) | ✅ | ✅ | ❌ |
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| [CvT](../en/model_doc/cvt) | ✅ | ✅ | ❌ |
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| [Data2VecAudio](../en/model_doc/data2vec) | ✅ | ❌ | ❌ |
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| [Data2VecText](../en/model_doc/data2vec) | ✅ | ❌ | ❌ |
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| [Data2VecVision](../en/model_doc/data2vec) | ✅ | ✅ | ❌ |
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| [DeBERTa](../en/model_doc/deberta) | ✅ | ✅ | ❌ |
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| [DeBERTa-v2](../en/model_doc/deberta-v2) | ✅ | ✅ | ❌ |
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| [Decision Transformer](../en/model_doc/decision_transformer) | ✅ | ❌ | ❌ |
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| [Deformable DETR](../en/model_doc/deformable_detr) | ✅ | ❌ | ❌ |
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| [DeiT](../en/model_doc/deit) | ✅ | ✅ | ❌ |
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| [DePlot](../en/model_doc/deplot) | ✅ | ❌ | ❌ |
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| [Depth Anything](../en/model_doc/depth_anything) | ✅ | ❌ | ❌ |
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| [DETA](../en/model_doc/deta) | ✅ | ❌ | ❌ |
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| [DETR](../en/model_doc/detr) | ✅ | ❌ | ❌ |
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| [DialoGPT](../en/model_doc/dialogpt) | ✅ | ✅ | ✅ |
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| [DiNAT](../en/model_doc/dinat) | ✅ | ❌ | ❌ |
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| [DINOv2](../en/model_doc/dinov2) | ✅ | ❌ | ❌ |
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| [DistilBERT](../en/model_doc/distilbert) | ✅ | ✅ | ✅ |
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| [DiT](../en/model_doc/dit) | ✅ | ❌ | ✅ |
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| [DonutSwin](../en/model_doc/donut) | ✅ | ❌ | ❌ |
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| [DPR](../en/model_doc/dpr) | ✅ | ✅ | ❌ |
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| [DPT](../en/model_doc/dpt) | ✅ | ❌ | ❌ |
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| [EfficientFormer](../en/model_doc/efficientformer) | ✅ | ✅ | ❌ |
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| [EfficientNet](../en/model_doc/efficientnet) | ✅ | ❌ | ❌ |
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| [ELECTRA](../en/model_doc/electra) | ✅ | ✅ | ✅ |
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| [EnCodec](../en/model_doc/encodec) | ✅ | ❌ | ❌ |
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| [Encoder decoder](../en/model_doc/encoder-decoder) | ✅ | ✅ | ✅ |
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| [ERNIE](../en/model_doc/ernie) | ✅ | ❌ | ❌ |
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| [ErnieM](../en/model_doc/ernie_m) | ✅ | ❌ | ❌ |
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| [ESM](../en/model_doc/esm) | ✅ | ✅ | ❌ |
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| [FairSeq Machine-Translation](../en/model_doc/fsmt) | ✅ | ❌ | ❌ |
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| [Falcon](../en/model_doc/falcon) | ✅ | ❌ | ❌ |
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| [FastSpeech2Conformer](../en/model_doc/fastspeech2_conformer) | ✅ | ❌ | ❌ |
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| [FLAN-T5](../en/model_doc/flan-t5) | ✅ | ✅ | ✅ |
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| [FLAN-UL2](../en/model_doc/flan-ul2) | ✅ | ✅ | ✅ |
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| [FlauBERT](../en/model_doc/flaubert) | ✅ | ✅ | ❌ |
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| [FLAVA](../en/model_doc/flava) | ✅ | ❌ | ❌ |
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| [FNet](../en/model_doc/fnet) | ✅ | ❌ | ❌ |
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| [FocalNet](../en/model_doc/focalnet) | ✅ | ❌ | ❌ |
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| [Funnel Transformer](../en/model_doc/funnel) | ✅ | ✅ | ❌ |
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| [Fuyu](../en/model_doc/fuyu) | ✅ | ❌ | ❌ |
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| [Gemma](../en/model_doc/gemma) | ✅ | ❌ | ✅ |
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| [GIT](../en/model_doc/git) | ✅ | ❌ | ❌ |
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| [GLPN](../en/model_doc/glpn) | ✅ | ❌ | ❌ |
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| [GPT Neo](../en/model_doc/gpt_neo) | ✅ | ❌ | ✅ |
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| [GPT NeoX](../en/model_doc/gpt_neox) | ✅ | ❌ | ❌ |
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| [GPT NeoX Japanese](../en/model_doc/gpt_neox_japanese) | ✅ | ❌ | ❌ |
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| [GPT-J](../en/model_doc/gptj) | ✅ | ✅ | ✅ |
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| [GPT-Sw3](../en/model_doc/gpt-sw3) | ✅ | ✅ | ✅ |
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| [GPTBigCode](../en/model_doc/gpt_bigcode) | ✅ | ❌ | ❌ |
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| [GPTSAN-japanese](../en/model_doc/gptsan-japanese) | ✅ | ❌ | ❌ |
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| [Graphormer](../en/model_doc/graphormer) | ✅ | ❌ | ❌ |
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| [GroupViT](../en/model_doc/groupvit) | ✅ | ✅ | ❌ |
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| [HerBERT](../en/model_doc/herbert) | ✅ | ✅ | ✅ |
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| [Hubert](../en/model_doc/hubert) | ✅ | ✅ | ❌ |
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| [I-BERT](../en/model_doc/ibert) | ✅ | ❌ | ❌ |
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| [IDEFICS](../en/model_doc/idefics) | ✅ | ❌ | ❌ |
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| [ImageGPT](../en/model_doc/imagegpt) | ✅ | ❌ | ❌ |
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| [Informer](../en/model_doc/informer) | ✅ | ❌ | ❌ |
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| [InstructBLIP](../en/model_doc/instructblip) | ✅ | ❌ | ❌ |
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| [Jukebox](../en/model_doc/jukebox) | ✅ | ❌ | ❌ |
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| [KOSMOS-2](../en/model_doc/kosmos-2) | ✅ | ❌ | ❌ |
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| [LayoutLM](../en/model_doc/layoutlm) | ✅ | ✅ | ❌ |
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| [LayoutLMv2](../en/model_doc/layoutlmv2) | ✅ | ❌ | ❌ |
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| [LayoutLMv3](../en/model_doc/layoutlmv3) | ✅ | ✅ | ❌ |
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| [LayoutXLM](../en/model_doc/layoutxlm) | ✅ | ❌ | ❌ |
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| [LED](../en/model_doc/led) | ✅ | ✅ | ❌ |
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| [LeViT](../en/model_doc/levit) | ✅ | ❌ | ❌ |
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| [LiLT](../en/model_doc/lilt) | ✅ | ❌ | ❌ |
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| [LLaMA](../en/model_doc/llama) | ✅ | ❌ | ✅ |
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| [Llama2](../en/model_doc/llama2) | ✅ | ❌ | ✅ |
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| [LLaVa](../en/model_doc/llava) | ✅ | ❌ | ❌ |
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| [Longformer](../en/model_doc/longformer) | ✅ | ✅ | ❌ |
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| [LongT5](../en/model_doc/longt5) | ✅ | ❌ | ✅ |
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| [LUKE](../en/model_doc/luke) | ✅ | ❌ | ❌ |
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| [LXMERT](../en/model_doc/lxmert) | ✅ | ✅ | ❌ |
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| [M-CTC-T](../en/model_doc/mctct) | ✅ | ❌ | ❌ |
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| [M2M100](../en/model_doc/m2m_100) | ✅ | ❌ | ❌ |
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| [MADLAD-400](../en/model_doc/madlad-400) | ✅ | ✅ | ✅ |
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| [Marian](../en/model_doc/marian) | ✅ | ✅ | ✅ |
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| [MarkupLM](../en/model_doc/markuplm) | ✅ | ❌ | ❌ |
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| [Mask2Former](../en/model_doc/mask2former) | ✅ | ❌ | ❌ |
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| [MaskFormer](../en/model_doc/maskformer) | ✅ | ❌ | ❌ |
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| [MatCha](../en/model_doc/matcha) | ✅ | ❌ | ❌ |
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| [mBART](../en/model_doc/mbart) | ✅ | ✅ | ✅ |
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| [mBART-50](../en/model_doc/mbart50) | ✅ | ✅ | ✅ |
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| [MEGA](../en/model_doc/mega) | ✅ | ❌ | ❌ |
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| [Megatron-BERT](../en/model_doc/megatron-bert) | ✅ | ❌ | ❌ |
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| [Megatron-GPT2](../en/model_doc/megatron_gpt2) | ✅ | ✅ | ✅ |
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| [MGP-STR](../en/model_doc/mgp-str) | ✅ | ❌ | ❌ |
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| [Mistral](../en/model_doc/mistral) | ✅ | ❌ | ✅ |
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| [Mixtral](../en/model_doc/mixtral) | ✅ | ❌ | ❌ |
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| [mLUKE](../en/model_doc/mluke) | ✅ | ❌ | ❌ |
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| [MMS](../en/model_doc/mms) | ✅ | ✅ | ✅ |
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| [MobileBERT](../en/model_doc/mobilebert) | ✅ | ✅ | ❌ |
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| [MobileNetV1](../en/model_doc/mobilenet_v1) | ✅ | ❌ | ❌ |
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| [MobileNetV2](../en/model_doc/mobilenet_v2) | ✅ | ❌ | ❌ |
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| [MobileViT](../en/model_doc/mobilevit) | ✅ | ✅ | ❌ |
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| [MobileViTV2](../en/model_doc/mobilevitv2) | ✅ | ❌ | ❌ |
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| [MPNet](../en/model_doc/mpnet) | ✅ | ✅ | ❌ |
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| [MPT](../en/model_doc/mpt) | ✅ | ❌ | ❌ |
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| [MRA](../en/model_doc/mra) | ✅ | ❌ | ❌ |
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| [MT5](../en/model_doc/mt5) | ✅ | ✅ | ✅ |
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| [MusicGen](../en/model_doc/musicgen) | ✅ | ❌ | ❌ |
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| [MVP](../en/model_doc/mvp) | ✅ | ❌ | ❌ |
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| [NAT](../en/model_doc/nat) | ✅ | ❌ | ❌ |
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| [Nezha](../en/model_doc/nezha) | ✅ | ❌ | ❌ |
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| [NLLB](../en/model_doc/nllb) | ✅ | ❌ | ❌ |
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| [NLLB-MOE](../en/model_doc/nllb-moe) | ✅ | ❌ | ❌ |
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| [Nougat](../en/model_doc/nougat) | ✅ | ✅ | ✅ |
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| [Nyströmformer](../en/model_doc/nystromformer) | ✅ | ❌ | ❌ |
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| [OneFormer](../en/model_doc/oneformer) | ✅ | ❌ | ❌ |
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| [OpenAI GPT](../en/model_doc/openai-gpt) | ✅ | ✅ | ❌ |
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| [OpenAI GPT-2](../en/model_doc/gpt2) | ✅ | ✅ | ✅ |
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| [OpenLlama](../en/model_doc/open-llama) | ✅ | ❌ | ❌ |
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| [OPT](../en/model_doc/opt) | ✅ | ✅ | ✅ |
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| [OWL-ViT](../en/model_doc/owlvit) | ✅ | ❌ | ❌ |
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| [OWLv2](../en/model_doc/owlv2) | ✅ | ❌ | ❌ |
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| [PatchTSMixer](../en/model_doc/patchtsmixer) | ✅ | ❌ | ❌ |
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| [PatchTST](../en/model_doc/patchtst) | ✅ | ❌ | ❌ |
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| [Pegasus](../en/model_doc/pegasus) | ✅ | ✅ | ✅ |
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| [PEGASUS-X](../en/model_doc/pegasus_x) | ✅ | ❌ | ❌ |
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| [Perceiver](../en/model_doc/perceiver) | ✅ | ❌ | ❌ |
|
||
| [Persimmon](../en/model_doc/persimmon) | ✅ | ❌ | ❌ |
|
||
| [Phi](../en/model_doc/phi) | ✅ | ❌ | ❌ |
|
||
| [PhoBERT](../en/model_doc/phobert) | ✅ | ✅ | ✅ |
|
||
| [Pix2Struct](../en/model_doc/pix2struct) | ✅ | ❌ | ❌ |
|
||
| [PLBart](../en/model_doc/plbart) | ✅ | ❌ | ❌ |
|
||
| [PoolFormer](../en/model_doc/poolformer) | ✅ | ❌ | ❌ |
|
||
| [Pop2Piano](../en/model_doc/pop2piano) | ✅ | ❌ | ❌ |
|
||
| [ProphetNet](../en/model_doc/prophetnet) | ✅ | ❌ | ❌ |
|
||
| [PVT](../en/model_doc/pvt) | ✅ | ❌ | ❌ |
|
||
| [QDQBert](../en/model_doc/qdqbert) | ✅ | ❌ | ❌ |
|
||
| [Qwen2](../en/model_doc/qwen2) | ✅ | ❌ | ❌ |
|
||
| [RAG](../en/model_doc/rag) | ✅ | ✅ | ❌ |
|
||
| [REALM](../en/model_doc/realm) | ✅ | ❌ | ❌ |
|
||
| [Reformer](../en/model_doc/reformer) | ✅ | ❌ | ❌ |
|
||
| [RegNet](../en/model_doc/regnet) | ✅ | ✅ | ✅ |
|
||
| [RemBERT](../en/model_doc/rembert) | ✅ | ✅ | ❌ |
|
||
| [ResNet](../en/model_doc/resnet) | ✅ | ✅ | ✅ |
|
||
| [RetriBERT](../en/model_doc/retribert) | ✅ | ❌ | ❌ |
|
||
| [RoBERTa](../en/model_doc/roberta) | ✅ | ✅ | ✅ |
|
||
| [RoBERTa-PreLayerNorm](../en/model_doc/roberta-prelayernorm) | ✅ | ✅ | ✅ |
|
||
| [RoCBert](../en/model_doc/roc_bert) | ✅ | ❌ | ❌ |
|
||
| [RoFormer](../en/model_doc/roformer) | ✅ | ✅ | ✅ |
|
||
| [RWKV](../en/model_doc/rwkv) | ✅ | ❌ | ❌ |
|
||
| [SAM](../en/model_doc/sam) | ✅ | ✅ | ❌ |
|
||
| [SeamlessM4T](../en/model_doc/seamless_m4t) | ✅ | ❌ | ❌ |
|
||
| [SeamlessM4Tv2](../en/model_doc/seamless_m4t_v2) | ✅ | ❌ | ❌ |
|
||
| [SegFormer](../en/model_doc/segformer) | ✅ | ✅ | ❌ |
|
||
| [SegGPT](../en/model_doc/seggpt) | ✅ | ❌ | ❌ |
|
||
| [SEW](../en/model_doc/sew) | ✅ | ❌ | ❌ |
|
||
| [SEW-D](../en/model_doc/sew-d) | ✅ | ❌ | ❌ |
|
||
| [SigLIP](../en/model_doc/siglip) | ✅ | ❌ | ❌ |
|
||
| [Speech Encoder decoder](../en/model_doc/speech-encoder-decoder) | ✅ | ❌ | ✅ |
|
||
| [Speech2Text](../en/model_doc/speech_to_text) | ✅ | ✅ | ❌ |
|
||
| [SpeechT5](../en/model_doc/speecht5) | ✅ | ❌ | ❌ |
|
||
| [Splinter](../en/model_doc/splinter) | ✅ | ❌ | ❌ |
|
||
| [SqueezeBERT](../en/model_doc/squeezebert) | ✅ | ❌ | ❌ |
|
||
| [StableLm](../en/model_doc/stablelm) | ✅ | ❌ | ❌ |
|
||
| [Starcoder2](../en/model_doc/starcoder2) | ✅ | ❌ | ❌ |
|
||
| [SwiftFormer](../en/model_doc/swiftformer) | ✅ | ❌ | ❌ |
|
||
| [Swin Transformer](../en/model_doc/swin) | ✅ | ✅ | ❌ |
|
||
| [Swin Transformer V2](../en/model_doc/swinv2) | ✅ | ❌ | ❌ |
|
||
| [Swin2SR](../en/model_doc/swin2sr) | ✅ | ❌ | ❌ |
|
||
| [SwitchTransformers](../en/model_doc/switch_transformers) | ✅ | ❌ | ❌ |
|
||
| [T5](../en/model_doc/t5) | ✅ | ✅ | ✅ |
|
||
| [T5v1.1](../en/model_doc/t5v1.1) | ✅ | ✅ | ✅ |
|
||
| [Table Transformer](../en/model_doc/table-transformer) | ✅ | ❌ | ❌ |
|
||
| [TAPAS](../en/model_doc/tapas) | ✅ | ✅ | ❌ |
|
||
| [TAPEX](../en/model_doc/tapex) | ✅ | ✅ | ✅ |
|
||
| [Time Series Transformer](../en/model_doc/time_series_transformer) | ✅ | ❌ | ❌ |
|
||
| [TimeSformer](../en/model_doc/timesformer) | ✅ | ❌ | ❌ |
|
||
| [Trajectory Transformer](../en/model_doc/trajectory_transformer) | ✅ | ❌ | ❌ |
|
||
| [Transformer-XL](../en/model_doc/transfo-xl) | ✅ | ✅ | ❌ |
|
||
| [TrOCR](../en/model_doc/trocr) | ✅ | ❌ | ❌ |
|
||
| [TVLT](../en/model_doc/tvlt) | ✅ | ❌ | ❌ |
|
||
| [TVP](../en/model_doc/tvp) | ✅ | ❌ | ❌ |
|
||
| [UL2](../en/model_doc/ul2) | ✅ | ✅ | ✅ |
|
||
| [UMT5](../en/model_doc/umt5) | ✅ | ❌ | ❌ |
|
||
| [UniSpeech](../en/model_doc/unispeech) | ✅ | ❌ | ❌ |
|
||
| [UniSpeechSat](../en/model_doc/unispeech-sat) | ✅ | ❌ | ❌ |
|
||
| [UnivNet](../en/model_doc/univnet) | ✅ | ❌ | ❌ |
|
||
| [UPerNet](../en/model_doc/upernet) | ✅ | ❌ | ❌ |
|
||
| [VAN](../en/model_doc/van) | ✅ | ❌ | ❌ |
|
||
| [VideoMAE](../en/model_doc/videomae) | ✅ | ❌ | ❌ |
|
||
| [ViLT](../en/model_doc/vilt) | ✅ | ❌ | ❌ |
|
||
| [VipLlava](../en/model_doc/vipllava) | ✅ | ❌ | ❌ |
|
||
| [Vision Encoder decoder](../en/model_doc/vision-encoder-decoder) | ✅ | ✅ | ✅ |
|
||
| [VisionTextDualEncoder](../en/model_doc/vision-text-dual-encoder) | ✅ | ✅ | ✅ |
|
||
| [VisualBERT](../en/model_doc/visual_bert) | ✅ | ❌ | ❌ |
|
||
| [ViT](../en/model_doc/vit) | ✅ | ✅ | ✅ |
|
||
| [ViT Hybrid](../en/model_doc/vit_hybrid) | ✅ | ❌ | ❌ |
|
||
| [VitDet](../en/model_doc/vitdet) | ✅ | ❌ | ❌ |
|
||
| [ViTMAE](../en/model_doc/vit_mae) | ✅ | ✅ | ❌ |
|
||
| [ViTMatte](../en/model_doc/vitmatte) | ✅ | ❌ | ❌ |
|
||
| [ViTMSN](../en/model_doc/vit_msn) | ✅ | ❌ | ❌ |
|
||
| [VITS](../en/model_doc/vits) | ✅ | ❌ | ❌ |
|
||
| [ViViT](../en/model_doc/vivit) | ✅ | ❌ | ❌ |
|
||
| [Wav2Vec2](../en/model_doc/wav2vec2) | ✅ | ✅ | ✅ |
|
||
| [Wav2Vec2-BERT](../en/model_doc/wav2vec2-bert) | ✅ | ❌ | ❌ |
|
||
| [Wav2Vec2-Conformer](../en/model_doc/wav2vec2-conformer) | ✅ | ❌ | ❌ |
|
||
| [Wav2Vec2Phoneme](../en/model_doc/wav2vec2_phoneme) | ✅ | ✅ | ✅ |
|
||
| [WavLM](../en/model_doc/wavlm) | ✅ | ❌ | ❌ |
|
||
| [Whisper](../en/model_doc/whisper) | ✅ | ✅ | ✅ |
|
||
| [X-CLIP](../en/model_doc/xclip) | ✅ | ❌ | ❌ |
|
||
| [X-MOD](../en/model_doc/xmod) | ✅ | ❌ | ❌ |
|
||
| [XGLM](../en/model_doc/xglm) | ✅ | ✅ | ✅ |
|
||
| [XLM](../en/model_doc/xlm) | ✅ | ✅ | ❌ |
|
||
| [XLM-ProphetNet](../en/model_doc/xlm-prophetnet) | ✅ | ❌ | ❌ |
|
||
| [XLM-RoBERTa](../en/model_doc/xlm-roberta) | ✅ | ✅ | ✅ |
|
||
| [XLM-RoBERTa-XL](../en/model_doc/xlm-roberta-xl) | ✅ | ❌ | ❌ |
|
||
| [XLM-V](../en/model_doc/xlm-v) | ✅ | ✅ | ✅ |
|
||
| [XLNet](../en/model_doc/xlnet) | ✅ | ✅ | ❌ |
|
||
| [XLS-R](../en/model_doc/xls_r) | ✅ | ✅ | ✅ |
|
||
| [XLSR-Wav2Vec2](../en/model_doc/xlsr_wav2vec2) | ✅ | ✅ | ✅ |
|
||
| [YOLOS](../en/model_doc/yolos) | ✅ | ❌ | ❌ |
|
||
| [YOSO](../en/model_doc/yoso) | ✅ | ❌ | ❌ |
|
||
|
||
<!-- End table-->
|