多くの場合、from_pretrained()
メソッドに与えられた事前学習済みモデルの名前やパスから、使用したいアーキテクチャを推測することができます。自動クラスはこの仕事をあなたに代わって行うためにここにありますので、事前学習済みの重み/設定/語彙への名前/パスを与えると自動的に関連するモデルを取得できます。
AutoConfig、AutoModel、AutoTokenizerのいずれかをインスタンス化すると、関連するアーキテクチャのクラスが直接作成されます。例えば、
model = AutoModel.from_pretrained("google-bert/bert-base-cased")
これはBertModelのインスタンスであるモデルを作成します。
各タスクごと、そして各バックエンド(PyTorch、TensorFlow、またはFlax)ごとにAutoModel
のクラスが存在します。
それぞれの自動クラスには、カスタムクラスで拡張するためのメソッドがあります。例えば、NewModel
というモデルのカスタムクラスを定義した場合、NewModelConfig
を確保しておけばこのようにして自動クラスに追加することができます:
from transformers import AutoConfig, AutoModel
AutoConfig.register("new-model", NewModelConfig)
AutoModel.register(NewModelConfig, NewModel)
その後、通常どおりauto classesを使用することができるようになります!
あなたのNewModelConfig
がPretrainedConfigのサブクラスである場合、そのmodel_type
属性がコンフィグを登録するときに使用するキー(ここでは"new-model"
)と同じに設定されていることを確認してください。
同様に、あなたのNewModel
がPreTrainedModelのサブクラスである場合、そのconfig_class
属性がモデルを登録する際に使用するクラス(ここではNewModelConfig
)と同じに設定されていることを確認してください。
This is a generic configuration class that will be instantiated as one of the configuration classes of the library when created with the from_pretrained() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( pretrained_model_name_or_path **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../my_model_directory/configuration.json
.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
If False
, then this function returns just the final configuration object.
If True
, then this functions returns a Tuple(config, unused_kwargs)
where unused_kwargs is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
part of kwargs
which has not been used to update config
and is otherwise ignored.
bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. return_unused_kwargs
keyword parameter. Instantiate one of the configuration classes of the library from a pretrained model configuration.
The configuration class to instantiate is selected based on the model_type
property of the config object that
is loaded, or when it’s missing, by falling back to using pattern matching on pretrained_model_name_or_path
:
LlamaConfig
(CodeLlama model)CohereConfig
(Cohere model)DbrxConfig
(DBRX model)DepthAnythingConfig
(Depth Anything model)Dinov2Config
(DINOv2 model)DistilBertConfig
(DistilBERT model)DonutSwinConfig
(DonutSwin model)DPRConfig
(DPR model)DPTConfig
(DPT model)EfficientFormerConfig
(EfficientFormer model)EfficientNetConfig
(EfficientNet model)ElectraConfig
(ELECTRA model)EncodecConfig
(EnCodec model)EncoderDecoderConfig
(Encoder decoder model)ErnieConfig
(ERNIE model)ErnieMConfig
(ErnieM model)EsmConfig
(ESM model)FalconConfig
(Falcon model)FastSpeech2ConformerConfig
(FastSpeech2Conformer model)FlaubertConfig
(FlauBERT model)FlavaConfig
(FLAVA model)FNetConfig
(FNet model)FocalNetConfig
(FocalNet model)FSMTConfig
(FairSeq Machine-Translation model)FunnelConfig
(Funnel Transformer model)FuyuConfig
(Fuyu model)GemmaConfig
(Gemma model)GitConfig
(GIT model)GLPNConfig
(GLPN model)GPT2Config
(GPT-Sw3 model)GPT2Config
(OpenAI GPT-2 model)GPTBigCodeConfig
(GPTBigCode model)GPTNeoConfig
(GPT Neo model)GPTNeoXConfig
(GPT NeoX model)GPTNeoXJapaneseConfig
(GPT NeoX Japanese model)GPTJConfig
(GPT-J model)GPTSanJapaneseConfig
(GPTSAN-japanese model)GraphormerConfig
(Graphormer model)GroundingDinoConfig
(Grounding DINO model)GroupViTConfig
(GroupViT model)HubertConfig
(Hubert model)IBertConfig
(I-BERT model)IdeficsConfig
(IDEFICS model)Idefics2Config
(Idefics2 model)ImageGPTConfig
(ImageGPT model)InformerConfig
(Informer model)InstructBlipConfig
(InstructBLIP model)JambaConfig
(Jamba model)JukeboxConfig
(Jukebox model)Kosmos2Config
(KOSMOS-2 model)LayoutLMConfig
(LayoutLM model)LayoutLMv2Config
(LayoutLMv2 model)LayoutLMv3Config
(LayoutLMv3 model)LEDConfig
(LED model)LevitConfig
(LeViT model)LiltConfig
(LiLT model)LlamaConfig
(LLaMA model)LlavaConfig
(LLaVa model)LlavaNextConfig
(LLaVA-NeXT model)LongformerConfig
(Longformer model)LongT5Config
(LongT5 model)LukeConfig
(LUKE model)LxmertConfig
(LXMERT model)M2M100Config
(M2M100 model)MambaConfig
(Mamba model)MarianConfig
(Marian model)MarkupLMConfig
(MarkupLM model)Mask2FormerConfig
(Mask2Former model)MaskFormerConfig
(MaskFormer model)MaskFormerSwinConfig
(MaskFormerSwin model)MBartConfig
(mBART model)MCTCTConfig
(M-CTC-T model)MegaConfig
(MEGA model)MegatronBertConfig
(Megatron-BERT model)MgpstrConfig
(MGP-STR model)MistralConfig
(Mistral model)MixtralConfig
(Mixtral model)MobileBertConfig
(MobileBERT model)MobileNetV1Config
(MobileNetV1 model)MobileNetV2Config
(MobileNetV2 model)MobileViTConfig
(MobileViT model)MobileViTV2Config
(MobileViTV2 model)MPNetConfig
(MPNet model)MptConfig
(MPT model)MraConfig
(MRA model)MT5Config
(MT5 model)MusicgenConfig
(MusicGen model)MusicgenMelodyConfig
(MusicGen Melody model)MvpConfig
(MVP model)NatConfig
(NAT model)NezhaConfig
(Nezha model)NllbMoeConfig
(NLLB-MOE model)VisionEncoderDecoderConfig
(Nougat model)NystromformerConfig
(Nyströmformer model)OlmoConfig
(OLMo model)OneFormerConfig
(OneFormer model)OpenLlamaConfig
(OpenLlama model)OpenAIGPTConfig
(OpenAI GPT model)OPTConfig
(OPT model)Owlv2Config
(OWLv2 model)OwlViTConfig
(OWL-ViT model)PatchTSMixerConfig
(PatchTSMixer model)PatchTSTConfig
(PatchTST model)PegasusConfig
(Pegasus model)PegasusXConfig
(PEGASUS-X model)PerceiverConfig
(Perceiver model)PersimmonConfig
(Persimmon model)PhiConfig
(Phi model)Phi3Config
(Phi3 model)Pix2StructConfig
(Pix2Struct model)PLBartConfig
(PLBart model)PoolFormerConfig
(PoolFormer model)Pop2PianoConfig
(Pop2Piano model)ProphetNetConfig
(ProphetNet model)PvtConfig
(PVT model)PvtV2Config
(PVTv2 model)QDQBertConfig
(QDQBert model)Qwen2Config
(Qwen2 model)Qwen2MoeConfig
(Qwen2MoE model)RagConfig
(RAG model)RealmConfig
(REALM model)RecurrentGemmaConfig
(RecurrentGemma model)ReformerConfig
(Reformer model)RegNetConfig
(RegNet model)RemBertConfig
(RemBERT model)ResNetConfig
(ResNet model)RetriBertConfig
(RetriBERT model)RobertaConfig
(RoBERTa model)RobertaPreLayerNormConfig
(RoBERTa-PreLayerNorm model)RoCBertConfig
(RoCBert model)RoFormerConfig
(RoFormer model)RwkvConfig
(RWKV model)SamConfig
(SAM model)SeamlessM4TConfig
(SeamlessM4T model)SeamlessM4Tv2Config
(SeamlessM4Tv2 model)SegformerConfig
(SegFormer model)SegGptConfig
(SegGPT model)SEWConfig
(SEW model)SEWDConfig
(SEW-D model)SiglipConfig
(SigLIP model)SiglipVisionConfig
(SiglipVisionModel model)SpeechEncoderDecoderConfig
(Speech Encoder decoder model)Speech2TextConfig
(Speech2Text model)Speech2Text2Config
(Speech2Text2 model)SpeechT5Config
(SpeechT5 model)SplinterConfig
(Splinter model)SqueezeBertConfig
(SqueezeBERT model)StableLmConfig
(StableLm model)Starcoder2Config
(Starcoder2 model)SuperPointConfig
(SuperPoint model)SwiftFormerConfig
(SwiftFormer model)SwinConfig
(Swin Transformer model)Swin2SRConfig
(Swin2SR model)Swinv2Config
(Swin Transformer V2 model)SwitchTransformersConfig
(SwitchTransformers model)T5Config
(T5 model)TableTransformerConfig
(Table Transformer model)TapasConfig
(TAPAS model)TimeSeriesTransformerConfig
(Time Series Transformer model)TimesformerConfig
(TimeSformer model)TimmBackboneConfig
(TimmBackbone model)TrajectoryTransformerConfig
(Trajectory Transformer model)TransfoXLConfig
(Transformer-XL model)TrOCRConfig
(TrOCR model)TvltConfig
(TVLT model)TvpConfig
(TVP model)UdopConfig
(UDOP model)UMT5Config
(UMT5 model)UniSpeechConfig
(UniSpeech model)UniSpeechSatConfig
(UniSpeechSat model)UnivNetConfig
(UnivNet model)UperNetConfig
(UPerNet model)VanConfig
(VAN model)VideoMAEConfig
(VideoMAE model)ViltConfig
(ViLT model)VipLlavaConfig
(VipLlava model)VisionEncoderDecoderConfig
(Vision Encoder decoder model)VisionTextDualEncoderConfig
(VisionTextDualEncoder model)VisualBertConfig
(VisualBERT model)ViTConfig
(ViT model)ViTHybridConfig
(ViT Hybrid model)ViTMAEConfig
(ViTMAE model)ViTMSNConfig
(ViTMSN model)VitDetConfig
(VitDet model)VitMatteConfig
(ViTMatte model)VitsConfig
(VITS model)VivitConfig
(ViViT model)Wav2Vec2Config
(Wav2Vec2 model)Wav2Vec2BertConfig
(Wav2Vec2-BERT model)Wav2Vec2ConformerConfig
(Wav2Vec2-Conformer model)WavLMConfig
(WavLM model)WhisperConfig
(Whisper model)XCLIPConfig
(X-CLIP model)XGLMConfig
(XGLM model)XLMConfig
(XLM model)XLMProphetNetConfig
(XLM-ProphetNet model)XLMRobertaConfig
(XLM-RoBERTa model)XLMRobertaXLConfig
(XLM-RoBERTa-XL model)XLNetConfig
(XLNet model)XmodConfig
(X-MOD model)YolosConfig
(YOLOS model)YosoConfig
(YOSO model)Examples:
>>> from transformers import AutoConfig
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained("google-bert/bert-base-uncased")
>>> # Download configuration from huggingface.co (user-uploaded) and cache.
>>> config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased")
>>> # If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*).
>>> config = AutoConfig.from_pretrained("./test/bert_saved_model/")
>>> # Load a specific configuration file.
>>> config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json")
>>> # Change some config attributes when loading a pretrained config.
>>> config = AutoConfig.from_pretrained("google-bert/bert-base-uncased", output_attentions=True, foo=False)
>>> config.output_attentions
True
>>> config, unused_kwargs = AutoConfig.from_pretrained(
... "google-bert/bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
... )
>>> config.output_attentions
True
>>> unused_kwargs
{'foo': False}
( model_type config exist_ok = False )
Parameters
str
) — The model type like “bert” or “gpt”. Register a new configuration for this class.
This is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when created with the AutoTokenizer.from_pretrained() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( pretrained_model_name_or_path *inputs **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../my_model_directory/vocab.txt
. (Not
applicable to all derived classes)__init__()
method. str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. str
, optional) —
In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
facebook/rag-token-base), specify it here. bool
, optional, defaults to True
) —
Use a fast Rust-based tokenizer if it is supported for
a given model. If a fast tokenizer is not available for a given model, a normal Python-based tokenizer
is returned instead. str
, optional) —
Tokenizer type to be loaded. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. __init__()
method. Can be used to set special tokens like
bos_token
, eos_token
, unk_token
, sep_token
, pad_token
, cls_token
, mask_token
,
additional_special_tokens
. See parameters in the __init__()
for more details. Instantiate one of the tokenizer classes of the library from a pretrained model vocabulary.
The tokenizer class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
PegasusTokenizer
or PegasusTokenizerFast
(BigBird-Pegasus model)GPT2Tokenizer
or GPT2TokenizerFast
(BLIP-2 model)RobertaTokenizer
or RobertaTokenizerFast
(BridgeTower model)RobertaTokenizer
or RobertaTokenizerFast
(CLAP model)CohereTokenizerFast
(Cohere model)Wav2Vec2CTCTokenizer
(Data2VecAudio model)RobertaTokenizer
or RobertaTokenizerFast
(Data2VecText model)GPT2Tokenizer
or GPT2TokenizerFast
(DBRX model)DistilBertTokenizer
or DistilBertTokenizerFast
(DistilBERT model)DPRQuestionEncoderTokenizer
or DPRQuestionEncoderTokenizerFast
(DPR model)ElectraTokenizer
or ElectraTokenizerFast
(ELECTRA model)ErnieMTokenizer
(ErnieM model)EsmTokenizer
(ESM model)FlaubertTokenizer
(FlauBERT model)FNetTokenizer
or FNetTokenizerFast
(FNet model)FSMTTokenizer
(FairSeq Machine-Translation model)FunnelTokenizer
or FunnelTokenizerFast
(Funnel Transformer model)GemmaTokenizer
or GemmaTokenizerFast
(Gemma model)GPTSw3Tokenizer
(GPT-Sw3 model)GPT2Tokenizer
or GPT2TokenizerFast
(OpenAI GPT-2 model)GPT2Tokenizer
or GPT2TokenizerFast
(GPTBigCode model)GPT2Tokenizer
or GPT2TokenizerFast
(GPT Neo model)GPTNeoXTokenizerFast
(GPT NeoX model)GPTNeoXJapaneseTokenizer
(GPT NeoX Japanese model)GPT2Tokenizer
or GPT2TokenizerFast
(GPT-J model)GPTSanJapaneseTokenizer
(GPTSAN-japanese model)HerbertTokenizer
or HerbertTokenizerFast
(HerBERT model)Wav2Vec2CTCTokenizer
(Hubert model)RobertaTokenizer
or RobertaTokenizerFast
(I-BERT model)LlamaTokenizerFast
(IDEFICS model)LlamaTokenizer
or LlamaTokenizerFast
(Idefics2 model)GPT2Tokenizer
or GPT2TokenizerFast
(InstructBLIP model)LlamaTokenizer
or LlamaTokenizerFast
(Jamba model)JukeboxTokenizer
(Jukebox model)XLMRobertaTokenizer
or XLMRobertaTokenizerFast
(KOSMOS-2 model)LayoutLMTokenizer
or LayoutLMTokenizerFast
(LayoutLM model)LayoutLMv2Tokenizer
or LayoutLMv2TokenizerFast
(LayoutLMv2 model)LayoutLMv3Tokenizer
or LayoutLMv3TokenizerFast
(LayoutLMv3 model)LayoutXLMTokenizer
or LayoutXLMTokenizerFast
(LayoutXLM model)LEDTokenizer
or LEDTokenizerFast
(LED model)LayoutLMv3Tokenizer
or LayoutLMv3TokenizerFast
(LiLT model)LlamaTokenizer
or LlamaTokenizerFast
(LLaMA model)LlamaTokenizer
or LlamaTokenizerFast
(LLaVa model)LlamaTokenizer
or LlamaTokenizerFast
(LLaVA-NeXT model)LongformerTokenizer
or LongformerTokenizerFast
(Longformer model)T5Tokenizer
or T5TokenizerFast
(LongT5 model)LukeTokenizer
(LUKE model)LxmertTokenizer
or LxmertTokenizerFast
(LXMERT model)M2M100Tokenizer
(M2M100 model)GPTNeoXTokenizerFast
(Mamba model)MarianTokenizer
(Marian model)MBartTokenizer
or MBartTokenizerFast
(mBART model)MBart50Tokenizer
or MBart50TokenizerFast
(mBART-50 model)RobertaTokenizer
or RobertaTokenizerFast
(MEGA model)MgpstrTokenizer
(MGP-STR model)LlamaTokenizer
or LlamaTokenizerFast
(Mistral model)LlamaTokenizer
or LlamaTokenizerFast
(Mixtral model)MLukeTokenizer
(mLUKE model)MobileBertTokenizer
or MobileBertTokenizerFast
(MobileBERT model)MPNetTokenizer
or MPNetTokenizerFast
(MPNet model)GPTNeoXTokenizerFast
(MPT model)RobertaTokenizer
or RobertaTokenizerFast
(MRA model)MT5Tokenizer
or MT5TokenizerFast
(MT5 model)T5Tokenizer
or T5TokenizerFast
(MusicGen model)T5Tokenizer
or T5TokenizerFast
(MusicGen Melody model)MvpTokenizer
or MvpTokenizerFast
(MVP model)NllbTokenizer
or NllbTokenizerFast
(NLLB model)NllbTokenizer
or NllbTokenizerFast
(NLLB-MOE model)GPTNeoXTokenizerFast
(OLMo model)OpenAIGPTTokenizer
or OpenAIGPTTokenizerFast
(OpenAI GPT model)GPT2Tokenizer
or GPT2TokenizerFast
(OPT model)PegasusTokenizer
or PegasusTokenizerFast
(Pegasus model)PegasusTokenizer
or PegasusTokenizerFast
(PEGASUS-X model)PerceiverTokenizer
(Perceiver model)LlamaTokenizer
or LlamaTokenizerFast
(Persimmon model)LlamaTokenizer
or LlamaTokenizerFast
(Phi3 model)PhobertTokenizer
(PhoBERT model)T5Tokenizer
or T5TokenizerFast
(Pix2Struct model)PLBartTokenizer
(PLBart model)ProphetNetTokenizer
(ProphetNet model)Qwen2Tokenizer
or Qwen2TokenizerFast
(Qwen2 model)Qwen2Tokenizer
or Qwen2TokenizerFast
(Qwen2MoE model)RagTokenizer
(RAG model)RealmTokenizer
or RealmTokenizerFast
(REALM model)GemmaTokenizer
or GemmaTokenizerFast
(RecurrentGemma model)ReformerTokenizer
or ReformerTokenizerFast
(Reformer model)RemBertTokenizer
or RemBertTokenizerFast
(RemBERT model)RetriBertTokenizer
or RetriBertTokenizerFast
(RetriBERT model)RobertaTokenizer
or RobertaTokenizerFast
(RoBERTa model)RobertaTokenizer
or RobertaTokenizerFast
(RoBERTa-PreLayerNorm model)RoCBertTokenizer
(RoCBert model)RoFormerTokenizer
or RoFormerTokenizerFast
(RoFormer model)GPTNeoXTokenizerFast
(RWKV model)SeamlessM4TTokenizer
or SeamlessM4TTokenizerFast
(SeamlessM4T model)SeamlessM4TTokenizer
or SeamlessM4TTokenizerFast
(SeamlessM4Tv2 model)SiglipTokenizer
(SigLIP model)Speech2TextTokenizer
(Speech2Text model)Speech2Text2Tokenizer
(Speech2Text2 model)SpeechT5Tokenizer
(SpeechT5 model)SplinterTokenizer
or SplinterTokenizerFast
(Splinter model)SqueezeBertTokenizer
or SqueezeBertTokenizerFast
(SqueezeBERT model)GPTNeoXTokenizerFast
(StableLm model)GPT2Tokenizer
or GPT2TokenizerFast
(Starcoder2 model)T5Tokenizer
or T5TokenizerFast
(SwitchTransformers model)T5Tokenizer
or T5TokenizerFast
(T5 model)TapasTokenizer
(TAPAS model)TapexTokenizer
(TAPEX model)TransfoXLTokenizer
(Transformer-XL model)UdopTokenizer
or UdopTokenizerFast
(UDOP model)T5Tokenizer
or T5TokenizerFast
(UMT5 model)LlamaTokenizer
or LlamaTokenizerFast
(VipLlava model)VitsTokenizer
(VITS model)Wav2Vec2CTCTokenizer
(Wav2Vec2 model)Wav2Vec2CTCTokenizer
(Wav2Vec2-BERT model)Wav2Vec2CTCTokenizer
(Wav2Vec2-Conformer model)Wav2Vec2PhonemeCTCTokenizer
(Wav2Vec2Phoneme model)WhisperTokenizer
or WhisperTokenizerFast
(Whisper model)XGLMTokenizer
or XGLMTokenizerFast
(XGLM model)XLMTokenizer
(XLM model)XLMProphetNetTokenizer
(XLM-ProphetNet model)XLMRobertaTokenizer
or XLMRobertaTokenizerFast
(XLM-RoBERTa model)XLMRobertaTokenizer
or XLMRobertaTokenizerFast
(XLM-RoBERTa-XL model)XLNetTokenizer
or XLNetTokenizerFast
(XLNet model)XLMRobertaTokenizer
or XLMRobertaTokenizerFast
(X-MOD model)Examples:
>>> from transformers import AutoTokenizer
>>> # Download vocabulary from huggingface.co and cache.
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
>>> # Download vocabulary from huggingface.co (user-uploaded) and cache.
>>> tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased")
>>> # If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*)
>>> # tokenizer = AutoTokenizer.from_pretrained("./test/bert_saved_model/")
>>> # Download vocabulary from huggingface.co and define model-specific arguments
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base", add_prefix_space=True)
( config_class slow_tokenizer_class = None fast_tokenizer_class = None exist_ok = False )
Parameters
PretrainedTokenizer
, optional) —
The slow tokenizer to register. PretrainedTokenizerFast
, optional) —
The fast tokenizer to register. Register a new tokenizer in this mapping.
This is a generic feature extractor class that will be instantiated as one of the feature extractor classes of the library when created with the AutoFeatureExtractor.from_pretrained() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( pretrained_model_name_or_path **kwargs )
Parameters
str
or os.PathLike
) —
This can be either:
./my_model_directory/
../my_model_directory/preprocessor_config.json
.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request. str
or bool, optional) —
The token to use as HTTP bearer authorization for remote files. If True
, will use the token generated
when running huggingface-cli login
(stored in ~/.huggingface
). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
If False
, then this function returns just the final feature extractor object. If True
, then this
functions returns a Tuple(feature_extractor, unused_kwargs)
where unused_kwargs is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
kwargs
which has not been used to update feature_extractor
and is otherwise ignored. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. Dict[str, Any]
, optional) —
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are not feature extractor attributes is
controlled by the return_unused_kwargs
keyword parameter. Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary.
The feature extractor class to instantiate is selected based on the model_type
property of the config object
(either passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s
missing, by falling back to using pattern matching on pretrained_model_name_or_path
:
ViTFeatureExtractor
(CLIPSeg model)Wav2Vec2FeatureExtractor
(Data2VecAudio model)ViTFeatureExtractor
(DiNAT model)DonutFeatureExtractor
(DonutSwin model)DPTFeatureExtractor
(DPT model)EncodecFeatureExtractor
(EnCodec model)FlavaFeatureExtractor
(FLAVA model)GLPNFeatureExtractor
(GLPN model)Wav2Vec2FeatureExtractor
(Hubert model)ImageGPTFeatureExtractor
(ImageGPT model)LayoutLMv2FeatureExtractor
(LayoutLMv2 model)LayoutLMv3FeatureExtractor
(LayoutLMv3 model)LevitFeatureExtractor
(LeViT model)MaskFormerFeatureExtractor
(MaskFormer model)MCTCTFeatureExtractor
(M-CTC-T model)MobileNetV1FeatureExtractor
(MobileNetV1 model)MobileNetV2FeatureExtractor
(MobileNetV2 model)MobileViTFeatureExtractor
(MobileViT model)ViTFeatureExtractor
(NAT model)OwlViTFeatureExtractor
(OWL-ViT model)PerceiverFeatureExtractor
(Perceiver model)PoolFormerFeatureExtractor
(PoolFormer model)Pop2PianoFeatureExtractor
(Pop2Piano model)SeamlessM4TFeatureExtractor
(SeamlessM4T model)SeamlessM4TFeatureExtractor
(SeamlessM4Tv2 model)SegformerFeatureExtractor
(SegFormer model)Wav2Vec2FeatureExtractor
(SEW model)Wav2Vec2FeatureExtractor
(SEW-D model)Speech2TextFeatureExtractor
(Speech2Text model)SpeechT5FeatureExtractor
(SpeechT5 model)ViTFeatureExtractor
(SwiftFormer model)ViTFeatureExtractor
(Swin Transformer model)ViTFeatureExtractor
(Swin Transformer V2 model)VideoMAEFeatureExtractor
(TimeSformer model)TvltFeatureExtractor
(TVLT model)Wav2Vec2FeatureExtractor
(UniSpeech model)Wav2Vec2FeatureExtractor
(UniSpeechSat model)UnivNetFeatureExtractor
(UnivNet model)VideoMAEFeatureExtractor
(VideoMAE model)ViltFeatureExtractor
(ViLT model)ViTFeatureExtractor
(ViT model)ViTFeatureExtractor
(ViTMAE model)ViTFeatureExtractor
(ViTMSN model)Wav2Vec2FeatureExtractor
(Wav2Vec2 model)Wav2Vec2FeatureExtractor
(Wav2Vec2-BERT model)Wav2Vec2FeatureExtractor
(Wav2Vec2-Conformer model)Wav2Vec2FeatureExtractor
(WavLM model)WhisperFeatureExtractor
(Whisper model)YolosFeatureExtractor
(YOLOS model)Passing token=True
is required when you want to use a private model.
Examples:
>>> from transformers import AutoFeatureExtractor
>>> # Download feature extractor from huggingface.co and cache.
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
>>> # If feature extractor files are in a directory (e.g. feature extractor was saved using *save_pretrained('./test/saved_model/')*)
>>> # feature_extractor = AutoFeatureExtractor.from_pretrained("./test/saved_model/")
( config_class feature_extractor_class exist_ok = False )
Parameters
FeatureExtractorMixin
) — The feature extractor to register. Register a new feature extractor for this class.
This is a generic image processor class that will be instantiated as one of the image processor classes of the library when created with the AutoImageProcessor.from_pretrained() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( pretrained_model_name_or_path **kwargs )
Parameters
str
or os.PathLike
) —
This can be either:
./my_model_directory/
../my_model_directory/preprocessor_config.json
.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model image processor should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Whether or not to force to (re-)download the image processor files and override the cached versions if
they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request. str
or bool, optional) —
The token to use as HTTP bearer authorization for remote files. If True
, will use the token generated
when running huggingface-cli login
(stored in ~/.huggingface
). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
If False
, then this function returns just the final image processor object. If True
, then this
functions returns a Tuple(image_processor, unused_kwargs)
where unused_kwargs is a dictionary
consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of
kwargs
which has not been used to update image_processor
and is otherwise ignored. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. Dict[str, Any]
, optional) —
The values in kwargs of any keys which are image processor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are not image processor attributes is
controlled by the return_unused_kwargs
keyword parameter. Instantiate one of the image processor classes of the library from a pretrained model vocabulary.
The image processor class to instantiate is selected based on the model_type
property of the config object
(either passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s
missing, by falling back to using pattern matching on pretrained_model_name_or_path
:
EfficientNetImageProcessor
(ALIGN model)ViTImageProcessor
(CLIPSeg model)DPTImageProcessor
(Depth Anything model)ViTImageProcessor
(DiNAT model)DonutImageProcessor
(DonutSwin model)DPTImageProcessor
(DPT model)EfficientFormerImageProcessor
(EfficientFormer model)EfficientNetImageProcessor
(EfficientNet model)FlavaImageProcessor
(FLAVA model)FuyuImageProcessor
(Fuyu model)GLPNImageProcessor
(GLPN model)GroundingDinoImageProcessor
(Grounding DINO model)IdeficsImageProcessor
(IDEFICS model)Idefics2ImageProcessor
(Idefics2 model)ImageGPTImageProcessor
(ImageGPT model)LayoutLMv2ImageProcessor
(LayoutLMv2 model)LayoutLMv3ImageProcessor
(LayoutLMv3 model)LevitImageProcessor
(LeViT model)LlavaNextImageProcessor
(LLaVA-NeXT model)Mask2FormerImageProcessor
(Mask2Former model)MaskFormerImageProcessor
(MaskFormer model)ViTImageProcessor
(MGP-STR model)MobileNetV1ImageProcessor
(MobileNetV1 model)MobileNetV2ImageProcessor
(MobileNetV2 model)MobileViTImageProcessor
(MobileViT model)MobileViTImageProcessor
(MobileViTV2 model)ViTImageProcessor
(NAT model)NougatImageProcessor
(Nougat model)OneFormerImageProcessor
(OneFormer model)Owlv2ImageProcessor
(OWLv2 model)OwlViTImageProcessor
(OWL-ViT model)PerceiverImageProcessor
(Perceiver model)Pix2StructImageProcessor
(Pix2Struct model)PoolFormerImageProcessor
(PoolFormer model)PvtImageProcessor
(PVT model)PvtImageProcessor
(PVTv2 model)SamImageProcessor
(SAM model)SegformerImageProcessor
(SegFormer model)SegGptImageProcessor
(SegGPT model)SiglipImageProcessor
(SigLIP model)ViTImageProcessor
(SwiftFormer model)ViTImageProcessor
(Swin Transformer model)Swin2SRImageProcessor
(Swin2SR model)ViTImageProcessor
(Swin Transformer V2 model)VideoMAEImageProcessor
(TimeSformer model)TvltImageProcessor
(TVLT model)TvpImageProcessor
(TVP model)LayoutLMv3ImageProcessor
(UDOP model)SegformerImageProcessor
(UPerNet model)VideoMAEImageProcessor
(VideoMAE model)ViltImageProcessor
(ViLT model)ViTImageProcessor
(ViT model)ViTHybridImageProcessor
(ViT Hybrid model)ViTImageProcessor
(ViTMAE model)ViTImageProcessor
(ViTMSN model)VitMatteImageProcessor
(ViTMatte model)YolosImageProcessor
(YOLOS model)Passing token=True
is required when you want to use a private model.
Examples:
>>> from transformers import AutoImageProcessor
>>> # Download image processor from huggingface.co and cache.
>>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
>>> # If image processor files are in a directory (e.g. image processor was saved using *save_pretrained('./test/saved_model/')*)
>>> # image_processor = AutoImageProcessor.from_pretrained("./test/saved_model/")
( config_class image_processor_class exist_ok = False )
Parameters
Register a new image processor for this class.
This is a generic processor class that will be instantiated as one of the processor classes of the library when created with the AutoProcessor.from_pretrained() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( pretrained_model_name_or_path **kwargs )
Parameters
str
or os.PathLike
) —
This can be either:
save_pretrained()
method,
e.g., ./my_model_directory/
.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request. str
or bool, optional) —
The token to use as HTTP bearer authorization for remote files. If True
, will use the token generated
when running huggingface-cli login
(stored in ~/.huggingface
). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
If False
, then this function returns just the final feature extractor object. If True
, then this
functions returns a Tuple(feature_extractor, unused_kwargs)
where unused_kwargs is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
kwargs
which has not been used to update feature_extractor
and is otherwise ignored. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. Dict[str, Any]
, optional) —
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are not feature extractor attributes is
controlled by the return_unused_kwargs
keyword parameter. Instantiate one of the processor classes of the library from a pretrained model vocabulary.
The processor class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible):
FlavaProcessor
(FLAVA model)FuyuProcessor
(Fuyu model)GitProcessor
(GIT model)Wav2Vec2Processor
(Hubert model)IdeficsProcessor
(IDEFICS model)Idefics2Processor
(Idefics2 model)InstructBlipProcessor
(InstructBLIP model)Kosmos2Processor
(KOSMOS-2 model)LayoutLMv2Processor
(LayoutLMv2 model)LayoutLMv3Processor
(LayoutLMv3 model)LlavaProcessor
(LLaVa model)LlavaNextProcessor
(LLaVA-NeXT model)MarkupLMProcessor
(MarkupLM model)MCTCTProcessor
(M-CTC-T model)MgpstrProcessor
(MGP-STR model)OneFormerProcessor
(OneFormer model)Owlv2Processor
(OWLv2 model)OwlViTProcessor
(OWL-ViT model)Pix2StructProcessor
(Pix2Struct model)Pop2PianoProcessor
(Pop2Piano model)SamProcessor
(SAM model)SeamlessM4TProcessor
(SeamlessM4T model)Wav2Vec2Processor
(SEW model)Wav2Vec2Processor
(SEW-D model)SiglipProcessor
(SigLIP model)Speech2TextProcessor
(Speech2Text model)Speech2Text2Processor
(Speech2Text2 model)SpeechT5Processor
(SpeechT5 model)TrOCRProcessor
(TrOCR model)TvltProcessor
(TVLT model)TvpProcessor
(TVP model)Wav2Vec2Processor
(UniSpeech model)Wav2Vec2Processor
(UniSpeechSat model)ViltProcessor
(ViLT model)LlavaProcessor
(VipLlava model)VisionTextDualEncoderProcessor
(VisionTextDualEncoder model)Wav2Vec2Processor
(Wav2Vec2 model)Wav2Vec2Processor
(Wav2Vec2-BERT model)Wav2Vec2Processor
(Wav2Vec2-Conformer model)Wav2Vec2Processor
(WavLM model)WhisperProcessor
(Whisper model)XCLIPProcessor
(X-CLIP model)Passing token=True
is required when you want to use a private model.
Examples:
>>> from transformers import AutoProcessor
>>> # Download processor from huggingface.co and cache.
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
>>> # If processor files are in a directory (e.g. processor was saved using *save_pretrained('./test/saved_model/')*)
>>> # processor = AutoProcessor.from_pretrained("./test/saved_model/")
( config_class processor_class exist_ok = False )
Parameters
FeatureExtractorMixin
) — The processor to register. Register a new processor for this class.
以下の自動クラスは、特定のヘッドを持たないベースモデルクラスをインスタンス化するために利用可能です。
This is a generic model class that will be instantiated as one of the base model classes of the library when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
CohereConfig
configuration class: CohereModel
(Cohere model)DPRConfig
configuration class: DPRQuestionEncoder
(DPR model)DPTConfig
configuration class: DPTModel
(DPT model)DbrxConfig
configuration class: DbrxModel
(DBRX model)Dinov2Config
configuration class: Dinov2Model
(DINOv2 model)DistilBertConfig
configuration class: DistilBertModel
(DistilBERT model)DonutSwinConfig
configuration class: DonutSwinModel
(DonutSwin model)EfficientFormerConfig
configuration class: EfficientFormerModel
(EfficientFormer model)EfficientNetConfig
configuration class: EfficientNetModel
(EfficientNet model)ElectraConfig
configuration class: ElectraModel
(ELECTRA model)EncodecConfig
configuration class: EncodecModel
(EnCodec model)ErnieConfig
configuration class: ErnieModel
(ERNIE model)ErnieMConfig
configuration class: ErnieMModel
(ErnieM model)EsmConfig
configuration class: EsmModel
(ESM model)FNetConfig
configuration class: FNetModel
(FNet model)FSMTConfig
configuration class: FSMTModel
(FairSeq Machine-Translation model)FalconConfig
configuration class: FalconModel
(Falcon model)FastSpeech2ConformerConfig
configuration class: FastSpeech2ConformerModel
(FastSpeech2Conformer model)FlaubertConfig
configuration class: FlaubertModel
(FlauBERT model)FlavaConfig
configuration class: FlavaModel
(FLAVA model)FocalNetConfig
configuration class: FocalNetModel
(FocalNet model)FunnelConfig
configuration class: FunnelModel
or FunnelBaseModel
(Funnel Transformer model)GLPNConfig
configuration class: GLPNModel
(GLPN model)GPT2Config
configuration class: GPT2Model
(OpenAI GPT-2 model)GPTBigCodeConfig
configuration class: GPTBigCodeModel
(GPTBigCode model)GPTJConfig
configuration class: GPTJModel
(GPT-J model)GPTNeoConfig
configuration class: GPTNeoModel
(GPT Neo model)GPTNeoXConfig
configuration class: GPTNeoXModel
(GPT NeoX model)GPTNeoXJapaneseConfig
configuration class: GPTNeoXJapaneseModel
(GPT NeoX Japanese model)GPTSanJapaneseConfig
configuration class: GPTSanJapaneseForConditionalGeneration
(GPTSAN-japanese model)GemmaConfig
configuration class: GemmaModel
(Gemma model)GitConfig
configuration class: GitModel
(GIT model)GraphormerConfig
configuration class: GraphormerModel
(Graphormer model)GroundingDinoConfig
configuration class: GroundingDinoModel
(Grounding DINO model)GroupViTConfig
configuration class: GroupViTModel
(GroupViT model)HubertConfig
configuration class: HubertModel
(Hubert model)IBertConfig
configuration class: IBertModel
(I-BERT model)Idefics2Config
configuration class: Idefics2Model
(Idefics2 model)IdeficsConfig
configuration class: IdeficsModel
(IDEFICS model)ImageGPTConfig
configuration class: ImageGPTModel
(ImageGPT model)InformerConfig
configuration class: InformerModel
(Informer model)JambaConfig
configuration class: JambaModel
(Jamba model)JukeboxConfig
configuration class: JukeboxModel
(Jukebox model)Kosmos2Config
configuration class: Kosmos2Model
(KOSMOS-2 model)LEDConfig
configuration class: LEDModel
(LED model)LayoutLMConfig
configuration class: LayoutLMModel
(LayoutLM model)LayoutLMv2Config
configuration class: LayoutLMv2Model
(LayoutLMv2 model)LayoutLMv3Config
configuration class: LayoutLMv3Model
(LayoutLMv3 model)LevitConfig
configuration class: LevitModel
(LeViT model)LiltConfig
configuration class: LiltModel
(LiLT model)LlamaConfig
configuration class: LlamaModel
(LLaMA model)LongT5Config
configuration class: LongT5Model
(LongT5 model)LongformerConfig
configuration class: LongformerModel
(Longformer model)LukeConfig
configuration class: LukeModel
(LUKE model)LxmertConfig
configuration class: LxmertModel
(LXMERT model)M2M100Config
configuration class: M2M100Model
(M2M100 model)MBartConfig
configuration class: MBartModel
(mBART model)MCTCTConfig
configuration class: MCTCTModel
(M-CTC-T model)MPNetConfig
configuration class: MPNetModel
(MPNet model)MT5Config
configuration class: MT5Model
(MT5 model)MambaConfig
configuration class: MambaModel
(Mamba model)MarianConfig
configuration class: MarianModel
(Marian model)MarkupLMConfig
configuration class: MarkupLMModel
(MarkupLM model)Mask2FormerConfig
configuration class: Mask2FormerModel
(Mask2Former model)MaskFormerConfig
configuration class: MaskFormerModel
(MaskFormer model)MaskFormerSwinConfig
configuration class: MaskFormerSwinModel
(MaskFormerSwin model)MegaConfig
configuration class: MegaModel
(MEGA model)MegatronBertConfig
configuration class: MegatronBertModel
(Megatron-BERT model)MgpstrConfig
configuration class: MgpstrForSceneTextRecognition
(MGP-STR model)MistralConfig
configuration class: MistralModel
(Mistral model)MixtralConfig
configuration class: MixtralModel
(Mixtral model)MobileBertConfig
configuration class: MobileBertModel
(MobileBERT model)MobileNetV1Config
configuration class: MobileNetV1Model
(MobileNetV1 model)MobileNetV2Config
configuration class: MobileNetV2Model
(MobileNetV2 model)MobileViTConfig
configuration class: MobileViTModel
(MobileViT model)MobileViTV2Config
configuration class: MobileViTV2Model
(MobileViTV2 model)MptConfig
configuration class: MptModel
(MPT model)MraConfig
configuration class: MraModel
(MRA model)MusicgenConfig
configuration class: MusicgenModel
(MusicGen model)MusicgenMelodyConfig
configuration class: MusicgenMelodyModel
(MusicGen Melody model)MvpConfig
configuration class: MvpModel
(MVP model)NatConfig
configuration class: NatModel
(NAT model)NezhaConfig
configuration class: NezhaModel
(Nezha model)NllbMoeConfig
configuration class: NllbMoeModel
(NLLB-MOE model)NystromformerConfig
configuration class: NystromformerModel
(Nyströmformer model)OPTConfig
configuration class: OPTModel
(OPT model)OlmoConfig
configuration class: OlmoModel
(OLMo model)OneFormerConfig
configuration class: OneFormerModel
(OneFormer model)OpenAIGPTConfig
configuration class: OpenAIGPTModel
(OpenAI GPT model)OpenLlamaConfig
configuration class: OpenLlamaModel
(OpenLlama model)OwlViTConfig
configuration class: OwlViTModel
(OWL-ViT model)Owlv2Config
configuration class: Owlv2Model
(OWLv2 model)PLBartConfig
configuration class: PLBartModel
(PLBart model)PatchTSMixerConfig
configuration class: PatchTSMixerModel
(PatchTSMixer model)PatchTSTConfig
configuration class: PatchTSTModel
(PatchTST model)PegasusConfig
configuration class: PegasusModel
(Pegasus model)PegasusXConfig
configuration class: PegasusXModel
(PEGASUS-X model)PerceiverConfig
configuration class: PerceiverModel
(Perceiver model)PersimmonConfig
configuration class: PersimmonModel
(Persimmon model)Phi3Config
configuration class: Phi3Model
(Phi3 model)PhiConfig
configuration class: PhiModel
(Phi model)PoolFormerConfig
configuration class: PoolFormerModel
(PoolFormer model)ProphetNetConfig
configuration class: ProphetNetModel
(ProphetNet model)PvtConfig
configuration class: PvtModel
(PVT model)PvtV2Config
configuration class: PvtV2Model
(PVTv2 model)QDQBertConfig
configuration class: QDQBertModel
(QDQBert model)Qwen2Config
configuration class: Qwen2Model
(Qwen2 model)Qwen2MoeConfig
configuration class: Qwen2MoeModel
(Qwen2MoE model)RecurrentGemmaConfig
configuration class: RecurrentGemmaModel
(RecurrentGemma model)ReformerConfig
configuration class: ReformerModel
(Reformer model)RegNetConfig
configuration class: RegNetModel
(RegNet model)RemBertConfig
configuration class: RemBertModel
(RemBERT model)ResNetConfig
configuration class: ResNetModel
(ResNet model)RetriBertConfig
configuration class: RetriBertModel
(RetriBERT model)RoCBertConfig
configuration class: RoCBertModel
(RoCBert model)RoFormerConfig
configuration class: RoFormerModel
(RoFormer model)RobertaConfig
configuration class: RobertaModel
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: RobertaPreLayerNormModel
(RoBERTa-PreLayerNorm model)RwkvConfig
configuration class: RwkvModel
(RWKV model)SEWConfig
configuration class: SEWModel
(SEW model)SEWDConfig
configuration class: SEWDModel
(SEW-D model)SamConfig
configuration class: SamModel
(SAM model)SeamlessM4TConfig
configuration class: SeamlessM4TModel
(SeamlessM4T model)SeamlessM4Tv2Config
configuration class: SeamlessM4Tv2Model
(SeamlessM4Tv2 model)SegGptConfig
configuration class: SegGptModel
(SegGPT model)SegformerConfig
configuration class: SegformerModel
(SegFormer model)SiglipConfig
configuration class: SiglipModel
(SigLIP model)SiglipVisionConfig
configuration class: SiglipVisionModel
(SiglipVisionModel model)Speech2TextConfig
configuration class: Speech2TextModel
(Speech2Text model)SpeechT5Config
configuration class: SpeechT5Model
(SpeechT5 model)SplinterConfig
configuration class: SplinterModel
(Splinter model)SqueezeBertConfig
configuration class: SqueezeBertModel
(SqueezeBERT model)StableLmConfig
configuration class: StableLmModel
(StableLm model)Starcoder2Config
configuration class: Starcoder2Model
(Starcoder2 model)SwiftFormerConfig
configuration class: SwiftFormerModel
(SwiftFormer model)Swin2SRConfig
configuration class: Swin2SRModel
(Swin2SR model)SwinConfig
configuration class: SwinModel
(Swin Transformer model)Swinv2Config
configuration class: Swinv2Model
(Swin Transformer V2 model)SwitchTransformersConfig
configuration class: SwitchTransformersModel
(SwitchTransformers model)T5Config
configuration class: T5Model
(T5 model)TableTransformerConfig
configuration class: TableTransformerModel
(Table Transformer model)TapasConfig
configuration class: TapasModel
(TAPAS model)TimeSeriesTransformerConfig
configuration class: TimeSeriesTransformerModel
(Time Series Transformer model)TimesformerConfig
configuration class: TimesformerModel
(TimeSformer model)TimmBackboneConfig
configuration class: TimmBackbone
(TimmBackbone model)TrajectoryTransformerConfig
configuration class: TrajectoryTransformerModel
(Trajectory Transformer model)TransfoXLConfig
configuration class: TransfoXLModel
(Transformer-XL model)TvltConfig
configuration class: TvltModel
(TVLT model)TvpConfig
configuration class: TvpModel
(TVP model)UMT5Config
configuration class: UMT5Model
(UMT5 model)UdopConfig
configuration class: UdopModel
(UDOP model)UniSpeechConfig
configuration class: UniSpeechModel
(UniSpeech model)UniSpeechSatConfig
configuration class: UniSpeechSatModel
(UniSpeechSat model)UnivNetConfig
configuration class: UnivNetModel
(UnivNet model)VanConfig
configuration class: VanModel
(VAN model)ViTConfig
configuration class: ViTModel
(ViT model)ViTHybridConfig
configuration class: ViTHybridModel
(ViT Hybrid model)ViTMAEConfig
configuration class: ViTMAEModel
(ViTMAE model)ViTMSNConfig
configuration class: ViTMSNModel
(ViTMSN model)VideoMAEConfig
configuration class: VideoMAEModel
(VideoMAE model)ViltConfig
configuration class: ViltModel
(ViLT model)VisionTextDualEncoderConfig
configuration class: VisionTextDualEncoderModel
(VisionTextDualEncoder model)VisualBertConfig
configuration class: VisualBertModel
(VisualBERT model)VitDetConfig
configuration class: VitDetModel
(VitDet model)VitsConfig
configuration class: VitsModel
(VITS model)VivitConfig
configuration class: VivitModel
(ViViT model)Wav2Vec2BertConfig
configuration class: Wav2Vec2BertModel
(Wav2Vec2-BERT model)Wav2Vec2Config
configuration class: Wav2Vec2Model
(Wav2Vec2 model)Wav2Vec2ConformerConfig
configuration class: Wav2Vec2ConformerModel
(Wav2Vec2-Conformer model)WavLMConfig
configuration class: WavLMModel
(WavLM model)WhisperConfig
configuration class: WhisperModel
(Whisper model)XCLIPConfig
configuration class: XCLIPModel
(X-CLIP model)XGLMConfig
configuration class: XGLMModel
(XGLM model)XLMConfig
configuration class: XLMModel
(XLM model)XLMProphetNetConfig
configuration class: XLMProphetNetModel
(XLM-ProphetNet model)XLMRobertaConfig
configuration class: XLMRobertaModel
(XLM-RoBERTa model)XLMRobertaXLConfig
configuration class: XLMRobertaXLModel
(XLM-RoBERTa-XL model)XLNetConfig
configuration class: XLNetModel
(XLNet model)XmodConfig
configuration class: XmodModel
(X-MOD model)YolosConfig
configuration class: YolosModel
(YOLOS model)YosoConfig
configuration class: YosoModel
(YOSO model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the base model classes of the library from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the base model classes of the library from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
LlamaModel
(CodeLlama model)CohereModel
(Cohere model)DbrxModel
(DBRX model)Dinov2Model
(DINOv2 model)DistilBertModel
(DistilBERT model)DonutSwinModel
(DonutSwin model)DPRQuestionEncoder
(DPR model)DPTModel
(DPT model)EfficientFormerModel
(EfficientFormer model)EfficientNetModel
(EfficientNet model)ElectraModel
(ELECTRA model)EncodecModel
(EnCodec model)ErnieModel
(ERNIE model)ErnieMModel
(ErnieM model)EsmModel
(ESM model)FalconModel
(Falcon model)FastSpeech2ConformerModel
(FastSpeech2Conformer model)FlaubertModel
(FlauBERT model)FlavaModel
(FLAVA model)FNetModel
(FNet model)FocalNetModel
(FocalNet model)FSMTModel
(FairSeq Machine-Translation model)FunnelModel
or FunnelBaseModel
(Funnel Transformer model)GemmaModel
(Gemma model)GitModel
(GIT model)GLPNModel
(GLPN model)GPT2Model
(GPT-Sw3 model)GPT2Model
(OpenAI GPT-2 model)GPTBigCodeModel
(GPTBigCode model)GPTNeoModel
(GPT Neo model)GPTNeoXModel
(GPT NeoX model)GPTNeoXJapaneseModel
(GPT NeoX Japanese model)GPTJModel
(GPT-J model)GPTSanJapaneseForConditionalGeneration
(GPTSAN-japanese model)GraphormerModel
(Graphormer model)GroundingDinoModel
(Grounding DINO model)GroupViTModel
(GroupViT model)HubertModel
(Hubert model)IBertModel
(I-BERT model)IdeficsModel
(IDEFICS model)Idefics2Model
(Idefics2 model)ImageGPTModel
(ImageGPT model)InformerModel
(Informer model)JambaModel
(Jamba model)JukeboxModel
(Jukebox model)Kosmos2Model
(KOSMOS-2 model)LayoutLMModel
(LayoutLM model)LayoutLMv2Model
(LayoutLMv2 model)LayoutLMv3Model
(LayoutLMv3 model)LEDModel
(LED model)LevitModel
(LeViT model)LiltModel
(LiLT model)LlamaModel
(LLaMA model)LongformerModel
(Longformer model)LongT5Model
(LongT5 model)LukeModel
(LUKE model)LxmertModel
(LXMERT model)M2M100Model
(M2M100 model)MambaModel
(Mamba model)MarianModel
(Marian model)MarkupLMModel
(MarkupLM model)Mask2FormerModel
(Mask2Former model)MaskFormerModel
(MaskFormer model)MaskFormerSwinModel
(MaskFormerSwin model)MBartModel
(mBART model)MCTCTModel
(M-CTC-T model)MegaModel
(MEGA model)MegatronBertModel
(Megatron-BERT model)MgpstrForSceneTextRecognition
(MGP-STR model)MistralModel
(Mistral model)MixtralModel
(Mixtral model)MobileBertModel
(MobileBERT model)MobileNetV1Model
(MobileNetV1 model)MobileNetV2Model
(MobileNetV2 model)MobileViTModel
(MobileViT model)MobileViTV2Model
(MobileViTV2 model)MPNetModel
(MPNet model)MptModel
(MPT model)MraModel
(MRA model)MT5Model
(MT5 model)MusicgenModel
(MusicGen model)MusicgenMelodyModel
(MusicGen Melody model)MvpModel
(MVP model)NatModel
(NAT model)NezhaModel
(Nezha model)NllbMoeModel
(NLLB-MOE model)NystromformerModel
(Nyströmformer model)OlmoModel
(OLMo model)OneFormerModel
(OneFormer model)OpenLlamaModel
(OpenLlama model)OpenAIGPTModel
(OpenAI GPT model)OPTModel
(OPT model)Owlv2Model
(OWLv2 model)OwlViTModel
(OWL-ViT model)PatchTSMixerModel
(PatchTSMixer model)PatchTSTModel
(PatchTST model)PegasusModel
(Pegasus model)PegasusXModel
(PEGASUS-X model)PerceiverModel
(Perceiver model)PersimmonModel
(Persimmon model)PhiModel
(Phi model)Phi3Model
(Phi3 model)PLBartModel
(PLBart model)PoolFormerModel
(PoolFormer model)ProphetNetModel
(ProphetNet model)PvtModel
(PVT model)PvtV2Model
(PVTv2 model)QDQBertModel
(QDQBert model)Qwen2Model
(Qwen2 model)Qwen2MoeModel
(Qwen2MoE model)RecurrentGemmaModel
(RecurrentGemma model)ReformerModel
(Reformer model)RegNetModel
(RegNet model)RemBertModel
(RemBERT model)ResNetModel
(ResNet model)RetriBertModel
(RetriBERT model)RobertaModel
(RoBERTa model)RobertaPreLayerNormModel
(RoBERTa-PreLayerNorm model)RoCBertModel
(RoCBert model)RoFormerModel
(RoFormer model)RwkvModel
(RWKV model)SamModel
(SAM model)SeamlessM4TModel
(SeamlessM4T model)SeamlessM4Tv2Model
(SeamlessM4Tv2 model)SegformerModel
(SegFormer model)SegGptModel
(SegGPT model)SEWModel
(SEW model)SEWDModel
(SEW-D model)SiglipModel
(SigLIP model)SiglipVisionModel
(SiglipVisionModel model)Speech2TextModel
(Speech2Text model)SpeechT5Model
(SpeechT5 model)SplinterModel
(Splinter model)SqueezeBertModel
(SqueezeBERT model)StableLmModel
(StableLm model)Starcoder2Model
(Starcoder2 model)SwiftFormerModel
(SwiftFormer model)SwinModel
(Swin Transformer model)Swin2SRModel
(Swin2SR model)Swinv2Model
(Swin Transformer V2 model)SwitchTransformersModel
(SwitchTransformers model)T5Model
(T5 model)TableTransformerModel
(Table Transformer model)TapasModel
(TAPAS model)TimeSeriesTransformerModel
(Time Series Transformer model)TimesformerModel
(TimeSformer model)TimmBackbone
(TimmBackbone model)TrajectoryTransformerModel
(Trajectory Transformer model)TransfoXLModel
(Transformer-XL model)TvltModel
(TVLT model)TvpModel
(TVP model)UdopModel
(UDOP model)UMT5Model
(UMT5 model)UniSpeechModel
(UniSpeech model)UniSpeechSatModel
(UniSpeechSat model)UnivNetModel
(UnivNet model)VanModel
(VAN model)VideoMAEModel
(VideoMAE model)ViltModel
(ViLT model)VisionTextDualEncoderModel
(VisionTextDualEncoder model)VisualBertModel
(VisualBERT model)ViTModel
(ViT model)ViTHybridModel
(ViT Hybrid model)ViTMAEModel
(ViTMAE model)ViTMSNModel
(ViTMSN model)VitDetModel
(VitDet model)VitsModel
(VITS model)VivitModel
(ViViT model)Wav2Vec2Model
(Wav2Vec2 model)Wav2Vec2BertModel
(Wav2Vec2-BERT model)Wav2Vec2ConformerModel
(Wav2Vec2-Conformer model)WavLMModel
(WavLM model)WhisperModel
(Whisper model)XCLIPModel
(X-CLIP model)XGLMModel
(XGLM model)XLMModel
(XLM model)XLMProphetNetModel
(XLM-ProphetNet model)XLMRobertaModel
(XLM-RoBERTa model)XLMRobertaXLModel
(XLM-RoBERTa-XL model)XLNetModel
(XLNet model)XmodModel
(X-MOD model)YolosModel
(YOLOS model)YosoModel
(YOSO model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModel
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModel.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModel.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModel.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the base model classes of the library when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DPRConfig
configuration class: TFDPRQuestionEncoder
(DPR model)DistilBertConfig
configuration class: TFDistilBertModel
(DistilBERT model)EfficientFormerConfig
configuration class: TFEfficientFormerModel
(EfficientFormer model)ElectraConfig
configuration class: TFElectraModel
(ELECTRA model)EsmConfig
configuration class: TFEsmModel
(ESM model)FlaubertConfig
configuration class: TFFlaubertModel
(FlauBERT model)FunnelConfig
configuration class: TFFunnelModel
or TFFunnelBaseModel
(Funnel Transformer model)GPT2Config
configuration class: TFGPT2Model
(OpenAI GPT-2 model)GPTJConfig
configuration class: TFGPTJModel
(GPT-J model)GroupViTConfig
configuration class: TFGroupViTModel
(GroupViT model)HubertConfig
configuration class: TFHubertModel
(Hubert model)LEDConfig
configuration class: TFLEDModel
(LED model)LayoutLMConfig
configuration class: TFLayoutLMModel
(LayoutLM model)LayoutLMv3Config
configuration class: TFLayoutLMv3Model
(LayoutLMv3 model)LongformerConfig
configuration class: TFLongformerModel
(Longformer model)LxmertConfig
configuration class: TFLxmertModel
(LXMERT model)MBartConfig
configuration class: TFMBartModel
(mBART model)MPNetConfig
configuration class: TFMPNetModel
(MPNet model)MT5Config
configuration class: TFMT5Model
(MT5 model)MarianConfig
configuration class: TFMarianModel
(Marian model)MobileBertConfig
configuration class: TFMobileBertModel
(MobileBERT model)MobileViTConfig
configuration class: TFMobileViTModel
(MobileViT model)OPTConfig
configuration class: TFOPTModel
(OPT model)OpenAIGPTConfig
configuration class: TFOpenAIGPTModel
(OpenAI GPT model)PegasusConfig
configuration class: TFPegasusModel
(Pegasus model)RegNetConfig
configuration class: TFRegNetModel
(RegNet model)RemBertConfig
configuration class: TFRemBertModel
(RemBERT model)ResNetConfig
configuration class: TFResNetModel
(ResNet model)RoFormerConfig
configuration class: TFRoFormerModel
(RoFormer model)RobertaConfig
configuration class: TFRobertaModel
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: TFRobertaPreLayerNormModel
(RoBERTa-PreLayerNorm model)SamConfig
configuration class: TFSamModel
(SAM model)SegformerConfig
configuration class: TFSegformerModel
(SegFormer model)Speech2TextConfig
configuration class: TFSpeech2TextModel
(Speech2Text model)SwiftFormerConfig
configuration class: TFSwiftFormerModel
(SwiftFormer model)SwinConfig
configuration class: TFSwinModel
(Swin Transformer model)T5Config
configuration class: TFT5Model
(T5 model)TapasConfig
configuration class: TFTapasModel
(TAPAS model)TransfoXLConfig
configuration class: TFTransfoXLModel
(Transformer-XL model)ViTConfig
configuration class: TFViTModel
(ViT model)ViTMAEConfig
configuration class: TFViTMAEModel
(ViTMAE model)VisionTextDualEncoderConfig
configuration class: TFVisionTextDualEncoderModel
(VisionTextDualEncoder model)Wav2Vec2Config
configuration class: TFWav2Vec2Model
(Wav2Vec2 model)WhisperConfig
configuration class: TFWhisperModel
(Whisper model)XGLMConfig
configuration class: TFXGLMModel
(XGLM model)XLMConfig
configuration class: TFXLMModel
(XLM model)XLMRobertaConfig
configuration class: TFXLMRobertaModel
(XLM-RoBERTa model)XLNetConfig
configuration class: TFXLNetModel
(XLNet model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the base model classes of the library from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the base model classes of the library from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFDistilBertModel
(DistilBERT model)TFDPRQuestionEncoder
(DPR model)TFEfficientFormerModel
(EfficientFormer model)TFElectraModel
(ELECTRA model)TFEsmModel
(ESM model)TFFlaubertModel
(FlauBERT model)TFFunnelModel
or TFFunnelBaseModel
(Funnel Transformer model)TFGPT2Model
(GPT-Sw3 model)TFGPT2Model
(OpenAI GPT-2 model)TFGPTJModel
(GPT-J model)TFGroupViTModel
(GroupViT model)TFHubertModel
(Hubert model)TFLayoutLMModel
(LayoutLM model)TFLayoutLMv3Model
(LayoutLMv3 model)TFLEDModel
(LED model)TFLongformerModel
(Longformer model)TFLxmertModel
(LXMERT model)TFMarianModel
(Marian model)TFMBartModel
(mBART model)TFMobileBertModel
(MobileBERT model)TFMobileViTModel
(MobileViT model)TFMPNetModel
(MPNet model)TFMT5Model
(MT5 model)TFOpenAIGPTModel
(OpenAI GPT model)TFOPTModel
(OPT model)TFPegasusModel
(Pegasus model)TFRegNetModel
(RegNet model)TFRemBertModel
(RemBERT model)TFResNetModel
(ResNet model)TFRobertaModel
(RoBERTa model)TFRobertaPreLayerNormModel
(RoBERTa-PreLayerNorm model)TFRoFormerModel
(RoFormer model)TFSamModel
(SAM model)TFSegformerModel
(SegFormer model)TFSpeech2TextModel
(Speech2Text model)TFSwiftFormerModel
(SwiftFormer model)TFSwinModel
(Swin Transformer model)TFT5Model
(T5 model)TFTapasModel
(TAPAS model)TFTransfoXLModel
(Transformer-XL model)TFVisionTextDualEncoderModel
(VisionTextDualEncoder model)TFViTModel
(ViT model)TFViTMAEModel
(ViTMAE model)TFWav2Vec2Model
(Wav2Vec2 model)TFWhisperModel
(Whisper model)TFXGLMModel
(XGLM model)TFXLMModel
(XLM model)TFXLMRobertaModel
(XLM-RoBERTa model)TFXLNetModel
(XLNet model)Examples:
>>> from transformers import AutoConfig, TFAutoModel
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModel.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the base model classes of the library when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: FlaxDistilBertModel
(DistilBERT model)ElectraConfig
configuration class: FlaxElectraModel
(ELECTRA model)GPT2Config
configuration class: FlaxGPT2Model
(OpenAI GPT-2 model)GPTJConfig
configuration class: FlaxGPTJModel
(GPT-J model)GPTNeoConfig
configuration class: FlaxGPTNeoModel
(GPT Neo model)GemmaConfig
configuration class: FlaxGemmaModel
(Gemma model)LlamaConfig
configuration class: FlaxLlamaModel
(LLaMA model)LongT5Config
configuration class: FlaxLongT5Model
(LongT5 model)MBartConfig
configuration class: FlaxMBartModel
(mBART model)MT5Config
configuration class: FlaxMT5Model
(MT5 model)MarianConfig
configuration class: FlaxMarianModel
(Marian model)MistralConfig
configuration class: FlaxMistralModel
(Mistral model)OPTConfig
configuration class: FlaxOPTModel
(OPT model)PegasusConfig
configuration class: FlaxPegasusModel
(Pegasus model)RegNetConfig
configuration class: FlaxRegNetModel
(RegNet model)ResNetConfig
configuration class: FlaxResNetModel
(ResNet model)RoFormerConfig
configuration class: FlaxRoFormerModel
(RoFormer model)RobertaConfig
configuration class: FlaxRobertaModel
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: FlaxRobertaPreLayerNormModel
(RoBERTa-PreLayerNorm model)T5Config
configuration class: FlaxT5Model
(T5 model)ViTConfig
configuration class: FlaxViTModel
(ViT model)VisionTextDualEncoderConfig
configuration class: FlaxVisionTextDualEncoderModel
(VisionTextDualEncoder model)Wav2Vec2Config
configuration class: FlaxWav2Vec2Model
(Wav2Vec2 model)WhisperConfig
configuration class: FlaxWhisperModel
(Whisper model)XGLMConfig
configuration class: FlaxXGLMModel
(XGLM model)XLMRobertaConfig
configuration class: FlaxXLMRobertaModel
(XLM-RoBERTa model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the base model classes of the library from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the base model classes of the library from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
FlaxDistilBertModel
(DistilBERT model)FlaxElectraModel
(ELECTRA model)FlaxGemmaModel
(Gemma model)FlaxGPT2Model
(GPT-Sw3 model)FlaxGPT2Model
(OpenAI GPT-2 model)FlaxGPTNeoModel
(GPT Neo model)FlaxGPTJModel
(GPT-J model)FlaxLlamaModel
(LLaMA model)FlaxLongT5Model
(LongT5 model)FlaxMarianModel
(Marian model)FlaxMBartModel
(mBART model)FlaxMistralModel
(Mistral model)FlaxMT5Model
(MT5 model)FlaxOPTModel
(OPT model)FlaxPegasusModel
(Pegasus model)FlaxRegNetModel
(RegNet model)FlaxResNetModel
(ResNet model)FlaxRobertaModel
(RoBERTa model)FlaxRobertaPreLayerNormModel
(RoBERTa-PreLayerNorm model)FlaxRoFormerModel
(RoFormer model)FlaxT5Model
(T5 model)FlaxVisionTextDualEncoderModel
(VisionTextDualEncoder model)FlaxViTModel
(ViT model)FlaxWav2Vec2Model
(Wav2Vec2 model)FlaxWhisperModel
(Whisper model)FlaxXGLMModel
(XGLM model)FlaxXLMRobertaModel
(XLM-RoBERTa model)Examples:
>>> from transformers import AutoConfig, FlaxAutoModel
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModel.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModel.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = FlaxAutoModel.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
以下の自動クラスは、事前学習ヘッドを持つモデルをインスタンス化するために利用可能です。
This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: DistilBertForMaskedLM
(DistilBERT model)ElectraConfig
configuration class: ElectraForPreTraining
(ELECTRA model)ErnieConfig
configuration class: ErnieForPreTraining
(ERNIE model)FNetConfig
configuration class: FNetForPreTraining
(FNet model)FSMTConfig
configuration class: FSMTForConditionalGeneration
(FairSeq Machine-Translation model)FlaubertConfig
configuration class: FlaubertWithLMHeadModel
(FlauBERT model)FlavaConfig
configuration class: FlavaForPreTraining
(FLAVA model)FunnelConfig
configuration class: FunnelForPreTraining
(Funnel Transformer model)GPT2Config
configuration class: GPT2LMHeadModel
(OpenAI GPT-2 model)GPTBigCodeConfig
configuration class: GPTBigCodeForCausalLM
(GPTBigCode model)GPTSanJapaneseConfig
configuration class: GPTSanJapaneseForConditionalGeneration
(GPTSAN-japanese model)IBertConfig
configuration class: IBertForMaskedLM
(I-BERT model)Idefics2Config
configuration class: Idefics2ForConditionalGeneration
(Idefics2 model)IdeficsConfig
configuration class: IdeficsForVisionText2Text
(IDEFICS model)LayoutLMConfig
configuration class: LayoutLMForMaskedLM
(LayoutLM model)LlavaConfig
configuration class: LlavaForConditionalGeneration
(LLaVa model)LlavaNextConfig
configuration class: LlavaNextForConditionalGeneration
(LLaVA-NeXT model)LongformerConfig
configuration class: LongformerForMaskedLM
(Longformer model)LukeConfig
configuration class: LukeForMaskedLM
(LUKE model)LxmertConfig
configuration class: LxmertForPreTraining
(LXMERT model)MPNetConfig
configuration class: MPNetForMaskedLM
(MPNet model)MambaConfig
configuration class: MambaForCausalLM
(Mamba model)MegaConfig
configuration class: MegaForMaskedLM
(MEGA model)MegatronBertConfig
configuration class: MegatronBertForPreTraining
(Megatron-BERT model)MobileBertConfig
configuration class: MobileBertForPreTraining
(MobileBERT model)MptConfig
configuration class: MptForCausalLM
(MPT model)MraConfig
configuration class: MraForMaskedLM
(MRA model)MvpConfig
configuration class: MvpForConditionalGeneration
(MVP model)NezhaConfig
configuration class: NezhaForPreTraining
(Nezha model)NllbMoeConfig
configuration class: NllbMoeForConditionalGeneration
(NLLB-MOE model)OpenAIGPTConfig
configuration class: OpenAIGPTLMHeadModel
(OpenAI GPT model)RetriBertConfig
configuration class: RetriBertModel
(RetriBERT model)RoCBertConfig
configuration class: RoCBertForPreTraining
(RoCBert model)RobertaConfig
configuration class: RobertaForMaskedLM
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: RobertaPreLayerNormForMaskedLM
(RoBERTa-PreLayerNorm model)RwkvConfig
configuration class: RwkvForCausalLM
(RWKV model)SplinterConfig
configuration class: SplinterForPreTraining
(Splinter model)SqueezeBertConfig
configuration class: SqueezeBertForMaskedLM
(SqueezeBERT model)SwitchTransformersConfig
configuration class: SwitchTransformersForConditionalGeneration
(SwitchTransformers model)T5Config
configuration class: T5ForConditionalGeneration
(T5 model)TapasConfig
configuration class: TapasForMaskedLM
(TAPAS model)TransfoXLConfig
configuration class: TransfoXLLMHeadModel
(Transformer-XL model)TvltConfig
configuration class: TvltForPreTraining
(TVLT model)UniSpeechConfig
configuration class: UniSpeechForPreTraining
(UniSpeech model)UniSpeechSatConfig
configuration class: UniSpeechSatForPreTraining
(UniSpeechSat model)ViTMAEConfig
configuration class: ViTMAEForPreTraining
(ViTMAE model)VideoMAEConfig
configuration class: VideoMAEForPreTraining
(VideoMAE model)VipLlavaConfig
configuration class: VipLlavaForConditionalGeneration
(VipLlava model)VisualBertConfig
configuration class: VisualBertForPreTraining
(VisualBERT model)Wav2Vec2Config
configuration class: Wav2Vec2ForPreTraining
(Wav2Vec2 model)Wav2Vec2ConformerConfig
configuration class: Wav2Vec2ConformerForPreTraining
(Wav2Vec2-Conformer model)XLMConfig
configuration class: XLMWithLMHeadModel
(XLM model)XLMRobertaConfig
configuration class: XLMRobertaForMaskedLM
(XLM-RoBERTa model)XLMRobertaXLConfig
configuration class: XLMRobertaXLForMaskedLM
(XLM-RoBERTa-XL model)XLNetConfig
configuration class: XLNetLMHeadModel
(XLNet model)XmodConfig
configuration class: XmodForMaskedLM
(X-MOD model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a pretraining head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
DistilBertForMaskedLM
(DistilBERT model)ElectraForPreTraining
(ELECTRA model)ErnieForPreTraining
(ERNIE model)FlaubertWithLMHeadModel
(FlauBERT model)FlavaForPreTraining
(FLAVA model)FNetForPreTraining
(FNet model)FSMTForConditionalGeneration
(FairSeq Machine-Translation model)FunnelForPreTraining
(Funnel Transformer model)GPT2LMHeadModel
(GPT-Sw3 model)GPT2LMHeadModel
(OpenAI GPT-2 model)GPTBigCodeForCausalLM
(GPTBigCode model)GPTSanJapaneseForConditionalGeneration
(GPTSAN-japanese model)IBertForMaskedLM
(I-BERT model)IdeficsForVisionText2Text
(IDEFICS model)Idefics2ForConditionalGeneration
(Idefics2 model)LayoutLMForMaskedLM
(LayoutLM model)LlavaForConditionalGeneration
(LLaVa model)LlavaNextForConditionalGeneration
(LLaVA-NeXT model)LongformerForMaskedLM
(Longformer model)LukeForMaskedLM
(LUKE model)LxmertForPreTraining
(LXMERT model)MambaForCausalLM
(Mamba model)MegaForMaskedLM
(MEGA model)MegatronBertForPreTraining
(Megatron-BERT model)MobileBertForPreTraining
(MobileBERT model)MPNetForMaskedLM
(MPNet model)MptForCausalLM
(MPT model)MraForMaskedLM
(MRA model)MvpForConditionalGeneration
(MVP model)NezhaForPreTraining
(Nezha model)NllbMoeForConditionalGeneration
(NLLB-MOE model)OpenAIGPTLMHeadModel
(OpenAI GPT model)RetriBertModel
(RetriBERT model)RobertaForMaskedLM
(RoBERTa model)RobertaPreLayerNormForMaskedLM
(RoBERTa-PreLayerNorm model)RoCBertForPreTraining
(RoCBert model)RwkvForCausalLM
(RWKV model)SplinterForPreTraining
(Splinter model)SqueezeBertForMaskedLM
(SqueezeBERT model)SwitchTransformersForConditionalGeneration
(SwitchTransformers model)T5ForConditionalGeneration
(T5 model)TapasForMaskedLM
(TAPAS model)TransfoXLLMHeadModel
(Transformer-XL model)TvltForPreTraining
(TVLT model)UniSpeechForPreTraining
(UniSpeech model)UniSpeechSatForPreTraining
(UniSpeechSat model)VideoMAEForPreTraining
(VideoMAE model)VipLlavaForConditionalGeneration
(VipLlava model)VisualBertForPreTraining
(VisualBERT model)ViTMAEForPreTraining
(ViTMAE model)Wav2Vec2ForPreTraining
(Wav2Vec2 model)Wav2Vec2ConformerForPreTraining
(Wav2Vec2-Conformer model)XLMWithLMHeadModel
(XLM model)XLMRobertaForMaskedLM
(XLM-RoBERTa model)XLMRobertaXLForMaskedLM
(XLM-RoBERTa-XL model)XLNetLMHeadModel
(XLNet model)XmodForMaskedLM
(X-MOD model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForPreTraining
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForPreTraining.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForPreTraining.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForPreTraining.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: TFDistilBertForMaskedLM
(DistilBERT model)ElectraConfig
configuration class: TFElectraForPreTraining
(ELECTRA model)FlaubertConfig
configuration class: TFFlaubertWithLMHeadModel
(FlauBERT model)FunnelConfig
configuration class: TFFunnelForPreTraining
(Funnel Transformer model)GPT2Config
configuration class: TFGPT2LMHeadModel
(OpenAI GPT-2 model)LayoutLMConfig
configuration class: TFLayoutLMForMaskedLM
(LayoutLM model)LxmertConfig
configuration class: TFLxmertForPreTraining
(LXMERT model)MPNetConfig
configuration class: TFMPNetForMaskedLM
(MPNet model)MobileBertConfig
configuration class: TFMobileBertForPreTraining
(MobileBERT model)OpenAIGPTConfig
configuration class: TFOpenAIGPTLMHeadModel
(OpenAI GPT model)RobertaConfig
configuration class: TFRobertaForMaskedLM
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: TFRobertaPreLayerNormForMaskedLM
(RoBERTa-PreLayerNorm model)T5Config
configuration class: TFT5ForConditionalGeneration
(T5 model)TapasConfig
configuration class: TFTapasForMaskedLM
(TAPAS model)TransfoXLConfig
configuration class: TFTransfoXLLMHeadModel
(Transformer-XL model)ViTMAEConfig
configuration class: TFViTMAEForPreTraining
(ViTMAE model)XLMConfig
configuration class: TFXLMWithLMHeadModel
(XLM model)XLMRobertaConfig
configuration class: TFXLMRobertaForMaskedLM
(XLM-RoBERTa model)XLNetConfig
configuration class: TFXLNetLMHeadModel
(XLNet model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a pretraining head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFDistilBertForMaskedLM
(DistilBERT model)TFElectraForPreTraining
(ELECTRA model)TFFlaubertWithLMHeadModel
(FlauBERT model)TFFunnelForPreTraining
(Funnel Transformer model)TFGPT2LMHeadModel
(GPT-Sw3 model)TFGPT2LMHeadModel
(OpenAI GPT-2 model)TFLayoutLMForMaskedLM
(LayoutLM model)TFLxmertForPreTraining
(LXMERT model)TFMobileBertForPreTraining
(MobileBERT model)TFMPNetForMaskedLM
(MPNet model)TFOpenAIGPTLMHeadModel
(OpenAI GPT model)TFRobertaForMaskedLM
(RoBERTa model)TFRobertaPreLayerNormForMaskedLM
(RoBERTa-PreLayerNorm model)TFT5ForConditionalGeneration
(T5 model)TFTapasForMaskedLM
(TAPAS model)TFTransfoXLLMHeadModel
(Transformer-XL model)TFViTMAEForPreTraining
(ViTMAE model)TFXLMWithLMHeadModel
(XLM model)TFXLMRobertaForMaskedLM
(XLM-RoBERTa model)TFXLNetLMHeadModel
(XLNet model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForPreTraining
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForPreTraining.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForPreTraining.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForPreTraining.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
ElectraConfig
configuration class: FlaxElectraForPreTraining
(ELECTRA model)LongT5Config
configuration class: FlaxLongT5ForConditionalGeneration
(LongT5 model)MBartConfig
configuration class: FlaxMBartForConditionalGeneration
(mBART model)MT5Config
configuration class: FlaxMT5ForConditionalGeneration
(MT5 model)RoFormerConfig
configuration class: FlaxRoFormerForMaskedLM
(RoFormer model)RobertaConfig
configuration class: FlaxRobertaForMaskedLM
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: FlaxRobertaPreLayerNormForMaskedLM
(RoBERTa-PreLayerNorm model)T5Config
configuration class: FlaxT5ForConditionalGeneration
(T5 model)Wav2Vec2Config
configuration class: FlaxWav2Vec2ForPreTraining
(Wav2Vec2 model)WhisperConfig
configuration class: FlaxWhisperForConditionalGeneration
(Whisper model)XLMRobertaConfig
configuration class: FlaxXLMRobertaForMaskedLM
(XLM-RoBERTa model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a pretraining head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
FlaxElectraForPreTraining
(ELECTRA model)FlaxLongT5ForConditionalGeneration
(LongT5 model)FlaxMBartForConditionalGeneration
(mBART model)FlaxMT5ForConditionalGeneration
(MT5 model)FlaxRobertaForMaskedLM
(RoBERTa model)FlaxRobertaPreLayerNormForMaskedLM
(RoBERTa-PreLayerNorm model)FlaxRoFormerForMaskedLM
(RoFormer model)FlaxT5ForConditionalGeneration
(T5 model)FlaxWav2Vec2ForPreTraining
(Wav2Vec2 model)FlaxWhisperForConditionalGeneration
(Whisper model)FlaxXLMRobertaForMaskedLM
(XLM-RoBERTa model)Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForPreTraining
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForPreTraining.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForPreTraining.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = FlaxAutoModelForPreTraining.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
以下の自動クラスは、次の自然言語処理タスクに利用可能です。
This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
CohereConfig
configuration class: CohereForCausalLM
(Cohere model)DbrxConfig
configuration class: DbrxForCausalLM
(DBRX model)ElectraConfig
configuration class: ElectraForCausalLM
(ELECTRA model)ErnieConfig
configuration class: ErnieForCausalLM
(ERNIE model)FalconConfig
configuration class: FalconForCausalLM
(Falcon model)FuyuConfig
configuration class: FuyuForCausalLM
(Fuyu model)GPT2Config
configuration class: GPT2LMHeadModel
(OpenAI GPT-2 model)GPTBigCodeConfig
configuration class: GPTBigCodeForCausalLM
(GPTBigCode model)GPTJConfig
configuration class: GPTJForCausalLM
(GPT-J model)GPTNeoConfig
configuration class: GPTNeoForCausalLM
(GPT Neo model)GPTNeoXConfig
configuration class: GPTNeoXForCausalLM
(GPT NeoX model)GPTNeoXJapaneseConfig
configuration class: GPTNeoXJapaneseForCausalLM
(GPT NeoX Japanese model)GemmaConfig
configuration class: GemmaForCausalLM
(Gemma model)GitConfig
configuration class: GitForCausalLM
(GIT model)JambaConfig
configuration class: JambaForCausalLM
(Jamba model)LlamaConfig
configuration class: LlamaForCausalLM
(LLaMA model)MBartConfig
configuration class: MBartForCausalLM
(mBART model)MambaConfig
configuration class: MambaForCausalLM
(Mamba model)MarianConfig
configuration class: MarianForCausalLM
(Marian model)MegaConfig
configuration class: MegaForCausalLM
(MEGA model)MegatronBertConfig
configuration class: MegatronBertForCausalLM
(Megatron-BERT model)MistralConfig
configuration class: MistralForCausalLM
(Mistral model)MixtralConfig
configuration class: MixtralForCausalLM
(Mixtral model)MptConfig
configuration class: MptForCausalLM
(MPT model)MusicgenConfig
configuration class: MusicgenForCausalLM
(MusicGen model)MusicgenMelodyConfig
configuration class: MusicgenMelodyForCausalLM
(MusicGen Melody model)MvpConfig
configuration class: MvpForCausalLM
(MVP model)OPTConfig
configuration class: OPTForCausalLM
(OPT model)OlmoConfig
configuration class: OlmoForCausalLM
(OLMo model)OpenAIGPTConfig
configuration class: OpenAIGPTLMHeadModel
(OpenAI GPT model)OpenLlamaConfig
configuration class: OpenLlamaForCausalLM
(OpenLlama model)PLBartConfig
configuration class: PLBartForCausalLM
(PLBart model)PegasusConfig
configuration class: PegasusForCausalLM
(Pegasus model)PersimmonConfig
configuration class: PersimmonForCausalLM
(Persimmon model)Phi3Config
configuration class: Phi3ForCausalLM
(Phi3 model)PhiConfig
configuration class: PhiForCausalLM
(Phi model)ProphetNetConfig
configuration class: ProphetNetForCausalLM
(ProphetNet model)QDQBertConfig
configuration class: QDQBertLMHeadModel
(QDQBert model)Qwen2Config
configuration class: Qwen2ForCausalLM
(Qwen2 model)Qwen2MoeConfig
configuration class: Qwen2MoeForCausalLM
(Qwen2MoE model)RecurrentGemmaConfig
configuration class: RecurrentGemmaForCausalLM
(RecurrentGemma model)ReformerConfig
configuration class: ReformerModelWithLMHead
(Reformer model)RemBertConfig
configuration class: RemBertForCausalLM
(RemBERT model)RoCBertConfig
configuration class: RoCBertForCausalLM
(RoCBert model)RoFormerConfig
configuration class: RoFormerForCausalLM
(RoFormer model)RobertaConfig
configuration class: RobertaForCausalLM
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: RobertaPreLayerNormForCausalLM
(RoBERTa-PreLayerNorm model)RwkvConfig
configuration class: RwkvForCausalLM
(RWKV model)Speech2Text2Config
configuration class: Speech2Text2ForCausalLM
(Speech2Text2 model)StableLmConfig
configuration class: StableLmForCausalLM
(StableLm model)Starcoder2Config
configuration class: Starcoder2ForCausalLM
(Starcoder2 model)TrOCRConfig
configuration class: TrOCRForCausalLM
(TrOCR model)TransfoXLConfig
configuration class: TransfoXLLMHeadModel
(Transformer-XL model)WhisperConfig
configuration class: WhisperForCausalLM
(Whisper model)XGLMConfig
configuration class: XGLMForCausalLM
(XGLM model)XLMConfig
configuration class: XLMWithLMHeadModel
(XLM model)XLMProphetNetConfig
configuration class: XLMProphetNetForCausalLM
(XLM-ProphetNet model)XLMRobertaConfig
configuration class: XLMRobertaForCausalLM
(XLM-RoBERTa model)XLMRobertaXLConfig
configuration class: XLMRobertaXLForCausalLM
(XLM-RoBERTa-XL model)XLNetConfig
configuration class: XLNetLMHeadModel
(XLNet model)XmodConfig
configuration class: XmodForCausalLM
(X-MOD model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
LlamaForCausalLM
(CodeLlama model)CohereForCausalLM
(Cohere model)DbrxForCausalLM
(DBRX model)ElectraForCausalLM
(ELECTRA model)ErnieForCausalLM
(ERNIE model)FalconForCausalLM
(Falcon model)FuyuForCausalLM
(Fuyu model)GemmaForCausalLM
(Gemma model)GitForCausalLM
(GIT model)GPT2LMHeadModel
(GPT-Sw3 model)GPT2LMHeadModel
(OpenAI GPT-2 model)GPTBigCodeForCausalLM
(GPTBigCode model)GPTNeoForCausalLM
(GPT Neo model)GPTNeoXForCausalLM
(GPT NeoX model)GPTNeoXJapaneseForCausalLM
(GPT NeoX Japanese model)GPTJForCausalLM
(GPT-J model)JambaForCausalLM
(Jamba model)LlamaForCausalLM
(LLaMA model)MambaForCausalLM
(Mamba model)MarianForCausalLM
(Marian model)MBartForCausalLM
(mBART model)MegaForCausalLM
(MEGA model)MegatronBertForCausalLM
(Megatron-BERT model)MistralForCausalLM
(Mistral model)MixtralForCausalLM
(Mixtral model)MptForCausalLM
(MPT model)MusicgenForCausalLM
(MusicGen model)MusicgenMelodyForCausalLM
(MusicGen Melody model)MvpForCausalLM
(MVP model)OlmoForCausalLM
(OLMo model)OpenLlamaForCausalLM
(OpenLlama model)OpenAIGPTLMHeadModel
(OpenAI GPT model)OPTForCausalLM
(OPT model)PegasusForCausalLM
(Pegasus model)PersimmonForCausalLM
(Persimmon model)PhiForCausalLM
(Phi model)Phi3ForCausalLM
(Phi3 model)PLBartForCausalLM
(PLBart model)ProphetNetForCausalLM
(ProphetNet model)QDQBertLMHeadModel
(QDQBert model)Qwen2ForCausalLM
(Qwen2 model)Qwen2MoeForCausalLM
(Qwen2MoE model)RecurrentGemmaForCausalLM
(RecurrentGemma model)ReformerModelWithLMHead
(Reformer model)RemBertForCausalLM
(RemBERT model)RobertaForCausalLM
(RoBERTa model)RobertaPreLayerNormForCausalLM
(RoBERTa-PreLayerNorm model)RoCBertForCausalLM
(RoCBert model)RoFormerForCausalLM
(RoFormer model)RwkvForCausalLM
(RWKV model)Speech2Text2ForCausalLM
(Speech2Text2 model)StableLmForCausalLM
(StableLm model)Starcoder2ForCausalLM
(Starcoder2 model)TransfoXLLMHeadModel
(Transformer-XL model)TrOCRForCausalLM
(TrOCR model)WhisperForCausalLM
(Whisper model)XGLMForCausalLM
(XGLM model)XLMWithLMHeadModel
(XLM model)XLMProphetNetForCausalLM
(XLM-ProphetNet model)XLMRobertaForCausalLM
(XLM-RoBERTa model)XLMRobertaXLForCausalLM
(XLM-RoBERTa-XL model)XLNetLMHeadModel
(XLNet model)XmodForCausalLM
(X-MOD model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForCausalLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForCausalLM.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForCausalLM.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForCausalLM.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
GPT2Config
configuration class: TFGPT2LMHeadModel
(OpenAI GPT-2 model)GPTJConfig
configuration class: TFGPTJForCausalLM
(GPT-J model)OPTConfig
configuration class: TFOPTForCausalLM
(OPT model)OpenAIGPTConfig
configuration class: TFOpenAIGPTLMHeadModel
(OpenAI GPT model)RemBertConfig
configuration class: TFRemBertForCausalLM
(RemBERT model)RoFormerConfig
configuration class: TFRoFormerForCausalLM
(RoFormer model)RobertaConfig
configuration class: TFRobertaForCausalLM
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: TFRobertaPreLayerNormForCausalLM
(RoBERTa-PreLayerNorm model)TransfoXLConfig
configuration class: TFTransfoXLLMHeadModel
(Transformer-XL model)XGLMConfig
configuration class: TFXGLMForCausalLM
(XGLM model)XLMConfig
configuration class: TFXLMWithLMHeadModel
(XLM model)XLMRobertaConfig
configuration class: TFXLMRobertaForCausalLM
(XLM-RoBERTa model)XLNetConfig
configuration class: TFXLNetLMHeadModel
(XLNet model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFGPT2LMHeadModel
(GPT-Sw3 model)TFGPT2LMHeadModel
(OpenAI GPT-2 model)TFGPTJForCausalLM
(GPT-J model)TFOpenAIGPTLMHeadModel
(OpenAI GPT model)TFOPTForCausalLM
(OPT model)TFRemBertForCausalLM
(RemBERT model)TFRobertaForCausalLM
(RoBERTa model)TFRobertaPreLayerNormForCausalLM
(RoBERTa-PreLayerNorm model)TFRoFormerForCausalLM
(RoFormer model)TFTransfoXLLMHeadModel
(Transformer-XL model)TFXGLMForCausalLM
(XGLM model)TFXLMWithLMHeadModel
(XLM model)TFXLMRobertaForCausalLM
(XLM-RoBERTa model)TFXLNetLMHeadModel
(XLNet model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForCausalLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForCausalLM.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForCausalLM.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForCausalLM.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
ElectraConfig
configuration class: FlaxElectraForCausalLM
(ELECTRA model)GPT2Config
configuration class: FlaxGPT2LMHeadModel
(OpenAI GPT-2 model)GPTJConfig
configuration class: FlaxGPTJForCausalLM
(GPT-J model)GPTNeoConfig
configuration class: FlaxGPTNeoForCausalLM
(GPT Neo model)GemmaConfig
configuration class: FlaxGemmaForCausalLM
(Gemma model)LlamaConfig
configuration class: FlaxLlamaForCausalLM
(LLaMA model)MistralConfig
configuration class: FlaxMistralForCausalLM
(Mistral model)OPTConfig
configuration class: FlaxOPTForCausalLM
(OPT model)RobertaConfig
configuration class: FlaxRobertaForCausalLM
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: FlaxRobertaPreLayerNormForCausalLM
(RoBERTa-PreLayerNorm model)XGLMConfig
configuration class: FlaxXGLMForCausalLM
(XGLM model)XLMRobertaConfig
configuration class: FlaxXLMRobertaForCausalLM
(XLM-RoBERTa model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
FlaxElectraForCausalLM
(ELECTRA model)FlaxGemmaForCausalLM
(Gemma model)FlaxGPT2LMHeadModel
(GPT-Sw3 model)FlaxGPT2LMHeadModel
(OpenAI GPT-2 model)FlaxGPTNeoForCausalLM
(GPT Neo model)FlaxGPTJForCausalLM
(GPT-J model)FlaxLlamaForCausalLM
(LLaMA model)FlaxMistralForCausalLM
(Mistral model)FlaxOPTForCausalLM
(OPT model)FlaxRobertaForCausalLM
(RoBERTa model)FlaxRobertaPreLayerNormForCausalLM
(RoBERTa-PreLayerNorm model)FlaxXGLMForCausalLM
(XGLM model)FlaxXLMRobertaForCausalLM
(XLM-RoBERTa model)Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForCausalLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForCausalLM.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForCausalLM.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = FlaxAutoModelForCausalLM.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: DistilBertForMaskedLM
(DistilBERT model)ElectraConfig
configuration class: ElectraForMaskedLM
(ELECTRA model)ErnieConfig
configuration class: ErnieForMaskedLM
(ERNIE model)EsmConfig
configuration class: EsmForMaskedLM
(ESM model)FNetConfig
configuration class: FNetForMaskedLM
(FNet model)FlaubertConfig
configuration class: FlaubertWithLMHeadModel
(FlauBERT model)FunnelConfig
configuration class: FunnelForMaskedLM
(Funnel Transformer model)IBertConfig
configuration class: IBertForMaskedLM
(I-BERT model)LayoutLMConfig
configuration class: LayoutLMForMaskedLM
(LayoutLM model)LongformerConfig
configuration class: LongformerForMaskedLM
(Longformer model)LukeConfig
configuration class: LukeForMaskedLM
(LUKE model)MBartConfig
configuration class: MBartForConditionalGeneration
(mBART model)MPNetConfig
configuration class: MPNetForMaskedLM
(MPNet model)MegaConfig
configuration class: MegaForMaskedLM
(MEGA model)MegatronBertConfig
configuration class: MegatronBertForMaskedLM
(Megatron-BERT model)MobileBertConfig
configuration class: MobileBertForMaskedLM
(MobileBERT model)MraConfig
configuration class: MraForMaskedLM
(MRA model)MvpConfig
configuration class: MvpForConditionalGeneration
(MVP model)NezhaConfig
configuration class: NezhaForMaskedLM
(Nezha model)NystromformerConfig
configuration class: NystromformerForMaskedLM
(Nyströmformer model)PerceiverConfig
configuration class: PerceiverForMaskedLM
(Perceiver model)QDQBertConfig
configuration class: QDQBertForMaskedLM
(QDQBert model)ReformerConfig
configuration class: ReformerForMaskedLM
(Reformer model)RemBertConfig
configuration class: RemBertForMaskedLM
(RemBERT model)RoCBertConfig
configuration class: RoCBertForMaskedLM
(RoCBert model)RoFormerConfig
configuration class: RoFormerForMaskedLM
(RoFormer model)RobertaConfig
configuration class: RobertaForMaskedLM
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: RobertaPreLayerNormForMaskedLM
(RoBERTa-PreLayerNorm model)SqueezeBertConfig
configuration class: SqueezeBertForMaskedLM
(SqueezeBERT model)TapasConfig
configuration class: TapasForMaskedLM
(TAPAS model)Wav2Vec2Config
configuration class: Wav2Vec2ForMaskedLM
(Wav2Vec2 model)XLMConfig
configuration class: XLMWithLMHeadModel
(XLM model)XLMRobertaConfig
configuration class: XLMRobertaForMaskedLM
(XLM-RoBERTa model)XLMRobertaXLConfig
configuration class: XLMRobertaXLForMaskedLM
(XLM-RoBERTa-XL model)XmodConfig
configuration class: XmodForMaskedLM
(X-MOD model)YosoConfig
configuration class: YosoForMaskedLM
(YOSO model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
DistilBertForMaskedLM
(DistilBERT model)ElectraForMaskedLM
(ELECTRA model)ErnieForMaskedLM
(ERNIE model)EsmForMaskedLM
(ESM model)FlaubertWithLMHeadModel
(FlauBERT model)FNetForMaskedLM
(FNet model)FunnelForMaskedLM
(Funnel Transformer model)IBertForMaskedLM
(I-BERT model)LayoutLMForMaskedLM
(LayoutLM model)LongformerForMaskedLM
(Longformer model)LukeForMaskedLM
(LUKE model)MBartForConditionalGeneration
(mBART model)MegaForMaskedLM
(MEGA model)MegatronBertForMaskedLM
(Megatron-BERT model)MobileBertForMaskedLM
(MobileBERT model)MPNetForMaskedLM
(MPNet model)MraForMaskedLM
(MRA model)MvpForConditionalGeneration
(MVP model)NezhaForMaskedLM
(Nezha model)NystromformerForMaskedLM
(Nyströmformer model)PerceiverForMaskedLM
(Perceiver model)QDQBertForMaskedLM
(QDQBert model)ReformerForMaskedLM
(Reformer model)RemBertForMaskedLM
(RemBERT model)RobertaForMaskedLM
(RoBERTa model)RobertaPreLayerNormForMaskedLM
(RoBERTa-PreLayerNorm model)RoCBertForMaskedLM
(RoCBert model)RoFormerForMaskedLM
(RoFormer model)SqueezeBertForMaskedLM
(SqueezeBERT model)TapasForMaskedLM
(TAPAS model)Wav2Vec2ForMaskedLM
(Wav2Vec2 model)XLMWithLMHeadModel
(XLM model)XLMRobertaForMaskedLM
(XLM-RoBERTa model)XLMRobertaXLForMaskedLM
(XLM-RoBERTa-XL model)XmodForMaskedLM
(X-MOD model)YosoForMaskedLM
(YOSO model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForMaskedLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForMaskedLM.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: TFDistilBertForMaskedLM
(DistilBERT model)ElectraConfig
configuration class: TFElectraForMaskedLM
(ELECTRA model)EsmConfig
configuration class: TFEsmForMaskedLM
(ESM model)FlaubertConfig
configuration class: TFFlaubertWithLMHeadModel
(FlauBERT model)FunnelConfig
configuration class: TFFunnelForMaskedLM
(Funnel Transformer model)LayoutLMConfig
configuration class: TFLayoutLMForMaskedLM
(LayoutLM model)LongformerConfig
configuration class: TFLongformerForMaskedLM
(Longformer model)MPNetConfig
configuration class: TFMPNetForMaskedLM
(MPNet model)MobileBertConfig
configuration class: TFMobileBertForMaskedLM
(MobileBERT model)RemBertConfig
configuration class: TFRemBertForMaskedLM
(RemBERT model)RoFormerConfig
configuration class: TFRoFormerForMaskedLM
(RoFormer model)RobertaConfig
configuration class: TFRobertaForMaskedLM
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: TFRobertaPreLayerNormForMaskedLM
(RoBERTa-PreLayerNorm model)TapasConfig
configuration class: TFTapasForMaskedLM
(TAPAS model)XLMConfig
configuration class: TFXLMWithLMHeadModel
(XLM model)XLMRobertaConfig
configuration class: TFXLMRobertaForMaskedLM
(XLM-RoBERTa model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFDistilBertForMaskedLM
(DistilBERT model)TFElectraForMaskedLM
(ELECTRA model)TFEsmForMaskedLM
(ESM model)TFFlaubertWithLMHeadModel
(FlauBERT model)TFFunnelForMaskedLM
(Funnel Transformer model)TFLayoutLMForMaskedLM
(LayoutLM model)TFLongformerForMaskedLM
(Longformer model)TFMobileBertForMaskedLM
(MobileBERT model)TFMPNetForMaskedLM
(MPNet model)TFRemBertForMaskedLM
(RemBERT model)TFRobertaForMaskedLM
(RoBERTa model)TFRobertaPreLayerNormForMaskedLM
(RoBERTa-PreLayerNorm model)TFRoFormerForMaskedLM
(RoFormer model)TFTapasForMaskedLM
(TAPAS model)TFXLMWithLMHeadModel
(XLM model)TFXLMRobertaForMaskedLM
(XLM-RoBERTa model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForMaskedLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForMaskedLM.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: FlaxDistilBertForMaskedLM
(DistilBERT model)ElectraConfig
configuration class: FlaxElectraForMaskedLM
(ELECTRA model)MBartConfig
configuration class: FlaxMBartForConditionalGeneration
(mBART model)RoFormerConfig
configuration class: FlaxRoFormerForMaskedLM
(RoFormer model)RobertaConfig
configuration class: FlaxRobertaForMaskedLM
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: FlaxRobertaPreLayerNormForMaskedLM
(RoBERTa-PreLayerNorm model)XLMRobertaConfig
configuration class: FlaxXLMRobertaForMaskedLM
(XLM-RoBERTa model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
FlaxDistilBertForMaskedLM
(DistilBERT model)FlaxElectraForMaskedLM
(ELECTRA model)FlaxMBartForConditionalGeneration
(mBART model)FlaxRobertaForMaskedLM
(RoBERTa model)FlaxRobertaPreLayerNormForMaskedLM
(RoBERTa-PreLayerNorm model)FlaxRoFormerForMaskedLM
(RoFormer model)FlaxXLMRobertaForMaskedLM
(XLM-RoBERTa model)Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForMaskedLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = FlaxAutoModelForMaskedLM.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
EncoderDecoderConfig
configuration class: EncoderDecoderModel
(Encoder decoder model)FSMTConfig
configuration class: FSMTForConditionalGeneration
(FairSeq Machine-Translation model)GPTSanJapaneseConfig
configuration class: GPTSanJapaneseForConditionalGeneration
(GPTSAN-japanese model)LEDConfig
configuration class: LEDForConditionalGeneration
(LED model)LongT5Config
configuration class: LongT5ForConditionalGeneration
(LongT5 model)M2M100Config
configuration class: M2M100ForConditionalGeneration
(M2M100 model)MBartConfig
configuration class: MBartForConditionalGeneration
(mBART model)MT5Config
configuration class: MT5ForConditionalGeneration
(MT5 model)MarianConfig
configuration class: MarianMTModel
(Marian model)MvpConfig
configuration class: MvpForConditionalGeneration
(MVP model)NllbMoeConfig
configuration class: NllbMoeForConditionalGeneration
(NLLB-MOE model)PLBartConfig
configuration class: PLBartForConditionalGeneration
(PLBart model)PegasusConfig
configuration class: PegasusForConditionalGeneration
(Pegasus model)PegasusXConfig
configuration class: PegasusXForConditionalGeneration
(PEGASUS-X model)ProphetNetConfig
configuration class: ProphetNetForConditionalGeneration
(ProphetNet model)SeamlessM4TConfig
configuration class: SeamlessM4TForTextToText
(SeamlessM4T model)SeamlessM4Tv2Config
configuration class: SeamlessM4Tv2ForTextToText
(SeamlessM4Tv2 model)SwitchTransformersConfig
configuration class: SwitchTransformersForConditionalGeneration
(SwitchTransformers model)T5Config
configuration class: T5ForConditionalGeneration
(T5 model)UMT5Config
configuration class: UMT5ForConditionalGeneration
(UMT5 model)XLMProphetNetConfig
configuration class: XLMProphetNetForConditionalGeneration
(XLM-ProphetNet model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
EncoderDecoderModel
(Encoder decoder model)FSMTForConditionalGeneration
(FairSeq Machine-Translation model)GPTSanJapaneseForConditionalGeneration
(GPTSAN-japanese model)LEDForConditionalGeneration
(LED model)LongT5ForConditionalGeneration
(LongT5 model)M2M100ForConditionalGeneration
(M2M100 model)MarianMTModel
(Marian model)MBartForConditionalGeneration
(mBART model)MT5ForConditionalGeneration
(MT5 model)MvpForConditionalGeneration
(MVP model)NllbMoeForConditionalGeneration
(NLLB-MOE model)PegasusForConditionalGeneration
(Pegasus model)PegasusXForConditionalGeneration
(PEGASUS-X model)PLBartForConditionalGeneration
(PLBart model)ProphetNetForConditionalGeneration
(ProphetNet model)SeamlessM4TForTextToText
(SeamlessM4T model)SeamlessM4Tv2ForTextToText
(SeamlessM4Tv2 model)SwitchTransformersForConditionalGeneration
(SwitchTransformers model)T5ForConditionalGeneration
(T5 model)UMT5ForConditionalGeneration
(UMT5 model)XLMProphetNetForConditionalGeneration
(XLM-ProphetNet model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForSeq2SeqLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")
>>> # Update configuration during loading
>>> model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/t5_tf_model_config.json")
>>> model = AutoModelForSeq2SeqLM.from_pretrained(
... "./tf_model/t5_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
EncoderDecoderConfig
configuration class: TFEncoderDecoderModel
(Encoder decoder model)LEDConfig
configuration class: TFLEDForConditionalGeneration
(LED model)MBartConfig
configuration class: TFMBartForConditionalGeneration
(mBART model)MT5Config
configuration class: TFMT5ForConditionalGeneration
(MT5 model)MarianConfig
configuration class: TFMarianMTModel
(Marian model)PegasusConfig
configuration class: TFPegasusForConditionalGeneration
(Pegasus model)T5Config
configuration class: TFT5ForConditionalGeneration
(T5 model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFEncoderDecoderModel
(Encoder decoder model)TFLEDForConditionalGeneration
(LED model)TFMarianMTModel
(Marian model)TFMBartForConditionalGeneration
(mBART model)TFMT5ForConditionalGeneration
(MT5 model)TFPegasusForConditionalGeneration
(Pegasus model)TFT5ForConditionalGeneration
(T5 model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForSeq2SeqLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")
>>> # Update configuration during loading
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/t5_pt_model_config.json")
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained(
... "./pt_model/t5_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
EncoderDecoderConfig
configuration class: FlaxEncoderDecoderModel
(Encoder decoder model)LongT5Config
configuration class: FlaxLongT5ForConditionalGeneration
(LongT5 model)MBartConfig
configuration class: FlaxMBartForConditionalGeneration
(mBART model)MT5Config
configuration class: FlaxMT5ForConditionalGeneration
(MT5 model)MarianConfig
configuration class: FlaxMarianMTModel
(Marian model)PegasusConfig
configuration class: FlaxPegasusForConditionalGeneration
(Pegasus model)T5Config
configuration class: FlaxT5ForConditionalGeneration
(T5 model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
FlaxEncoderDecoderModel
(Encoder decoder model)FlaxLongT5ForConditionalGeneration
(LongT5 model)FlaxMarianMTModel
(Marian model)FlaxMBartForConditionalGeneration
(mBART model)FlaxMT5ForConditionalGeneration
(MT5 model)FlaxPegasusForConditionalGeneration
(Pegasus model)FlaxT5ForConditionalGeneration
(T5 model)Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/t5_pt_model_config.json")
>>> model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
... "./pt_model/t5_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: DistilBertForSequenceClassification
(DistilBERT model)ElectraConfig
configuration class: ElectraForSequenceClassification
(ELECTRA model)ErnieConfig
configuration class: ErnieForSequenceClassification
(ERNIE model)ErnieMConfig
configuration class: ErnieMForSequenceClassification
(ErnieM model)EsmConfig
configuration class: EsmForSequenceClassification
(ESM model)FNetConfig
configuration class: FNetForSequenceClassification
(FNet model)FalconConfig
configuration class: FalconForSequenceClassification
(Falcon model)FlaubertConfig
configuration class: FlaubertForSequenceClassification
(FlauBERT model)FunnelConfig
configuration class: FunnelForSequenceClassification
(Funnel Transformer model)GPT2Config
configuration class: GPT2ForSequenceClassification
(OpenAI GPT-2 model)GPTBigCodeConfig
configuration class: GPTBigCodeForSequenceClassification
(GPTBigCode model)GPTJConfig
configuration class: GPTJForSequenceClassification
(GPT-J model)GPTNeoConfig
configuration class: GPTNeoForSequenceClassification
(GPT Neo model)GPTNeoXConfig
configuration class: GPTNeoXForSequenceClassification
(GPT NeoX model)GemmaConfig
configuration class: GemmaForSequenceClassification
(Gemma model)IBertConfig
configuration class: IBertForSequenceClassification
(I-BERT model)JambaConfig
configuration class: JambaForSequenceClassification
(Jamba model)LEDConfig
configuration class: LEDForSequenceClassification
(LED model)LayoutLMConfig
configuration class: LayoutLMForSequenceClassification
(LayoutLM model)LayoutLMv2Config
configuration class: LayoutLMv2ForSequenceClassification
(LayoutLMv2 model)LayoutLMv3Config
configuration class: LayoutLMv3ForSequenceClassification
(LayoutLMv3 model)LiltConfig
configuration class: LiltForSequenceClassification
(LiLT model)LlamaConfig
configuration class: LlamaForSequenceClassification
(LLaMA model)LongformerConfig
configuration class: LongformerForSequenceClassification
(Longformer model)LukeConfig
configuration class: LukeForSequenceClassification
(LUKE model)MBartConfig
configuration class: MBartForSequenceClassification
(mBART model)MPNetConfig
configuration class: MPNetForSequenceClassification
(MPNet model)MT5Config
configuration class: MT5ForSequenceClassification
(MT5 model)MarkupLMConfig
configuration class: MarkupLMForSequenceClassification
(MarkupLM model)MegaConfig
configuration class: MegaForSequenceClassification
(MEGA model)MegatronBertConfig
configuration class: MegatronBertForSequenceClassification
(Megatron-BERT model)MistralConfig
configuration class: MistralForSequenceClassification
(Mistral model)MixtralConfig
configuration class: MixtralForSequenceClassification
(Mixtral model)MobileBertConfig
configuration class: MobileBertForSequenceClassification
(MobileBERT model)MptConfig
configuration class: MptForSequenceClassification
(MPT model)MraConfig
configuration class: MraForSequenceClassification
(MRA model)MvpConfig
configuration class: MvpForSequenceClassification
(MVP model)NezhaConfig
configuration class: NezhaForSequenceClassification
(Nezha model)NystromformerConfig
configuration class: NystromformerForSequenceClassification
(Nyströmformer model)OPTConfig
configuration class: OPTForSequenceClassification
(OPT model)OpenAIGPTConfig
configuration class: OpenAIGPTForSequenceClassification
(OpenAI GPT model)OpenLlamaConfig
configuration class: OpenLlamaForSequenceClassification
(OpenLlama model)PLBartConfig
configuration class: PLBartForSequenceClassification
(PLBart model)PerceiverConfig
configuration class: PerceiverForSequenceClassification
(Perceiver model)PersimmonConfig
configuration class: PersimmonForSequenceClassification
(Persimmon model)Phi3Config
configuration class: Phi3ForSequenceClassification
(Phi3 model)PhiConfig
configuration class: PhiForSequenceClassification
(Phi model)QDQBertConfig
configuration class: QDQBertForSequenceClassification
(QDQBert model)Qwen2Config
configuration class: Qwen2ForSequenceClassification
(Qwen2 model)Qwen2MoeConfig
configuration class: Qwen2MoeForSequenceClassification
(Qwen2MoE model)ReformerConfig
configuration class: ReformerForSequenceClassification
(Reformer model)RemBertConfig
configuration class: RemBertForSequenceClassification
(RemBERT model)RoCBertConfig
configuration class: RoCBertForSequenceClassification
(RoCBert model)RoFormerConfig
configuration class: RoFormerForSequenceClassification
(RoFormer model)RobertaConfig
configuration class: RobertaForSequenceClassification
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: RobertaPreLayerNormForSequenceClassification
(RoBERTa-PreLayerNorm model)SqueezeBertConfig
configuration class: SqueezeBertForSequenceClassification
(SqueezeBERT model)StableLmConfig
configuration class: StableLmForSequenceClassification
(StableLm model)Starcoder2Config
configuration class: Starcoder2ForSequenceClassification
(Starcoder2 model)T5Config
configuration class: T5ForSequenceClassification
(T5 model)TapasConfig
configuration class: TapasForSequenceClassification
(TAPAS model)TransfoXLConfig
configuration class: TransfoXLForSequenceClassification
(Transformer-XL model)UMT5Config
configuration class: UMT5ForSequenceClassification
(UMT5 model)XLMConfig
configuration class: XLMForSequenceClassification
(XLM model)XLMRobertaConfig
configuration class: XLMRobertaForSequenceClassification
(XLM-RoBERTa model)XLMRobertaXLConfig
configuration class: XLMRobertaXLForSequenceClassification
(XLM-RoBERTa-XL model)XLNetConfig
configuration class: XLNetForSequenceClassification
(XLNet model)XmodConfig
configuration class: XmodForSequenceClassification
(X-MOD model)YosoConfig
configuration class: YosoForSequenceClassification
(YOSO model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
LlamaForSequenceClassification
(CodeLlama model)DistilBertForSequenceClassification
(DistilBERT model)ElectraForSequenceClassification
(ELECTRA model)ErnieForSequenceClassification
(ERNIE model)ErnieMForSequenceClassification
(ErnieM model)EsmForSequenceClassification
(ESM model)FalconForSequenceClassification
(Falcon model)FlaubertForSequenceClassification
(FlauBERT model)FNetForSequenceClassification
(FNet model)FunnelForSequenceClassification
(Funnel Transformer model)GemmaForSequenceClassification
(Gemma model)GPT2ForSequenceClassification
(GPT-Sw3 model)GPT2ForSequenceClassification
(OpenAI GPT-2 model)GPTBigCodeForSequenceClassification
(GPTBigCode model)GPTNeoForSequenceClassification
(GPT Neo model)GPTNeoXForSequenceClassification
(GPT NeoX model)GPTJForSequenceClassification
(GPT-J model)IBertForSequenceClassification
(I-BERT model)JambaForSequenceClassification
(Jamba model)LayoutLMForSequenceClassification
(LayoutLM model)LayoutLMv2ForSequenceClassification
(LayoutLMv2 model)LayoutLMv3ForSequenceClassification
(LayoutLMv3 model)LEDForSequenceClassification
(LED model)LiltForSequenceClassification
(LiLT model)LlamaForSequenceClassification
(LLaMA model)LongformerForSequenceClassification
(Longformer model)LukeForSequenceClassification
(LUKE model)MarkupLMForSequenceClassification
(MarkupLM model)MBartForSequenceClassification
(mBART model)MegaForSequenceClassification
(MEGA model)MegatronBertForSequenceClassification
(Megatron-BERT model)MistralForSequenceClassification
(Mistral model)MixtralForSequenceClassification
(Mixtral model)MobileBertForSequenceClassification
(MobileBERT model)MPNetForSequenceClassification
(MPNet model)MptForSequenceClassification
(MPT model)MraForSequenceClassification
(MRA model)MT5ForSequenceClassification
(MT5 model)MvpForSequenceClassification
(MVP model)NezhaForSequenceClassification
(Nezha model)NystromformerForSequenceClassification
(Nyströmformer model)OpenLlamaForSequenceClassification
(OpenLlama model)OpenAIGPTForSequenceClassification
(OpenAI GPT model)OPTForSequenceClassification
(OPT model)PerceiverForSequenceClassification
(Perceiver model)PersimmonForSequenceClassification
(Persimmon model)PhiForSequenceClassification
(Phi model)Phi3ForSequenceClassification
(Phi3 model)PLBartForSequenceClassification
(PLBart model)QDQBertForSequenceClassification
(QDQBert model)Qwen2ForSequenceClassification
(Qwen2 model)Qwen2MoeForSequenceClassification
(Qwen2MoE model)ReformerForSequenceClassification
(Reformer model)RemBertForSequenceClassification
(RemBERT model)RobertaForSequenceClassification
(RoBERTa model)RobertaPreLayerNormForSequenceClassification
(RoBERTa-PreLayerNorm model)RoCBertForSequenceClassification
(RoCBert model)RoFormerForSequenceClassification
(RoFormer model)SqueezeBertForSequenceClassification
(SqueezeBERT model)StableLmForSequenceClassification
(StableLm model)Starcoder2ForSequenceClassification
(Starcoder2 model)T5ForSequenceClassification
(T5 model)TapasForSequenceClassification
(TAPAS model)TransfoXLForSequenceClassification
(Transformer-XL model)UMT5ForSequenceClassification
(UMT5 model)XLMForSequenceClassification
(XLM model)XLMRobertaForSequenceClassification
(XLM-RoBERTa model)XLMRobertaXLForSequenceClassification
(XLM-RoBERTa-XL model)XLNetForSequenceClassification
(XLNet model)XmodForSequenceClassification
(X-MOD model)YosoForSequenceClassification
(YOSO model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForSequenceClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForSequenceClassification.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: TFDistilBertForSequenceClassification
(DistilBERT model)ElectraConfig
configuration class: TFElectraForSequenceClassification
(ELECTRA model)EsmConfig
configuration class: TFEsmForSequenceClassification
(ESM model)FlaubertConfig
configuration class: TFFlaubertForSequenceClassification
(FlauBERT model)FunnelConfig
configuration class: TFFunnelForSequenceClassification
(Funnel Transformer model)GPT2Config
configuration class: TFGPT2ForSequenceClassification
(OpenAI GPT-2 model)GPTJConfig
configuration class: TFGPTJForSequenceClassification
(GPT-J model)LayoutLMConfig
configuration class: TFLayoutLMForSequenceClassification
(LayoutLM model)LayoutLMv3Config
configuration class: TFLayoutLMv3ForSequenceClassification
(LayoutLMv3 model)LongformerConfig
configuration class: TFLongformerForSequenceClassification
(Longformer model)MPNetConfig
configuration class: TFMPNetForSequenceClassification
(MPNet model)MobileBertConfig
configuration class: TFMobileBertForSequenceClassification
(MobileBERT model)OpenAIGPTConfig
configuration class: TFOpenAIGPTForSequenceClassification
(OpenAI GPT model)RemBertConfig
configuration class: TFRemBertForSequenceClassification
(RemBERT model)RoFormerConfig
configuration class: TFRoFormerForSequenceClassification
(RoFormer model)RobertaConfig
configuration class: TFRobertaForSequenceClassification
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: TFRobertaPreLayerNormForSequenceClassification
(RoBERTa-PreLayerNorm model)TapasConfig
configuration class: TFTapasForSequenceClassification
(TAPAS model)TransfoXLConfig
configuration class: TFTransfoXLForSequenceClassification
(Transformer-XL model)XLMConfig
configuration class: TFXLMForSequenceClassification
(XLM model)XLMRobertaConfig
configuration class: TFXLMRobertaForSequenceClassification
(XLM-RoBERTa model)XLNetConfig
configuration class: TFXLNetForSequenceClassification
(XLNet model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFDistilBertForSequenceClassification
(DistilBERT model)TFElectraForSequenceClassification
(ELECTRA model)TFEsmForSequenceClassification
(ESM model)TFFlaubertForSequenceClassification
(FlauBERT model)TFFunnelForSequenceClassification
(Funnel Transformer model)TFGPT2ForSequenceClassification
(GPT-Sw3 model)TFGPT2ForSequenceClassification
(OpenAI GPT-2 model)TFGPTJForSequenceClassification
(GPT-J model)TFLayoutLMForSequenceClassification
(LayoutLM model)TFLayoutLMv3ForSequenceClassification
(LayoutLMv3 model)TFLongformerForSequenceClassification
(Longformer model)TFMobileBertForSequenceClassification
(MobileBERT model)TFMPNetForSequenceClassification
(MPNet model)TFOpenAIGPTForSequenceClassification
(OpenAI GPT model)TFRemBertForSequenceClassification
(RemBERT model)TFRobertaForSequenceClassification
(RoBERTa model)TFRobertaPreLayerNormForSequenceClassification
(RoBERTa-PreLayerNorm model)TFRoFormerForSequenceClassification
(RoFormer model)TFTapasForSequenceClassification
(TAPAS model)TFTransfoXLForSequenceClassification
(Transformer-XL model)TFXLMForSequenceClassification
(XLM model)TFXLMRobertaForSequenceClassification
(XLM-RoBERTa model)TFXLNetForSequenceClassification
(XLNet model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForSequenceClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForSequenceClassification.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: FlaxDistilBertForSequenceClassification
(DistilBERT model)ElectraConfig
configuration class: FlaxElectraForSequenceClassification
(ELECTRA model)MBartConfig
configuration class: FlaxMBartForSequenceClassification
(mBART model)RoFormerConfig
configuration class: FlaxRoFormerForSequenceClassification
(RoFormer model)RobertaConfig
configuration class: FlaxRobertaForSequenceClassification
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: FlaxRobertaPreLayerNormForSequenceClassification
(RoBERTa-PreLayerNorm model)XLMRobertaConfig
configuration class: FlaxXLMRobertaForSequenceClassification
(XLM-RoBERTa model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
FlaxDistilBertForSequenceClassification
(DistilBERT model)FlaxElectraForSequenceClassification
(ELECTRA model)FlaxMBartForSequenceClassification
(mBART model)FlaxRobertaForSequenceClassification
(RoBERTa model)FlaxRobertaPreLayerNormForSequenceClassification
(RoBERTa-PreLayerNorm model)FlaxRoFormerForSequenceClassification
(RoFormer model)FlaxXLMRobertaForSequenceClassification
(XLM-RoBERTa model)Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForSequenceClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = FlaxAutoModelForSequenceClassification.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: DistilBertForMultipleChoice
(DistilBERT model)ElectraConfig
configuration class: ElectraForMultipleChoice
(ELECTRA model)ErnieConfig
configuration class: ErnieForMultipleChoice
(ERNIE model)ErnieMConfig
configuration class: ErnieMForMultipleChoice
(ErnieM model)FNetConfig
configuration class: FNetForMultipleChoice
(FNet model)FlaubertConfig
configuration class: FlaubertForMultipleChoice
(FlauBERT model)FunnelConfig
configuration class: FunnelForMultipleChoice
(Funnel Transformer model)IBertConfig
configuration class: IBertForMultipleChoice
(I-BERT model)LongformerConfig
configuration class: LongformerForMultipleChoice
(Longformer model)LukeConfig
configuration class: LukeForMultipleChoice
(LUKE model)MPNetConfig
configuration class: MPNetForMultipleChoice
(MPNet model)MegaConfig
configuration class: MegaForMultipleChoice
(MEGA model)MegatronBertConfig
configuration class: MegatronBertForMultipleChoice
(Megatron-BERT model)MobileBertConfig
configuration class: MobileBertForMultipleChoice
(MobileBERT model)MraConfig
configuration class: MraForMultipleChoice
(MRA model)NezhaConfig
configuration class: NezhaForMultipleChoice
(Nezha model)NystromformerConfig
configuration class: NystromformerForMultipleChoice
(Nyströmformer model)QDQBertConfig
configuration class: QDQBertForMultipleChoice
(QDQBert model)RemBertConfig
configuration class: RemBertForMultipleChoice
(RemBERT model)RoCBertConfig
configuration class: RoCBertForMultipleChoice
(RoCBert model)RoFormerConfig
configuration class: RoFormerForMultipleChoice
(RoFormer model)RobertaConfig
configuration class: RobertaForMultipleChoice
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: RobertaPreLayerNormForMultipleChoice
(RoBERTa-PreLayerNorm model)SqueezeBertConfig
configuration class: SqueezeBertForMultipleChoice
(SqueezeBERT model)XLMConfig
configuration class: XLMForMultipleChoice
(XLM model)XLMRobertaConfig
configuration class: XLMRobertaForMultipleChoice
(XLM-RoBERTa model)XLMRobertaXLConfig
configuration class: XLMRobertaXLForMultipleChoice
(XLM-RoBERTa-XL model)XLNetConfig
configuration class: XLNetForMultipleChoice
(XLNet model)XmodConfig
configuration class: XmodForMultipleChoice
(X-MOD model)YosoConfig
configuration class: YosoForMultipleChoice
(YOSO model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
DistilBertForMultipleChoice
(DistilBERT model)ElectraForMultipleChoice
(ELECTRA model)ErnieForMultipleChoice
(ERNIE model)ErnieMForMultipleChoice
(ErnieM model)FlaubertForMultipleChoice
(FlauBERT model)FNetForMultipleChoice
(FNet model)FunnelForMultipleChoice
(Funnel Transformer model)IBertForMultipleChoice
(I-BERT model)LongformerForMultipleChoice
(Longformer model)LukeForMultipleChoice
(LUKE model)MegaForMultipleChoice
(MEGA model)MegatronBertForMultipleChoice
(Megatron-BERT model)MobileBertForMultipleChoice
(MobileBERT model)MPNetForMultipleChoice
(MPNet model)MraForMultipleChoice
(MRA model)NezhaForMultipleChoice
(Nezha model)NystromformerForMultipleChoice
(Nyströmformer model)QDQBertForMultipleChoice
(QDQBert model)RemBertForMultipleChoice
(RemBERT model)RobertaForMultipleChoice
(RoBERTa model)RobertaPreLayerNormForMultipleChoice
(RoBERTa-PreLayerNorm model)RoCBertForMultipleChoice
(RoCBert model)RoFormerForMultipleChoice
(RoFormer model)SqueezeBertForMultipleChoice
(SqueezeBERT model)XLMForMultipleChoice
(XLM model)XLMRobertaForMultipleChoice
(XLM-RoBERTa model)XLMRobertaXLForMultipleChoice
(XLM-RoBERTa-XL model)XLNetForMultipleChoice
(XLNet model)XmodForMultipleChoice
(X-MOD model)YosoForMultipleChoice
(YOSO model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForMultipleChoice
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForMultipleChoice.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: TFDistilBertForMultipleChoice
(DistilBERT model)ElectraConfig
configuration class: TFElectraForMultipleChoice
(ELECTRA model)FlaubertConfig
configuration class: TFFlaubertForMultipleChoice
(FlauBERT model)FunnelConfig
configuration class: TFFunnelForMultipleChoice
(Funnel Transformer model)LongformerConfig
configuration class: TFLongformerForMultipleChoice
(Longformer model)MPNetConfig
configuration class: TFMPNetForMultipleChoice
(MPNet model)MobileBertConfig
configuration class: TFMobileBertForMultipleChoice
(MobileBERT model)RemBertConfig
configuration class: TFRemBertForMultipleChoice
(RemBERT model)RoFormerConfig
configuration class: TFRoFormerForMultipleChoice
(RoFormer model)RobertaConfig
configuration class: TFRobertaForMultipleChoice
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: TFRobertaPreLayerNormForMultipleChoice
(RoBERTa-PreLayerNorm model)XLMConfig
configuration class: TFXLMForMultipleChoice
(XLM model)XLMRobertaConfig
configuration class: TFXLMRobertaForMultipleChoice
(XLM-RoBERTa model)XLNetConfig
configuration class: TFXLNetForMultipleChoice
(XLNet model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFDistilBertForMultipleChoice
(DistilBERT model)TFElectraForMultipleChoice
(ELECTRA model)TFFlaubertForMultipleChoice
(FlauBERT model)TFFunnelForMultipleChoice
(Funnel Transformer model)TFLongformerForMultipleChoice
(Longformer model)TFMobileBertForMultipleChoice
(MobileBERT model)TFMPNetForMultipleChoice
(MPNet model)TFRemBertForMultipleChoice
(RemBERT model)TFRobertaForMultipleChoice
(RoBERTa model)TFRobertaPreLayerNormForMultipleChoice
(RoBERTa-PreLayerNorm model)TFRoFormerForMultipleChoice
(RoFormer model)TFXLMForMultipleChoice
(XLM model)TFXLMRobertaForMultipleChoice
(XLM-RoBERTa model)TFXLNetForMultipleChoice
(XLNet model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForMultipleChoice
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForMultipleChoice.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: FlaxDistilBertForMultipleChoice
(DistilBERT model)ElectraConfig
configuration class: FlaxElectraForMultipleChoice
(ELECTRA model)RoFormerConfig
configuration class: FlaxRoFormerForMultipleChoice
(RoFormer model)RobertaConfig
configuration class: FlaxRobertaForMultipleChoice
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: FlaxRobertaPreLayerNormForMultipleChoice
(RoBERTa-PreLayerNorm model)XLMRobertaConfig
configuration class: FlaxXLMRobertaForMultipleChoice
(XLM-RoBERTa model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
FlaxDistilBertForMultipleChoice
(DistilBERT model)FlaxElectraForMultipleChoice
(ELECTRA model)FlaxRobertaForMultipleChoice
(RoBERTa model)FlaxRobertaPreLayerNormForMultipleChoice
(RoBERTa-PreLayerNorm model)FlaxRoFormerForMultipleChoice
(RoFormer model)FlaxXLMRobertaForMultipleChoice
(XLM-RoBERTa model)Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForMultipleChoice
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = FlaxAutoModelForMultipleChoice.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
ErnieConfig
configuration class: ErnieForNextSentencePrediction
(ERNIE model)FNetConfig
configuration class: FNetForNextSentencePrediction
(FNet model)MegatronBertConfig
configuration class: MegatronBertForNextSentencePrediction
(Megatron-BERT model)MobileBertConfig
configuration class: MobileBertForNextSentencePrediction
(MobileBERT model)NezhaConfig
configuration class: NezhaForNextSentencePrediction
(Nezha model)QDQBertConfig
configuration class: QDQBertForNextSentencePrediction
(QDQBert model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
ErnieForNextSentencePrediction
(ERNIE model)FNetForNextSentencePrediction
(FNet model)MegatronBertForNextSentencePrediction
(Megatron-BERT model)MobileBertForNextSentencePrediction
(MobileBERT model)NezhaForNextSentencePrediction
(Nezha model)QDQBertForNextSentencePrediction
(QDQBert model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForNextSentencePrediction
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForNextSentencePrediction.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForNextSentencePrediction.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForNextSentencePrediction.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
MobileBertConfig
configuration class: TFMobileBertForNextSentencePrediction
(MobileBERT model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFMobileBertForNextSentencePrediction
(MobileBERT model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForNextSentencePrediction
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForNextSentencePrediction.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForNextSentencePrediction.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForNextSentencePrediction.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForNextSentencePrediction
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForNextSentencePrediction.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForNextSentencePrediction.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = FlaxAutoModelForNextSentencePrediction.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: DistilBertForTokenClassification
(DistilBERT model)ElectraConfig
configuration class: ElectraForTokenClassification
(ELECTRA model)ErnieConfig
configuration class: ErnieForTokenClassification
(ERNIE model)ErnieMConfig
configuration class: ErnieMForTokenClassification
(ErnieM model)EsmConfig
configuration class: EsmForTokenClassification
(ESM model)FNetConfig
configuration class: FNetForTokenClassification
(FNet model)FalconConfig
configuration class: FalconForTokenClassification
(Falcon model)FlaubertConfig
configuration class: FlaubertForTokenClassification
(FlauBERT model)FunnelConfig
configuration class: FunnelForTokenClassification
(Funnel Transformer model)GPT2Config
configuration class: GPT2ForTokenClassification
(OpenAI GPT-2 model)GPTBigCodeConfig
configuration class: GPTBigCodeForTokenClassification
(GPTBigCode model)GPTNeoConfig
configuration class: GPTNeoForTokenClassification
(GPT Neo model)GPTNeoXConfig
configuration class: GPTNeoXForTokenClassification
(GPT NeoX model)IBertConfig
configuration class: IBertForTokenClassification
(I-BERT model)LayoutLMConfig
configuration class: LayoutLMForTokenClassification
(LayoutLM model)LayoutLMv2Config
configuration class: LayoutLMv2ForTokenClassification
(LayoutLMv2 model)LayoutLMv3Config
configuration class: LayoutLMv3ForTokenClassification
(LayoutLMv3 model)LiltConfig
configuration class: LiltForTokenClassification
(LiLT model)LongformerConfig
configuration class: LongformerForTokenClassification
(Longformer model)LukeConfig
configuration class: LukeForTokenClassification
(LUKE model)MPNetConfig
configuration class: MPNetForTokenClassification
(MPNet model)MT5Config
configuration class: MT5ForTokenClassification
(MT5 model)MarkupLMConfig
configuration class: MarkupLMForTokenClassification
(MarkupLM model)MegaConfig
configuration class: MegaForTokenClassification
(MEGA model)MegatronBertConfig
configuration class: MegatronBertForTokenClassification
(Megatron-BERT model)MobileBertConfig
configuration class: MobileBertForTokenClassification
(MobileBERT model)MptConfig
configuration class: MptForTokenClassification
(MPT model)MraConfig
configuration class: MraForTokenClassification
(MRA model)NezhaConfig
configuration class: NezhaForTokenClassification
(Nezha model)NystromformerConfig
configuration class: NystromformerForTokenClassification
(Nyströmformer model)Phi3Config
configuration class: Phi3ForTokenClassification
(Phi3 model)PhiConfig
configuration class: PhiForTokenClassification
(Phi model)QDQBertConfig
configuration class: QDQBertForTokenClassification
(QDQBert model)RemBertConfig
configuration class: RemBertForTokenClassification
(RemBERT model)RoCBertConfig
configuration class: RoCBertForTokenClassification
(RoCBert model)RoFormerConfig
configuration class: RoFormerForTokenClassification
(RoFormer model)RobertaConfig
configuration class: RobertaForTokenClassification
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: RobertaPreLayerNormForTokenClassification
(RoBERTa-PreLayerNorm model)SqueezeBertConfig
configuration class: SqueezeBertForTokenClassification
(SqueezeBERT model)T5Config
configuration class: T5ForTokenClassification
(T5 model)UMT5Config
configuration class: UMT5ForTokenClassification
(UMT5 model)XLMConfig
configuration class: XLMForTokenClassification
(XLM model)XLMRobertaConfig
configuration class: XLMRobertaForTokenClassification
(XLM-RoBERTa model)XLMRobertaXLConfig
configuration class: XLMRobertaXLForTokenClassification
(XLM-RoBERTa-XL model)XLNetConfig
configuration class: XLNetForTokenClassification
(XLNet model)XmodConfig
configuration class: XmodForTokenClassification
(X-MOD model)YosoConfig
configuration class: YosoForTokenClassification
(YOSO model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a token classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
DistilBertForTokenClassification
(DistilBERT model)ElectraForTokenClassification
(ELECTRA model)ErnieForTokenClassification
(ERNIE model)ErnieMForTokenClassification
(ErnieM model)EsmForTokenClassification
(ESM model)FalconForTokenClassification
(Falcon model)FlaubertForTokenClassification
(FlauBERT model)FNetForTokenClassification
(FNet model)FunnelForTokenClassification
(Funnel Transformer model)GPT2ForTokenClassification
(GPT-Sw3 model)GPT2ForTokenClassification
(OpenAI GPT-2 model)GPTBigCodeForTokenClassification
(GPTBigCode model)GPTNeoForTokenClassification
(GPT Neo model)GPTNeoXForTokenClassification
(GPT NeoX model)IBertForTokenClassification
(I-BERT model)LayoutLMForTokenClassification
(LayoutLM model)LayoutLMv2ForTokenClassification
(LayoutLMv2 model)LayoutLMv3ForTokenClassification
(LayoutLMv3 model)LiltForTokenClassification
(LiLT model)LongformerForTokenClassification
(Longformer model)LukeForTokenClassification
(LUKE model)MarkupLMForTokenClassification
(MarkupLM model)MegaForTokenClassification
(MEGA model)MegatronBertForTokenClassification
(Megatron-BERT model)MobileBertForTokenClassification
(MobileBERT model)MPNetForTokenClassification
(MPNet model)MptForTokenClassification
(MPT model)MraForTokenClassification
(MRA model)MT5ForTokenClassification
(MT5 model)NezhaForTokenClassification
(Nezha model)NystromformerForTokenClassification
(Nyströmformer model)PhiForTokenClassification
(Phi model)Phi3ForTokenClassification
(Phi3 model)QDQBertForTokenClassification
(QDQBert model)RemBertForTokenClassification
(RemBERT model)RobertaForTokenClassification
(RoBERTa model)RobertaPreLayerNormForTokenClassification
(RoBERTa-PreLayerNorm model)RoCBertForTokenClassification
(RoCBert model)RoFormerForTokenClassification
(RoFormer model)SqueezeBertForTokenClassification
(SqueezeBERT model)T5ForTokenClassification
(T5 model)UMT5ForTokenClassification
(UMT5 model)XLMForTokenClassification
(XLM model)XLMRobertaForTokenClassification
(XLM-RoBERTa model)XLMRobertaXLForTokenClassification
(XLM-RoBERTa-XL model)XLNetForTokenClassification
(XLNet model)XmodForTokenClassification
(X-MOD model)YosoForTokenClassification
(YOSO model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForTokenClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForTokenClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForTokenClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForTokenClassification.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: TFDistilBertForTokenClassification
(DistilBERT model)ElectraConfig
configuration class: TFElectraForTokenClassification
(ELECTRA model)EsmConfig
configuration class: TFEsmForTokenClassification
(ESM model)FlaubertConfig
configuration class: TFFlaubertForTokenClassification
(FlauBERT model)FunnelConfig
configuration class: TFFunnelForTokenClassification
(Funnel Transformer model)LayoutLMConfig
configuration class: TFLayoutLMForTokenClassification
(LayoutLM model)LayoutLMv3Config
configuration class: TFLayoutLMv3ForTokenClassification
(LayoutLMv3 model)LongformerConfig
configuration class: TFLongformerForTokenClassification
(Longformer model)MPNetConfig
configuration class: TFMPNetForTokenClassification
(MPNet model)MobileBertConfig
configuration class: TFMobileBertForTokenClassification
(MobileBERT model)RemBertConfig
configuration class: TFRemBertForTokenClassification
(RemBERT model)RoFormerConfig
configuration class: TFRoFormerForTokenClassification
(RoFormer model)RobertaConfig
configuration class: TFRobertaForTokenClassification
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: TFRobertaPreLayerNormForTokenClassification
(RoBERTa-PreLayerNorm model)XLMConfig
configuration class: TFXLMForTokenClassification
(XLM model)XLMRobertaConfig
configuration class: TFXLMRobertaForTokenClassification
(XLM-RoBERTa model)XLNetConfig
configuration class: TFXLNetForTokenClassification
(XLNet model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a token classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFDistilBertForTokenClassification
(DistilBERT model)TFElectraForTokenClassification
(ELECTRA model)TFEsmForTokenClassification
(ESM model)TFFlaubertForTokenClassification
(FlauBERT model)TFFunnelForTokenClassification
(Funnel Transformer model)TFLayoutLMForTokenClassification
(LayoutLM model)TFLayoutLMv3ForTokenClassification
(LayoutLMv3 model)TFLongformerForTokenClassification
(Longformer model)TFMobileBertForTokenClassification
(MobileBERT model)TFMPNetForTokenClassification
(MPNet model)TFRemBertForTokenClassification
(RemBERT model)TFRobertaForTokenClassification
(RoBERTa model)TFRobertaPreLayerNormForTokenClassification
(RoBERTa-PreLayerNorm model)TFRoFormerForTokenClassification
(RoFormer model)TFXLMForTokenClassification
(XLM model)TFXLMRobertaForTokenClassification
(XLM-RoBERTa model)TFXLNetForTokenClassification
(XLNet model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForTokenClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForTokenClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForTokenClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForTokenClassification.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: FlaxDistilBertForTokenClassification
(DistilBERT model)ElectraConfig
configuration class: FlaxElectraForTokenClassification
(ELECTRA model)RoFormerConfig
configuration class: FlaxRoFormerForTokenClassification
(RoFormer model)RobertaConfig
configuration class: FlaxRobertaForTokenClassification
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: FlaxRobertaPreLayerNormForTokenClassification
(RoBERTa-PreLayerNorm model)XLMRobertaConfig
configuration class: FlaxXLMRobertaForTokenClassification
(XLM-RoBERTa model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a token classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
FlaxDistilBertForTokenClassification
(DistilBERT model)FlaxElectraForTokenClassification
(ELECTRA model)FlaxRobertaForTokenClassification
(RoBERTa model)FlaxRobertaPreLayerNormForTokenClassification
(RoBERTa-PreLayerNorm model)FlaxRoFormerForTokenClassification
(RoFormer model)FlaxXLMRobertaForTokenClassification
(XLM-RoBERTa model)Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForTokenClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForTokenClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForTokenClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = FlaxAutoModelForTokenClassification.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: DistilBertForQuestionAnswering
(DistilBERT model)ElectraConfig
configuration class: ElectraForQuestionAnswering
(ELECTRA model)ErnieConfig
configuration class: ErnieForQuestionAnswering
(ERNIE model)ErnieMConfig
configuration class: ErnieMForQuestionAnswering
(ErnieM model)FNetConfig
configuration class: FNetForQuestionAnswering
(FNet model)FalconConfig
configuration class: FalconForQuestionAnswering
(Falcon model)FlaubertConfig
configuration class: FlaubertForQuestionAnsweringSimple
(FlauBERT model)FunnelConfig
configuration class: FunnelForQuestionAnswering
(Funnel Transformer model)GPT2Config
configuration class: GPT2ForQuestionAnswering
(OpenAI GPT-2 model)GPTJConfig
configuration class: GPTJForQuestionAnswering
(GPT-J model)GPTNeoConfig
configuration class: GPTNeoForQuestionAnswering
(GPT Neo model)GPTNeoXConfig
configuration class: GPTNeoXForQuestionAnswering
(GPT NeoX model)IBertConfig
configuration class: IBertForQuestionAnswering
(I-BERT model)LEDConfig
configuration class: LEDForQuestionAnswering
(LED model)LayoutLMv2Config
configuration class: LayoutLMv2ForQuestionAnswering
(LayoutLMv2 model)LayoutLMv3Config
configuration class: LayoutLMv3ForQuestionAnswering
(LayoutLMv3 model)LiltConfig
configuration class: LiltForQuestionAnswering
(LiLT model)LlamaConfig
configuration class: LlamaForQuestionAnswering
(LLaMA model)LongformerConfig
configuration class: LongformerForQuestionAnswering
(Longformer model)LukeConfig
configuration class: LukeForQuestionAnswering
(LUKE model)LxmertConfig
configuration class: LxmertForQuestionAnswering
(LXMERT model)MBartConfig
configuration class: MBartForQuestionAnswering
(mBART model)MPNetConfig
configuration class: MPNetForQuestionAnswering
(MPNet model)MT5Config
configuration class: MT5ForQuestionAnswering
(MT5 model)MarkupLMConfig
configuration class: MarkupLMForQuestionAnswering
(MarkupLM model)MegaConfig
configuration class: MegaForQuestionAnswering
(MEGA model)MegatronBertConfig
configuration class: MegatronBertForQuestionAnswering
(Megatron-BERT model)MobileBertConfig
configuration class: MobileBertForQuestionAnswering
(MobileBERT model)MptConfig
configuration class: MptForQuestionAnswering
(MPT model)MraConfig
configuration class: MraForQuestionAnswering
(MRA model)MvpConfig
configuration class: MvpForQuestionAnswering
(MVP model)NezhaConfig
configuration class: NezhaForQuestionAnswering
(Nezha model)NystromformerConfig
configuration class: NystromformerForQuestionAnswering
(Nyströmformer model)OPTConfig
configuration class: OPTForQuestionAnswering
(OPT model)QDQBertConfig
configuration class: QDQBertForQuestionAnswering
(QDQBert model)ReformerConfig
configuration class: ReformerForQuestionAnswering
(Reformer model)RemBertConfig
configuration class: RemBertForQuestionAnswering
(RemBERT model)RoCBertConfig
configuration class: RoCBertForQuestionAnswering
(RoCBert model)RoFormerConfig
configuration class: RoFormerForQuestionAnswering
(RoFormer model)RobertaConfig
configuration class: RobertaForQuestionAnswering
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: RobertaPreLayerNormForQuestionAnswering
(RoBERTa-PreLayerNorm model)SplinterConfig
configuration class: SplinterForQuestionAnswering
(Splinter model)SqueezeBertConfig
configuration class: SqueezeBertForQuestionAnswering
(SqueezeBERT model)T5Config
configuration class: T5ForQuestionAnswering
(T5 model)UMT5Config
configuration class: UMT5ForQuestionAnswering
(UMT5 model)XLMConfig
configuration class: XLMForQuestionAnsweringSimple
(XLM model)XLMRobertaConfig
configuration class: XLMRobertaForQuestionAnswering
(XLM-RoBERTa model)XLMRobertaXLConfig
configuration class: XLMRobertaXLForQuestionAnswering
(XLM-RoBERTa-XL model)XLNetConfig
configuration class: XLNetForQuestionAnsweringSimple
(XLNet model)XmodConfig
configuration class: XmodForQuestionAnswering
(X-MOD model)YosoConfig
configuration class: YosoForQuestionAnswering
(YOSO model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a question answering head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
DistilBertForQuestionAnswering
(DistilBERT model)ElectraForQuestionAnswering
(ELECTRA model)ErnieForQuestionAnswering
(ERNIE model)ErnieMForQuestionAnswering
(ErnieM model)FalconForQuestionAnswering
(Falcon model)FlaubertForQuestionAnsweringSimple
(FlauBERT model)FNetForQuestionAnswering
(FNet model)FunnelForQuestionAnswering
(Funnel Transformer model)GPT2ForQuestionAnswering
(OpenAI GPT-2 model)GPTNeoForQuestionAnswering
(GPT Neo model)GPTNeoXForQuestionAnswering
(GPT NeoX model)GPTJForQuestionAnswering
(GPT-J model)IBertForQuestionAnswering
(I-BERT model)LayoutLMv2ForQuestionAnswering
(LayoutLMv2 model)LayoutLMv3ForQuestionAnswering
(LayoutLMv3 model)LEDForQuestionAnswering
(LED model)LiltForQuestionAnswering
(LiLT model)LlamaForQuestionAnswering
(LLaMA model)LongformerForQuestionAnswering
(Longformer model)LukeForQuestionAnswering
(LUKE model)LxmertForQuestionAnswering
(LXMERT model)MarkupLMForQuestionAnswering
(MarkupLM model)MBartForQuestionAnswering
(mBART model)MegaForQuestionAnswering
(MEGA model)MegatronBertForQuestionAnswering
(Megatron-BERT model)MobileBertForQuestionAnswering
(MobileBERT model)MPNetForQuestionAnswering
(MPNet model)MptForQuestionAnswering
(MPT model)MraForQuestionAnswering
(MRA model)MT5ForQuestionAnswering
(MT5 model)MvpForQuestionAnswering
(MVP model)NezhaForQuestionAnswering
(Nezha model)NystromformerForQuestionAnswering
(Nyströmformer model)OPTForQuestionAnswering
(OPT model)QDQBertForQuestionAnswering
(QDQBert model)ReformerForQuestionAnswering
(Reformer model)RemBertForQuestionAnswering
(RemBERT model)RobertaForQuestionAnswering
(RoBERTa model)RobertaPreLayerNormForQuestionAnswering
(RoBERTa-PreLayerNorm model)RoCBertForQuestionAnswering
(RoCBert model)RoFormerForQuestionAnswering
(RoFormer model)SplinterForQuestionAnswering
(Splinter model)SqueezeBertForQuestionAnswering
(SqueezeBERT model)T5ForQuestionAnswering
(T5 model)UMT5ForQuestionAnswering
(UMT5 model)XLMForQuestionAnsweringSimple
(XLM model)XLMRobertaForQuestionAnswering
(XLM-RoBERTa model)XLMRobertaXLForQuestionAnswering
(XLM-RoBERTa-XL model)XLNetForQuestionAnsweringSimple
(XLNet model)XmodForQuestionAnswering
(X-MOD model)YosoForQuestionAnswering
(YOSO model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForQuestionAnswering
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForQuestionAnswering.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForQuestionAnswering.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForQuestionAnswering.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: TFDistilBertForQuestionAnswering
(DistilBERT model)ElectraConfig
configuration class: TFElectraForQuestionAnswering
(ELECTRA model)FlaubertConfig
configuration class: TFFlaubertForQuestionAnsweringSimple
(FlauBERT model)FunnelConfig
configuration class: TFFunnelForQuestionAnswering
(Funnel Transformer model)GPTJConfig
configuration class: TFGPTJForQuestionAnswering
(GPT-J model)LayoutLMv3Config
configuration class: TFLayoutLMv3ForQuestionAnswering
(LayoutLMv3 model)LongformerConfig
configuration class: TFLongformerForQuestionAnswering
(Longformer model)MPNetConfig
configuration class: TFMPNetForQuestionAnswering
(MPNet model)MobileBertConfig
configuration class: TFMobileBertForQuestionAnswering
(MobileBERT model)RemBertConfig
configuration class: TFRemBertForQuestionAnswering
(RemBERT model)RoFormerConfig
configuration class: TFRoFormerForQuestionAnswering
(RoFormer model)RobertaConfig
configuration class: TFRobertaForQuestionAnswering
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: TFRobertaPreLayerNormForQuestionAnswering
(RoBERTa-PreLayerNorm model)XLMConfig
configuration class: TFXLMForQuestionAnsweringSimple
(XLM model)XLMRobertaConfig
configuration class: TFXLMRobertaForQuestionAnswering
(XLM-RoBERTa model)XLNetConfig
configuration class: TFXLNetForQuestionAnsweringSimple
(XLNet model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a question answering head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFDistilBertForQuestionAnswering
(DistilBERT model)TFElectraForQuestionAnswering
(ELECTRA model)TFFlaubertForQuestionAnsweringSimple
(FlauBERT model)TFFunnelForQuestionAnswering
(Funnel Transformer model)TFGPTJForQuestionAnswering
(GPT-J model)TFLayoutLMv3ForQuestionAnswering
(LayoutLMv3 model)TFLongformerForQuestionAnswering
(Longformer model)TFMobileBertForQuestionAnswering
(MobileBERT model)TFMPNetForQuestionAnswering
(MPNet model)TFRemBertForQuestionAnswering
(RemBERT model)TFRobertaForQuestionAnswering
(RoBERTa model)TFRobertaPreLayerNormForQuestionAnswering
(RoBERTa-PreLayerNorm model)TFRoFormerForQuestionAnswering
(RoFormer model)TFXLMForQuestionAnsweringSimple
(XLM model)TFXLMRobertaForQuestionAnswering
(XLM-RoBERTa model)TFXLNetForQuestionAnsweringSimple
(XLNet model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForQuestionAnswering
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForQuestionAnswering.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForQuestionAnswering.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForQuestionAnswering.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DistilBertConfig
configuration class: FlaxDistilBertForQuestionAnswering
(DistilBERT model)ElectraConfig
configuration class: FlaxElectraForQuestionAnswering
(ELECTRA model)MBartConfig
configuration class: FlaxMBartForQuestionAnswering
(mBART model)RoFormerConfig
configuration class: FlaxRoFormerForQuestionAnswering
(RoFormer model)RobertaConfig
configuration class: FlaxRobertaForQuestionAnswering
(RoBERTa model)RobertaPreLayerNormConfig
configuration class: FlaxRobertaPreLayerNormForQuestionAnswering
(RoBERTa-PreLayerNorm model)XLMRobertaConfig
configuration class: FlaxXLMRobertaForQuestionAnswering
(XLM-RoBERTa model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a question answering head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
FlaxDistilBertForQuestionAnswering
(DistilBERT model)FlaxElectraForQuestionAnswering
(ELECTRA model)FlaxMBartForQuestionAnswering
(mBART model)FlaxRobertaForQuestionAnswering
(RoBERTa model)FlaxRobertaPreLayerNormForQuestionAnswering
(RoBERTa-PreLayerNorm model)FlaxRoFormerForQuestionAnswering
(RoFormer model)FlaxXLMRobertaForQuestionAnswering
(XLM-RoBERTa model)Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForQuestionAnswering
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForQuestionAnswering.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForQuestionAnswering.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = FlaxAutoModelForQuestionAnswering.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
以下の自動クラスは、次のコンピュータービジョンタスクに利用可能です。
This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DPTConfig
configuration class: DPTForDepthEstimation
(DPT model)DepthAnythingConfig
configuration class: DepthAnythingForDepthEstimation
(Depth Anything model)GLPNConfig
configuration class: GLPNForDepthEstimation
(GLPN model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a depth estimation head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
DepthAnythingForDepthEstimation
(Depth Anything model)DPTForDepthEstimation
(DPT model)GLPNForDepthEstimation
(GLPN model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForDepthEstimation
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForDepthEstimation.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForDepthEstimation.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForDepthEstimation.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
CLIPForImageClassification
(CLIP model)Dinov2Config
configuration class: Dinov2ForImageClassification
(DINOv2 model)EfficientFormerConfig
configuration class: EfficientFormerForImageClassification
or EfficientFormerForImageClassificationWithTeacher
(EfficientFormer model)EfficientNetConfig
configuration class: EfficientNetForImageClassification
(EfficientNet model)FocalNetConfig
configuration class: FocalNetForImageClassification
(FocalNet model)ImageGPTConfig
configuration class: ImageGPTForImageClassification
(ImageGPT model)LevitConfig
configuration class: LevitForImageClassification
or LevitForImageClassificationWithTeacher
(LeViT model)MobileNetV1Config
configuration class: MobileNetV1ForImageClassification
(MobileNetV1 model)MobileNetV2Config
configuration class: MobileNetV2ForImageClassification
(MobileNetV2 model)MobileViTConfig
configuration class: MobileViTForImageClassification
(MobileViT model)MobileViTV2Config
configuration class: MobileViTV2ForImageClassification
(MobileViTV2 model)NatConfig
configuration class: NatForImageClassification
(NAT model)PerceiverConfig
configuration class: PerceiverForImageClassificationLearned
or PerceiverForImageClassificationFourier
or PerceiverForImageClassificationConvProcessing
(Perceiver model)PoolFormerConfig
configuration class: PoolFormerForImageClassification
(PoolFormer model)PvtConfig
configuration class: PvtForImageClassification
(PVT model)PvtV2Config
configuration class: PvtV2ForImageClassification
(PVTv2 model)RegNetConfig
configuration class: RegNetForImageClassification
(RegNet model)ResNetConfig
configuration class: ResNetForImageClassification
(ResNet model)SegformerConfig
configuration class: SegformerForImageClassification
(SegFormer model)SiglipConfig
configuration class: SiglipForImageClassification
(SigLIP model)SwiftFormerConfig
configuration class: SwiftFormerForImageClassification
(SwiftFormer model)SwinConfig
configuration class: SwinForImageClassification
(Swin Transformer model)Swinv2Config
configuration class: Swinv2ForImageClassification
(Swin Transformer V2 model)VanConfig
configuration class: VanForImageClassification
(VAN model)ViTConfig
configuration class: ViTForImageClassification
(ViT model)ViTHybridConfig
configuration class: ViTHybridForImageClassification
(ViT Hybrid model)ViTMSNConfig
configuration class: ViTMSNForImageClassification
(ViTMSN model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a image classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
CLIPForImageClassification
(CLIP model)Dinov2ForImageClassification
(DINOv2 model)EfficientFormerForImageClassification
or EfficientFormerForImageClassificationWithTeacher
(EfficientFormer model)EfficientNetForImageClassification
(EfficientNet model)FocalNetForImageClassification
(FocalNet model)ImageGPTForImageClassification
(ImageGPT model)LevitForImageClassification
or LevitForImageClassificationWithTeacher
(LeViT model)MobileNetV1ForImageClassification
(MobileNetV1 model)MobileNetV2ForImageClassification
(MobileNetV2 model)MobileViTForImageClassification
(MobileViT model)MobileViTV2ForImageClassification
(MobileViTV2 model)NatForImageClassification
(NAT model)PerceiverForImageClassificationLearned
or PerceiverForImageClassificationFourier
or PerceiverForImageClassificationConvProcessing
(Perceiver model)PoolFormerForImageClassification
(PoolFormer model)PvtForImageClassification
(PVT model)PvtV2ForImageClassification
(PVTv2 model)RegNetForImageClassification
(RegNet model)ResNetForImageClassification
(ResNet model)SegformerForImageClassification
(SegFormer model)SiglipForImageClassification
(SigLIP model)SwiftFormerForImageClassification
(SwiftFormer model)SwinForImageClassification
(Swin Transformer model)Swinv2ForImageClassification
(Swin Transformer V2 model)VanForImageClassification
(VAN model)ViTForImageClassification
(ViT model)ViTHybridForImageClassification
(ViT Hybrid model)ViTMSNForImageClassification
(ViTMSN model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForImageClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForImageClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForImageClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForImageClassification.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
EfficientFormerConfig
configuration class: TFEfficientFormerForImageClassification
or TFEfficientFormerForImageClassificationWithTeacher
(EfficientFormer model)MobileViTConfig
configuration class: TFMobileViTForImageClassification
(MobileViT model)RegNetConfig
configuration class: TFRegNetForImageClassification
(RegNet model)ResNetConfig
configuration class: TFResNetForImageClassification
(ResNet model)SegformerConfig
configuration class: TFSegformerForImageClassification
(SegFormer model)SwiftFormerConfig
configuration class: TFSwiftFormerForImageClassification
(SwiftFormer model)SwinConfig
configuration class: TFSwinForImageClassification
(Swin Transformer model)ViTConfig
configuration class: TFViTForImageClassification
(ViT model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a image classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFEfficientFormerForImageClassification
or TFEfficientFormerForImageClassificationWithTeacher
(EfficientFormer model)TFMobileViTForImageClassification
(MobileViT model)TFRegNetForImageClassification
(RegNet model)TFResNetForImageClassification
(ResNet model)TFSegformerForImageClassification
(SegFormer model)TFSwiftFormerForImageClassification
(SwiftFormer model)TFSwinForImageClassification
(Swin Transformer model)TFViTForImageClassification
(ViT model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForImageClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForImageClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForImageClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForImageClassification.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
RegNetConfig
configuration class: FlaxRegNetForImageClassification
(RegNet model)ResNetConfig
configuration class: FlaxResNetForImageClassification
(ResNet model)ViTConfig
configuration class: FlaxViTForImageClassification
(ViT model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a image classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
FlaxRegNetForImageClassification
(RegNet model)FlaxResNetForImageClassification
(ResNet model)FlaxViTForImageClassification
(ViT model)Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForImageClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForImageClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForImageClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = FlaxAutoModelForImageClassification.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
TimesformerConfig
configuration class: TimesformerForVideoClassification
(TimeSformer model)VideoMAEConfig
configuration class: VideoMAEForVideoClassification
(VideoMAE model)VivitConfig
configuration class: VivitForVideoClassification
(ViViT model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a video classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a video classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TimesformerForVideoClassification
(TimeSformer model)VideoMAEForVideoClassification
(VideoMAE model)VivitForVideoClassification
(ViViT model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForVideoClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForVideoClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForVideoClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForVideoClassification.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
FocalNetConfig
configuration class: FocalNetForMaskedImageModeling
(FocalNet model)SwinConfig
configuration class: SwinForMaskedImageModeling
(Swin Transformer model)Swinv2Config
configuration class: Swinv2ForMaskedImageModeling
(Swin Transformer V2 model)ViTConfig
configuration class: ViTForMaskedImageModeling
(ViT model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
FocalNetForMaskedImageModeling
(FocalNet model)SwinForMaskedImageModeling
(Swin Transformer model)Swinv2ForMaskedImageModeling
(Swin Transformer V2 model)ViTForMaskedImageModeling
(ViT model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForMaskedImageModeling
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForMaskedImageModeling.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForMaskedImageModeling.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForMaskedImageModeling.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
SwinConfig
configuration class: TFSwinForMaskedImageModeling
(Swin Transformer model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFSwinForMaskedImageModeling
(Swin Transformer model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForMaskedImageModeling
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForMaskedImageModeling.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForMaskedImageModeling.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForMaskedImageModeling.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
TableTransformerConfig
configuration class: TableTransformerForObjectDetection
(Table Transformer model)YolosConfig
configuration class: YolosForObjectDetection
(YOLOS model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a object detection head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a object detection head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TableTransformerForObjectDetection
(Table Transformer model)YolosForObjectDetection
(YOLOS model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForObjectDetection
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForObjectDetection.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForObjectDetection.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForObjectDetection.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a image segmentation head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForImageSegmentation
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForImageSegmentation.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForImageSegmentation.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForImageSegmentation.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
DPTConfig
configuration class: DPTForSemanticSegmentation
(DPT model)MobileNetV2Config
configuration class: MobileNetV2ForSemanticSegmentation
(MobileNetV2 model)MobileViTConfig
configuration class: MobileViTForSemanticSegmentation
(MobileViT model)MobileViTV2Config
configuration class: MobileViTV2ForSemanticSegmentation
(MobileViTV2 model)SegformerConfig
configuration class: SegformerForSemanticSegmentation
(SegFormer model)UperNetConfig
configuration class: UperNetForSemanticSegmentation
(UPerNet model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
DPTForSemanticSegmentation
(DPT model)MobileNetV2ForSemanticSegmentation
(MobileNetV2 model)MobileViTForSemanticSegmentation
(MobileViT model)MobileViTV2ForSemanticSegmentation
(MobileViTV2 model)SegformerForSemanticSegmentation
(SegFormer model)UperNetForSemanticSegmentation
(UPerNet model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForSemanticSegmentation
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForSemanticSegmentation.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForSemanticSegmentation.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForSemanticSegmentation.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
MobileViTConfig
configuration class: TFMobileViTForSemanticSegmentation
(MobileViT model)SegformerConfig
configuration class: TFSegformerForSemanticSegmentation
(SegFormer model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFMobileViTForSemanticSegmentation
(MobileViT model)TFSegformerForSemanticSegmentation
(SegFormer model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForSemanticSegmentation
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForSemanticSegmentation.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForSemanticSegmentation.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForSemanticSegmentation.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
MaskFormerConfig
configuration class: MaskFormerForInstanceSegmentation
(MaskFormer model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
MaskFormerForInstanceSegmentation
(MaskFormer model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForInstanceSegmentation
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForInstanceSegmentation.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForInstanceSegmentation.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForInstanceSegmentation.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a universal image segmentation head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
Mask2FormerConfig
configuration class: Mask2FormerForUniversalSegmentation
(Mask2Former model)MaskFormerConfig
configuration class: MaskFormerForInstanceSegmentation
(MaskFormer model)OneFormerConfig
configuration class: OneFormerForUniversalSegmentation
(OneFormer model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
Mask2FormerForUniversalSegmentation
(Mask2Former model)MaskFormerForInstanceSegmentation
(MaskFormer model)OneFormerForUniversalSegmentation
(OneFormer model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForUniversalSegmentation
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForUniversalSegmentation.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForUniversalSegmentation.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForUniversalSegmentation.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
SiglipConfig
configuration class: SiglipModel
(SigLIP model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
SiglipModel
(SigLIP model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForZeroShotImageClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForZeroShotImageClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForZeroShotImageClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForZeroShotImageClassification.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
Examples:
>>> from transformers import AutoConfig, TFAutoModelForZeroShotImageClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForZeroShotImageClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForZeroShotImageClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForZeroShotImageClassification.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
GroundingDinoConfig
configuration class: GroundingDinoForObjectDetection
(Grounding DINO model)OwlViTConfig
configuration class: OwlViTForObjectDetection
(OWL-ViT model)Owlv2Config
configuration class: Owlv2ForObjectDetection
(OWLv2 model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
GroundingDinoForObjectDetection
(Grounding DINO model)Owlv2ForObjectDetection
(OWLv2 model)OwlViTForObjectDetection
(OWL-ViT model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForZeroShotObjectDetection
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForZeroShotObjectDetection.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForZeroShotObjectDetection.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForZeroShotObjectDetection.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
以下の自動クラスは、次の音声タスクに利用可能です。
This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
HubertConfig
configuration class: HubertForSequenceClassification
(Hubert model)SEWConfig
configuration class: SEWForSequenceClassification
(SEW model)SEWDConfig
configuration class: SEWDForSequenceClassification
(SEW-D model)UniSpeechConfig
configuration class: UniSpeechForSequenceClassification
(UniSpeech model)UniSpeechSatConfig
configuration class: UniSpeechSatForSequenceClassification
(UniSpeechSat model)Wav2Vec2BertConfig
configuration class: Wav2Vec2BertForSequenceClassification
(Wav2Vec2-BERT model)Wav2Vec2Config
configuration class: Wav2Vec2ForSequenceClassification
(Wav2Vec2 model)Wav2Vec2ConformerConfig
configuration class: Wav2Vec2ConformerForSequenceClassification
(Wav2Vec2-Conformer model)WavLMConfig
configuration class: WavLMForSequenceClassification
(WavLM model)WhisperConfig
configuration class: WhisperForAudioClassification
(Whisper model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a audio classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
HubertForSequenceClassification
(Hubert model)SEWForSequenceClassification
(SEW model)SEWDForSequenceClassification
(SEW-D model)UniSpeechForSequenceClassification
(UniSpeech model)UniSpeechSatForSequenceClassification
(UniSpeechSat model)Wav2Vec2ForSequenceClassification
(Wav2Vec2 model)Wav2Vec2BertForSequenceClassification
(Wav2Vec2-BERT model)Wav2Vec2ConformerForSequenceClassification
(Wav2Vec2-Conformer model)WavLMForSequenceClassification
(WavLM model)WhisperForAudioClassification
(Whisper model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForAudioClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForAudioClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForAudioClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForAudioClassification.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
Wav2Vec2Config
configuration class: TFWav2Vec2ForSequenceClassification
(Wav2Vec2 model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a audio classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFWav2Vec2ForSequenceClassification
(Wav2Vec2 model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForAudioClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForAudioClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForAudioClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForAudioClassification.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
UniSpeechSatConfig
configuration class: UniSpeechSatForAudioFrameClassification
(UniSpeechSat model)Wav2Vec2BertConfig
configuration class: Wav2Vec2BertForAudioFrameClassification
(Wav2Vec2-BERT model)Wav2Vec2Config
configuration class: Wav2Vec2ForAudioFrameClassification
(Wav2Vec2 model)Wav2Vec2ConformerConfig
configuration class: Wav2Vec2ConformerForAudioFrameClassification
(Wav2Vec2-Conformer model)WavLMConfig
configuration class: WavLMForAudioFrameClassification
(WavLM model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
UniSpeechSatForAudioFrameClassification
(UniSpeechSat model)Wav2Vec2ForAudioFrameClassification
(Wav2Vec2 model)Wav2Vec2BertForAudioFrameClassification
(Wav2Vec2-BERT model)Wav2Vec2ConformerForAudioFrameClassification
(Wav2Vec2-Conformer model)WavLMForAudioFrameClassification
(WavLM model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForAudioFrameClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForAudioFrameClassification.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForAudioFrameClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForAudioFrameClassification.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
HubertConfig
configuration class: HubertForCTC
(Hubert model)MCTCTConfig
configuration class: MCTCTForCTC
(M-CTC-T model)SEWConfig
configuration class: SEWForCTC
(SEW model)SEWDConfig
configuration class: SEWDForCTC
(SEW-D model)UniSpeechConfig
configuration class: UniSpeechForCTC
(UniSpeech model)UniSpeechSatConfig
configuration class: UniSpeechSatForCTC
(UniSpeechSat model)Wav2Vec2BertConfig
configuration class: Wav2Vec2BertForCTC
(Wav2Vec2-BERT model)Wav2Vec2Config
configuration class: Wav2Vec2ForCTC
(Wav2Vec2 model)Wav2Vec2ConformerConfig
configuration class: Wav2Vec2ConformerForCTC
(Wav2Vec2-Conformer model)WavLMConfig
configuration class: WavLMForCTC
(WavLM model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
HubertForCTC
(Hubert model)MCTCTForCTC
(M-CTC-T model)SEWForCTC
(SEW model)SEWDForCTC
(SEW-D model)UniSpeechForCTC
(UniSpeech model)UniSpeechSatForCTC
(UniSpeechSat model)Wav2Vec2ForCTC
(Wav2Vec2 model)Wav2Vec2BertForCTC
(Wav2Vec2-BERT model)Wav2Vec2ConformerForCTC
(Wav2Vec2-Conformer model)WavLMForCTC
(WavLM model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForCTC
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForCTC.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForCTC.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForCTC.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
Pop2PianoConfig
configuration class: Pop2PianoForConditionalGeneration
(Pop2Piano model)SeamlessM4TConfig
configuration class: SeamlessM4TForSpeechToText
(SeamlessM4T model)SeamlessM4Tv2Config
configuration class: SeamlessM4Tv2ForSpeechToText
(SeamlessM4Tv2 model)Speech2TextConfig
configuration class: Speech2TextForConditionalGeneration
(Speech2Text model)SpeechEncoderDecoderConfig
configuration class: SpeechEncoderDecoderModel
(Speech Encoder decoder model)SpeechT5Config
configuration class: SpeechT5ForSpeechToText
(SpeechT5 model)WhisperConfig
configuration class: WhisperForConditionalGeneration
(Whisper model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
Pop2PianoForConditionalGeneration
(Pop2Piano model)SeamlessM4TForSpeechToText
(SeamlessM4T model)SeamlessM4Tv2ForSpeechToText
(SeamlessM4Tv2 model)SpeechEncoderDecoderModel
(Speech Encoder decoder model)Speech2TextForConditionalGeneration
(Speech2Text model)SpeechT5ForSpeechToText
(SpeechT5 model)WhisperForConditionalGeneration
(Whisper model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForSpeechSeq2Seq
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForSpeechSeq2Seq.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForSpeechSeq2Seq.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForSpeechSeq2Seq.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
Speech2TextConfig
configuration class: TFSpeech2TextForConditionalGeneration
(Speech2Text model)WhisperConfig
configuration class: TFWhisperForConditionalGeneration
(Whisper model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFSpeech2TextForConditionalGeneration
(Speech2Text model)TFWhisperForConditionalGeneration
(Whisper model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForSpeechSeq2Seq
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForSpeechSeq2Seq.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForSpeechSeq2Seq.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForSpeechSeq2Seq.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
SpeechEncoderDecoderConfig
configuration class: FlaxSpeechEncoderDecoderModel
(Speech Encoder decoder model)WhisperConfig
configuration class: FlaxWhisperForConditionalGeneration
(Whisper model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
FlaxSpeechEncoderDecoderModel
(Speech Encoder decoder model)FlaxWhisperForConditionalGeneration
(Whisper model)Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForSpeechSeq2Seq
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
UniSpeechSatConfig
configuration class: UniSpeechSatForXVector
(UniSpeechSat model)Wav2Vec2BertConfig
configuration class: Wav2Vec2BertForXVector
(Wav2Vec2-BERT model)Wav2Vec2Config
configuration class: Wav2Vec2ForXVector
(Wav2Vec2 model)Wav2Vec2ConformerConfig
configuration class: Wav2Vec2ConformerForXVector
(Wav2Vec2-Conformer model)WavLMConfig
configuration class: WavLMForXVector
(WavLM model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
UniSpeechSatForXVector
(UniSpeechSat model)Wav2Vec2ForXVector
(Wav2Vec2 model)Wav2Vec2BertForXVector
(Wav2Vec2-BERT model)Wav2Vec2ConformerForXVector
(Wav2Vec2-Conformer model)WavLMForXVector
(WavLM model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForAudioXVector
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForAudioXVector.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForAudioXVector.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForAudioXVector.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
以下の自動クラスは、次のマルチモーダルタスクに利用可能です。
This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
TapasConfig
configuration class: TapasForQuestionAnswering
(TAPAS model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a table question answering head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TapasForQuestionAnswering
(TAPAS model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForTableQuestionAnswering
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq")
>>> # Update configuration during loading
>>> model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/tapas_tf_model_config.json")
>>> model = AutoModelForTableQuestionAnswering.from_pretrained(
... "./tf_model/tapas_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
TapasConfig
configuration class: TFTapasForQuestionAnswering
(TAPAS model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a table question answering head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFTapasForQuestionAnswering
(TAPAS model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForTableQuestionAnswering
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq")
>>> # Update configuration during loading
>>> model = TFAutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/tapas_pt_model_config.json")
>>> model = TFAutoModelForTableQuestionAnswering.from_pretrained(
... "./pt_model/tapas_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
LayoutLMConfig
configuration class: LayoutLMForQuestionAnswering
(LayoutLM model)LayoutLMv2Config
configuration class: LayoutLMv2ForQuestionAnswering
(LayoutLMv2 model)LayoutLMv3Config
configuration class: LayoutLMv3ForQuestionAnswering
(LayoutLMv3 model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a document question answering head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
Examples:
>>> from transformers import AutoConfig, AutoModelForDocumentQuestionAnswering
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")
>>> model = AutoModelForDocumentQuestionAnswering.from_config(config)
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
LayoutLMForQuestionAnswering
(LayoutLM model)LayoutLMv2ForQuestionAnswering
(LayoutLMv2 model)LayoutLMv3ForQuestionAnswering
(LayoutLMv3 model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForDocumentQuestionAnswering
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")
>>> # Update configuration during loading
>>> model = AutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/layoutlm_tf_model_config.json")
>>> model = AutoModelForDocumentQuestionAnswering.from_pretrained(
... "./tf_model/layoutlm_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
LayoutLMConfig
configuration class: TFLayoutLMForQuestionAnswering
(LayoutLM model)LayoutLMv3Config
configuration class: TFLayoutLMv3ForQuestionAnswering
(LayoutLMv3 model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a document question answering head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
Examples:
>>> from transformers import AutoConfig, TFAutoModelForDocumentQuestionAnswering
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")
>>> model = TFAutoModelForDocumentQuestionAnswering.from_config(config)
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFLayoutLMForQuestionAnswering
(LayoutLM model)TFLayoutLMv3ForQuestionAnswering
(LayoutLMv3 model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForDocumentQuestionAnswering
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")
>>> # Update configuration during loading
>>> model = TFAutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/layoutlm_pt_model_config.json")
>>> model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(
... "./pt_model/layoutlm_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a visual question answering head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
ViltConfig
configuration class: ViltForQuestionAnswering
(ViLT model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a visual question answering head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
ViltForQuestionAnswering
(ViLT model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForVisualQuestionAnswering
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForVisualQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
>>> # Update configuration during loading
>>> model = AutoModelForVisualQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/vilt_tf_model_config.json")
>>> model = AutoModelForVisualQuestionAnswering.from_pretrained(
... "./tf_model/vilt_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
GitConfig
configuration class: GitForCausalLM
(GIT model)Idefics2Config
configuration class: Idefics2ForConditionalGeneration
(Idefics2 model)InstructBlipConfig
configuration class: InstructBlipForConditionalGeneration
(InstructBLIP model)Kosmos2Config
configuration class: Kosmos2ForConditionalGeneration
(KOSMOS-2 model)LlavaConfig
configuration class: LlavaForConditionalGeneration
(LLaVa model)LlavaNextConfig
configuration class: LlavaNextForConditionalGeneration
(LLaVA-NeXT model)Pix2StructConfig
configuration class: Pix2StructForConditionalGeneration
(Pix2Struct model)VipLlavaConfig
configuration class: VipLlavaForConditionalGeneration
(VipLlava model)VisionEncoderDecoderConfig
configuration class: VisionEncoderDecoderModel
(Vision Encoder decoder model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../tf_model/model.ckpt.index
). In
this case, from_tf
should be set to True
and a configuration object should be provided as
config
argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.
str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a TensorFlow checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
GitForCausalLM
(GIT model)Idefics2ForConditionalGeneration
(Idefics2 model)InstructBlipForConditionalGeneration
(InstructBLIP model)Kosmos2ForConditionalGeneration
(KOSMOS-2 model)LlavaForConditionalGeneration
(LLaVa model)LlavaNextForConditionalGeneration
(LLaVA-NeXT model)Pix2StructForConditionalGeneration
(Pix2Struct model)VipLlavaForConditionalGeneration
(VipLlava model)VisionEncoderDecoderModel
(Vision Encoder decoder model)The model is set in evaluation mode by default using model.eval()
(so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with model.train()
Examples:
>>> from transformers import AutoConfig, AutoModelForVision2Seq
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForVision2Seq.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForVision2Seq.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
>>> model = AutoModelForVision2Seq.from_pretrained(
... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
VisionEncoderDecoderConfig
configuration class: TFVisionEncoderDecoderModel
(Vision Encoder decoder model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
TFVisionEncoderDecoderModel
(Vision Encoder decoder model)Examples:
>>> from transformers import AutoConfig, TFAutoModelForVision2Seq
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForVision2Seq.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForVision2Seq.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForVision2Seq.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created with the from_pretrained() class method or the from_config() class method.
This class cannot be instantiated directly using __init__()
(throws an error).
( **kwargs )
Parameters
VisionEncoderDecoderConfig
configuration class: FlaxVisionEncoderDecoderModel
(Vision Encoder decoder model)str
, optional) —
The attention implementation to use in the model (if relevant). Can be any of "eager"
(manual implementation of the attention), "sdpa"
(using F.scaled_dot_product_attention
), or "flash_attention_2"
(using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager"
implementation. Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.
Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.
( *model_args **kwargs )
Parameters
str
or os.PathLike
) —
Can be either:
./my_model_directory/
../pt_model/pytorch_model.bin
). In this
case, from_pt
should be set to True
and a configuration object should be provided as config
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.__init__()
method. pretrained_model_name_or_path
and a
configuration JSON file named config.json is found in the directory.str
or os.PathLike
, optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. bool
, optional, defaults to False
) —
Load the model weights from a PyTorch checkpoint save file (see docstring of
pretrained_model_name_or_path
argument). bool
, optional, defaults to False
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. bool
, optional, defaults to False
) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists. Dict[str, str]
, optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}
. The proxies are used on each request. bool
, optional, defaults to False
) —
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. bool
, optional, defaults to False
) —
Whether or not to only look at local files (e.g., not try downloading the model). str
, optional, defaults to "main"
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. bool
, optional, defaults to False
) —
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to True
for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine. str
, optional, defaults to "main"
) —
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so revision
can be any identifier
allowed by git. output_attentions=True
). Behaves differently depending on whether a config
is provided or
automatically loaded:
config
, **kwargs
will be directly passed to the
underlying model’s __init__
method (we assume all relevant updates to the configuration have
already been done)kwargs
will be first passed to the configuration class
initialization function (from_pretrained()). Each key of kwargs
that
corresponds to a configuration attribute will be used to override said attribute with the
supplied kwargs
value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model’s __init__
function.Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.
The model class to instantiate is selected based on the model_type
property of the config object (either
passed as an argument or loaded from pretrained_model_name_or_path
if possible), or when it’s missing, by
falling back to using pattern matching on pretrained_model_name_or_path
:
FlaxVisionEncoderDecoderModel
(Vision Encoder decoder model)Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForVision2Seq
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForVision2Seq.from_pretrained("google-bert/bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForVision2Seq.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = FlaxAutoModelForVision2Seq.from_pretrained(
... "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )