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Remove task guides auto-update in favor of links towards task pages (#30429)
This commit is contained in:
Lysandre Debut
GitHub
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commit
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@ -208,7 +208,6 @@ jobs:
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- run: python utils/check_doctest_list.py
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- run: make deps_table_check_updated
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- run: python utils/update_metadata.py --check-only
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- run: python utils/check_task_guides.py
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- run: python utils/check_docstrings.py
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- run: python utils/check_support_list.py
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2
Makefile
2
Makefile
@ -44,7 +44,6 @@ repo-consistency:
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python utils/check_config_attributes.py
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python utils/check_doctest_list.py
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python utils/update_metadata.py --check-only
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python utils/check_task_guides.py
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python utils/check_docstrings.py
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python utils/check_support_list.py
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@ -85,7 +84,6 @@ fix-copies:
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python utils/check_table.py --fix_and_overwrite
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python utils/check_dummies.py --fix_and_overwrite
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python utils/check_doctest_list.py --fix_and_overwrite
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python utils/check_task_guides.py --fix_and_overwrite
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python utils/check_docstrings.py --fix_and_overwrite
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# Run tests for the library
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@ -28,13 +28,8 @@ This guide will show you how to:
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2. Use your finetuned model for inference.
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<Tip>
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The task illustrated in this tutorial is supported by the following model architectures:
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
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[Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [M-CTC-T](../model_doc/mctct), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-BERT](../model_doc/wav2vec2-bert), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm)
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<!--End of the generated tip-->
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To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/automatic-speech-recognition)
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</Tip>
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@ -28,13 +28,8 @@ This guide will show you how to:
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2. Use your finetuned model for inference.
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<Tip>
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The task illustrated in this tutorial is supported by the following model architectures:
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
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[Audio Spectrogram Transformer](../model_doc/audio-spectrogram-transformer), [Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-BERT](../model_doc/wav2vec2-bert), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm), [Whisper](../model_doc/whisper)
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<!--End of the generated tip-->
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To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/audio-classification)
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</Tip>
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@ -30,13 +30,7 @@ This guide illustrates how to:
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<Tip>
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The task illustrated in this tutorial is supported by the following model architectures:
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
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[LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3)
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<!--End of the generated tip-->
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To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/image-to-text)
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</Tip>
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@ -30,13 +30,8 @@ This guide illustrates how to:
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2. Use your fine-tuned model for inference.
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<Tip>
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The task illustrated in this tutorial is supported by the following model architectures:
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
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[BEiT](../model_doc/beit), [BiT](../model_doc/bit), [CLIP](../model_doc/clip), [ConvNeXT](../model_doc/convnext), [ConvNeXTV2](../model_doc/convnextv2), [CvT](../model_doc/cvt), [Data2VecVision](../model_doc/data2vec-vision), [DeiT](../model_doc/deit), [DiNAT](../model_doc/dinat), [DINOv2](../model_doc/dinov2), [EfficientFormer](../model_doc/efficientformer), [EfficientNet](../model_doc/efficientnet), [FocalNet](../model_doc/focalnet), [ImageGPT](../model_doc/imagegpt), [LeViT](../model_doc/levit), [MobileNetV1](../model_doc/mobilenet_v1), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [NAT](../model_doc/nat), [Perceiver](../model_doc/perceiver), [PoolFormer](../model_doc/poolformer), [PVT](../model_doc/pvt), [PVTv2](../model_doc/pvt_v2), [RegNet](../model_doc/regnet), [ResNet](../model_doc/resnet), [SegFormer](../model_doc/segformer), [SigLIP](../model_doc/siglip), [SwiftFormer](../model_doc/swiftformer), [Swin Transformer](../model_doc/swin), [Swin Transformer V2](../model_doc/swinv2), [VAN](../model_doc/van), [ViT](../model_doc/vit), [ViT Hybrid](../model_doc/vit_hybrid), [ViTMSN](../model_doc/vit_msn)
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<!--End of the generated tip-->
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To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/image-classification)
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</Tip>
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@ -33,16 +33,8 @@ This guide will show you how to:
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2. Use your finetuned model for inference.
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<Tip>
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You can finetune other architectures for causal language modeling following the same steps in this guide.
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Choose one of the following architectures:
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
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[BART](../model_doc/bart), [BERT](../model_doc/bert), [Bert Generation](../model_doc/bert-generation), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BioGpt](../model_doc/biogpt), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CodeLlama](../model_doc/code_llama), [CodeGen](../model_doc/codegen), [Cohere](../model_doc/cohere), [CPM-Ant](../model_doc/cpmant), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [DBRX](../model_doc/dbrx), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [Falcon](../model_doc/falcon), [Fuyu](../model_doc/fuyu), [Gemma](../model_doc/gemma), [GIT](../model_doc/git), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT NeoX Japanese](../model_doc/gpt_neox_japanese), [GPT-J](../model_doc/gptj), [Jamba](../model_doc/jamba), [LLaMA](../model_doc/llama), [Mamba](../model_doc/mamba), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [Mistral](../model_doc/mistral), [Mixtral](../model_doc/mixtral), [MPT](../model_doc/mpt), [MusicGen](../model_doc/musicgen), [MusicGen Melody](../model_doc/musicgen_melody), [MVP](../model_doc/mvp), [OLMo](../model_doc/olmo), [OpenLlama](../model_doc/open-llama), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Pegasus](../model_doc/pegasus), [Persimmon](../model_doc/persimmon), [Phi](../model_doc/phi), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [QDQBert](../model_doc/qdqbert), [Qwen2](../model_doc/qwen2), [Qwen2MoE](../model_doc/qwen2_moe), [RecurrentGemma](../model_doc/recurrent_gemma), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [RWKV](../model_doc/rwkv), [Speech2Text2](../model_doc/speech_to_text_2), [StableLm](../model_doc/stablelm), [Starcoder2](../model_doc/starcoder2), [Transformer-XL](../model_doc/transfo-xl), [TrOCR](../model_doc/trocr), [Whisper](../model_doc/whisper), [XGLM](../model_doc/xglm), [XLM](../model_doc/xlm), [XLM-ProphetNet](../model_doc/xlm-prophetnet), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod)
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<!--End of the generated tip-->
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To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/text-generation)
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</Tip>
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@ -30,14 +30,8 @@ This guide will show you how to:
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2. Use your finetuned model for inference.
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<Tip>
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You can finetune other architectures for masked language modeling following the same steps in this guide.
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Choose one of the following architectures:
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
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[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [CamemBERT](../model_doc/camembert), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MRA](../model_doc/mra), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [Perceiver](../model_doc/perceiver), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [TAPAS](../model_doc/tapas), [Wav2Vec2](../model_doc/wav2vec2), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
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<!--End of the generated tip-->
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To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/fill-mask)
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</Tip>
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@ -26,13 +26,8 @@ in the scene and the corresponding depth information, which can be affected by f
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occlusion, and texture.
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<Tip>
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The task illustrated in this tutorial is supported by the following model architectures:
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
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[Depth Anything](../model_doc/depth_anything), [DPT](../model_doc/dpt), [GLPN](../model_doc/glpn)
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<!--End of the generated tip-->
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To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/depth-anything)
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</Tip>
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@ -25,17 +25,6 @@ This guide will show you how to:
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1. Finetune [BERT](https://huggingface.co/google-bert/bert-base-uncased) on the `regular` configuration of the [SWAG](https://huggingface.co/datasets/swag) dataset to select the best answer given multiple options and some context.
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2. Use your finetuned model for inference.
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<Tip>
|
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The task illustrated in this tutorial is supported by the following model architectures:
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
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[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [I-BERT](../model_doc/ibert), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MRA](../model_doc/mra), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
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<!--End of the generated tip-->
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</Tip>
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Before you begin, make sure you have all the necessary libraries installed:
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```bash
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@ -33,13 +33,8 @@ In this guide, you will learn how to:
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2. Use your finetuned model for inference.
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<Tip>
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The task illustrated in this tutorial is supported by the following model architectures:
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
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[Conditional DETR](../model_doc/conditional_detr), [Deformable DETR](../model_doc/deformable_detr), [DETA](../model_doc/deta), [DETR](../model_doc/detr), [Table Transformer](../model_doc/table-transformer), [YOLOS](../model_doc/yolos)
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<!--End of the generated tip-->
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To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/object-detection)
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</Tip>
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@ -31,15 +31,8 @@ This guide will show you how to:
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2. Use your finetuned model for inference.
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<Tip>
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The task illustrated in this tutorial is supported by the following model architectures:
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
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[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [Falcon](../model_doc/falcon), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [OpenAI GPT-2](../model_doc/gpt2), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [LLaMA](../model_doc/llama), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [LXMERT](../model_doc/lxmert), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MPT](../model_doc/mpt), [MRA](../model_doc/mra), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OPT](../model_doc/opt), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [Splinter](../model_doc/splinter), [SqueezeBERT](../model_doc/squeezebert), [T5](../model_doc/t5), [UMT5](../model_doc/umt5), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
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<!--End of the generated tip-->
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To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/question-answering)
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</Tip>
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@ -201,13 +201,8 @@ We will now:
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2. Use your fine-tuned model for inference.
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<Tip>
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The task illustrated in this tutorial is supported by the following model architectures:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
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[BEiT](../model_doc/beit), [Data2VecVision](../model_doc/data2vec-vision), [DPT](../model_doc/dpt), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [SegFormer](../model_doc/segformer), [UPerNet](../model_doc/upernet)
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<!--End of the generated tip-->
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To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/image-segmentation)
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</Tip>
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@ -28,16 +28,8 @@ This guide will show you how to:
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2. Use your finetuned model for inference.
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<Tip>
|
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The task illustrated in this tutorial is supported by the following model architectures:
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||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
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|
||||
[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BioGpt](../model_doc/biogpt), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [CodeLlama](../model_doc/code_llama), [ConvBERT](../model_doc/convbert), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [ESM](../model_doc/esm), [Falcon](../model_doc/falcon), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [Gemma](../model_doc/gemma), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [Jamba](../model_doc/jamba), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [LLaMA](../model_doc/llama), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [Mistral](../model_doc/mistral), [Mixtral](../model_doc/mixtral), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MPT](../model_doc/mpt), [MRA](../model_doc/mra), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OpenLlama](../model_doc/open-llama), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Perceiver](../model_doc/perceiver), [Persimmon](../model_doc/persimmon), [Phi](../model_doc/phi), [PLBart](../model_doc/plbart), [QDQBert](../model_doc/qdqbert), [Qwen2](../model_doc/qwen2), [Qwen2MoE](../model_doc/qwen2_moe), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [StableLm](../model_doc/stablelm), [Starcoder2](../model_doc/starcoder2), [T5](../model_doc/t5), [TAPAS](../model_doc/tapas), [Transformer-XL](../model_doc/transfo-xl), [UMT5](../model_doc/umt5), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
|
||||
|
||||
|
||||
|
||||
<!--End of the generated tip-->
|
||||
To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/text-classification).
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -31,13 +31,8 @@ This guide will show you how to:
|
||||
2. Use your finetuned model for inference.
|
||||
|
||||
<Tip>
|
||||
The task illustrated in this tutorial is supported by the following model architectures:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[BART](../model_doc/bart), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [Encoder decoder](../model_doc/encoder-decoder), [FairSeq Machine-Translation](../model_doc/fsmt), [GPTSAN-japanese](../model_doc/gptsan-japanese), [LED](../model_doc/led), [LongT5](../model_doc/longt5), [M2M100](../model_doc/m2m_100), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [NLLB](../model_doc/nllb), [NLLB-MOE](../model_doc/nllb-moe), [Pegasus](../model_doc/pegasus), [PEGASUS-X](../model_doc/pegasus_x), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [SeamlessM4T](../model_doc/seamless_m4t), [SeamlessM4Tv2](../model_doc/seamless_m4t_v2), [SwitchTransformers](../model_doc/switch_transformers), [T5](../model_doc/t5), [UMT5](../model_doc/umt5), [XLM-ProphetNet](../model_doc/xlm-prophetnet)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/summarization)
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -28,13 +28,8 @@ This guide will show you how to:
|
||||
2. Use your finetuned model for inference.
|
||||
|
||||
<Tip>
|
||||
The task illustrated in this tutorial is supported by the following model architectures:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BioGpt](../model_doc/biogpt), [BLOOM](../model_doc/bloom), [BROS](../model_doc/bros), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [ESM](../model_doc/esm), [Falcon](../model_doc/falcon), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MPT](../model_doc/mpt), [MRA](../model_doc/mra), [MT5](../model_doc/mt5), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [Phi](../model_doc/phi), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [T5](../model_doc/t5), [UMT5](../model_doc/umt5), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/token-classification).
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -28,13 +28,8 @@ This guide will show you how to:
|
||||
2. Use your finetuned model for inference.
|
||||
|
||||
<Tip>
|
||||
The task illustrated in this tutorial is supported by the following model architectures:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[BART](../model_doc/bart), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [Encoder decoder](../model_doc/encoder-decoder), [FairSeq Machine-Translation](../model_doc/fsmt), [GPTSAN-japanese](../model_doc/gptsan-japanese), [LED](../model_doc/led), [LongT5](../model_doc/longt5), [M2M100](../model_doc/m2m_100), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [NLLB](../model_doc/nllb), [NLLB-MOE](../model_doc/nllb-moe), [Pegasus](../model_doc/pegasus), [PEGASUS-X](../model_doc/pegasus_x), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [SeamlessM4T](../model_doc/seamless_m4t), [SeamlessM4Tv2](../model_doc/seamless_m4t_v2), [SwitchTransformers](../model_doc/switch_transformers), [T5](../model_doc/t5), [UMT5](../model_doc/umt5), [XLM-ProphetNet](../model_doc/xlm-prophetnet)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/translation).
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -26,13 +26,8 @@ This guide will show you how to:
|
||||
2. Use your fine-tuned model for inference.
|
||||
|
||||
<Tip>
|
||||
The task illustrated in this tutorial is supported by the following model architectures:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[TimeSformer](../model_doc/timesformer), [VideoMAE](../model_doc/videomae), [ViViT](../model_doc/vivit)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/video-classification).
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -30,8 +30,6 @@ Esta guía te mostrará cómo realizar fine-tuning [DistilGPT2](https://huggingf
|
||||
|
||||
<Tip>
|
||||
|
||||
Puedes realizar fine-tuning a otras arquitecturas para modelos de lenguaje como [GPT-Neo](https://huggingface.co/EleutherAI/gpt-neo-125M), [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) y [BERT](https://huggingface.co/google-bert/bert-base-uncased) siguiendo los mismos pasos presentados en esta guía!
|
||||
|
||||
Mira la [página de tarea](https://huggingface.co/tasks/text-generation) para generación de texto y la [página de tarea](https://huggingface.co/tasks/fill-mask) para modelos de lenguajes por enmascaramiento para obtener más información sobre los modelos, datasets, y métricas asociadas.
|
||||
|
||||
</Tip>
|
||||
|
||||
@ -28,13 +28,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 微調整したモデルを推論に使用します。
|
||||
|
||||
<Tip>
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [M-CTC-T](../model_doc/mctct), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/automatic-speech-recognition) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -29,18 +29,11 @@ rendered properly in your Markdown viewer.
|
||||
2. 微調整したモデルを推論に使用します。
|
||||
|
||||
<Tip>
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[Audio Spectrogram Transformer](../model_doc/audio-spectrogram-transformer), [Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm), [Whisper](../model_doc/whisper)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/audio-classification) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
始める前に、必要なライブラリがすべてインストールされていることを確認してください。
|
||||
|
||||
```bash
|
||||
pip install transformers datasets evaluate
|
||||
```
|
||||
|
||||
@ -30,14 +30,7 @@ rendered properly in your Markdown viewer.
|
||||
|
||||
<Tip>
|
||||
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/image-to-text) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -31,13 +31,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 微調整したモデルを推論に使用します。
|
||||
|
||||
<Tip>
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[BEiT](../model_doc/beit), [BiT](../model_doc/bit), [ConvNeXT](../model_doc/convnext), [ConvNeXTV2](../model_doc/convnextv2), [CvT](../model_doc/cvt), [Data2VecVision](../model_doc/data2vec-vision), [DeiT](../model_doc/deit), [DiNAT](../model_doc/dinat), [DINOv2](../model_doc/dinov2), [EfficientFormer](../model_doc/efficientformer), [EfficientNet](../model_doc/efficientnet), [FocalNet](../model_doc/focalnet), [ImageGPT](../model_doc/imagegpt), [LeViT](../model_doc/levit), [MobileNetV1](../model_doc/mobilenet_v1), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [NAT](../model_doc/nat), [Perceiver](../model_doc/perceiver), [PoolFormer](../model_doc/poolformer), [PVT](../model_doc/pvt), [RegNet](../model_doc/regnet), [ResNet](../model_doc/resnet), [SegFormer](../model_doc/segformer), [SwiftFormer](../model_doc/swiftformer), [Swin Transformer](../model_doc/swin), [Swin Transformer V2](../model_doc/swinv2), [VAN](../model_doc/van), [ViT](../model_doc/vit), [ViT Hybrid](../model_doc/vit_hybrid), [ViTMSN](../model_doc/vit_msn)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/image-classification) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -37,14 +37,7 @@ rendered properly in your Markdown viewer.
|
||||
|
||||
<Tip>
|
||||
|
||||
このガイドと同じ手順に従って、因果言語モデリング用に他のアーキテクチャを微調整できます。
|
||||
次のアーキテクチャのいずれかを選択します。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
[BART](../model_doc/bart), [BERT](../model_doc/bert), [Bert Generation](../model_doc/bert-generation), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BioGpt](../model_doc/biogpt), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CodeLlama](../model_doc/code_llama), [CodeGen](../model_doc/codegen), [CPM-Ant](../model_doc/cpmant), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [Falcon](../model_doc/falcon), [Fuyu](../model_doc/fuyu), [GIT](../model_doc/git), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT NeoX Japanese](../model_doc/gpt_neox_japanese), [GPT-J](../model_doc/gptj), [LLaMA](../model_doc/llama), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [Mistral](../model_doc/mistral), [MPT](../model_doc/mpt), [MusicGen](../model_doc/musicgen), [MVP](../model_doc/mvp), [OpenLlama](../model_doc/open-llama), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Pegasus](../model_doc/pegasus), [Persimmon](../model_doc/persimmon), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [RWKV](../model_doc/rwkv), [Speech2Text2](../model_doc/speech_to_text_2), [Transformer-XL](../model_doc/transfo-xl), [TrOCR](../model_doc/trocr), [XGLM](../model_doc/xglm), [XLM](../model_doc/xlm), [XLM-ProphetNet](../model_doc/xlm-prophetnet), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod)
|
||||
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/text-generation) を確認することをお勧めします。u
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -30,14 +30,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 微調整したモデルを推論に使用します。
|
||||
|
||||
<Tip>
|
||||
このガイドと同じ手順に従って、マスクされた言語モデリング用に他のアーキテクチャを微調整できます。
|
||||
次のアーキテクチャのいずれかを選択します。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [CamemBERT](../model_doc/camembert), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MRA](../model_doc/mra), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [Perceiver](../model_doc/perceiver), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [TAPAS](../model_doc/tapas), [Wav2Vec2](../model_doc/wav2vec2), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/fill-mask) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -26,13 +26,8 @@ rendered properly in your Markdown viewer.
|
||||
オクルージョンとテクスチャ。
|
||||
|
||||
<Tip>
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[DPT](../model_doc/dpt), [GLPN](../model_doc/glpn)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/depth-estimation) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -25,17 +25,6 @@ rendered properly in your Markdown viewer.
|
||||
1. [SWAG](https://huggingface.co/datasets/swag) データセットの「通常」構成で [BERT](https://huggingface.co/google-bert/bert-base-uncased) を微調整して、最適なデータセットを選択します複数の選択肢と何らかのコンテキストを考慮して回答します。
|
||||
2. 微調整したモデルを推論に使用します。
|
||||
|
||||
<Tip>
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [I-BERT](../model_doc/ibert), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MRA](../model_doc/mra), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
|
||||
</Tip>
|
||||
|
||||
始める前に、必要なライブラリがすべてインストールされていることを確認してください。
|
||||
|
||||
```bash
|
||||
|
||||
@ -33,13 +33,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 微調整したモデルを推論に使用します。
|
||||
|
||||
<Tip>
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[Conditional DETR](../model_doc/conditional_detr), [Deformable DETR](../model_doc/deformable_detr), [DETA](../model_doc/deta), [DETR](../model_doc/detr), [Table Transformer](../model_doc/table-transformer), [YOLOS](../model_doc/yolos)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/object-detection) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -31,15 +31,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 微調整したモデルを推論に使用します。
|
||||
|
||||
<Tip>
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
|
||||
[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [Falcon](../model_doc/falcon), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [OpenAI GPT-2](../model_doc/gpt2), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [LXMERT](../model_doc/lxmert), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MPT](../model_doc/mpt), [MRA](../model_doc/mra), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OPT](../model_doc/opt), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [Splinter](../model_doc/splinter), [SqueezeBERT](../model_doc/squeezebert), [T5](../model_doc/t5), [UMT5](../model_doc/umt5), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
|
||||
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/question-answering) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -29,13 +29,7 @@ rendered properly in your Markdown viewer.
|
||||
|
||||
<Tip>
|
||||
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[BEiT](../model_doc/beit), [Data2VecVision](../model_doc/data2vec-vision), [DPT](../model_doc/dpt), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [SegFormer](../model_doc/segformer), [UPerNet](../model_doc/upernet)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/image-segmentation) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -28,13 +28,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 微調整したモデルを推論に使用します。
|
||||
|
||||
<Tip>
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[BEiT](../model_doc/beit), [Data2VecVision](../model_doc/data2vec-vision), [DPT](../model_doc/dpt), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [SegFormer](../model_doc/segformer), [UPerNet](../model_doc/upernet)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/text-classification) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -31,13 +31,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 微調整したモデルを推論に使用します。
|
||||
|
||||
<Tip>
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[BART](../model_doc/bart), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [Encoder decoder](../model_doc/encoder-decoder), [FairSeq Machine-Translation](../model_doc/fsmt), [GPTSAN-japanese](../model_doc/gptsan-japanese), [LED](../model_doc/led), [LongT5](../model_doc/longt5), [M2M100](../model_doc/m2m_100), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [NLLB](../model_doc/nllb), [NLLB-MOE](../model_doc/nllb-moe), [Pegasus](../model_doc/pegasus), [PEGASUS-X](../model_doc/pegasus_x), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [SwitchTransformers](../model_doc/switch_transformers), [T5](../model_doc/t5), [UMT5](../model_doc/umt5), [XLM-ProphetNet](../model_doc/xlm-prophetnet)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/summarization) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -28,12 +28,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 微調整されたモデルを推論に使用します。
|
||||
|
||||
<Tip>
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BioGpt](../model_doc/biogpt), [BLOOM](../model_doc/bloom), [BROS](../model_doc/bros), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [ESM](../model_doc/esm), [Falcon](../model_doc/falcon), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MPT](../model_doc/mpt), [MRA](../model_doc/mra), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/token-classification) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -28,13 +28,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 微調整されたモデルを推論に使用します。
|
||||
|
||||
<Tip>
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[BART](../model_doc/bart), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [Encoder decoder](../model_doc/encoder-decoder), [FairSeq Machine-Translation](../model_doc/fsmt), [GPTSAN-japanese](../model_doc/gptsan-japanese), [LED](../model_doc/led), [LongT5](../model_doc/longt5), [M2M100](../model_doc/m2m_100), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [NLLB](../model_doc/nllb), [NLLB-MOE](../model_doc/nllb-moe), [Pegasus](../model_doc/pegasus), [PEGASUS-X](../model_doc/pegasus_x), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [SwitchTransformers](../model_doc/switch_transformers), [T5](../model_doc/t5), [UMT5](../model_doc/umt5), [XLM-ProphetNet](../model_doc/xlm-prophetnet)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/translation) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -27,13 +27,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 微調整したモデルを推論に使用します。
|
||||
|
||||
<Tip>
|
||||
このチュートリアルで説明するタスクは、次のモデル アーキテクチャでサポートされています。
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[TimeSformer](../model_doc/timesformer), [VideoMAE](../model_doc/videomae), [ViViT](../model_doc/vivit)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/video-classification) を確認することをお勧めします。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -29,13 +29,8 @@ Siri와 Alexa와 같은 가상 어시스턴트는 ASR 모델을 사용하여 일
|
||||
2. 미세 조정한 모델을 추론에 사용합니다.
|
||||
|
||||
<Tip>
|
||||
이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에 의해 지원됩니다:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [M-CTC-T](../model_doc/mctct), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/automatic-speech-recognition)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -28,13 +28,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 추론에 미세 조정된 모델을 사용하세요.
|
||||
|
||||
<Tip>
|
||||
이 튜토리얼에서 설명하는 작업은 아래의 모델 아키텍처에서 지원됩니다:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[Audio Spectrogram Transformer](../model_doc/audio-spectrogram-transformer), [Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm), [Whisper](../model_doc/whisper)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/audio-classification)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -29,13 +29,7 @@ rendered properly in your Markdown viewer.
|
||||
|
||||
<Tip>
|
||||
|
||||
이 튜토리얼에서 설명하는 태스크는 다음과 같은 모델 아키텍처에서 지원됩니다:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/image-to-text)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -30,12 +30,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 추론을 위해 미세 조정 모델을 사용합니다.
|
||||
|
||||
<Tip>
|
||||
이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에 의해 지원됩니다:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[BEiT](../model_doc/beit), [BiT](../model_doc/bit), [ConvNeXT](../model_doc/convnext), [ConvNeXTV2](../model_doc/convnextv2), [CvT](../model_doc/cvt), [Data2VecVision](../model_doc/data2vec-vision), [DeiT](../model_doc/deit), [DiNAT](../model_doc/dinat), [EfficientFormer](../model_doc/efficientformer), [EfficientNet](../model_doc/efficientnet), [FocalNet](../model_doc/focalnet), [ImageGPT](../model_doc/imagegpt), [LeViT](../model_doc/levit), [MobileNetV1](../model_doc/mobilenet_v1), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [NAT](../model_doc/nat), [Perceiver](../model_doc/perceiver), [PoolFormer](../model_doc/poolformer), [RegNet](../model_doc/regnet), [ResNet](../model_doc/resnet), [SegFormer](../model_doc/segformer), [Swin Transformer](../model_doc/swin), [Swin Transformer V2](../model_doc/swinv2), [VAN](../model_doc/van), [ViT](../model_doc/vit), [ViT Hybrid](../model_doc/vit_hybrid), [ViTMSN](../model_doc/vit_msn)
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/image-classification)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -33,14 +33,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 미세 조정된 모델을 추론에 사용
|
||||
|
||||
<Tip>
|
||||
이 안내서의 단계와 동일한 방법으로 인과 언어 모델링을 위해 다른 아키텍처를 미세 조정할 수 있습니다.
|
||||
다음 아키텍처 중 하나를 선택하세요:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
[BART](../model_doc/bart), [BERT](../model_doc/bert), [Bert Generation](../model_doc/bert-generation), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BioGpt](../model_doc/biogpt), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CodeGen](../model_doc/codegen), [CPM-Ant](../model_doc/cpmant), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [GIT](../model_doc/git), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT NeoX Japanese](../model_doc/gpt_neox_japanese), [GPT-J](../model_doc/gptj), [LLaMA](../model_doc/llama), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MVP](../model_doc/mvp), [OpenLlama](../model_doc/open-llama), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Pegasus](../model_doc/pegasus), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [RWKV](../model_doc/rwkv), [Speech2Text2](../model_doc/speech_to_text_2), [Transformer-XL](../model_doc/transfo-xl), [TrOCR](../model_doc/trocr), [XGLM](../model_doc/xglm), [XLM](../model_doc/xlm), [XLM-ProphetNet](../model_doc/xlm-prophetnet), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod)
|
||||
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/text-generation)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -30,15 +30,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 추론 시에 직접 미세 조정한 모델을 사용합니다.
|
||||
|
||||
<Tip>
|
||||
이번 가이드에서처럼 다른 아키텍처를 미세 조정해 마스킹된 언어 모델링을 할 수 있습니다.
|
||||
|
||||
다음 아키텍쳐 중 하나를 선택하세요:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [CamemBERT](../model_doc/camembert), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [Perceiver](../model_doc/perceiver), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [TAPAS](../model_doc/tapas), [Wav2Vec2](../model_doc/wav2vec2), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/fill-mask)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -24,13 +24,8 @@ rendered properly in your Markdown viewer.
|
||||
|
||||
|
||||
<Tip>
|
||||
이 튜토리얼에서 다루는 작업은 다음 모델 아키텍처에서 지원됩니다:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[DPT](../model_doc/dpt), [GLPN](../model_doc/glpn)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/depth-estimation)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -25,17 +25,6 @@ rendered properly in your Markdown viewer.
|
||||
1. [SWAG](https://huggingface.co/datasets/swag) 데이터 세트의 'regular' 구성으로 [BERT](https://huggingface.co/google-bert/bert-base-uncased)를 미세 조정하여 여러 옵션과 일부 컨텍스트가 주어졌을 때 가장 적합한 답을 선택합니다.
|
||||
2. 추론에 미세 조정된 모델을 사용합니다.
|
||||
|
||||
<Tip>
|
||||
이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에서 지원됩니다:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [I-BERT](../model_doc/ibert), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
|
||||
</Tip>
|
||||
|
||||
시작하기 전에 필요한 라이브러리가 모두 설치되어 있는지 확인하세요:
|
||||
|
||||
```bash
|
||||
|
||||
@ -30,13 +30,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 미세조정 한 모델을 추론에 사용하기.
|
||||
|
||||
<Tip>
|
||||
이 튜토리얼의 태스크는 다음 모델 아키텍처에서 지원됩니다:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[Conditional DETR](../model_doc/conditional_detr), [Deformable DETR](../model_doc/deformable_detr), [DETA](../model_doc/deta), [DETR](../model_doc/detr), [Table Transformer](../model_doc/table-transformer), [YOLOS](../model_doc/yolos)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/object-detection)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -31,14 +31,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 추론에 미세 조정된 모델 사용하기
|
||||
|
||||
<Tip>
|
||||
이 튜토리얼에서 설명하는 태스크는 다음과 같은 모델 아키텍처에서 지원됩니다.
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [LXMERT](../model_doc/lxmert), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OPT](../model_doc/opt), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [Splinter](../model_doc/splinter), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
|
||||
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/question-answering)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -29,13 +29,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 미세 조정된 모델을 추론에 사용하기.
|
||||
|
||||
<Tip>
|
||||
이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에서 지원됩니다:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[BEiT](../model_doc/beit), [Data2VecVision](../model_doc/data2vec-vision), [DPT](../model_doc/dpt), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [SegFormer](../model_doc/segformer), [UPerNet](../model_doc/upernet)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/image-segmentation)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -28,14 +28,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 추론을 위해 파인 튜닝 모델을 사용합니다.
|
||||
|
||||
<Tip>
|
||||
이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에 의해 지원됩니다:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPT Neo](../model_doc/gpt_neo), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [LLaMA](../model_doc/llama), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Perceiver](../model_doc/perceiver), [PLBart](../model_doc/plbart), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [TAPAS](../model_doc/tapas), [Transformer-XL](../model_doc/transfo-xl), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
|
||||
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/text-classification)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -33,13 +33,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 파인튜닝된 모델을 사용하여 추론합니다.
|
||||
|
||||
<Tip>
|
||||
이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에서 지원됩니다:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[BART](../model_doc/bart), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [Encoder decoder](../model_doc/encoder-decoder), [FairSeq Machine-Translation](../model_doc/fsmt), [GPTSAN-japanese](../model_doc/gptsan-japanese), [LED](../model_doc/led), [LongT5](../model_doc/longt5), [M2M100](../model_doc/m2m_100), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [NLLB](../model_doc/nllb), [NLLB-MOE](../model_doc/nllb-moe), [Pegasus](../model_doc/pegasus), [PEGASUS-X](../model_doc/pegasus_x), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [SwitchTransformers](../model_doc/switch_transformers), [T5](../model_doc/t5), [XLM-ProphetNet](../model_doc/xlm-prophetnet)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/summarization)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -28,13 +28,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 추론을 위해 파인 튜닝 모델을 사용합니다.
|
||||
|
||||
<Tip>
|
||||
이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에 의해 지원됩니다:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BioGpt](../model_doc/biogpt), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/token-classification)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -28,13 +28,8 @@ rendered properly in your Markdown viewer.
|
||||
2. 파인튜닝된 모델을 추론에 사용하는 방법입니다.
|
||||
|
||||
<Tip>
|
||||
이 태스크 가이드는 아래 모델 아키텍처에도 응용할 수 있습니다.
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[BART](../model_doc/bart), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [Encoder decoder](../model_doc/encoder-decoder), [FairSeq Machine-Translation](../model_doc/fsmt), [GPTSAN-japanese](../model_doc/gptsan-japanese), [LED](../model_doc/led), [LongT5](../model_doc/longt5), [M2M100](../model_doc/m2m_100), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [NLLB](../model_doc/nllb), [NLLB-MOE](../model_doc/nllb-moe), [Pegasus](../model_doc/pegasus), [PEGASUS-X](../model_doc/pegasus_x), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [SwitchTransformers](../model_doc/switch_transformers), [T5](../model_doc/t5), [XLM-ProphetNet](../model_doc/xlm-prophetnet)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/translation)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -28,13 +28,7 @@ rendered properly in your Markdown viewer.
|
||||
|
||||
<Tip>
|
||||
|
||||
이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에서 지원됩니다:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[TimeSformer](../model_doc/timesformer), [VideoMAE](../model_doc/videomae)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/video-classification)를 확인하는 것이 좋습니다.
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -31,13 +31,7 @@ Siri 和 Alexa 这类虚拟助手使用 ASR 模型来帮助用户日常生活，
|
||||
|
||||
<Tip>
|
||||
|
||||
本教程中展示的任务受以下模型架构的支持：
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
[Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [M-CTC-T](../model_doc/mctct), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-BERT](../model_doc/wav2vec2-bert), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm)
|
||||
|
||||
<!--End of the generated tip-->
|
||||
如果您想查看所有与本任务兼容的架构和检查点，最好查看[任务页](https://huggingface.co/tasks/automatic-speech-recognition)。
|
||||
|
||||
</Tip>
|
||||
|
||||
|
||||
@ -1,168 +0,0 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2023 The HuggingFace Inc. team.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
Utility that checks the list of models in the tips in the task-specific pages of the doc is up to date and potentially
|
||||
fixes it.
|
||||
|
||||
Use from the root of the repo with:
|
||||
|
||||
```bash
|
||||
python utils/check_task_guides.py
|
||||
```
|
||||
|
||||
for a check that will error in case of inconsistencies (used by `make repo-consistency`).
|
||||
|
||||
To auto-fix issues run:
|
||||
|
||||
```bash
|
||||
python utils/check_task_guides.py --fix_and_overwrite
|
||||
```
|
||||
|
||||
which is used by `make fix-copies`.
|
||||
"""
|
||||
import argparse
|
||||
import os
|
||||
|
||||
from transformers.utils import direct_transformers_import
|
||||
|
||||
|
||||
# All paths are set with the intent you should run this script from the root of the repo with the command
|
||||
# python utils/check_task_guides.py
|
||||
TRANSFORMERS_PATH = "src/transformers"
|
||||
PATH_TO_TASK_GUIDES = "docs/source/en/tasks"
|
||||
|
||||
|
||||
def _find_text_in_file(filename: str, start_prompt: str, end_prompt: str) -> str:
|
||||
"""
|
||||
Find the text in filename between two prompts.
|
||||
|
||||
Args:
|
||||
filename (`str`): The file to search into.
|
||||
start_prompt (`str`): A string to look for at the start of the content searched.
|
||||
end_prompt (`str`): A string that will mark the end of the content to look for.
|
||||
|
||||
Returns:
|
||||
`str`: The content between the prompts.
|
||||
"""
|
||||
with open(filename, "r", encoding="utf-8", newline="\n") as f:
|
||||
lines = f.readlines()
|
||||
# Find the start prompt.
|
||||
start_index = 0
|
||||
while not lines[start_index].startswith(start_prompt):
|
||||
start_index += 1
|
||||
start_index += 1
|
||||
|
||||
# Now go until the end prompt.
|
||||
end_index = start_index
|
||||
while not lines[end_index].startswith(end_prompt):
|
||||
end_index += 1
|
||||
end_index -= 1
|
||||
|
||||
while len(lines[start_index]) <= 1:
|
||||
start_index += 1
|
||||
while len(lines[end_index]) <= 1:
|
||||
end_index -= 1
|
||||
end_index += 1
|
||||
return "".join(lines[start_index:end_index]), start_index, end_index, lines
|
||||
|
||||
|
||||
# This is to make sure the transformers module imported is the one in the repo.
|
||||
transformers_module = direct_transformers_import(TRANSFORMERS_PATH)
|
||||
|
||||
# Map between a task guide and the corresponding auto class.
|
||||
TASK_GUIDE_TO_MODELS = {
|
||||
"asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
|
||||
"audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
|
||||
"language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
|
||||
"image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
|
||||
"masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
|
||||
"multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
|
||||
"object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
|
||||
"question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
|
||||
"semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
|
||||
"sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
|
||||
"summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
|
||||
"token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
|
||||
"translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
|
||||
"video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
|
||||
"document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
|
||||
"monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
|
||||
}
|
||||
|
||||
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
|
||||
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
|
||||
SPECIAL_TASK_GUIDE_TO_MODEL_TYPES = {
|
||||
"summarization.md": ("nllb",),
|
||||
"translation.md": ("nllb",),
|
||||
}
|
||||
|
||||
|
||||
def get_model_list_for_task(task_guide: str) -> str:
|
||||
"""
|
||||
Return the list of models supporting a given task.
|
||||
|
||||
Args:
|
||||
task_guide (`str`): The name of the task guide to check.
|
||||
|
||||
Returns:
|
||||
`str`: The list of models supporting this task, as links to their respective doc pages separated by commas.
|
||||
"""
|
||||
model_maping_names = TASK_GUIDE_TO_MODELS[task_guide]
|
||||
special_model_types = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(task_guide, set())
|
||||
model_names = {
|
||||
code: name
|
||||
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
|
||||
if (code in model_maping_names or code in special_model_types)
|
||||
}
|
||||
return ", ".join([f"[{name}](../model_doc/{code})" for code, name in model_names.items()]) + "\n"
|
||||
|
||||
|
||||
def check_model_list_for_task(task_guide: str, overwrite: bool = False):
|
||||
"""
|
||||
For a given task guide, checks the model list in the generated tip for consistency with the state of the lib and
|
||||
updates it if needed.
|
||||
|
||||
Args:
|
||||
task_guide (`str`):
|
||||
The name of the task guide to check.
|
||||
overwrite (`bool`, *optional*, defaults to `False`):
|
||||
Whether or not to overwrite the table when it's not up to date.
|
||||
"""
|
||||
current_list, start_index, end_index, lines = _find_text_in_file(
|
||||
filename=os.path.join(PATH_TO_TASK_GUIDES, task_guide),
|
||||
start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->",
|
||||
end_prompt="<!--End of the generated tip-->",
|
||||
)
|
||||
|
||||
new_list = get_model_list_for_task(task_guide)
|
||||
|
||||
if current_list != new_list:
|
||||
if overwrite:
|
||||
with open(os.path.join(PATH_TO_TASK_GUIDES, task_guide), "w", encoding="utf-8", newline="\n") as f:
|
||||
f.writelines(lines[:start_index] + [new_list] + lines[end_index:])
|
||||
else:
|
||||
raise ValueError(
|
||||
f"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
|
||||
" to fix this."
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
|
||||
args = parser.parse_args()
|
||||
|
||||
for task_guide in TASK_GUIDE_TO_MODELS.keys():
|
||||
check_model_list_for_task(task_guide, args.fix_and_overwrite)
|
||||
Loading…
Reference in New Issue
Block a user