diff --git a/.circleci/config.yml b/.circleci/config.yml index d5e9ac799fe..044493315d2 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -208,7 +208,6 @@ jobs: - run: python utils/check_doctest_list.py - run: make deps_table_check_updated - run: python utils/update_metadata.py --check-only - - run: python utils/check_task_guides.py - run: python utils/check_docstrings.py - run: python utils/check_support_list.py diff --git a/Makefile b/Makefile index 49535b5694d..ebc66d922cd 100644 --- a/Makefile +++ b/Makefile @@ -44,7 +44,6 @@ repo-consistency: python utils/check_config_attributes.py python utils/check_doctest_list.py python utils/update_metadata.py --check-only - python utils/check_task_guides.py python utils/check_docstrings.py python utils/check_support_list.py @@ -85,7 +84,6 @@ fix-copies: python utils/check_table.py --fix_and_overwrite python utils/check_dummies.py --fix_and_overwrite python utils/check_doctest_list.py --fix_and_overwrite - python utils/check_task_guides.py --fix_and_overwrite python utils/check_docstrings.py --fix_and_overwrite # Run tests for the library diff --git a/docs/source/en/tasks/asr.md b/docs/source/en/tasks/asr.md index a1a96271102..3222f70c4d2 100644 --- a/docs/source/en/tasks/asr.md +++ b/docs/source/en/tasks/asr.md @@ -28,13 +28,8 @@ This guide will show you how to: 2. Use your finetuned model for inference. -The task illustrated in this tutorial is supported by the following model architectures: - - -[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) - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/automatic-speech-recognition) diff --git a/docs/source/en/tasks/audio_classification.md b/docs/source/en/tasks/audio_classification.md index 5ea3567f4c3..c50107e44f1 100644 --- a/docs/source/en/tasks/audio_classification.md +++ b/docs/source/en/tasks/audio_classification.md @@ -28,13 +28,8 @@ This guide will show you how to: 2. Use your finetuned model for inference. -The task illustrated in this tutorial is supported by the following model architectures: - - -[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) - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/audio-classification) diff --git a/docs/source/en/tasks/document_question_answering.md b/docs/source/en/tasks/document_question_answering.md index 3d3acf0541d..54c0cd5aef3 100644 --- a/docs/source/en/tasks/document_question_answering.md +++ b/docs/source/en/tasks/document_question_answering.md @@ -30,13 +30,7 @@ This guide illustrates how to: -The task illustrated in this tutorial is supported by the following model architectures: - - - -[LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3) - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/image-to-text) diff --git a/docs/source/en/tasks/image_classification.md b/docs/source/en/tasks/image_classification.md index 25f232bc00a..81ff45c4c8d 100644 --- a/docs/source/en/tasks/image_classification.md +++ b/docs/source/en/tasks/image_classification.md @@ -30,13 +30,8 @@ This guide illustrates how to: 2. Use your fine-tuned model for inference. -The task illustrated in this tutorial is supported by the following model architectures: - - -[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) - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/image-classification) diff --git a/docs/source/en/tasks/language_modeling.md b/docs/source/en/tasks/language_modeling.md index 5c7bcd8595c..af26ab1e44a 100644 --- a/docs/source/en/tasks/language_modeling.md +++ b/docs/source/en/tasks/language_modeling.md @@ -33,16 +33,8 @@ This guide will show you how to: 2. Use your finetuned model for inference. -You can finetune other architectures for causal language modeling following the same steps in this guide. -Choose one of the following architectures: - -[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) - - - - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/text-generation) diff --git a/docs/source/en/tasks/masked_language_modeling.md b/docs/source/en/tasks/masked_language_modeling.md index 1736e858eeb..5987e0193f1 100644 --- a/docs/source/en/tasks/masked_language_modeling.md +++ b/docs/source/en/tasks/masked_language_modeling.md @@ -30,14 +30,8 @@ This guide will show you how to: 2. Use your finetuned model for inference. -You can finetune other architectures for masked language modeling following the same steps in this guide. -Choose one of the following architectures: - - -[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) - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/fill-mask) diff --git a/docs/source/en/tasks/monocular_depth_estimation.md b/docs/source/en/tasks/monocular_depth_estimation.md index aea18299893..d3cc8f3c3c8 100644 --- a/docs/source/en/tasks/monocular_depth_estimation.md +++ b/docs/source/en/tasks/monocular_depth_estimation.md @@ -26,13 +26,8 @@ in the scene and the corresponding depth information, which can be affected by f occlusion, and texture. -The task illustrated in this tutorial is supported by the following model architectures: - - -[Depth Anything](../model_doc/depth_anything), [DPT](../model_doc/dpt), [GLPN](../model_doc/glpn) - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/depth-anything) diff --git a/docs/source/en/tasks/multiple_choice.md b/docs/source/en/tasks/multiple_choice.md index 9baa0eea5d5..4adcad52328 100644 --- a/docs/source/en/tasks/multiple_choice.md +++ b/docs/source/en/tasks/multiple_choice.md @@ -25,17 +25,6 @@ This guide will show you how to: 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. 2. Use your finetuned model for inference. - -The task illustrated in this tutorial is supported by the following model architectures: - - - -[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) - - - - - Before you begin, make sure you have all the necessary libraries installed: ```bash diff --git a/docs/source/en/tasks/object_detection.md b/docs/source/en/tasks/object_detection.md index 2513591f545..273484bbb3e 100644 --- a/docs/source/en/tasks/object_detection.md +++ b/docs/source/en/tasks/object_detection.md @@ -33,13 +33,8 @@ In this guide, you will learn how to: 2. Use your finetuned model for inference. -The task illustrated in this tutorial is supported by the following model architectures: - - -[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) - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/object-detection) diff --git a/docs/source/en/tasks/question_answering.md b/docs/source/en/tasks/question_answering.md index 724e51d0dc9..367e35b1211 100644 --- a/docs/source/en/tasks/question_answering.md +++ b/docs/source/en/tasks/question_answering.md @@ -31,15 +31,8 @@ This guide will show you how to: 2. Use your finetuned model for inference. -The task illustrated in this tutorial is supported by the following model architectures: - - - -[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) - - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/question-answering) diff --git a/docs/source/en/tasks/semantic_segmentation.md b/docs/source/en/tasks/semantic_segmentation.md index 048a1d38d00..ac444730018 100644 --- a/docs/source/en/tasks/semantic_segmentation.md +++ b/docs/source/en/tasks/semantic_segmentation.md @@ -201,13 +201,8 @@ We will now: 2. Use your fine-tuned model for inference. -The task illustrated in this tutorial is supported by the following model architectures: - - -[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) - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/image-segmentation) diff --git a/docs/source/en/tasks/sequence_classification.md b/docs/source/en/tasks/sequence_classification.md index 67fde97d090..572d6493ba4 100644 --- a/docs/source/en/tasks/sequence_classification.md +++ b/docs/source/en/tasks/sequence_classification.md @@ -28,16 +28,8 @@ This guide will show you how to: 2. Use your finetuned model for inference. -The task illustrated in this tutorial is supported by the following model architectures: - - - -[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) - - - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/text-classification). diff --git a/docs/source/en/tasks/summarization.md b/docs/source/en/tasks/summarization.md index 37a305a4ac0..e9e77189d46 100644 --- a/docs/source/en/tasks/summarization.md +++ b/docs/source/en/tasks/summarization.md @@ -31,13 +31,8 @@ This guide will show you how to: 2. Use your finetuned model for inference. -The task illustrated in this tutorial is supported by the following model architectures: - - -[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) - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/summarization) diff --git a/docs/source/en/tasks/token_classification.md b/docs/source/en/tasks/token_classification.md index d0e4e87963f..444d8421727 100644 --- a/docs/source/en/tasks/token_classification.md +++ b/docs/source/en/tasks/token_classification.md @@ -28,13 +28,8 @@ This guide will show you how to: 2. Use your finetuned model for inference. -The task illustrated in this tutorial is supported by the following model architectures: - - -[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) - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/token-classification). diff --git a/docs/source/en/tasks/translation.md b/docs/source/en/tasks/translation.md index c03ed34e58a..e7838ea6be9 100644 --- a/docs/source/en/tasks/translation.md +++ b/docs/source/en/tasks/translation.md @@ -28,13 +28,8 @@ This guide will show you how to: 2. Use your finetuned model for inference. -The task illustrated in this tutorial is supported by the following model architectures: - - -[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) - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/translation). diff --git a/docs/source/en/tasks/video_classification.md b/docs/source/en/tasks/video_classification.md index 1a0b8deeb1d..e3e998c7d67 100644 --- a/docs/source/en/tasks/video_classification.md +++ b/docs/source/en/tasks/video_classification.md @@ -26,13 +26,8 @@ This guide will show you how to: 2. Use your fine-tuned model for inference. -The task illustrated in this tutorial is supported by the following model architectures: - - -[TimeSformer](../model_doc/timesformer), [VideoMAE](../model_doc/videomae), [ViViT](../model_doc/vivit) - - +To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/video-classification). diff --git a/docs/source/es/tasks/language_modeling.md b/docs/source/es/tasks/language_modeling.md index 73bfc4d650f..9516876a006 100644 --- a/docs/source/es/tasks/language_modeling.md +++ b/docs/source/es/tasks/language_modeling.md @@ -30,8 +30,6 @@ Esta guía te mostrará cómo realizar fine-tuning [DistilGPT2](https://huggingf -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. diff --git a/docs/source/ja/tasks/asr.md b/docs/source/ja/tasks/asr.md index 6d5f65461d2..9226f5b414f 100644 --- a/docs/source/ja/tasks/asr.md +++ b/docs/source/ja/tasks/asr.md @@ -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。 -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。 - - -[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) - - +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。 diff --git a/docs/source/ja/tasks/audio_classification.md b/docs/source/ja/tasks/audio_classification.md index 6f4d0dd1718..d32050072f9 100644 --- a/docs/source/ja/tasks/audio_classification.md +++ b/docs/source/ja/tasks/audio_classification.md @@ -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。 -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。 - - -[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) - - +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。 -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 ``` diff --git a/docs/source/ja/tasks/document_question_answering.md b/docs/source/ja/tasks/document_question_answering.md index ec88f262086..847ec8441cc 100644 --- a/docs/source/ja/tasks/document_question_answering.md +++ b/docs/source/ja/tasks/document_question_answering.md @@ -30,14 +30,7 @@ rendered properly in your Markdown viewer. -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。 - - - - -[LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3) - - +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。 diff --git a/docs/source/ja/tasks/image_classification.md b/docs/source/ja/tasks/image_classification.md index f16e46c26fc..2202dc3a4f6 100644 --- a/docs/source/ja/tasks/image_classification.md +++ b/docs/source/ja/tasks/image_classification.md @@ -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。 -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。 - - -[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) - - +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。 diff --git a/docs/source/ja/tasks/language_modeling.md b/docs/source/ja/tasks/language_modeling.md index 1d1bcab0b37..b65d60102ef 100644 --- a/docs/source/ja/tasks/language_modeling.md +++ b/docs/source/ja/tasks/language_modeling.md @@ -37,14 +37,7 @@ rendered properly in your Markdown viewer. -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。 - - -[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) - - - +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 diff --git a/docs/source/ja/tasks/masked_language_modeling.md b/docs/source/ja/tasks/masked_language_modeling.md index 29488d5c71e..29d7b73ae5d 100644 --- a/docs/source/ja/tasks/masked_language_modeling.md +++ b/docs/source/ja/tasks/masked_language_modeling.md @@ -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。 -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。 - - -[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) - - +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。 diff --git a/docs/source/ja/tasks/monocular_depth_estimation.md b/docs/source/ja/tasks/monocular_depth_estimation.md index 984631fd3d5..e7a3a994a60 100644 --- a/docs/source/ja/tasks/monocular_depth_estimation.md +++ b/docs/source/ja/tasks/monocular_depth_estimation.md @@ -26,13 +26,8 @@ rendered properly in your Markdown viewer. オクルージョンとテクスチャ。 -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。 - - -[DPT](../model_doc/dpt), [GLPN](../model_doc/glpn) - - +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。 diff --git a/docs/source/ja/tasks/multiple_choice.md b/docs/source/ja/tasks/multiple_choice.md index 045c9112932..98e258f161b 100644 --- a/docs/source/ja/tasks/multiple_choice.md +++ b/docs/source/ja/tasks/multiple_choice.md @@ -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。 - -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。 - - - -[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) - - - - - 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 diff --git a/docs/source/ja/tasks/object_detection.md b/docs/source/ja/tasks/object_detection.md index 389e7bdf2f4..1b1bfb3f815 100644 --- a/docs/source/ja/tasks/object_detection.md +++ b/docs/source/ja/tasks/object_detection.md @@ -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。 -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。 - - -[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) - - +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。 diff --git a/docs/source/ja/tasks/question_answering.md b/docs/source/ja/tasks/question_answering.md index d7feac56076..b039272f45e 100644 --- a/docs/source/ja/tasks/question_answering.md +++ b/docs/source/ja/tasks/question_answering.md @@ -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。 -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。 - - - -[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) - - - +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。 diff --git a/docs/source/ja/tasks/semantic_segmentation.md b/docs/source/ja/tasks/semantic_segmentation.md index 572280c1962..56fb47d52f7 100644 --- a/docs/source/ja/tasks/semantic_segmentation.md +++ b/docs/source/ja/tasks/semantic_segmentation.md @@ -29,13 +29,7 @@ rendered properly in your Markdown viewer. -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。 - - - -[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) - - +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。 diff --git a/docs/source/ja/tasks/sequence_classification.md b/docs/source/ja/tasks/sequence_classification.md index c97644ca10f..4c2a70ab8a3 100644 --- a/docs/source/ja/tasks/sequence_classification.md +++ b/docs/source/ja/tasks/sequence_classification.md @@ -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。 -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。 - - -[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) - - +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。 diff --git a/docs/source/ja/tasks/summarization.md b/docs/source/ja/tasks/summarization.md index 04f1a53d13f..a4385f73792 100644 --- a/docs/source/ja/tasks/summarization.md +++ b/docs/source/ja/tasks/summarization.md @@ -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。 -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。 - - -[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) - - +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。 diff --git a/docs/source/ja/tasks/token_classification.md b/docs/source/ja/tasks/token_classification.md index 49758467425..a7f5097f685 100644 --- a/docs/source/ja/tasks/token_classification.md +++ b/docs/source/ja/tasks/token_classification.md @@ -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。 -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。 - -[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) - - +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。 diff --git a/docs/source/ja/tasks/translation.md b/docs/source/ja/tasks/translation.md index b68cddd86e5..f683581cd11 100644 --- a/docs/source/ja/tasks/translation.md +++ b/docs/source/ja/tasks/translation.md @@ -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。 -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。 - - -[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) - - +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。 diff --git a/docs/source/ja/tasks/video_classification.md b/docs/source/ja/tasks/video_classification.md index 688cb701496..ecfae843f2a 100644 --- a/docs/source/ja/tasks/video_classification.md +++ b/docs/source/ja/tasks/video_classification.md @@ -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。 -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。 - - -[TimeSformer](../model_doc/timesformer), [VideoMAE](../model_doc/videomae), [ViViT](../model_doc/vivit) - - +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。 diff --git a/docs/source/ko/tasks/asr.md b/docs/source/ko/tasks/asr.md index 474d60bf2d1..2247537678a 100644 --- a/docs/source/ko/tasks/asr.md +++ b/docs/source/ko/tasks/asr.md @@ -29,13 +29,8 @@ Siri와 Alexa와 같은 가상 어시스턴트는 ASR 모델을 사용하여 일 2. 미세 조정한 모델을 추론에 사용합니다. -이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에 의해 지원됩니다: - - -[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) - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/automatic-speech-recognition)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/audio_classification.md b/docs/source/ko/tasks/audio_classification.md index c9ef810e8ef..73932100b0c 100644 --- a/docs/source/ko/tasks/audio_classification.md +++ b/docs/source/ko/tasks/audio_classification.md @@ -28,13 +28,8 @@ rendered properly in your Markdown viewer. 2. 추론에 미세 조정된 모델을 사용하세요. -이 튜토리얼에서 설명하는 작업은 아래의 모델 아키텍처에서 지원됩니다: - - -[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) - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/audio-classification)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/document_question_answering.md b/docs/source/ko/tasks/document_question_answering.md index 920eb99ea52..3d943ab96e6 100644 --- a/docs/source/ko/tasks/document_question_answering.md +++ b/docs/source/ko/tasks/document_question_answering.md @@ -29,13 +29,7 @@ rendered properly in your Markdown viewer. -이 튜토리얼에서 설명하는 태스크는 다음과 같은 모델 아키텍처에서 지원됩니다: - - - -[LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3) - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/image-to-text)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/image_classification.md b/docs/source/ko/tasks/image_classification.md index d647b4512b0..91ff3a9ca9b 100644 --- a/docs/source/ko/tasks/image_classification.md +++ b/docs/source/ko/tasks/image_classification.md @@ -30,12 +30,8 @@ rendered properly in your Markdown viewer. 2. 추론을 위해 미세 조정 모델을 사용합니다. -이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에 의해 지원됩니다: - - -[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) - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/image-classification)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/language_modeling.md b/docs/source/ko/tasks/language_modeling.md index b98c64dcc3a..ff2a47c24ec 100644 --- a/docs/source/ko/tasks/language_modeling.md +++ b/docs/source/ko/tasks/language_modeling.md @@ -33,14 +33,8 @@ rendered properly in your Markdown viewer. 2. 미세 조정된 모델을 추론에 사용 -이 안내서의 단계와 동일한 방법으로 인과 언어 모델링을 위해 다른 아키텍처를 미세 조정할 수 있습니다. -다음 아키텍처 중 하나를 선택하세요: - -[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) - - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/text-generation)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/masked_language_modeling.md b/docs/source/ko/tasks/masked_language_modeling.md index c710dbf168e..74df085c5b5 100644 --- a/docs/source/ko/tasks/masked_language_modeling.md +++ b/docs/source/ko/tasks/masked_language_modeling.md @@ -30,15 +30,8 @@ rendered properly in your Markdown viewer. 2. 추론 시에 직접 미세 조정한 모델을 사용합니다. -이번 가이드에서처럼 다른 아키텍처를 미세 조정해 마스킹된 언어 모델링을 할 수 있습니다. -다음 아키텍쳐 중 하나를 선택하세요: - - - -[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) - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/fill-mask)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/monocular_depth_estimation.md b/docs/source/ko/tasks/monocular_depth_estimation.md index e02dd5466b7..2c640d2a86d 100644 --- a/docs/source/ko/tasks/monocular_depth_estimation.md +++ b/docs/source/ko/tasks/monocular_depth_estimation.md @@ -24,13 +24,8 @@ rendered properly in your Markdown viewer. -이 튜토리얼에서 다루는 작업은 다음 모델 아키텍처에서 지원됩니다: - - -[DPT](../model_doc/dpt), [GLPN](../model_doc/glpn) - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/depth-estimation)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/multiple_choice.md b/docs/source/ko/tasks/multiple_choice.md index b28654ea4f1..607bc047479 100644 --- a/docs/source/ko/tasks/multiple_choice.md +++ b/docs/source/ko/tasks/multiple_choice.md @@ -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. - -이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에서 지원됩니다: - - - -[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) - - - - - 시작하기 전에 필요한 라이브러리가 모두 설치되어 있는지 확인하세요: ```bash diff --git a/docs/source/ko/tasks/object_detection.md b/docs/source/ko/tasks/object_detection.md index 0076bba6f84..2b92d7edb59 100644 --- a/docs/source/ko/tasks/object_detection.md +++ b/docs/source/ko/tasks/object_detection.md @@ -30,13 +30,8 @@ rendered properly in your Markdown viewer. 2. 미세조정 한 모델을 추론에 사용하기. -이 튜토리얼의 태스크는 다음 모델 아키텍처에서 지원됩니다: - - -[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) - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/object-detection)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/question_answering.md b/docs/source/ko/tasks/question_answering.md index 7fe8ba3a5f0..cebd9e1a78a 100644 --- a/docs/source/ko/tasks/question_answering.md +++ b/docs/source/ko/tasks/question_answering.md @@ -31,14 +31,8 @@ rendered properly in your Markdown viewer. 2. 추론에 미세 조정된 모델 사용하기 -이 튜토리얼에서 설명하는 태스크는 다음과 같은 모델 아키텍처에서 지원됩니다. - - -[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) - - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/question-answering)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/semantic_segmentation.md b/docs/source/ko/tasks/semantic_segmentation.md index 0afa4bbe020..8a5e20228d6 100644 --- a/docs/source/ko/tasks/semantic_segmentation.md +++ b/docs/source/ko/tasks/semantic_segmentation.md @@ -29,13 +29,8 @@ rendered properly in your Markdown viewer. 2. 미세 조정된 모델을 추론에 사용하기. -이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에서 지원됩니다: - - -[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) - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/image-segmentation)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/sequence_classification.md b/docs/source/ko/tasks/sequence_classification.md index 9cf6b9f5243..b9812e63b06 100644 --- a/docs/source/ko/tasks/sequence_classification.md +++ b/docs/source/ko/tasks/sequence_classification.md @@ -28,14 +28,8 @@ rendered properly in your Markdown viewer. 2. 추론을 위해 파인 튜닝 모델을 사용합니다. -이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에 의해 지원됩니다: - - -[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) - - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/text-classification)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/summarization.md b/docs/source/ko/tasks/summarization.md index 62e410757e4..fc09d6a86e1 100644 --- a/docs/source/ko/tasks/summarization.md +++ b/docs/source/ko/tasks/summarization.md @@ -33,13 +33,8 @@ rendered properly in your Markdown viewer. 2. 파인튜닝된 모델을 사용하여 추론합니다. -이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에서 지원됩니다: - - -[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) - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/summarization)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/token_classification.md b/docs/source/ko/tasks/token_classification.md index 5bb3989d459..e32a18e1ee0 100644 --- a/docs/source/ko/tasks/token_classification.md +++ b/docs/source/ko/tasks/token_classification.md @@ -28,13 +28,8 @@ rendered properly in your Markdown viewer. 2. 추론을 위해 파인 튜닝 모델을 사용합니다. -이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에 의해 지원됩니다: - - -[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) - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/token-classification)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/translation.md b/docs/source/ko/tasks/translation.md index 982142c84ea..b05ecf2d5a2 100644 --- a/docs/source/ko/tasks/translation.md +++ b/docs/source/ko/tasks/translation.md @@ -28,13 +28,8 @@ rendered properly in your Markdown viewer. 2. 파인튜닝된 모델을 추론에 사용하는 방법입니다. -이 태스크 가이드는 아래 모델 아키텍처에도 응용할 수 있습니다. - - -[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) - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/translation)를 확인하는 것이 좋습니다. diff --git a/docs/source/ko/tasks/video_classification.md b/docs/source/ko/tasks/video_classification.md index 762716c9ff7..f18ef918fa9 100644 --- a/docs/source/ko/tasks/video_classification.md +++ b/docs/source/ko/tasks/video_classification.md @@ -28,13 +28,7 @@ rendered properly in your Markdown viewer. -이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에서 지원됩니다: - - - -[TimeSformer](../model_doc/timesformer), [VideoMAE](../model_doc/videomae) - - +이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/video-classification)를 확인하는 것이 좋습니다. diff --git a/docs/source/zh/tasks/asr.md b/docs/source/zh/tasks/asr.md index 48ab94cb7d9..b4366d72040 100644 --- a/docs/source/zh/tasks/asr.md +++ b/docs/source/zh/tasks/asr.md @@ -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， -本教程中展示的任务受以下模型架构的支持： - - - -[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) - - +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)。 diff --git a/utils/check_task_guides.py b/utils/check_task_guides.py deleted file mode 100644 index b00ff1dc1a5..00000000000 --- a/utils/check_task_guides.py +++ /dev/null @@ -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="", - end_prompt="", - ) - - 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)