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Auto Classes
In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
are supplying to the from_pretrained() method. AutoClasses are here to do this job for you so that you
automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary.
Instantiating one of AutoConfig, AutoModel, and AutoTokenizer will directly create a class of the relevant architecture. For instance
model = AutoModel.from_pretrained("bert-base-cased")will create a model that is an instance of BertModel.
There is one class of AutoModel for each task, and for each backend (PyTorch, TensorFlow, or Flax).
Extending the Auto Classes
Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a
custom class of model NewModel, make sure you have a NewModelConfig then you can add those to the auto
classes like this:
from transformers import AutoConfig, AutoModel
AutoConfig.register("new-model", NewModelConfig)
AutoModel.register(NewModelConfig, NewModel)You will then be able to use the auto classes like you would usually do!
If your NewModelConfig is a subclass of ~transformer.PretrainedConfig, make sure its
model_type attribute is set to the same key you use when registering the config (here "new-model").
Likewise, if your NewModel is a subclass of PreTrainedModel, make sure its
config_class attribute is set to the same class you use when registering the model (here
NewModelConfig).
AutoConfig
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).
from_pretrained
< source >( pretrained_model_name_or_path **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing a configuration file saved using the
save_pretrained() method, or the save_pretrained() method,
e.g.,
./my_model_directory/. - A path or url to a saved configuration JSON file, e.g.,
./my_model_directory/configuration.json.
- A string, the model id of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download the model weights and configuration files and override the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
revision (
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, sorevisioncan be any identifier allowed by git. -
return_unused_kwargs (
bool, optional, defaults toFalse) — IfFalse, then this function returns just the final configuration object.If
True, then this functions returns aTuple(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 ofkwargswhich has not been used to updateconfigand is otherwise ignored. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs(additional keyword arguments, optional) —
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are not configuration attributes is controlled
by the
return_unused_kwargskeyword 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:
- albert — AlbertConfig (ALBERT model)
- audio-spectrogram-transformer — ASTConfig (Audio Spectrogram Transformer model)
- bart — BartConfig (BART model)
- beit — BeitConfig (BEiT model)
- bert — BertConfig (BERT model)
- bert-generation — BertGenerationConfig (Bert Generation model)
- big_bird — BigBirdConfig (BigBird model)
- bigbird_pegasus — BigBirdPegasusConfig (BigBird-Pegasus model)
- blenderbot — BlenderbotConfig (Blenderbot model)
- blenderbot-small — BlenderbotSmallConfig (BlenderbotSmall model)
- bloom — BloomConfig (BLOOM model)
- camembert — CamembertConfig (CamemBERT model)
- canine — CanineConfig (CANINE model)
- chinese_clip — ChineseCLIPConfig (Chinese-CLIP model)
- clip — CLIPConfig (CLIP model)
- clipseg — CLIPSegConfig (CLIPSeg model)
- codegen — CodeGenConfig (CodeGen model)
- conditional_detr — ConditionalDetrConfig (Conditional DETR model)
- convbert — ConvBertConfig (ConvBERT model)
- convnext — ConvNextConfig (ConvNeXT model)
- ctrl — CTRLConfig (CTRL model)
- cvt — CvtConfig (CvT model)
- data2vec-audio — Data2VecAudioConfig (Data2VecAudio model)
- data2vec-text — Data2VecTextConfig (Data2VecText model)
- data2vec-vision — Data2VecVisionConfig (Data2VecVision model)
- deberta — DebertaConfig (DeBERTa model)
- deberta-v2 — DebertaV2Config (DeBERTa-v2 model)
- decision_transformer — DecisionTransformerConfig (Decision Transformer model)
- deformable_detr — DeformableDetrConfig (Deformable DETR model)
- deit — DeiTConfig (DeiT model)
- detr — DetrConfig (DETR model)
- dinat — DinatConfig (DiNAT model)
- distilbert — DistilBertConfig (DistilBERT model)
- donut-swin — DonutSwinConfig (DonutSwin model)
- dpr — DPRConfig (DPR model)
- dpt — DPTConfig (DPT model)
- electra — ElectraConfig (ELECTRA model)
- encoder-decoder — EncoderDecoderConfig (Encoder decoder model)
- ernie — ErnieConfig (ERNIE model)
- esm — EsmConfig (ESM model)
- flaubert — FlaubertConfig (FlauBERT model)
- flava — FlavaConfig (FLAVA model)
- fnet — FNetConfig (FNet model)
- fsmt — FSMTConfig (FairSeq Machine-Translation model)
- funnel — FunnelConfig (Funnel Transformer model)
- glpn — GLPNConfig (GLPN model)
- gpt2 — GPT2Config (OpenAI GPT-2 model)
- gpt_neo — GPTNeoConfig (GPT Neo model)
- gpt_neox — GPTNeoXConfig (GPT NeoX model)
- gpt_neox_japanese — GPTNeoXJapaneseConfig (GPT NeoX Japanese model)
- gptj — GPTJConfig (GPT-J model)
- groupvit — GroupViTConfig (GroupViT model)
- hubert — HubertConfig (Hubert model)
- ibert — IBertConfig (I-BERT model)
- imagegpt — ImageGPTConfig (ImageGPT model)
- jukebox — JukeboxConfig (Jukebox model)
- layoutlm — LayoutLMConfig (LayoutLM model)
- layoutlmv2 — LayoutLMv2Config (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3Config (LayoutLMv3 model)
- led — LEDConfig (LED model)
- levit — LevitConfig (LeViT model)
- lilt — LiltConfig (LiLT model)
- longformer — LongformerConfig (Longformer model)
- longt5 — LongT5Config (LongT5 model)
- luke — LukeConfig (LUKE model)
- lxmert — LxmertConfig (LXMERT model)
- m2m_100 — M2M100Config (M2M100 model)
- marian — MarianConfig (Marian model)
- markuplm — MarkupLMConfig (MarkupLM model)
- maskformer — MaskFormerConfig (MaskFormer model)
- maskformer-swin —
MaskFormerSwinConfig(MaskFormerSwin model) - mbart — MBartConfig (mBART model)
- mctct — MCTCTConfig (M-CTC-T model)
- megatron-bert — MegatronBertConfig (Megatron-BERT model)
- mobilebert — MobileBertConfig (MobileBERT model)
- mobilenet_v1 — MobileNetV1Config (MobileNetV1 model)
- mobilenet_v2 — MobileNetV2Config (MobileNetV2 model)
- mobilevit — MobileViTConfig (MobileViT model)
- mpnet — MPNetConfig (MPNet model)
- mt5 — MT5Config (MT5 model)
- mvp — MvpConfig (MVP model)
- nat — NatConfig (NAT model)
- nezha — NezhaConfig (Nezha model)
- nystromformer — NystromformerConfig (Nyströmformer model)
- openai-gpt — OpenAIGPTConfig (OpenAI GPT model)
- opt — OPTConfig (OPT model)
- owlvit — OwlViTConfig (OWL-ViT model)
- pegasus — PegasusConfig (Pegasus model)
- pegasus_x — PegasusXConfig (PEGASUS-X model)
- perceiver — PerceiverConfig (Perceiver model)
- plbart — PLBartConfig (PLBart model)
- poolformer — PoolFormerConfig (PoolFormer model)
- prophetnet — ProphetNetConfig (ProphetNet model)
- qdqbert — QDQBertConfig (QDQBert model)
- rag — RagConfig (RAG model)
- realm — RealmConfig (REALM model)
- reformer — ReformerConfig (Reformer model)
- regnet — RegNetConfig (RegNet model)
- rembert — RemBertConfig (RemBERT model)
- resnet — ResNetConfig (ResNet model)
- retribert — RetriBertConfig (RetriBERT model)
- roberta — RobertaConfig (RoBERTa model)
- roc_bert — RoCBertConfig (RoCBert model)
- roformer — RoFormerConfig (RoFormer model)
- segformer — SegformerConfig (SegFormer model)
- sew — SEWConfig (SEW model)
- sew-d — SEWDConfig (SEW-D model)
- speech-encoder-decoder — SpeechEncoderDecoderConfig (Speech Encoder decoder model)
- speech_to_text — Speech2TextConfig (Speech2Text model)
- speech_to_text_2 — Speech2Text2Config (Speech2Text2 model)
- splinter — SplinterConfig (Splinter model)
- squeezebert — SqueezeBertConfig (SqueezeBERT model)
- swin — SwinConfig (Swin Transformer model)
- swinv2 — Swinv2Config (Swin Transformer V2 model)
- switch_transformers — SwitchTransformersConfig (SwitchTransformers model)
- t5 — T5Config (T5 model)
- table-transformer — TableTransformerConfig (Table Transformer model)
- tapas — TapasConfig (TAPAS model)
- time_series_transformer — TimeSeriesTransformerConfig (Time Series Transformer model)
- trajectory_transformer — TrajectoryTransformerConfig (Trajectory Transformer model)
- transfo-xl — TransfoXLConfig (Transformer-XL model)
- trocr — TrOCRConfig (TrOCR model)
- unispeech — UniSpeechConfig (UniSpeech model)
- unispeech-sat — UniSpeechSatConfig (UniSpeechSat model)
- van — VanConfig (VAN model)
- videomae — VideoMAEConfig (VideoMAE model)
- vilt — ViltConfig (ViLT model)
- vision-encoder-decoder — VisionEncoderDecoderConfig (Vision Encoder decoder model)
- vision-text-dual-encoder — VisionTextDualEncoderConfig (VisionTextDualEncoder model)
- visual_bert — VisualBertConfig (VisualBERT model)
- vit — ViTConfig (ViT model)
- vit_mae — ViTMAEConfig (ViTMAE model)
- vit_msn — ViTMSNConfig (ViTMSN model)
- wav2vec2 — Wav2Vec2Config (Wav2Vec2 model)
- wav2vec2-conformer — Wav2Vec2ConformerConfig (Wav2Vec2-Conformer model)
- wavlm — WavLMConfig (WavLM model)
- whisper — WhisperConfig (Whisper model)
- xclip — XCLIPConfig (X-CLIP model)
- xglm — XGLMConfig (XGLM model)
- xlm — XLMConfig (XLM model)
- xlm-prophetnet — XLMProphetNetConfig (XLM-ProphetNet model)
- xlm-roberta — XLMRobertaConfig (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLConfig (XLM-RoBERTa-XL model)
- xlnet — XLNetConfig (XLNet model)
- yolos — YolosConfig (YOLOS model)
- yoso — YosoConfig (YOSO model)
Examples:
>>> from transformers import AutoConfig
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained("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("bert-base-uncased", output_attentions=True, foo=False)
>>> config.output_attentions
True
>>> config, unused_kwargs = AutoConfig.from_pretrained(
... "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
... )
>>> config.output_attentions
True
>>> unused_kwargs
{'foo': False}register
< source >( model_type config )
Parameters
-
model_type (
str) — The model type like “bert” or “gpt”. - config (PretrainedConfig) — The config to register.
Register a new configuration for this class.
AutoTokenizer
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).
from_pretrained
< source >( pretrained_model_name_or_path *inputs **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a predefined tokenizer hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing vocabulary files required by the tokenizer, for instance saved
using the save_pretrained() method, e.g.,
./my_model_directory/. - A path or url to a single saved vocabulary file if and only if the tokenizer only requires a
single vocabulary file (like Bert or XLNet), e.g.:
./my_model_directory/vocab.txt. (Not applicable to all derived classes)
- A string, the model id of a predefined tokenizer hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
inputs (additional positional arguments, optional) —
Will be passed along to the Tokenizer
__init__()method. - config (PretrainedConfig, optional) — The configuration object used to dertermine the tokenizer class to instantiate.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download the model weights and configuration files and override the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
revision (
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, sorevisioncan be any identifier allowed by git. -
subfolder (
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. -
use_fast (
bool, optional, defaults toTrue) — Whether or not to try to load the fast version of the tokenizer. -
tokenizer_type (
str, optional) — Tokenizer type to be loaded. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Will be passed to the Tokenizer
__init__()method. Can be used to set special tokens likebos_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:
- albert — AlbertTokenizer or AlbertTokenizerFast (ALBERT model)
- bart — BartTokenizer or BartTokenizerFast (BART model)
- barthez — BarthezTokenizer or BarthezTokenizerFast (BARThez model)
- bartpho — BartphoTokenizer (BARTpho model)
- bert — BertTokenizer or BertTokenizerFast (BERT model)
- bert-generation — BertGenerationTokenizer (Bert Generation model)
- bert-japanese — BertJapaneseTokenizer (BertJapanese model)
- bertweet — BertweetTokenizer (BERTweet model)
- big_bird — BigBirdTokenizer or BigBirdTokenizerFast (BigBird model)
- bigbird_pegasus — PegasusTokenizer or PegasusTokenizerFast (BigBird-Pegasus model)
- blenderbot — BlenderbotTokenizer or BlenderbotTokenizerFast (Blenderbot model)
- blenderbot-small — BlenderbotSmallTokenizer (BlenderbotSmall model)
- bloom — BloomTokenizerFast (BLOOM model)
- byt5 — ByT5Tokenizer (ByT5 model)
- camembert — CamembertTokenizer or CamembertTokenizerFast (CamemBERT model)
- canine — CanineTokenizer (CANINE model)
- chinese_clip — BertTokenizer or BertTokenizerFast (Chinese-CLIP model)
- clip — CLIPTokenizer or CLIPTokenizerFast (CLIP model)
- clipseg — CLIPTokenizer or CLIPTokenizerFast (CLIPSeg model)
- codegen — CodeGenTokenizer or CodeGenTokenizerFast (CodeGen model)
- convbert — ConvBertTokenizer or ConvBertTokenizerFast (ConvBERT model)
- cpm — CpmTokenizer or CpmTokenizerFast (CPM model)
- ctrl — CTRLTokenizer (CTRL model)
- data2vec-text — RobertaTokenizer or RobertaTokenizerFast (Data2VecText model)
- deberta — DebertaTokenizer or DebertaTokenizerFast (DeBERTa model)
- deberta-v2 — DebertaV2Tokenizer or DebertaV2TokenizerFast (DeBERTa-v2 model)
- distilbert — DistilBertTokenizer or DistilBertTokenizerFast (DistilBERT model)
- dpr — DPRQuestionEncoderTokenizer or DPRQuestionEncoderTokenizerFast (DPR model)
- electra — ElectraTokenizer or ElectraTokenizerFast (ELECTRA model)
- ernie — BertTokenizer or BertTokenizerFast (ERNIE model)
- esm — EsmTokenizer (ESM model)
- flaubert — FlaubertTokenizer (FlauBERT model)
- fnet — FNetTokenizer or FNetTokenizerFast (FNet model)
- fsmt — FSMTTokenizer (FairSeq Machine-Translation model)
- funnel — FunnelTokenizer or FunnelTokenizerFast (Funnel Transformer model)
- gpt2 — GPT2Tokenizer or GPT2TokenizerFast (OpenAI GPT-2 model)
- gpt_neo — GPT2Tokenizer or GPT2TokenizerFast (GPT Neo model)
- gpt_neox — GPTNeoXTokenizerFast (GPT NeoX model)
- gpt_neox_japanese — GPTNeoXJapaneseTokenizer (GPT NeoX Japanese model)
- gptj — GPT2Tokenizer or GPT2TokenizerFast (GPT-J model)
- groupvit — CLIPTokenizer or CLIPTokenizerFast (GroupViT model)
- herbert — HerbertTokenizer or HerbertTokenizerFast (HerBERT model)
- hubert — Wav2Vec2CTCTokenizer (Hubert model)
- ibert — RobertaTokenizer or RobertaTokenizerFast (I-BERT model)
- jukebox — JukeboxTokenizer (Jukebox model)
- layoutlm — LayoutLMTokenizer or LayoutLMTokenizerFast (LayoutLM model)
- layoutlmv2 — LayoutLMv2Tokenizer or LayoutLMv2TokenizerFast (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3Tokenizer or LayoutLMv3TokenizerFast (LayoutLMv3 model)
- layoutxlm — LayoutXLMTokenizer or LayoutXLMTokenizerFast (LayoutXLM model)
- led — LEDTokenizer or LEDTokenizerFast (LED model)
- lilt — LayoutLMv3Tokenizer or LayoutLMv3TokenizerFast (LiLT model)
- longformer — LongformerTokenizer or LongformerTokenizerFast (Longformer model)
- longt5 — T5Tokenizer or T5TokenizerFast (LongT5 model)
- luke — LukeTokenizer (LUKE model)
- lxmert — LxmertTokenizer or LxmertTokenizerFast (LXMERT model)
- m2m_100 — M2M100Tokenizer (M2M100 model)
- marian — MarianTokenizer (Marian model)
- mbart — MBartTokenizer or MBartTokenizerFast (mBART model)
- mbart50 — MBart50Tokenizer or MBart50TokenizerFast (mBART-50 model)
- megatron-bert — BertTokenizer or BertTokenizerFast (Megatron-BERT model)
- mluke — MLukeTokenizer (mLUKE model)
- mobilebert — MobileBertTokenizer or MobileBertTokenizerFast (MobileBERT model)
- mpnet — MPNetTokenizer or MPNetTokenizerFast (MPNet model)
- mt5 — MT5Tokenizer or MT5TokenizerFast (MT5 model)
- mvp — MvpTokenizer or MvpTokenizerFast (MVP model)
- nezha — BertTokenizer or BertTokenizerFast (Nezha model)
- nllb — NllbTokenizer or NllbTokenizerFast (NLLB model)
- nystromformer — AlbertTokenizer or AlbertTokenizerFast (Nyströmformer model)
- openai-gpt — OpenAIGPTTokenizer or OpenAIGPTTokenizerFast (OpenAI GPT model)
- opt — GPT2Tokenizer (OPT model)
- owlvit — CLIPTokenizer or CLIPTokenizerFast (OWL-ViT model)
- pegasus — PegasusTokenizer or PegasusTokenizerFast (Pegasus model)
- pegasus_x — PegasusTokenizer or PegasusTokenizerFast (PEGASUS-X model)
- perceiver — PerceiverTokenizer (Perceiver model)
- phobert — PhobertTokenizer (PhoBERT model)
- plbart — PLBartTokenizer (PLBart model)
- prophetnet — ProphetNetTokenizer (ProphetNet model)
- qdqbert — BertTokenizer or BertTokenizerFast (QDQBert model)
- rag — RagTokenizer (RAG model)
- realm — RealmTokenizer or RealmTokenizerFast (REALM model)
- reformer — ReformerTokenizer or ReformerTokenizerFast (Reformer model)
- rembert — RemBertTokenizer or RemBertTokenizerFast (RemBERT model)
- retribert — RetriBertTokenizer or RetriBertTokenizerFast (RetriBERT model)
- roberta — RobertaTokenizer or RobertaTokenizerFast (RoBERTa model)
- roc_bert — RoCBertTokenizer (RoCBert model)
- roformer — RoFormerTokenizer or RoFormerTokenizerFast (RoFormer model)
- speech_to_text — Speech2TextTokenizer (Speech2Text model)
- speech_to_text_2 — Speech2Text2Tokenizer (Speech2Text2 model)
- splinter — SplinterTokenizer or SplinterTokenizerFast (Splinter model)
- squeezebert — SqueezeBertTokenizer or SqueezeBertTokenizerFast (SqueezeBERT model)
- switch_transformers — T5Tokenizer or T5TokenizerFast (SwitchTransformers model)
- t5 — T5Tokenizer or T5TokenizerFast (T5 model)
- tapas — TapasTokenizer (TAPAS model)
- tapex — TapexTokenizer (TAPEX model)
- transfo-xl — TransfoXLTokenizer (Transformer-XL model)
- vilt — BertTokenizer or BertTokenizerFast (ViLT model)
- visual_bert — BertTokenizer or BertTokenizerFast (VisualBERT model)
- wav2vec2 — Wav2Vec2CTCTokenizer (Wav2Vec2 model)
- wav2vec2-conformer — Wav2Vec2CTCTokenizer (Wav2Vec2-Conformer model)
- wav2vec2_phoneme — Wav2Vec2PhonemeCTCTokenizer (Wav2Vec2Phoneme model)
- whisper — WhisperTokenizer (Whisper model)
- xclip — CLIPTokenizer or CLIPTokenizerFast (X-CLIP model)
- xglm — XGLMTokenizer or XGLMTokenizerFast (XGLM model)
- xlm — XLMTokenizer (XLM model)
- xlm-prophetnet — XLMProphetNetTokenizer (XLM-ProphetNet model)
- xlm-roberta — XLMRobertaTokenizer or XLMRobertaTokenizerFast (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaTokenizer or XLMRobertaTokenizerFast (XLM-RoBERTa-XL model)
- xlnet — XLNetTokenizer or XLNetTokenizerFast (XLNet model)
- yoso — AlbertTokenizer or AlbertTokenizerFast (YOSO model)
Examples:
>>> from transformers import AutoTokenizer
>>> # Download vocabulary from huggingface.co and cache.
>>> tokenizer = AutoTokenizer.from_pretrained("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("roberta-base", add_prefix_space=True)register
< source >( config_class slow_tokenizer_class = None fast_tokenizer_class = None )
Parameters
- config_class (PretrainedConfig) — The configuration corresponding to the model to register.
-
slow_tokenizer_class (
PretrainedTokenizer, optional) — The slow tokenizer to register. -
slow_tokenizer_class (
PretrainedTokenizerFast, optional) — The fast tokenizer to register.
Register a new tokenizer in this mapping.
AutoFeatureExtractor
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).
from_pretrained
< source >( pretrained_model_name_or_path **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — This can be either:- a string, the model id of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - a path to a directory containing a feature extractor file saved using the
save_pretrained() method, e.g.,
./my_model_directory/. - a path or url to a saved feature extractor JSON file, e.g.,
./my_model_directory/preprocessor_config.json.
- a string, the model id of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like
-
cache_dir (
stroros.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. -
force_download (
bool, optional, defaults toFalse) — Whether or not to force to (re-)download the feature extractor files and override the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists. -
proxies (
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. -
use_auth_token (
stror bool, optional) — The token to use as HTTP bearer authorization for remote files. IfTrue, will use the token generated when runninghuggingface-cli login(stored in~/.huggingface). -
revision (
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, sorevisioncan be any identifier allowed by git. -
return_unused_kwargs (
bool, optional, defaults toFalse) — IfFalse, then this function returns just the final feature extractor object. IfTrue, then this functions returns aTuple(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 ofkwargswhich has not been used to updatefeature_extractorand is otherwise ignored. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (
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 thereturn_unused_kwargskeyword 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:
- audio-spectrogram-transformer — ASTFeatureExtractor (Audio Spectrogram Transformer model)
- beit — BeitFeatureExtractor (BEiT model)
- chinese_clip — ChineseCLIPFeatureExtractor (Chinese-CLIP model)
- clip — CLIPFeatureExtractor (CLIP model)
- clipseg — ViTFeatureExtractor (CLIPSeg model)
- conditional_detr — ConditionalDetrFeatureExtractor (Conditional DETR model)
- convnext — ConvNextFeatureExtractor (ConvNeXT model)
- cvt — ConvNextFeatureExtractor (CvT model)
- data2vec-audio — Wav2Vec2FeatureExtractor (Data2VecAudio model)
- data2vec-vision — BeitFeatureExtractor (Data2VecVision model)
- deformable_detr — DeformableDetrFeatureExtractor (Deformable DETR model)
- deit — DeiTFeatureExtractor (DeiT model)
- detr — DetrFeatureExtractor (DETR model)
- dinat — ViTFeatureExtractor (DiNAT model)
- donut-swin — DonutFeatureExtractor (DonutSwin model)
- dpt — DPTFeatureExtractor (DPT model)
- flava — FlavaFeatureExtractor (FLAVA model)
- glpn — GLPNFeatureExtractor (GLPN model)
- groupvit — CLIPFeatureExtractor (GroupViT model)
- hubert — Wav2Vec2FeatureExtractor (Hubert model)
- imagegpt — ImageGPTFeatureExtractor (ImageGPT model)
- layoutlmv2 — LayoutLMv2FeatureExtractor (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3FeatureExtractor (LayoutLMv3 model)
- levit — LevitFeatureExtractor (LeViT model)
- maskformer — MaskFormerFeatureExtractor (MaskFormer model)
- mctct — MCTCTFeatureExtractor (M-CTC-T model)
- mobilenet_v1 — MobileNetV1FeatureExtractor (MobileNetV1 model)
- mobilenet_v2 — MobileNetV2FeatureExtractor (MobileNetV2 model)
- mobilevit — MobileViTFeatureExtractor (MobileViT model)
- nat — ViTFeatureExtractor (NAT model)
- owlvit — OwlViTFeatureExtractor (OWL-ViT model)
- perceiver — PerceiverFeatureExtractor (Perceiver model)
- poolformer — PoolFormerFeatureExtractor (PoolFormer model)
- regnet — ConvNextFeatureExtractor (RegNet model)
- resnet — ConvNextFeatureExtractor (ResNet model)
- segformer — SegformerFeatureExtractor (SegFormer model)
- speech_to_text — Speech2TextFeatureExtractor (Speech2Text model)
- swin — ViTFeatureExtractor (Swin Transformer model)
- swinv2 — ViTFeatureExtractor (Swin Transformer V2 model)
- table-transformer — DetrFeatureExtractor (Table Transformer model)
- van — ConvNextFeatureExtractor (VAN model)
- videomae — VideoMAEFeatureExtractor (VideoMAE model)
- vilt — ViltFeatureExtractor (ViLT model)
- vit — ViTFeatureExtractor (ViT model)
- vit_mae — ViTFeatureExtractor (ViTMAE model)
- vit_msn — ViTFeatureExtractor (ViTMSN model)
- wav2vec2 — Wav2Vec2FeatureExtractor (Wav2Vec2 model)
- wav2vec2-conformer — Wav2Vec2FeatureExtractor (Wav2Vec2-Conformer model)
- whisper — WhisperFeatureExtractor (Whisper model)
- xclip — CLIPFeatureExtractor (X-CLIP model)
- yolos — YolosFeatureExtractor (YOLOS model)
Passing use_auth_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/")register
< source >( config_class feature_extractor_class )
Parameters
- config_class (PretrainedConfig) — The configuration corresponding to the model to register.
-
feature_extractor_class (
FeatureExtractorMixin) — The feature extractor to register.
Register a new feature extractor for this class.
AutoImageProcessor
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).
from_pretrained
< source >( pretrained_model_name_or_path **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — This can be either:- a string, the model id of a pretrained image_processor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - a path to a directory containing a image processor file saved using the
save_pretrained() method, e.g.,
./my_model_directory/. - a path or url to a saved image processor JSON file, e.g.,
./my_model_directory/preprocessor_config.json.
- a string, the model id of a pretrained image_processor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like
-
cache_dir (
stroros.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. -
force_download (
bool, optional, defaults toFalse) — Whether or not to force to (re-)download the image processor files and override the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists. -
proxies (
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. -
use_auth_token (
stror bool, optional) — The token to use as HTTP bearer authorization for remote files. IfTrue, will use the token generated when runninghuggingface-cli login(stored in~/.huggingface). -
revision (
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, sorevisioncan be any identifier allowed by git. -
return_unused_kwargs (
bool, optional, defaults toFalse) — IfFalse, then this function returns just the final image processor object. IfTrue, then this functions returns aTuple(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 ofkwargswhich has not been used to updateimage_processorand is otherwise ignored. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (
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 thereturn_unused_kwargskeyword 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:
- beit — BeitImageProcessor (BEiT model)
- chinese_clip — ChineseCLIPImageProcessor (Chinese-CLIP model)
- clip — CLIPImageProcessor (CLIP model)
- clipseg — ViTImageProcessor (CLIPSeg model)
- conditional_detr — ConditionalDetrImageProcessor (Conditional DETR model)
- convnext — ConvNextImageProcessor (ConvNeXT model)
- cvt — ConvNextImageProcessor (CvT model)
- data2vec-vision — BeitImageProcessor (Data2VecVision model)
- deformable_detr — DeformableDetrImageProcessor (Deformable DETR model)
- deit — DeiTImageProcessor (DeiT model)
- detr — DetrImageProcessor (DETR model)
- dinat — ViTImageProcessor (DiNAT model)
- donut-swin — DonutImageProcessor (DonutSwin model)
- dpt — DPTImageProcessor (DPT model)
- flava — FlavaImageProcessor (FLAVA model)
- glpn — GLPNImageProcessor (GLPN model)
- groupvit — CLIPImageProcessor (GroupViT model)
- imagegpt — ImageGPTImageProcessor (ImageGPT model)
- layoutlmv2 — LayoutLMv2ImageProcessor (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3ImageProcessor (LayoutLMv3 model)
- levit — LevitImageProcessor (LeViT model)
- maskformer — MaskFormerImageProcessor (MaskFormer model)
- mobilenet_v1 — MobileNetV1ImageProcessor (MobileNetV1 model)
- mobilenet_v2 — MobileNetV2ImageProcessor (MobileNetV2 model)
- mobilevit — MobileViTImageProcessor (MobileViT model)
- nat — ViTImageProcessor (NAT model)
- owlvit — OwlViTImageProcessor (OWL-ViT model)
- perceiver — PerceiverImageProcessor (Perceiver model)
- poolformer — PoolFormerImageProcessor (PoolFormer model)
- regnet — ConvNextImageProcessor (RegNet model)
- resnet — ConvNextImageProcessor (ResNet model)
- segformer — SegformerImageProcessor (SegFormer model)
- swin — ViTImageProcessor (Swin Transformer model)
- swinv2 — ViTImageProcessor (Swin Transformer V2 model)
- table-transformer — DetrImageProcessor (Table Transformer model)
- van — ConvNextImageProcessor (VAN model)
- videomae — VideoMAEImageProcessor (VideoMAE model)
- vilt — ViltImageProcessor (ViLT model)
- vit — ViTImageProcessor (ViT model)
- vit_mae — ViTImageProcessor (ViTMAE model)
- vit_msn — ViTImageProcessor (ViTMSN model)
- xclip — CLIPImageProcessor (X-CLIP model)
- yolos — YolosImageProcessor (YOLOS model)
Passing use_auth_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/")register
< source >( config_class image_processor_class )
Parameters
- config_class (PretrainedConfig) — The configuration corresponding to the model to register.
- image_processor_class (ImageProcessingMixin) — The image processor to register.
Register a new image processor for this class.
AutoProcessor
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).
from_pretrained
< source >( pretrained_model_name_or_path **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — This can be either:- a string, the model id of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - a path to a directory containing a processor files saved using the
save_pretrained()method, e.g.,./my_model_directory/.
- a string, the model id of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like
-
cache_dir (
stroros.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. -
force_download (
bool, optional, defaults toFalse) — Whether or not to force to (re-)download the feature extractor files and override the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists. -
proxies (
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. -
use_auth_token (
stror bool, optional) — The token to use as HTTP bearer authorization for remote files. IfTrue, will use the token generated when runninghuggingface-cli login(stored in~/.huggingface). -
revision (
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, sorevisioncan be any identifier allowed by git. -
return_unused_kwargs (
bool, optional, defaults toFalse) — IfFalse, then this function returns just the final feature extractor object. IfTrue, then this functions returns aTuple(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 ofkwargswhich has not been used to updatefeature_extractorand is otherwise ignored. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (
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 thereturn_unused_kwargskeyword 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):
- chinese_clip — ChineseCLIPProcessor (Chinese-CLIP model)
- clip — CLIPProcessor (CLIP model)
- clipseg — CLIPSegProcessor (CLIPSeg model)
- flava — FlavaProcessor (FLAVA model)
- groupvit — CLIPProcessor (GroupViT model)
- layoutlmv2 — LayoutLMv2Processor (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3Processor (LayoutLMv3 model)
- layoutxlm — LayoutXLMProcessor (LayoutXLM model)
- markuplm — MarkupLMProcessor (MarkupLM model)
- owlvit — OwlViTProcessor (OWL-ViT model)
- sew — Wav2Vec2Processor (SEW model)
- sew-d — Wav2Vec2Processor (SEW-D model)
- speech_to_text — Speech2TextProcessor (Speech2Text model)
- speech_to_text_2 — Speech2Text2Processor (Speech2Text2 model)
- trocr — TrOCRProcessor (TrOCR model)
- unispeech — Wav2Vec2Processor (UniSpeech model)
- unispeech-sat — Wav2Vec2Processor (UniSpeechSat model)
- vilt — ViltProcessor (ViLT model)
- vision-text-dual-encoder — VisionTextDualEncoderProcessor (VisionTextDualEncoder model)
- wav2vec2 — Wav2Vec2Processor (Wav2Vec2 model)
- wav2vec2-conformer — Wav2Vec2Processor (Wav2Vec2-Conformer model)
- wavlm — Wav2Vec2Processor (WavLM model)
- whisper — WhisperProcessor (Whisper model)
- xclip — XCLIPProcessor (X-CLIP model)
Passing use_auth_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/")register
< source >( config_class processor_class )
Parameters
- config_class (PretrainedConfig) — The configuration corresponding to the model to register.
-
processor_class (
FeatureExtractorMixin) — The processor to register.
Register a new processor for this class.
AutoModel
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- ASTConfig configuration class: ASTModel (Audio Spectrogram Transformer model)
- AlbertConfig configuration class: AlbertModel (ALBERT model)
- BartConfig configuration class: BartModel (BART model)
- BeitConfig configuration class: BeitModel (BEiT model)
- BertConfig configuration class: BertModel (BERT model)
- BertGenerationConfig configuration class: BertGenerationEncoder (Bert Generation model)
- BigBirdConfig configuration class: BigBirdModel (BigBird model)
- BigBirdPegasusConfig configuration class: BigBirdPegasusModel (BigBird-Pegasus model)
- BlenderbotConfig configuration class: BlenderbotModel (Blenderbot model)
- BlenderbotSmallConfig configuration class: BlenderbotSmallModel (BlenderbotSmall model)
- BloomConfig configuration class: BloomModel (BLOOM model)
- CLIPConfig configuration class: CLIPModel (CLIP model)
- CLIPSegConfig configuration class: CLIPSegModel (CLIPSeg model)
- CTRLConfig configuration class: CTRLModel (CTRL model)
- CamembertConfig configuration class: CamembertModel (CamemBERT model)
- CanineConfig configuration class: CanineModel (CANINE model)
- ChineseCLIPConfig configuration class: ChineseCLIPModel (Chinese-CLIP model)
- CodeGenConfig configuration class: CodeGenModel (CodeGen model)
- ConditionalDetrConfig configuration class: ConditionalDetrModel (Conditional DETR model)
- ConvBertConfig configuration class: ConvBertModel (ConvBERT model)
- ConvNextConfig configuration class: ConvNextModel (ConvNeXT model)
- CvtConfig configuration class: CvtModel (CvT model)
- DPRConfig configuration class: DPRQuestionEncoder (DPR model)
- DPTConfig configuration class: DPTModel (DPT model)
- Data2VecAudioConfig configuration class: Data2VecAudioModel (Data2VecAudio model)
- Data2VecTextConfig configuration class: Data2VecTextModel (Data2VecText model)
- Data2VecVisionConfig configuration class: Data2VecVisionModel (Data2VecVision model)
- DebertaConfig configuration class: DebertaModel (DeBERTa model)
- DebertaV2Config configuration class: DebertaV2Model (DeBERTa-v2 model)
- DecisionTransformerConfig configuration class: DecisionTransformerModel (Decision Transformer model)
- DeformableDetrConfig configuration class: DeformableDetrModel (Deformable DETR model)
- DeiTConfig configuration class: DeiTModel (DeiT model)
- DetrConfig configuration class: DetrModel (DETR model)
- DinatConfig configuration class: DinatModel (DiNAT model)
- DistilBertConfig configuration class: DistilBertModel (DistilBERT model)
- DonutSwinConfig configuration class: DonutSwinModel (DonutSwin model)
- ElectraConfig configuration class: ElectraModel (ELECTRA model)
- ErnieConfig configuration class: ErnieModel (ERNIE model)
- EsmConfig configuration class: EsmModel (ESM model)
- FNetConfig configuration class: FNetModel (FNet model)
- FSMTConfig configuration class: FSMTModel (FairSeq Machine-Translation model)
- FlaubertConfig configuration class: FlaubertModel (FlauBERT model)
- FlavaConfig configuration class: FlavaModel (FLAVA model)
- FunnelConfig configuration class: FunnelModel or FunnelBaseModel (Funnel Transformer model)
- GLPNConfig configuration class: GLPNModel (GLPN model)
- GPT2Config configuration class: GPT2Model (OpenAI GPT-2 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)
- GroupViTConfig configuration class: GroupViTModel (GroupViT model)
- HubertConfig configuration class: HubertModel (Hubert model)
- IBertConfig configuration class: IBertModel (I-BERT model)
- ImageGPTConfig configuration class: ImageGPTModel (ImageGPT model)
- JukeboxConfig configuration class: JukeboxModel (Jukebox 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)
- 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)
- MarianConfig configuration class: MarianModel (Marian model)
- MarkupLMConfig configuration class: MarkupLMModel (MarkupLM model)
- MaskFormerConfig configuration class: MaskFormerModel (MaskFormer model)
MaskFormerSwinConfigconfiguration class:MaskFormerSwinModel(MaskFormerSwin model)- MegatronBertConfig configuration class: MegatronBertModel (Megatron-BERT 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)
- MvpConfig configuration class: MvpModel (MVP model)
- NatConfig configuration class: NatModel (NAT model)
- NezhaConfig configuration class: NezhaModel (Nezha model)
- NystromformerConfig configuration class: NystromformerModel (Nyströmformer model)
- OPTConfig configuration class: OPTModel (OPT model)
- OpenAIGPTConfig configuration class: OpenAIGPTModel (OpenAI GPT model)
- OwlViTConfig configuration class: OwlViTModel (OWL-ViT model)
- PLBartConfig configuration class: PLBartModel (PLBart model)
- PegasusConfig configuration class: PegasusModel (Pegasus model)
- PegasusXConfig configuration class: PegasusXModel (PEGASUS-X model)
- PerceiverConfig configuration class: PerceiverModel (Perceiver model)
- PoolFormerConfig configuration class: PoolFormerModel (PoolFormer model)
- ProphetNetConfig configuration class: ProphetNetModel (ProphetNet model)
- QDQBertConfig configuration class: QDQBertModel (QDQBert 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)
- SEWConfig configuration class: SEWModel (SEW model)
- SEWDConfig configuration class: SEWDModel (SEW-D model)
- SegformerConfig configuration class: SegformerModel (SegFormer model)
- Speech2TextConfig configuration class: Speech2TextModel (Speech2Text model)
- SplinterConfig configuration class: SplinterModel (Splinter model)
- SqueezeBertConfig configuration class: SqueezeBertModel (SqueezeBERT 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)
- TrajectoryTransformerConfig configuration class: TrajectoryTransformerModel (Trajectory Transformer model)
- TransfoXLConfig configuration class: TransfoXLModel (Transformer-XL model)
- UniSpeechConfig configuration class: UniSpeechModel (UniSpeech model)
- UniSpeechSatConfig configuration class: UniSpeechSatModel (UniSpeechSat model)
- VanConfig configuration class: VanModel (VAN model)
- ViTConfig configuration class: ViTModel (ViT 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)
- 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)
- YolosConfig configuration class: YolosModel (YOLOS model)
- YosoConfig configuration class: YosoModel (YOSO model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — AlbertModel (ALBERT model)
- audio-spectrogram-transformer — ASTModel (Audio Spectrogram Transformer model)
- bart — BartModel (BART model)
- beit — BeitModel (BEiT model)
- bert — BertModel (BERT model)
- bert-generation — BertGenerationEncoder (Bert Generation model)
- big_bird — BigBirdModel (BigBird model)
- bigbird_pegasus — BigBirdPegasusModel (BigBird-Pegasus model)
- blenderbot — BlenderbotModel (Blenderbot model)
- blenderbot-small — BlenderbotSmallModel (BlenderbotSmall model)
- bloom — BloomModel (BLOOM model)
- camembert — CamembertModel (CamemBERT model)
- canine — CanineModel (CANINE model)
- chinese_clip — ChineseCLIPModel (Chinese-CLIP model)
- clip — CLIPModel (CLIP model)
- clipseg — CLIPSegModel (CLIPSeg model)
- codegen — CodeGenModel (CodeGen model)
- conditional_detr — ConditionalDetrModel (Conditional DETR model)
- convbert — ConvBertModel (ConvBERT model)
- convnext — ConvNextModel (ConvNeXT model)
- ctrl — CTRLModel (CTRL model)
- cvt — CvtModel (CvT model)
- data2vec-audio — Data2VecAudioModel (Data2VecAudio model)
- data2vec-text — Data2VecTextModel (Data2VecText model)
- data2vec-vision — Data2VecVisionModel (Data2VecVision model)
- deberta — DebertaModel (DeBERTa model)
- deberta-v2 — DebertaV2Model (DeBERTa-v2 model)
- decision_transformer — DecisionTransformerModel (Decision Transformer model)
- deformable_detr — DeformableDetrModel (Deformable DETR model)
- deit — DeiTModel (DeiT model)
- detr — DetrModel (DETR model)
- dinat — DinatModel (DiNAT model)
- distilbert — DistilBertModel (DistilBERT model)
- donut-swin — DonutSwinModel (DonutSwin model)
- dpr — DPRQuestionEncoder (DPR model)
- dpt — DPTModel (DPT model)
- electra — ElectraModel (ELECTRA model)
- ernie — ErnieModel (ERNIE model)
- esm — EsmModel (ESM model)
- flaubert — FlaubertModel (FlauBERT model)
- flava — FlavaModel (FLAVA model)
- fnet — FNetModel (FNet model)
- fsmt — FSMTModel (FairSeq Machine-Translation model)
- funnel — FunnelModel or FunnelBaseModel (Funnel Transformer model)
- glpn — GLPNModel (GLPN model)
- gpt2 — GPT2Model (OpenAI GPT-2 model)
- gpt_neo — GPTNeoModel (GPT Neo model)
- gpt_neox — GPTNeoXModel (GPT NeoX model)
- gpt_neox_japanese — GPTNeoXJapaneseModel (GPT NeoX Japanese model)
- gptj — GPTJModel (GPT-J model)
- groupvit — GroupViTModel (GroupViT model)
- hubert — HubertModel (Hubert model)
- ibert — IBertModel (I-BERT model)
- imagegpt — ImageGPTModel (ImageGPT model)
- jukebox — JukeboxModel (Jukebox model)
- layoutlm — LayoutLMModel (LayoutLM model)
- layoutlmv2 — LayoutLMv2Model (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3Model (LayoutLMv3 model)
- led — LEDModel (LED model)
- levit — LevitModel (LeViT model)
- lilt — LiltModel (LiLT model)
- longformer — LongformerModel (Longformer model)
- longt5 — LongT5Model (LongT5 model)
- luke — LukeModel (LUKE model)
- lxmert — LxmertModel (LXMERT model)
- m2m_100 — M2M100Model (M2M100 model)
- marian — MarianModel (Marian model)
- markuplm — MarkupLMModel (MarkupLM model)
- maskformer — MaskFormerModel (MaskFormer model)
- maskformer-swin —
MaskFormerSwinModel(MaskFormerSwin model) - mbart — MBartModel (mBART model)
- mctct — MCTCTModel (M-CTC-T model)
- megatron-bert — MegatronBertModel (Megatron-BERT model)
- mobilebert — MobileBertModel (MobileBERT model)
- mobilenet_v1 — MobileNetV1Model (MobileNetV1 model)
- mobilenet_v2 — MobileNetV2Model (MobileNetV2 model)
- mobilevit — MobileViTModel (MobileViT model)
- mpnet — MPNetModel (MPNet model)
- mt5 — MT5Model (MT5 model)
- mvp — MvpModel (MVP model)
- nat — NatModel (NAT model)
- nezha — NezhaModel (Nezha model)
- nllb — M2M100Model (NLLB model)
- nystromformer — NystromformerModel (Nyströmformer model)
- openai-gpt — OpenAIGPTModel (OpenAI GPT model)
- opt — OPTModel (OPT model)
- owlvit — OwlViTModel (OWL-ViT model)
- pegasus — PegasusModel (Pegasus model)
- pegasus_x — PegasusXModel (PEGASUS-X model)
- perceiver — PerceiverModel (Perceiver model)
- plbart — PLBartModel (PLBart model)
- poolformer — PoolFormerModel (PoolFormer model)
- prophetnet — ProphetNetModel (ProphetNet model)
- qdqbert — QDQBertModel (QDQBert model)
- reformer — ReformerModel (Reformer model)
- regnet — RegNetModel (RegNet model)
- rembert — RemBertModel (RemBERT model)
- resnet — ResNetModel (ResNet model)
- retribert — RetriBertModel (RetriBERT model)
- roberta — RobertaModel (RoBERTa model)
- roc_bert — RoCBertModel (RoCBert model)
- roformer — RoFormerModel (RoFormer model)
- segformer — SegformerModel (SegFormer model)
- sew — SEWModel (SEW model)
- sew-d — SEWDModel (SEW-D model)
- speech_to_text — Speech2TextModel (Speech2Text model)
- splinter — SplinterModel (Splinter model)
- squeezebert — SqueezeBertModel (SqueezeBERT model)
- swin — SwinModel (Swin Transformer model)
- swinv2 — Swinv2Model (Swin Transformer V2 model)
- switch_transformers — SwitchTransformersModel (SwitchTransformers model)
- t5 — T5Model (T5 model)
- table-transformer — TableTransformerModel (Table Transformer model)
- tapas — TapasModel (TAPAS model)
- time_series_transformer — TimeSeriesTransformerModel (Time Series Transformer model)
- trajectory_transformer — TrajectoryTransformerModel (Trajectory Transformer model)
- transfo-xl — TransfoXLModel (Transformer-XL model)
- unispeech — UniSpeechModel (UniSpeech model)
- unispeech-sat — UniSpeechSatModel (UniSpeechSat model)
- van — VanModel (VAN model)
- videomae — VideoMAEModel (VideoMAE model)
- vilt — ViltModel (ViLT model)
- vision-text-dual-encoder — VisionTextDualEncoderModel (VisionTextDualEncoder model)
- visual_bert — VisualBertModel (VisualBERT model)
- vit — ViTModel (ViT model)
- vit_mae — ViTMAEModel (ViTMAE model)
- vit_msn — ViTMSNModel (ViTMSN model)
- wav2vec2 — Wav2Vec2Model (Wav2Vec2 model)
- wav2vec2-conformer — Wav2Vec2ConformerModel (Wav2Vec2-Conformer model)
- wavlm — WavLMModel (WavLM model)
- whisper — WhisperModel (Whisper model)
- xclip — XCLIPModel (X-CLIP model)
- xglm — XGLMModel (XGLM model)
- xlm — XLMModel (XLM model)
- xlm-prophetnet — XLMProphetNetModel (XLM-ProphetNet model)
- xlm-roberta — XLMRobertaModel (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLModel (XLM-RoBERTa-XL model)
- xlnet — XLNetModel (XLNet model)
- yolos — YolosModel (YOLOS model)
- yoso — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModel.from_pretrained("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
... )AutoModelForPreTraining
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: AlbertForPreTraining (ALBERT model)
- BartConfig configuration class: BartForConditionalGeneration (BART model)
- BertConfig configuration class: BertForPreTraining (BERT model)
- BigBirdConfig configuration class: BigBirdForPreTraining (BigBird model)
- BloomConfig configuration class: BloomForCausalLM (BLOOM model)
- CTRLConfig configuration class: CTRLLMHeadModel (CTRL model)
- CamembertConfig configuration class: CamembertForMaskedLM (CamemBERT model)
- Data2VecTextConfig configuration class: Data2VecTextForMaskedLM (Data2VecText model)
- DebertaConfig configuration class: DebertaForMaskedLM (DeBERTa model)
- DebertaV2Config configuration class: DebertaV2ForMaskedLM (DeBERTa-v2 model)
- 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)
- 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)
- LxmertConfig configuration class: LxmertForPreTraining (LXMERT model)
- MPNetConfig configuration class: MPNetForMaskedLM (MPNet model)
- MegatronBertConfig configuration class: MegatronBertForPreTraining (Megatron-BERT model)
- MobileBertConfig configuration class: MobileBertForPreTraining (MobileBERT model)
- MvpConfig configuration class: MvpForConditionalGeneration (MVP model)
- NezhaConfig configuration class: NezhaForPreTraining (Nezha 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)
- 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)
- UniSpeechConfig configuration class: UniSpeechForPreTraining (UniSpeech model)
- UniSpeechSatConfig configuration class: UniSpeechSatForPreTraining (UniSpeechSat model)
- ViTMAEConfig configuration class: ViTMAEForPreTraining (ViTMAE model)
- VideoMAEConfig configuration class: VideoMAEForPreTraining (VideoMAE 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)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — AlbertForPreTraining (ALBERT model)
- bart — BartForConditionalGeneration (BART model)
- bert — BertForPreTraining (BERT model)
- big_bird — BigBirdForPreTraining (BigBird model)
- bloom — BloomForCausalLM (BLOOM model)
- camembert — CamembertForMaskedLM (CamemBERT model)
- ctrl — CTRLLMHeadModel (CTRL model)
- data2vec-text — Data2VecTextForMaskedLM (Data2VecText model)
- deberta — DebertaForMaskedLM (DeBERTa model)
- deberta-v2 — DebertaV2ForMaskedLM (DeBERTa-v2 model)
- distilbert — DistilBertForMaskedLM (DistilBERT model)
- electra — ElectraForPreTraining (ELECTRA model)
- ernie — ErnieForPreTraining (ERNIE model)
- flaubert — FlaubertWithLMHeadModel (FlauBERT model)
- flava — FlavaForPreTraining (FLAVA model)
- fnet — FNetForPreTraining (FNet model)
- fsmt — FSMTForConditionalGeneration (FairSeq Machine-Translation model)
- funnel — FunnelForPreTraining (Funnel Transformer model)
- gpt2 — GPT2LMHeadModel (OpenAI GPT-2 model)
- ibert — IBertForMaskedLM (I-BERT model)
- layoutlm — LayoutLMForMaskedLM (LayoutLM model)
- longformer — LongformerForMaskedLM (Longformer model)
- luke — LukeForMaskedLM (LUKE model)
- lxmert — LxmertForPreTraining (LXMERT model)
- megatron-bert — MegatronBertForPreTraining (Megatron-BERT model)
- mobilebert — MobileBertForPreTraining (MobileBERT model)
- mpnet — MPNetForMaskedLM (MPNet model)
- mvp — MvpForConditionalGeneration (MVP model)
- nezha — NezhaForPreTraining (Nezha model)
- openai-gpt — OpenAIGPTLMHeadModel (OpenAI GPT model)
- retribert — RetriBertModel (RetriBERT model)
- roberta — RobertaForMaskedLM (RoBERTa model)
- roc_bert — RoCBertForPreTraining (RoCBert model)
- splinter — SplinterForPreTraining (Splinter model)
- squeezebert — SqueezeBertForMaskedLM (SqueezeBERT model)
- switch_transformers — SwitchTransformersForConditionalGeneration (SwitchTransformers model)
- t5 — T5ForConditionalGeneration (T5 model)
- tapas — TapasForMaskedLM (TAPAS model)
- transfo-xl — TransfoXLLMHeadModel (Transformer-XL model)
- unispeech — UniSpeechForPreTraining (UniSpeech model)
- unispeech-sat — UniSpeechSatForPreTraining (UniSpeechSat model)
- videomae — VideoMAEForPreTraining (VideoMAE model)
- visual_bert — VisualBertForPreTraining (VisualBERT model)
- vit_mae — ViTMAEForPreTraining (ViTMAE model)
- wav2vec2 — Wav2Vec2ForPreTraining (Wav2Vec2 model)
- wav2vec2-conformer — Wav2Vec2ConformerForPreTraining (Wav2Vec2-Conformer model)
- xlm — XLMWithLMHeadModel (XLM model)
- xlm-roberta — XLMRobertaForMaskedLM (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForMaskedLM (XLM-RoBERTa-XL model)
- xlnet — XLNetLMHeadModel (XLNet 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForPreTraining.from_pretrained("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
... )AutoModelForCausalLM
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- BartConfig configuration class: BartForCausalLM (BART model)
- BertConfig configuration class: BertLMHeadModel (BERT model)
- BertGenerationConfig configuration class: BertGenerationDecoder (Bert Generation model)
- BigBirdConfig configuration class: BigBirdForCausalLM (BigBird model)
- BigBirdPegasusConfig configuration class: BigBirdPegasusForCausalLM (BigBird-Pegasus model)
- BlenderbotConfig configuration class: BlenderbotForCausalLM (Blenderbot model)
- BlenderbotSmallConfig configuration class: BlenderbotSmallForCausalLM (BlenderbotSmall model)
- BloomConfig configuration class: BloomForCausalLM (BLOOM model)
- CTRLConfig configuration class: CTRLLMHeadModel (CTRL model)
- CamembertConfig configuration class: CamembertForCausalLM (CamemBERT model)
- CodeGenConfig configuration class: CodeGenForCausalLM (CodeGen model)
- Data2VecTextConfig configuration class: Data2VecTextForCausalLM (Data2VecText model)
- ElectraConfig configuration class: ElectraForCausalLM (ELECTRA model)
- ErnieConfig configuration class: ErnieForCausalLM (ERNIE model)
- GPT2Config configuration class: GPT2LMHeadModel (OpenAI GPT-2 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)
- MBartConfig configuration class: MBartForCausalLM (mBART model)
- MarianConfig configuration class: MarianForCausalLM (Marian model)
- MegatronBertConfig configuration class: MegatronBertForCausalLM (Megatron-BERT model)
- MvpConfig configuration class: MvpForCausalLM (MVP model)
- OPTConfig configuration class: OPTForCausalLM (OPT model)
- OpenAIGPTConfig configuration class: OpenAIGPTLMHeadModel (OpenAI GPT model)
- PLBartConfig configuration class: PLBartForCausalLM (PLBart model)
- PegasusConfig configuration class: PegasusForCausalLM (Pegasus model)
- ProphetNetConfig configuration class: ProphetNetForCausalLM (ProphetNet model)
- QDQBertConfig configuration class: QDQBertLMHeadModel (QDQBert 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)
- Speech2Text2Config configuration class: Speech2Text2ForCausalLM (Speech2Text2 model)
- TrOCRConfig configuration class: TrOCRForCausalLM (TrOCR model)
- TransfoXLConfig configuration class: TransfoXLLMHeadModel (Transformer-XL 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)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- bart — BartForCausalLM (BART model)
- bert — BertLMHeadModel (BERT model)
- bert-generation — BertGenerationDecoder (Bert Generation model)
- big_bird — BigBirdForCausalLM (BigBird model)
- bigbird_pegasus — BigBirdPegasusForCausalLM (BigBird-Pegasus model)
- blenderbot — BlenderbotForCausalLM (Blenderbot model)
- blenderbot-small — BlenderbotSmallForCausalLM (BlenderbotSmall model)
- bloom — BloomForCausalLM (BLOOM model)
- camembert — CamembertForCausalLM (CamemBERT model)
- codegen — CodeGenForCausalLM (CodeGen model)
- ctrl — CTRLLMHeadModel (CTRL model)
- data2vec-text — Data2VecTextForCausalLM (Data2VecText model)
- electra — ElectraForCausalLM (ELECTRA model)
- ernie — ErnieForCausalLM (ERNIE model)
- gpt2 — GPT2LMHeadModel (OpenAI GPT-2 model)
- gpt_neo — GPTNeoForCausalLM (GPT Neo model)
- gpt_neox — GPTNeoXForCausalLM (GPT NeoX model)
- gpt_neox_japanese — GPTNeoXJapaneseForCausalLM (GPT NeoX Japanese model)
- gptj — GPTJForCausalLM (GPT-J model)
- marian — MarianForCausalLM (Marian model)
- mbart — MBartForCausalLM (mBART model)
- megatron-bert — MegatronBertForCausalLM (Megatron-BERT model)
- mvp — MvpForCausalLM (MVP model)
- openai-gpt — OpenAIGPTLMHeadModel (OpenAI GPT model)
- opt — OPTForCausalLM (OPT model)
- pegasus — PegasusForCausalLM (Pegasus model)
- plbart — PLBartForCausalLM (PLBart model)
- prophetnet — ProphetNetForCausalLM (ProphetNet model)
- qdqbert — QDQBertLMHeadModel (QDQBert model)
- reformer — ReformerModelWithLMHead (Reformer model)
- rembert — RemBertForCausalLM (RemBERT model)
- roberta — RobertaForCausalLM (RoBERTa model)
- roc_bert — RoCBertForCausalLM (RoCBert model)
- roformer — RoFormerForCausalLM (RoFormer model)
- speech_to_text_2 — Speech2Text2ForCausalLM (Speech2Text2 model)
- transfo-xl — TransfoXLLMHeadModel (Transformer-XL model)
- trocr — TrOCRForCausalLM (TrOCR model)
- xglm — XGLMForCausalLM (XGLM model)
- xlm — XLMWithLMHeadModel (XLM model)
- xlm-prophetnet — XLMProphetNetForCausalLM (XLM-ProphetNet model)
- xlm-roberta — XLMRobertaForCausalLM (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForCausalLM (XLM-RoBERTa-XL model)
- xlnet — XLNetLMHeadModel (XLNet 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForCausalLM.from_pretrained("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
... )AutoModelForDepthEstimation
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- DPTConfig configuration class: DPTForDepthEstimation (DPT model)
- GLPNConfig configuration class: GLPNForDepthEstimation (GLPN model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- dpt — DPTForDepthEstimation (DPT model)
- glpn — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForDepthEstimation.from_pretrained("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
... )AutoModelForMaskedLM
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: AlbertForMaskedLM (ALBERT model)
- BartConfig configuration class: BartForConditionalGeneration (BART model)
- BertConfig configuration class: BertForMaskedLM (BERT model)
- BigBirdConfig configuration class: BigBirdForMaskedLM (BigBird model)
- CamembertConfig configuration class: CamembertForMaskedLM (CamemBERT model)
- ConvBertConfig configuration class: ConvBertForMaskedLM (ConvBERT model)
- Data2VecTextConfig configuration class: Data2VecTextForMaskedLM (Data2VecText model)
- DebertaConfig configuration class: DebertaForMaskedLM (DeBERTa model)
- DebertaV2Config configuration class: DebertaV2ForMaskedLM (DeBERTa-v2 model)
- 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)
- MegatronBertConfig configuration class: MegatronBertForMaskedLM (Megatron-BERT model)
- MobileBertConfig configuration class: MobileBertForMaskedLM (MobileBERT 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)
- 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)
- YosoConfig configuration class: YosoForMaskedLM (YOSO model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — AlbertForMaskedLM (ALBERT model)
- bart — BartForConditionalGeneration (BART model)
- bert — BertForMaskedLM (BERT model)
- big_bird — BigBirdForMaskedLM (BigBird model)
- camembert — CamembertForMaskedLM (CamemBERT model)
- convbert — ConvBertForMaskedLM (ConvBERT model)
- data2vec-text — Data2VecTextForMaskedLM (Data2VecText model)
- deberta — DebertaForMaskedLM (DeBERTa model)
- deberta-v2 — DebertaV2ForMaskedLM (DeBERTa-v2 model)
- distilbert — DistilBertForMaskedLM (DistilBERT model)
- electra — ElectraForMaskedLM (ELECTRA model)
- ernie — ErnieForMaskedLM (ERNIE model)
- esm — EsmForMaskedLM (ESM model)
- flaubert — FlaubertWithLMHeadModel (FlauBERT model)
- fnet — FNetForMaskedLM (FNet model)
- funnel — FunnelForMaskedLM (Funnel Transformer model)
- ibert — IBertForMaskedLM (I-BERT model)
- layoutlm — LayoutLMForMaskedLM (LayoutLM model)
- longformer — LongformerForMaskedLM (Longformer model)
- luke — LukeForMaskedLM (LUKE model)
- mbart — MBartForConditionalGeneration (mBART model)
- megatron-bert — MegatronBertForMaskedLM (Megatron-BERT model)
- mobilebert — MobileBertForMaskedLM (MobileBERT model)
- mpnet — MPNetForMaskedLM (MPNet model)
- mvp — MvpForConditionalGeneration (MVP model)
- nezha — NezhaForMaskedLM (Nezha model)
- nystromformer — NystromformerForMaskedLM (Nyströmformer model)
- perceiver — PerceiverForMaskedLM (Perceiver model)
- qdqbert — QDQBertForMaskedLM (QDQBert model)
- reformer — ReformerForMaskedLM (Reformer model)
- rembert — RemBertForMaskedLM (RemBERT model)
- roberta — RobertaForMaskedLM (RoBERTa model)
- roc_bert — RoCBertForMaskedLM (RoCBert model)
- roformer — RoFormerForMaskedLM (RoFormer model)
- squeezebert — SqueezeBertForMaskedLM (SqueezeBERT model)
- tapas — TapasForMaskedLM (TAPAS model)
- wav2vec2 —
Wav2Vec2ForMaskedLM(Wav2Vec2 model) - xlm — XLMWithLMHeadModel (XLM model)
- xlm-roberta — XLMRobertaForMaskedLM (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForMaskedLM (XLM-RoBERTa-XL model)
- yoso — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForMaskedLM.from_pretrained("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
... )AutoModelForSeq2SeqLM
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- BartConfig configuration class: BartForConditionalGeneration (BART model)
- BigBirdPegasusConfig configuration class: BigBirdPegasusForConditionalGeneration (BigBird-Pegasus model)
- BlenderbotConfig configuration class: BlenderbotForConditionalGeneration (Blenderbot model)
- BlenderbotSmallConfig configuration class: BlenderbotSmallForConditionalGeneration (BlenderbotSmall model)
- EncoderDecoderConfig configuration class: EncoderDecoderModel (Encoder decoder model)
- FSMTConfig configuration class: FSMTForConditionalGeneration (FairSeq Machine-Translation 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)
- 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)
- SwitchTransformersConfig configuration class: SwitchTransformersForConditionalGeneration (SwitchTransformers model)
- T5Config configuration class: T5ForConditionalGeneration (T5 model)
- XLMProphetNetConfig configuration class: XLMProphetNetForConditionalGeneration (XLM-ProphetNet model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- bart — BartForConditionalGeneration (BART model)
- bigbird_pegasus — BigBirdPegasusForConditionalGeneration (BigBird-Pegasus model)
- blenderbot — BlenderbotForConditionalGeneration (Blenderbot model)
- blenderbot-small — BlenderbotSmallForConditionalGeneration (BlenderbotSmall model)
- encoder-decoder — EncoderDecoderModel (Encoder decoder model)
- fsmt — FSMTForConditionalGeneration (FairSeq Machine-Translation model)
- led — LEDForConditionalGeneration (LED model)
- longt5 — LongT5ForConditionalGeneration (LongT5 model)
- m2m_100 — M2M100ForConditionalGeneration (M2M100 model)
- marian — MarianMTModel (Marian model)
- mbart — MBartForConditionalGeneration (mBART model)
- mt5 — MT5ForConditionalGeneration (MT5 model)
- mvp — MvpForConditionalGeneration (MVP model)
- nllb — M2M100ForConditionalGeneration (NLLB model)
- pegasus — PegasusForConditionalGeneration (Pegasus model)
- pegasus_x — PegasusXForConditionalGeneration (PEGASUS-X model)
- plbart — PLBartForConditionalGeneration (PLBart model)
- prophetnet — ProphetNetForConditionalGeneration (ProphetNet model)
- switch_transformers — SwitchTransformersForConditionalGeneration (SwitchTransformers model)
- t5 — T5ForConditionalGeneration (T5 model)
- xlm-prophetnet — 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("t5-base")
>>> # Update configuration during loading
>>> model = AutoModelForSeq2SeqLM.from_pretrained("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
... )AutoModelForSequenceClassification
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: AlbertForSequenceClassification (ALBERT model)
- BartConfig configuration class: BartForSequenceClassification (BART model)
- BertConfig configuration class: BertForSequenceClassification (BERT model)
- BigBirdConfig configuration class: BigBirdForSequenceClassification (BigBird model)
- BigBirdPegasusConfig configuration class: BigBirdPegasusForSequenceClassification (BigBird-Pegasus model)
- BloomConfig configuration class: BloomForSequenceClassification (BLOOM model)
- CTRLConfig configuration class: CTRLForSequenceClassification (CTRL model)
- CamembertConfig configuration class: CamembertForSequenceClassification (CamemBERT model)
- CanineConfig configuration class: CanineForSequenceClassification (CANINE model)
- ConvBertConfig configuration class: ConvBertForSequenceClassification (ConvBERT model)
- Data2VecTextConfig configuration class: Data2VecTextForSequenceClassification (Data2VecText model)
- DebertaConfig configuration class: DebertaForSequenceClassification (DeBERTa model)
- DebertaV2Config configuration class: DebertaV2ForSequenceClassification (DeBERTa-v2 model)
- DistilBertConfig configuration class: DistilBertForSequenceClassification (DistilBERT model)
- ElectraConfig configuration class: ElectraForSequenceClassification (ELECTRA model)
- ErnieConfig configuration class: ErnieForSequenceClassification (ERNIE model)
- EsmConfig configuration class: EsmForSequenceClassification (ESM model)
- FNetConfig configuration class: FNetForSequenceClassification (FNet model)
- FlaubertConfig configuration class: FlaubertForSequenceClassification (FlauBERT model)
- FunnelConfig configuration class: FunnelForSequenceClassification (Funnel Transformer model)
- GPT2Config configuration class: GPT2ForSequenceClassification (OpenAI GPT-2 model)
- GPTJConfig configuration class: GPTJForSequenceClassification (GPT-J model)
- GPTNeoConfig configuration class: GPTNeoForSequenceClassification (GPT Neo model)
- IBertConfig configuration class: IBertForSequenceClassification (I-BERT 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)
- LongformerConfig configuration class: LongformerForSequenceClassification (Longformer model)
- LukeConfig configuration class: LukeForSequenceClassification (LUKE model)
- MBartConfig configuration class: MBartForSequenceClassification (mBART model)
- MPNetConfig configuration class: MPNetForSequenceClassification (MPNet model)
- MarkupLMConfig configuration class: MarkupLMForSequenceClassification (MarkupLM model)
- MegatronBertConfig configuration class: MegatronBertForSequenceClassification (Megatron-BERT model)
- MobileBertConfig configuration class: MobileBertForSequenceClassification (MobileBERT 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)
- PLBartConfig configuration class: PLBartForSequenceClassification (PLBart model)
- PerceiverConfig configuration class: PerceiverForSequenceClassification (Perceiver model)
- QDQBertConfig configuration class: QDQBertForSequenceClassification (QDQBert 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)
- SqueezeBertConfig configuration class: SqueezeBertForSequenceClassification (SqueezeBERT model)
- TapasConfig configuration class: TapasForSequenceClassification (TAPAS model)
- TransfoXLConfig configuration class: TransfoXLForSequenceClassification (Transformer-XL 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)
- YosoConfig configuration class: YosoForSequenceClassification (YOSO model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — AlbertForSequenceClassification (ALBERT model)
- bart — BartForSequenceClassification (BART model)
- bert — BertForSequenceClassification (BERT model)
- big_bird — BigBirdForSequenceClassification (BigBird model)
- bigbird_pegasus — BigBirdPegasusForSequenceClassification (BigBird-Pegasus model)
- bloom — BloomForSequenceClassification (BLOOM model)
- camembert — CamembertForSequenceClassification (CamemBERT model)
- canine — CanineForSequenceClassification (CANINE model)
- convbert — ConvBertForSequenceClassification (ConvBERT model)
- ctrl — CTRLForSequenceClassification (CTRL model)
- data2vec-text — Data2VecTextForSequenceClassification (Data2VecText model)
- deberta — DebertaForSequenceClassification (DeBERTa model)
- deberta-v2 — DebertaV2ForSequenceClassification (DeBERTa-v2 model)
- distilbert — DistilBertForSequenceClassification (DistilBERT model)
- electra — ElectraForSequenceClassification (ELECTRA model)
- ernie — ErnieForSequenceClassification (ERNIE model)
- esm — EsmForSequenceClassification (ESM model)
- flaubert — FlaubertForSequenceClassification (FlauBERT model)
- fnet — FNetForSequenceClassification (FNet model)
- funnel — FunnelForSequenceClassification (Funnel Transformer model)
- gpt2 — GPT2ForSequenceClassification (OpenAI GPT-2 model)
- gpt_neo — GPTNeoForSequenceClassification (GPT Neo model)
- gptj — GPTJForSequenceClassification (GPT-J model)
- ibert — IBertForSequenceClassification (I-BERT model)
- layoutlm — LayoutLMForSequenceClassification (LayoutLM model)
- layoutlmv2 — LayoutLMv2ForSequenceClassification (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3ForSequenceClassification (LayoutLMv3 model)
- led — LEDForSequenceClassification (LED model)
- lilt — LiltForSequenceClassification (LiLT model)
- longformer — LongformerForSequenceClassification (Longformer model)
- luke — LukeForSequenceClassification (LUKE model)
- markuplm — MarkupLMForSequenceClassification (MarkupLM model)
- mbart — MBartForSequenceClassification (mBART model)
- megatron-bert — MegatronBertForSequenceClassification (Megatron-BERT model)
- mobilebert — MobileBertForSequenceClassification (MobileBERT model)
- mpnet — MPNetForSequenceClassification (MPNet model)
- mvp — MvpForSequenceClassification (MVP model)
- nezha — NezhaForSequenceClassification (Nezha model)
- nystromformer — NystromformerForSequenceClassification (Nyströmformer model)
- openai-gpt — OpenAIGPTForSequenceClassification (OpenAI GPT model)
- opt — OPTForSequenceClassification (OPT model)
- perceiver — PerceiverForSequenceClassification (Perceiver model)
- plbart — PLBartForSequenceClassification (PLBart model)
- qdqbert — QDQBertForSequenceClassification (QDQBert model)
- reformer — ReformerForSequenceClassification (Reformer model)
- rembert — RemBertForSequenceClassification (RemBERT model)
- roberta — RobertaForSequenceClassification (RoBERTa model)
- roc_bert — RoCBertForSequenceClassification (RoCBert model)
- roformer — RoFormerForSequenceClassification (RoFormer model)
- squeezebert — SqueezeBertForSequenceClassification (SqueezeBERT model)
- tapas — TapasForSequenceClassification (TAPAS model)
- transfo-xl — TransfoXLForSequenceClassification (Transformer-XL model)
- xlm — XLMForSequenceClassification (XLM model)
- xlm-roberta — XLMRobertaForSequenceClassification (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForSequenceClassification (XLM-RoBERTa-XL model)
- xlnet — XLNetForSequenceClassification (XLNet model)
- yoso — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForSequenceClassification.from_pretrained("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
... )AutoModelForMultipleChoice
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: AlbertForMultipleChoice (ALBERT model)
- BertConfig configuration class: BertForMultipleChoice (BERT model)
- BigBirdConfig configuration class: BigBirdForMultipleChoice (BigBird model)
- CamembertConfig configuration class: CamembertForMultipleChoice (CamemBERT model)
- CanineConfig configuration class: CanineForMultipleChoice (CANINE model)
- ConvBertConfig configuration class: ConvBertForMultipleChoice (ConvBERT model)
- Data2VecTextConfig configuration class: Data2VecTextForMultipleChoice (Data2VecText model)
- DebertaV2Config configuration class: DebertaV2ForMultipleChoice (DeBERTa-v2 model)
- DistilBertConfig configuration class: DistilBertForMultipleChoice (DistilBERT model)
- ElectraConfig configuration class: ElectraForMultipleChoice (ELECTRA model)
- ErnieConfig configuration class: ErnieForMultipleChoice (ERNIE 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)
- MegatronBertConfig configuration class: MegatronBertForMultipleChoice (Megatron-BERT model)
- MobileBertConfig configuration class: MobileBertForMultipleChoice (MobileBERT 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)
- 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)
- YosoConfig configuration class: YosoForMultipleChoice (YOSO model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — AlbertForMultipleChoice (ALBERT model)
- bert — BertForMultipleChoice (BERT model)
- big_bird — BigBirdForMultipleChoice (BigBird model)
- camembert — CamembertForMultipleChoice (CamemBERT model)
- canine — CanineForMultipleChoice (CANINE model)
- convbert — ConvBertForMultipleChoice (ConvBERT model)
- data2vec-text — Data2VecTextForMultipleChoice (Data2VecText model)
- deberta-v2 — DebertaV2ForMultipleChoice (DeBERTa-v2 model)
- distilbert — DistilBertForMultipleChoice (DistilBERT model)
- electra — ElectraForMultipleChoice (ELECTRA model)
- ernie — ErnieForMultipleChoice (ERNIE model)
- flaubert — FlaubertForMultipleChoice (FlauBERT model)
- fnet — FNetForMultipleChoice (FNet model)
- funnel — FunnelForMultipleChoice (Funnel Transformer model)
- ibert — IBertForMultipleChoice (I-BERT model)
- longformer — LongformerForMultipleChoice (Longformer model)
- luke — LukeForMultipleChoice (LUKE model)
- megatron-bert — MegatronBertForMultipleChoice (Megatron-BERT model)
- mobilebert — MobileBertForMultipleChoice (MobileBERT model)
- mpnet — MPNetForMultipleChoice (MPNet model)
- nezha — NezhaForMultipleChoice (Nezha model)
- nystromformer — NystromformerForMultipleChoice (Nyströmformer model)
- qdqbert — QDQBertForMultipleChoice (QDQBert model)
- rembert — RemBertForMultipleChoice (RemBERT model)
- roberta — RobertaForMultipleChoice (RoBERTa model)
- roc_bert — RoCBertForMultipleChoice (RoCBert model)
- roformer — RoFormerForMultipleChoice (RoFormer model)
- squeezebert — SqueezeBertForMultipleChoice (SqueezeBERT model)
- xlm — XLMForMultipleChoice (XLM model)
- xlm-roberta — XLMRobertaForMultipleChoice (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForMultipleChoice (XLM-RoBERTa-XL model)
- xlnet — XLNetForMultipleChoice (XLNet model)
- yoso — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForMultipleChoice.from_pretrained("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
... )AutoModelForNextSentencePrediction
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- BertConfig configuration class: BertForNextSentencePrediction (BERT model)
- 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)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- bert — BertForNextSentencePrediction (BERT model)
- ernie — ErnieForNextSentencePrediction (ERNIE model)
- fnet — FNetForNextSentencePrediction (FNet model)
- megatron-bert — MegatronBertForNextSentencePrediction (Megatron-BERT model)
- mobilebert — MobileBertForNextSentencePrediction (MobileBERT model)
- nezha — NezhaForNextSentencePrediction (Nezha model)
- qdqbert — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForNextSentencePrediction.from_pretrained("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
... )AutoModelForTokenClassification
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: AlbertForTokenClassification (ALBERT model)
- BertConfig configuration class: BertForTokenClassification (BERT model)
- BigBirdConfig configuration class: BigBirdForTokenClassification (BigBird model)
- BloomConfig configuration class: BloomForTokenClassification (BLOOM model)
- CamembertConfig configuration class: CamembertForTokenClassification (CamemBERT model)
- CanineConfig configuration class: CanineForTokenClassification (CANINE model)
- ConvBertConfig configuration class: ConvBertForTokenClassification (ConvBERT model)
- Data2VecTextConfig configuration class: Data2VecTextForTokenClassification (Data2VecText model)
- DebertaConfig configuration class: DebertaForTokenClassification (DeBERTa model)
- DebertaV2Config configuration class: DebertaV2ForTokenClassification (DeBERTa-v2 model)
- DistilBertConfig configuration class: DistilBertForTokenClassification (DistilBERT model)
- ElectraConfig configuration class: ElectraForTokenClassification (ELECTRA model)
- ErnieConfig configuration class: ErnieForTokenClassification (ERNIE model)
- EsmConfig configuration class: EsmForTokenClassification (ESM model)
- FNetConfig configuration class: FNetForTokenClassification (FNet model)
- FlaubertConfig configuration class: FlaubertForTokenClassification (FlauBERT model)
- FunnelConfig configuration class: FunnelForTokenClassification (Funnel Transformer model)
- GPT2Config configuration class: GPT2ForTokenClassification (OpenAI GPT-2 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)
- MarkupLMConfig configuration class: MarkupLMForTokenClassification (MarkupLM model)
- MegatronBertConfig configuration class: MegatronBertForTokenClassification (Megatron-BERT model)
- MobileBertConfig configuration class: MobileBertForTokenClassification (MobileBERT model)
- NezhaConfig configuration class: NezhaForTokenClassification (Nezha model)
- NystromformerConfig configuration class: NystromformerForTokenClassification (Nyströmformer 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)
- SqueezeBertConfig configuration class: SqueezeBertForTokenClassification (SqueezeBERT 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)
- YosoConfig configuration class: YosoForTokenClassification (YOSO model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — AlbertForTokenClassification (ALBERT model)
- bert — BertForTokenClassification (BERT model)
- big_bird — BigBirdForTokenClassification (BigBird model)
- bloom — BloomForTokenClassification (BLOOM model)
- camembert — CamembertForTokenClassification (CamemBERT model)
- canine — CanineForTokenClassification (CANINE model)
- convbert — ConvBertForTokenClassification (ConvBERT model)
- data2vec-text — Data2VecTextForTokenClassification (Data2VecText model)
- deberta — DebertaForTokenClassification (DeBERTa model)
- deberta-v2 — DebertaV2ForTokenClassification (DeBERTa-v2 model)
- distilbert — DistilBertForTokenClassification (DistilBERT model)
- electra — ElectraForTokenClassification (ELECTRA model)
- ernie — ErnieForTokenClassification (ERNIE model)
- esm — EsmForTokenClassification (ESM model)
- flaubert — FlaubertForTokenClassification (FlauBERT model)
- fnet — FNetForTokenClassification (FNet model)
- funnel — FunnelForTokenClassification (Funnel Transformer model)
- gpt2 — GPT2ForTokenClassification (OpenAI GPT-2 model)
- ibert — IBertForTokenClassification (I-BERT model)
- layoutlm — LayoutLMForTokenClassification (LayoutLM model)
- layoutlmv2 — LayoutLMv2ForTokenClassification (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3ForTokenClassification (LayoutLMv3 model)
- lilt — LiltForTokenClassification (LiLT model)
- longformer — LongformerForTokenClassification (Longformer model)
- luke — LukeForTokenClassification (LUKE model)
- markuplm — MarkupLMForTokenClassification (MarkupLM model)
- megatron-bert — MegatronBertForTokenClassification (Megatron-BERT model)
- mobilebert — MobileBertForTokenClassification (MobileBERT model)
- mpnet — MPNetForTokenClassification (MPNet model)
- nezha — NezhaForTokenClassification (Nezha model)
- nystromformer — NystromformerForTokenClassification (Nyströmformer model)
- qdqbert — QDQBertForTokenClassification (QDQBert model)
- rembert — RemBertForTokenClassification (RemBERT model)
- roberta — RobertaForTokenClassification (RoBERTa model)
- roc_bert — RoCBertForTokenClassification (RoCBert model)
- roformer — RoFormerForTokenClassification (RoFormer model)
- squeezebert — SqueezeBertForTokenClassification (SqueezeBERT model)
- xlm — XLMForTokenClassification (XLM model)
- xlm-roberta — XLMRobertaForTokenClassification (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForTokenClassification (XLM-RoBERTa-XL model)
- xlnet — XLNetForTokenClassification (XLNet model)
- yoso — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForTokenClassification.from_pretrained("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
... )AutoModelForQuestionAnswering
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: AlbertForQuestionAnswering (ALBERT model)
- BartConfig configuration class: BartForQuestionAnswering (BART model)
- BertConfig configuration class: BertForQuestionAnswering (BERT model)
- BigBirdConfig configuration class: BigBirdForQuestionAnswering (BigBird model)
- BigBirdPegasusConfig configuration class: BigBirdPegasusForQuestionAnswering (BigBird-Pegasus model)
- BloomConfig configuration class: BloomForQuestionAnswering (BLOOM model)
- CamembertConfig configuration class: CamembertForQuestionAnswering (CamemBERT model)
- CanineConfig configuration class: CanineForQuestionAnswering (CANINE model)
- ConvBertConfig configuration class: ConvBertForQuestionAnswering (ConvBERT model)
- Data2VecTextConfig configuration class: Data2VecTextForQuestionAnswering (Data2VecText model)
- DebertaConfig configuration class: DebertaForQuestionAnswering (DeBERTa model)
- DebertaV2Config configuration class: DebertaV2ForQuestionAnswering (DeBERTa-v2 model)
- DistilBertConfig configuration class: DistilBertForQuestionAnswering (DistilBERT model)
- ElectraConfig configuration class: ElectraForQuestionAnswering (ELECTRA model)
- ErnieConfig configuration class: ErnieForQuestionAnswering (ERNIE model)
- FNetConfig configuration class: FNetForQuestionAnswering (FNet model)
- FlaubertConfig configuration class: FlaubertForQuestionAnsweringSimple (FlauBERT model)
- FunnelConfig configuration class: FunnelForQuestionAnswering (Funnel Transformer model)
- GPTJConfig configuration class: GPTJForQuestionAnswering (GPT-J 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)
- 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)
- MarkupLMConfig configuration class: MarkupLMForQuestionAnswering (MarkupLM model)
- MegatronBertConfig configuration class: MegatronBertForQuestionAnswering (Megatron-BERT model)
- MobileBertConfig configuration class: MobileBertForQuestionAnswering (MobileBERT 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)
- SplinterConfig configuration class: SplinterForQuestionAnswering (Splinter model)
- SqueezeBertConfig configuration class: SqueezeBertForQuestionAnswering (SqueezeBERT 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)
- YosoConfig configuration class: YosoForQuestionAnswering (YOSO model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — AlbertForQuestionAnswering (ALBERT model)
- bart — BartForQuestionAnswering (BART model)
- bert — BertForQuestionAnswering (BERT model)
- big_bird — BigBirdForQuestionAnswering (BigBird model)
- bigbird_pegasus — BigBirdPegasusForQuestionAnswering (BigBird-Pegasus model)
- bloom — BloomForQuestionAnswering (BLOOM model)
- camembert — CamembertForQuestionAnswering (CamemBERT model)
- canine — CanineForQuestionAnswering (CANINE model)
- convbert — ConvBertForQuestionAnswering (ConvBERT model)
- data2vec-text — Data2VecTextForQuestionAnswering (Data2VecText model)
- deberta — DebertaForQuestionAnswering (DeBERTa model)
- deberta-v2 — DebertaV2ForQuestionAnswering (DeBERTa-v2 model)
- distilbert — DistilBertForQuestionAnswering (DistilBERT model)
- electra — ElectraForQuestionAnswering (ELECTRA model)
- ernie — ErnieForQuestionAnswering (ERNIE model)
- flaubert — FlaubertForQuestionAnsweringSimple (FlauBERT model)
- fnet — FNetForQuestionAnswering (FNet model)
- funnel — FunnelForQuestionAnswering (Funnel Transformer model)
- gptj — GPTJForQuestionAnswering (GPT-J model)
- ibert — IBertForQuestionAnswering (I-BERT model)
- layoutlmv2 — LayoutLMv2ForQuestionAnswering (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3ForQuestionAnswering (LayoutLMv3 model)
- led — LEDForQuestionAnswering (LED model)
- lilt — LiltForQuestionAnswering (LiLT model)
- longformer — LongformerForQuestionAnswering (Longformer model)
- luke — LukeForQuestionAnswering (LUKE model)
- lxmert — LxmertForQuestionAnswering (LXMERT model)
- markuplm — MarkupLMForQuestionAnswering (MarkupLM model)
- mbart — MBartForQuestionAnswering (mBART model)
- megatron-bert — MegatronBertForQuestionAnswering (Megatron-BERT model)
- mobilebert — MobileBertForQuestionAnswering (MobileBERT model)
- mpnet — MPNetForQuestionAnswering (MPNet model)
- mvp — MvpForQuestionAnswering (MVP model)
- nezha — NezhaForQuestionAnswering (Nezha model)
- nystromformer — NystromformerForQuestionAnswering (Nyströmformer model)
- opt — OPTForQuestionAnswering (OPT model)
- qdqbert — QDQBertForQuestionAnswering (QDQBert model)
- reformer — ReformerForQuestionAnswering (Reformer model)
- rembert — RemBertForQuestionAnswering (RemBERT model)
- roberta — RobertaForQuestionAnswering (RoBERTa model)
- roc_bert — RoCBertForQuestionAnswering (RoCBert model)
- roformer — RoFormerForQuestionAnswering (RoFormer model)
- splinter — SplinterForQuestionAnswering (Splinter model)
- squeezebert — SqueezeBertForQuestionAnswering (SqueezeBERT model)
- xlm — XLMForQuestionAnsweringSimple (XLM model)
- xlm-roberta — XLMRobertaForQuestionAnswering (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForQuestionAnswering (XLM-RoBERTa-XL model)
- xlnet — XLNetForQuestionAnsweringSimple (XLNet model)
- yoso — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForQuestionAnswering.from_pretrained("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
... )AutoModelForTableQuestionAnswering
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- TapasConfig configuration class: TapasForQuestionAnswering (TAPAS model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- tapas — 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
... )AutoModelForDocumentQuestionAnswering
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- LayoutLMConfig configuration class: LayoutLMForQuestionAnswering (LayoutLM model)
- LayoutLMv2Config configuration class: LayoutLMv2ForQuestionAnswering (LayoutLMv2 model)
- LayoutLMv3Config configuration class: LayoutLMv3ForQuestionAnswering (LayoutLMv3 model)
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)from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- layoutlm — LayoutLMForQuestionAnswering (LayoutLM model)
- layoutlmv2 — LayoutLMv2ForQuestionAnswering (LayoutLMv2 model)
- layoutlmv3 — 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
... )AutoModelForImageClassification
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- BeitConfig configuration class: BeitForImageClassification (BEiT model)
- ConvNextConfig configuration class: ConvNextForImageClassification (ConvNeXT model)
- CvtConfig configuration class: CvtForImageClassification (CvT model)
- Data2VecVisionConfig configuration class: Data2VecVisionForImageClassification (Data2VecVision model)
- DeiTConfig configuration class: DeiTForImageClassification or DeiTForImageClassificationWithTeacher (DeiT model)
- DinatConfig configuration class: DinatForImageClassification (DiNAT 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)
- NatConfig configuration class: NatForImageClassification (NAT model)
- PerceiverConfig configuration class: PerceiverForImageClassificationLearned or PerceiverForImageClassificationFourier or PerceiverForImageClassificationConvProcessing (Perceiver model)
- PoolFormerConfig configuration class: PoolFormerForImageClassification (PoolFormer model)
- RegNetConfig configuration class: RegNetForImageClassification (RegNet model)
- ResNetConfig configuration class: ResNetForImageClassification (ResNet model)
- SegformerConfig configuration class: SegformerForImageClassification (SegFormer 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)
- ViTMSNConfig configuration class: ViTMSNForImageClassification (ViTMSN model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- beit — BeitForImageClassification (BEiT model)
- convnext — ConvNextForImageClassification (ConvNeXT model)
- cvt — CvtForImageClassification (CvT model)
- data2vec-vision — Data2VecVisionForImageClassification (Data2VecVision model)
- deit — DeiTForImageClassification or DeiTForImageClassificationWithTeacher (DeiT model)
- dinat — DinatForImageClassification (DiNAT model)
- imagegpt — ImageGPTForImageClassification (ImageGPT model)
- levit — LevitForImageClassification or LevitForImageClassificationWithTeacher (LeViT model)
- mobilenet_v1 — MobileNetV1ForImageClassification (MobileNetV1 model)
- mobilenet_v2 — MobileNetV2ForImageClassification (MobileNetV2 model)
- mobilevit — MobileViTForImageClassification (MobileViT model)
- nat — NatForImageClassification (NAT model)
- perceiver — PerceiverForImageClassificationLearned or PerceiverForImageClassificationFourier or PerceiverForImageClassificationConvProcessing (Perceiver model)
- poolformer — PoolFormerForImageClassification (PoolFormer model)
- regnet — RegNetForImageClassification (RegNet model)
- resnet — ResNetForImageClassification (ResNet model)
- segformer — SegformerForImageClassification (SegFormer model)
- swin — SwinForImageClassification (Swin Transformer model)
- swinv2 — Swinv2ForImageClassification (Swin Transformer V2 model)
- van — VanForImageClassification (VAN model)
- vit — ViTForImageClassification (ViT model)
- vit_msn — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForImageClassification.from_pretrained("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
... )AutoModelForVideoClassification
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- VideoMAEConfig configuration class: VideoMAEForVideoClassification (VideoMAE model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- videomae — VideoMAEForVideoClassification (VideoMAE 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForVideoClassification.from_pretrained("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
... )AutoModelForVision2Seq
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- VisionEncoderDecoderConfig configuration class: VisionEncoderDecoderModel (Vision Encoder decoder model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- vision-encoder-decoder — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForVision2Seq.from_pretrained("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
... )AutoModelForVisualQuestionAnswering
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- ViltConfig configuration class: ViltForQuestionAnswering (ViLT model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- vilt — 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
... )AutoModelForAudioClassification
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- ASTConfig configuration class: ASTForAudioClassification (Audio Spectrogram Transformer model)
- Data2VecAudioConfig configuration class: Data2VecAudioForSequenceClassification (Data2VecAudio model)
- 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)
- Wav2Vec2Config configuration class: Wav2Vec2ForSequenceClassification (Wav2Vec2 model)
- Wav2Vec2ConformerConfig configuration class: Wav2Vec2ConformerForSequenceClassification (Wav2Vec2-Conformer model)
- WavLMConfig configuration class: WavLMForSequenceClassification (WavLM model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- audio-spectrogram-transformer — ASTForAudioClassification (Audio Spectrogram Transformer model)
- data2vec-audio — Data2VecAudioForSequenceClassification (Data2VecAudio model)
- hubert — HubertForSequenceClassification (Hubert model)
- sew — SEWForSequenceClassification (SEW model)
- sew-d — SEWDForSequenceClassification (SEW-D model)
- unispeech — UniSpeechForSequenceClassification (UniSpeech model)
- unispeech-sat — UniSpeechSatForSequenceClassification (UniSpeechSat model)
- wav2vec2 — Wav2Vec2ForSequenceClassification (Wav2Vec2 model)
- wav2vec2-conformer — Wav2Vec2ConformerForSequenceClassification (Wav2Vec2-Conformer model)
- wavlm — WavLMForSequenceClassification (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, AutoModelForAudioClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForAudioClassification.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForAudioClassification.from_pretrained("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
... )AutoModelForAudioFrameClassification
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- Data2VecAudioConfig configuration class: Data2VecAudioForAudioFrameClassification (Data2VecAudio model)
- UniSpeechSatConfig configuration class: UniSpeechSatForAudioFrameClassification (UniSpeechSat model)
- Wav2Vec2Config configuration class: Wav2Vec2ForAudioFrameClassification (Wav2Vec2 model)
- Wav2Vec2ConformerConfig configuration class: Wav2Vec2ConformerForAudioFrameClassification (Wav2Vec2-Conformer model)
- WavLMConfig configuration class: WavLMForAudioFrameClassification (WavLM model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- data2vec-audio — Data2VecAudioForAudioFrameClassification (Data2VecAudio model)
- unispeech-sat — UniSpeechSatForAudioFrameClassification (UniSpeechSat model)
- wav2vec2 — Wav2Vec2ForAudioFrameClassification (Wav2Vec2 model)
- wav2vec2-conformer — Wav2Vec2ConformerForAudioFrameClassification (Wav2Vec2-Conformer model)
- wavlm — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForAudioFrameClassification.from_pretrained("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
... )AutoModelForCTC
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- Data2VecAudioConfig configuration class: Data2VecAudioForCTC (Data2VecAudio model)
- 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)
- Wav2Vec2Config configuration class: Wav2Vec2ForCTC (Wav2Vec2 model)
- Wav2Vec2ConformerConfig configuration class: Wav2Vec2ConformerForCTC (Wav2Vec2-Conformer model)
- WavLMConfig configuration class: WavLMForCTC (WavLM model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- data2vec-audio — Data2VecAudioForCTC (Data2VecAudio model)
- hubert — HubertForCTC (Hubert model)
- mctct — MCTCTForCTC (M-CTC-T model)
- sew — SEWForCTC (SEW model)
- sew-d — SEWDForCTC (SEW-D model)
- unispeech — UniSpeechForCTC (UniSpeech model)
- unispeech-sat — UniSpeechSatForCTC (UniSpeechSat model)
- wav2vec2 — Wav2Vec2ForCTC (Wav2Vec2 model)
- wav2vec2-conformer — Wav2Vec2ConformerForCTC (Wav2Vec2-Conformer model)
- wavlm — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForCTC.from_pretrained("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
... )AutoModelForSpeechSeq2Seq
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- Speech2TextConfig configuration class: Speech2TextForConditionalGeneration (Speech2Text model)
- SpeechEncoderDecoderConfig configuration class: SpeechEncoderDecoderModel (Speech Encoder decoder model)
- WhisperConfig configuration class: WhisperForConditionalGeneration (Whisper model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- speech-encoder-decoder — SpeechEncoderDecoderModel (Speech Encoder decoder model)
- speech_to_text — Speech2TextForConditionalGeneration (Speech2Text model)
- whisper — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForSpeechSeq2Seq.from_pretrained("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
... )AutoModelForAudioXVector
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- Data2VecAudioConfig configuration class: Data2VecAudioForXVector (Data2VecAudio model)
- UniSpeechSatConfig configuration class: UniSpeechSatForXVector (UniSpeechSat model)
- Wav2Vec2Config configuration class: Wav2Vec2ForXVector (Wav2Vec2 model)
- Wav2Vec2ConformerConfig configuration class: Wav2Vec2ConformerForXVector (Wav2Vec2-Conformer model)
- WavLMConfig configuration class: WavLMForXVector (WavLM model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- data2vec-audio — Data2VecAudioForXVector (Data2VecAudio model)
- unispeech-sat — UniSpeechSatForXVector (UniSpeechSat model)
- wav2vec2 — Wav2Vec2ForXVector (Wav2Vec2 model)
- wav2vec2-conformer — Wav2Vec2ConformerForXVector (Wav2Vec2-Conformer model)
- wavlm — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForAudioXVector.from_pretrained("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
... )AutoModelForMaskedImageModeling
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- DeiTConfig configuration class: DeiTForMaskedImageModeling (DeiT model)
- SwinConfig configuration class: SwinForMaskedImageModeling (Swin Transformer model)
- Swinv2Config configuration class: Swinv2ForMaskedImageModeling (Swin Transformer V2 model)
- ViTConfig configuration class: ViTForMaskedImageModeling (ViT model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- deit — DeiTForMaskedImageModeling (DeiT model)
- swin — SwinForMaskedImageModeling (Swin Transformer model)
- swinv2 — Swinv2ForMaskedImageModeling (Swin Transformer V2 model)
- vit — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForMaskedImageModeling.from_pretrained("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
... )AutoModelForObjectDetection
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- ConditionalDetrConfig configuration class: ConditionalDetrForObjectDetection (Conditional DETR model)
- DeformableDetrConfig configuration class: DeformableDetrForObjectDetection (Deformable DETR model)
- DetrConfig configuration class: DetrForObjectDetection (DETR model)
- TableTransformerConfig configuration class: TableTransformerForObjectDetection (Table Transformer model)
- YolosConfig configuration class: YolosForObjectDetection (YOLOS model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- conditional_detr — ConditionalDetrForObjectDetection (Conditional DETR model)
- deformable_detr — DeformableDetrForObjectDetection (Deformable DETR model)
- detr — DetrForObjectDetection (DETR model)
- table-transformer — TableTransformerForObjectDetection (Table Transformer model)
- yolos — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForObjectDetection.from_pretrained("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
... )AutoModelForImageSegmentation
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- DetrConfig configuration class: DetrForSegmentation (DETR model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- detr — DetrForSegmentation (DETR 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, AutoModelForImageSegmentation
>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForImageSegmentation.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForImageSegmentation.from_pretrained("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
... )AutoModelForSemanticSegmentation
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- BeitConfig configuration class: BeitForSemanticSegmentation (BEiT model)
- DPTConfig configuration class: DPTForSemanticSegmentation (DPT model)
- Data2VecVisionConfig configuration class: Data2VecVisionForSemanticSegmentation (Data2VecVision model)
- MobileNetV2Config configuration class: MobileNetV2ForSemanticSegmentation (MobileNetV2 model)
- MobileViTConfig configuration class: MobileViTForSemanticSegmentation (MobileViT model)
- SegformerConfig configuration class: SegformerForSemanticSegmentation (SegFormer model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- beit — BeitForSemanticSegmentation (BEiT model)
- data2vec-vision — Data2VecVisionForSemanticSegmentation (Data2VecVision model)
- dpt — DPTForSemanticSegmentation (DPT model)
- mobilenet_v2 — MobileNetV2ForSemanticSegmentation (MobileNetV2 model)
- mobilevit — MobileViTForSemanticSegmentation (MobileViT model)
- segformer — SegformerForSemanticSegmentation (SegFormer 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForSemanticSegmentation.from_pretrained("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
... )AutoModelForInstanceSegmentation
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- MaskFormerConfig configuration class: MaskFormerForInstanceSegmentation (MaskFormer model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- maskformer — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForInstanceSegmentation.from_pretrained("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
... )AutoModelForZeroShotObjectDetection
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- OwlViTConfig configuration class: OwlViTForObjectDetection (OWL-ViT model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a tensorflow index checkpoint file (e.g,
./tf_model/model.ckpt.index). In this case,from_tfshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
state_dict (Dict[str, torch.Tensor], optional) —
A state dictionary to use instead of a state dictionary loaded from saved weights file.
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.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_tf (
bool, optional, defaults toFalse) — Load the model weights from a TensorFlow checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- owlvit — 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("bert-base-cased")
>>> # Update configuration during loading
>>> model = AutoModelForZeroShotObjectDetection.from_pretrained("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
... )TFAutoModel
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: TFAlbertModel (ALBERT model)
- BartConfig configuration class: TFBartModel (BART model)
- BertConfig configuration class: TFBertModel (BERT model)
- BlenderbotConfig configuration class: TFBlenderbotModel (Blenderbot model)
- BlenderbotSmallConfig configuration class: TFBlenderbotSmallModel (BlenderbotSmall model)
- CLIPConfig configuration class: TFCLIPModel (CLIP model)
- CTRLConfig configuration class: TFCTRLModel (CTRL model)
- CamembertConfig configuration class: TFCamembertModel (CamemBERT model)
- ConvBertConfig configuration class: TFConvBertModel (ConvBERT model)
- ConvNextConfig configuration class: TFConvNextModel (ConvNeXT model)
- CvtConfig configuration class: TFCvtModel (CvT model)
- DPRConfig configuration class: TFDPRQuestionEncoder (DPR model)
- Data2VecVisionConfig configuration class: TFData2VecVisionModel (Data2VecVision model)
- DebertaConfig configuration class: TFDebertaModel (DeBERTa model)
- DebertaV2Config configuration class: TFDebertaV2Model (DeBERTa-v2 model)
- DeiTConfig configuration class: TFDeiTModel (DeiT model)
- DistilBertConfig configuration class: TFDistilBertModel (DistilBERT 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)
- SegformerConfig configuration class: TFSegformerModel (SegFormer model)
- Speech2TextConfig configuration class: TFSpeech2TextModel (Speech2Text 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)
- 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)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — TFAlbertModel (ALBERT model)
- bart — TFBartModel (BART model)
- bert — TFBertModel (BERT model)
- blenderbot — TFBlenderbotModel (Blenderbot model)
- blenderbot-small — TFBlenderbotSmallModel (BlenderbotSmall model)
- camembert — TFCamembertModel (CamemBERT model)
- clip — TFCLIPModel (CLIP model)
- convbert — TFConvBertModel (ConvBERT model)
- convnext — TFConvNextModel (ConvNeXT model)
- ctrl — TFCTRLModel (CTRL model)
- cvt — TFCvtModel (CvT model)
- data2vec-vision — TFData2VecVisionModel (Data2VecVision model)
- deberta — TFDebertaModel (DeBERTa model)
- deberta-v2 — TFDebertaV2Model (DeBERTa-v2 model)
- deit — TFDeiTModel (DeiT model)
- distilbert — TFDistilBertModel (DistilBERT model)
- dpr — TFDPRQuestionEncoder (DPR model)
- electra — TFElectraModel (ELECTRA model)
- esm — TFEsmModel (ESM model)
- flaubert — TFFlaubertModel (FlauBERT model)
- funnel — TFFunnelModel or TFFunnelBaseModel (Funnel Transformer model)
- gpt2 — TFGPT2Model (OpenAI GPT-2 model)
- gptj — TFGPTJModel (GPT-J model)
- groupvit — TFGroupViTModel (GroupViT model)
- hubert — TFHubertModel (Hubert model)
- layoutlm — TFLayoutLMModel (LayoutLM model)
- layoutlmv3 — TFLayoutLMv3Model (LayoutLMv3 model)
- led — TFLEDModel (LED model)
- longformer — TFLongformerModel (Longformer model)
- lxmert — TFLxmertModel (LXMERT model)
- marian — TFMarianModel (Marian model)
- mbart — TFMBartModel (mBART model)
- mobilebert — TFMobileBertModel (MobileBERT model)
- mobilevit — TFMobileViTModel (MobileViT model)
- mpnet — TFMPNetModel (MPNet model)
- mt5 — TFMT5Model (MT5 model)
- openai-gpt — TFOpenAIGPTModel (OpenAI GPT model)
- opt — TFOPTModel (OPT model)
- pegasus — TFPegasusModel (Pegasus model)
- regnet — TFRegNetModel (RegNet model)
- rembert — TFRemBertModel (RemBERT model)
- resnet — TFResNetModel (ResNet model)
- roberta — TFRobertaModel (RoBERTa model)
- roformer — TFRoFormerModel (RoFormer model)
- segformer — TFSegformerModel (SegFormer model)
- speech_to_text — TFSpeech2TextModel (Speech2Text model)
- swin — TFSwinModel (Swin Transformer model)
- t5 — TFT5Model (T5 model)
- tapas — TFTapasModel (TAPAS model)
- transfo-xl — TFTransfoXLModel (Transformer-XL model)
- vit — TFViTModel (ViT model)
- vit_mae — TFViTMAEModel (ViTMAE model)
- wav2vec2 — TFWav2Vec2Model (Wav2Vec2 model)
- whisper — TFWhisperModel (Whisper model)
- xglm — TFXGLMModel (XGLM model)
- xlm — TFXLMModel (XLM model)
- xlm-roberta — TFXLMRobertaModel (XLM-RoBERTa model)
- xlnet — TFXLNetModel (XLNet model)
Examples:
>>> from transformers import AutoConfig, TFAutoModel
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModel.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModel.from_pretrained("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
... )TFAutoModelForPreTraining
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: TFAlbertForPreTraining (ALBERT model)
- BartConfig configuration class: TFBartForConditionalGeneration (BART model)
- BertConfig configuration class: TFBertForPreTraining (BERT model)
- CTRLConfig configuration class: TFCTRLLMHeadModel (CTRL model)
- CamembertConfig configuration class: TFCamembertForMaskedLM (CamemBERT model)
- 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)
- 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)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — TFAlbertForPreTraining (ALBERT model)
- bart — TFBartForConditionalGeneration (BART model)
- bert — TFBertForPreTraining (BERT model)
- camembert — TFCamembertForMaskedLM (CamemBERT model)
- ctrl — TFCTRLLMHeadModel (CTRL model)
- distilbert — TFDistilBertForMaskedLM (DistilBERT model)
- electra — TFElectraForPreTraining (ELECTRA model)
- flaubert — TFFlaubertWithLMHeadModel (FlauBERT model)
- funnel — TFFunnelForPreTraining (Funnel Transformer model)
- gpt2 — TFGPT2LMHeadModel (OpenAI GPT-2 model)
- layoutlm — TFLayoutLMForMaskedLM (LayoutLM model)
- lxmert — TFLxmertForPreTraining (LXMERT model)
- mobilebert — TFMobileBertForPreTraining (MobileBERT model)
- mpnet — TFMPNetForMaskedLM (MPNet model)
- openai-gpt — TFOpenAIGPTLMHeadModel (OpenAI GPT model)
- roberta — TFRobertaForMaskedLM (RoBERTa model)
- t5 — TFT5ForConditionalGeneration (T5 model)
- tapas — TFTapasForMaskedLM (TAPAS model)
- transfo-xl — TFTransfoXLLMHeadModel (Transformer-XL model)
- vit_mae — TFViTMAEForPreTraining (ViTMAE model)
- xlm — TFXLMWithLMHeadModel (XLM model)
- xlm-roberta — TFXLMRobertaForMaskedLM (XLM-RoBERTa model)
- xlnet — TFXLNetLMHeadModel (XLNet model)
Examples:
>>> from transformers import AutoConfig, TFAutoModelForPreTraining
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForPreTraining.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForPreTraining.from_pretrained("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
... )TFAutoModelForCausalLM
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- BertConfig configuration class: TFBertLMHeadModel (BERT model)
- CTRLConfig configuration class: TFCTRLLMHeadModel (CTRL model)
- CamembertConfig configuration class: TFCamembertForCausalLM (CamemBERT model)
- 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)
- TransfoXLConfig configuration class: TFTransfoXLLMHeadModel (Transformer-XL model)
- XGLMConfig configuration class: TFXGLMForCausalLM (XGLM model)
- XLMConfig configuration class: TFXLMWithLMHeadModel (XLM model)
- XLNetConfig configuration class: TFXLNetLMHeadModel (XLNet model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- bert — TFBertLMHeadModel (BERT model)
- camembert — TFCamembertForCausalLM (CamemBERT model)
- ctrl — TFCTRLLMHeadModel (CTRL model)
- gpt2 — TFGPT2LMHeadModel (OpenAI GPT-2 model)
- gptj — TFGPTJForCausalLM (GPT-J model)
- openai-gpt — TFOpenAIGPTLMHeadModel (OpenAI GPT model)
- opt — TFOPTForCausalLM (OPT model)
- rembert — TFRemBertForCausalLM (RemBERT model)
- roberta — TFRobertaForCausalLM (RoBERTa model)
- roformer — TFRoFormerForCausalLM (RoFormer model)
- transfo-xl — TFTransfoXLLMHeadModel (Transformer-XL model)
- xglm — TFXGLMForCausalLM (XGLM model)
- xlm — TFXLMWithLMHeadModel (XLM model)
- xlnet — TFXLNetLMHeadModel (XLNet model)
Examples:
>>> from transformers import AutoConfig, TFAutoModelForCausalLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForCausalLM.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForCausalLM.from_pretrained("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
... )TFAutoModelForImageClassification
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- ConvNextConfig configuration class: TFConvNextForImageClassification (ConvNeXT model)
- CvtConfig configuration class: TFCvtForImageClassification (CvT model)
- Data2VecVisionConfig configuration class: TFData2VecVisionForImageClassification (Data2VecVision model)
- DeiTConfig configuration class: TFDeiTForImageClassification or TFDeiTForImageClassificationWithTeacher (DeiT 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)
- SwinConfig configuration class: TFSwinForImageClassification (Swin Transformer model)
- ViTConfig configuration class: TFViTForImageClassification (ViT model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- convnext — TFConvNextForImageClassification (ConvNeXT model)
- cvt — TFCvtForImageClassification (CvT model)
- data2vec-vision — TFData2VecVisionForImageClassification (Data2VecVision model)
- deit — TFDeiTForImageClassification or TFDeiTForImageClassificationWithTeacher (DeiT model)
- mobilevit — TFMobileViTForImageClassification (MobileViT model)
- regnet — TFRegNetForImageClassification (RegNet model)
- resnet — TFResNetForImageClassification (ResNet model)
- segformer — TFSegformerForImageClassification (SegFormer model)
- swin — TFSwinForImageClassification (Swin Transformer model)
- vit — TFViTForImageClassification (ViT model)
Examples:
>>> from transformers import AutoConfig, TFAutoModelForImageClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForImageClassification.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForImageClassification.from_pretrained("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
... )TFAutoModelForSemanticSegmentation
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- Data2VecVisionConfig configuration class: TFData2VecVisionForSemanticSegmentation (Data2VecVision model)
- MobileViTConfig configuration class: TFMobileViTForSemanticSegmentation (MobileViT model)
- SegformerConfig configuration class: TFSegformerForSemanticSegmentation (SegFormer model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- data2vec-vision — TFData2VecVisionForSemanticSegmentation (Data2VecVision model)
- mobilevit — TFMobileViTForSemanticSegmentation (MobileViT model)
- segformer — TFSegformerForSemanticSegmentation (SegFormer model)
Examples:
>>> from transformers import AutoConfig, TFAutoModelForSemanticSegmentation
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForSemanticSegmentation.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForSemanticSegmentation.from_pretrained("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
... )TFAutoModelForMaskedLM
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: TFAlbertForMaskedLM (ALBERT model)
- BertConfig configuration class: TFBertForMaskedLM (BERT model)
- CamembertConfig configuration class: TFCamembertForMaskedLM (CamemBERT model)
- ConvBertConfig configuration class: TFConvBertForMaskedLM (ConvBERT model)
- DebertaConfig configuration class: TFDebertaForMaskedLM (DeBERTa model)
- DebertaV2Config configuration class: TFDebertaV2ForMaskedLM (DeBERTa-v2 model)
- 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)
- TapasConfig configuration class: TFTapasForMaskedLM (TAPAS model)
- XLMConfig configuration class: TFXLMWithLMHeadModel (XLM model)
- XLMRobertaConfig configuration class: TFXLMRobertaForMaskedLM (XLM-RoBERTa model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — TFAlbertForMaskedLM (ALBERT model)
- bert — TFBertForMaskedLM (BERT model)
- camembert — TFCamembertForMaskedLM (CamemBERT model)
- convbert — TFConvBertForMaskedLM (ConvBERT model)
- deberta — TFDebertaForMaskedLM (DeBERTa model)
- deberta-v2 — TFDebertaV2ForMaskedLM (DeBERTa-v2 model)
- distilbert — TFDistilBertForMaskedLM (DistilBERT model)
- electra — TFElectraForMaskedLM (ELECTRA model)
- esm — TFEsmForMaskedLM (ESM model)
- flaubert — TFFlaubertWithLMHeadModel (FlauBERT model)
- funnel — TFFunnelForMaskedLM (Funnel Transformer model)
- layoutlm — TFLayoutLMForMaskedLM (LayoutLM model)
- longformer — TFLongformerForMaskedLM (Longformer model)
- mobilebert — TFMobileBertForMaskedLM (MobileBERT model)
- mpnet — TFMPNetForMaskedLM (MPNet model)
- rembert — TFRemBertForMaskedLM (RemBERT model)
- roberta — TFRobertaForMaskedLM (RoBERTa model)
- roformer — TFRoFormerForMaskedLM (RoFormer model)
- tapas — TFTapasForMaskedLM (TAPAS model)
- xlm — TFXLMWithLMHeadModel (XLM model)
- xlm-roberta — TFXLMRobertaForMaskedLM (XLM-RoBERTa model)
Examples:
>>> from transformers import AutoConfig, TFAutoModelForMaskedLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForMaskedLM.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForMaskedLM.from_pretrained("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
... )TFAutoModelForSeq2SeqLM
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- BartConfig configuration class: TFBartForConditionalGeneration (BART model)
- BlenderbotConfig configuration class: TFBlenderbotForConditionalGeneration (Blenderbot model)
- BlenderbotSmallConfig configuration class: TFBlenderbotSmallForConditionalGeneration (BlenderbotSmall model)
- 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)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- bart — TFBartForConditionalGeneration (BART model)
- blenderbot — TFBlenderbotForConditionalGeneration (Blenderbot model)
- blenderbot-small — TFBlenderbotSmallForConditionalGeneration (BlenderbotSmall model)
- encoder-decoder — TFEncoderDecoderModel (Encoder decoder model)
- led — TFLEDForConditionalGeneration (LED model)
- marian — TFMarianMTModel (Marian model)
- mbart — TFMBartForConditionalGeneration (mBART model)
- mt5 — TFMT5ForConditionalGeneration (MT5 model)
- pegasus — TFPegasusForConditionalGeneration (Pegasus model)
- t5 — TFT5ForConditionalGeneration (T5 model)
Examples:
>>> from transformers import AutoConfig, TFAutoModelForSeq2SeqLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> # Update configuration during loading
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("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
... )TFAutoModelForSequenceClassification
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: TFAlbertForSequenceClassification (ALBERT model)
- BertConfig configuration class: TFBertForSequenceClassification (BERT model)
- CTRLConfig configuration class: TFCTRLForSequenceClassification (CTRL model)
- CamembertConfig configuration class: TFCamembertForSequenceClassification (CamemBERT model)
- ConvBertConfig configuration class: TFConvBertForSequenceClassification (ConvBERT model)
- DebertaConfig configuration class: TFDebertaForSequenceClassification (DeBERTa model)
- DebertaV2Config configuration class: TFDebertaV2ForSequenceClassification (DeBERTa-v2 model)
- 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)
- 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)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — TFAlbertForSequenceClassification (ALBERT model)
- bert — TFBertForSequenceClassification (BERT model)
- camembert — TFCamembertForSequenceClassification (CamemBERT model)
- convbert — TFConvBertForSequenceClassification (ConvBERT model)
- ctrl — TFCTRLForSequenceClassification (CTRL model)
- deberta — TFDebertaForSequenceClassification (DeBERTa model)
- deberta-v2 — TFDebertaV2ForSequenceClassification (DeBERTa-v2 model)
- distilbert — TFDistilBertForSequenceClassification (DistilBERT model)
- electra — TFElectraForSequenceClassification (ELECTRA model)
- esm — TFEsmForSequenceClassification (ESM model)
- flaubert — TFFlaubertForSequenceClassification (FlauBERT model)
- funnel — TFFunnelForSequenceClassification (Funnel Transformer model)
- gpt2 — TFGPT2ForSequenceClassification (OpenAI GPT-2 model)
- gptj — TFGPTJForSequenceClassification (GPT-J model)
- layoutlm — TFLayoutLMForSequenceClassification (LayoutLM model)
- layoutlmv3 — TFLayoutLMv3ForSequenceClassification (LayoutLMv3 model)
- longformer — TFLongformerForSequenceClassification (Longformer model)
- mobilebert — TFMobileBertForSequenceClassification (MobileBERT model)
- mpnet — TFMPNetForSequenceClassification (MPNet model)
- openai-gpt — TFOpenAIGPTForSequenceClassification (OpenAI GPT model)
- rembert — TFRemBertForSequenceClassification (RemBERT model)
- roberta — TFRobertaForSequenceClassification (RoBERTa model)
- roformer — TFRoFormerForSequenceClassification (RoFormer model)
- tapas — TFTapasForSequenceClassification (TAPAS model)
- transfo-xl — TFTransfoXLForSequenceClassification (Transformer-XL model)
- xlm — TFXLMForSequenceClassification (XLM model)
- xlm-roberta — TFXLMRobertaForSequenceClassification (XLM-RoBERTa model)
- xlnet — TFXLNetForSequenceClassification (XLNet model)
Examples:
>>> from transformers import AutoConfig, TFAutoModelForSequenceClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForSequenceClassification.from_pretrained("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
... )TFAutoModelForMultipleChoice
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: TFAlbertForMultipleChoice (ALBERT model)
- BertConfig configuration class: TFBertForMultipleChoice (BERT model)
- CamembertConfig configuration class: TFCamembertForMultipleChoice (CamemBERT model)
- ConvBertConfig configuration class: TFConvBertForMultipleChoice (ConvBERT model)
- 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)
- XLMConfig configuration class: TFXLMForMultipleChoice (XLM model)
- XLMRobertaConfig configuration class: TFXLMRobertaForMultipleChoice (XLM-RoBERTa model)
- XLNetConfig configuration class: TFXLNetForMultipleChoice (XLNet model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — TFAlbertForMultipleChoice (ALBERT model)
- bert — TFBertForMultipleChoice (BERT model)
- camembert — TFCamembertForMultipleChoice (CamemBERT model)
- convbert — TFConvBertForMultipleChoice (ConvBERT model)
- distilbert — TFDistilBertForMultipleChoice (DistilBERT model)
- electra — TFElectraForMultipleChoice (ELECTRA model)
- flaubert — TFFlaubertForMultipleChoice (FlauBERT model)
- funnel — TFFunnelForMultipleChoice (Funnel Transformer model)
- longformer — TFLongformerForMultipleChoice (Longformer model)
- mobilebert — TFMobileBertForMultipleChoice (MobileBERT model)
- mpnet — TFMPNetForMultipleChoice (MPNet model)
- rembert — TFRemBertForMultipleChoice (RemBERT model)
- roberta — TFRobertaForMultipleChoice (RoBERTa model)
- roformer — TFRoFormerForMultipleChoice (RoFormer model)
- xlm — TFXLMForMultipleChoice (XLM model)
- xlm-roberta — TFXLMRobertaForMultipleChoice (XLM-RoBERTa model)
- xlnet — TFXLNetForMultipleChoice (XLNet model)
Examples:
>>> from transformers import AutoConfig, TFAutoModelForMultipleChoice
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForMultipleChoice.from_pretrained("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
... )TFAutoModelForNextSentencePrediction
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- BertConfig configuration class: TFBertForNextSentencePrediction (BERT model)
- MobileBertConfig configuration class: TFMobileBertForNextSentencePrediction (MobileBERT model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- bert — TFBertForNextSentencePrediction (BERT model)
- mobilebert — TFMobileBertForNextSentencePrediction (MobileBERT model)
Examples:
>>> from transformers import AutoConfig, TFAutoModelForNextSentencePrediction
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForNextSentencePrediction.from_pretrained("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
... )TFAutoModelForTableQuestionAnswering
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- TapasConfig configuration class: TFTapasForQuestionAnswering (TAPAS model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- tapas — 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
... )TFAutoModelForDocumentQuestionAnswering
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- LayoutLMConfig configuration class: TFLayoutLMForQuestionAnswering (LayoutLM model)
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)from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- layoutlm — TFLayoutLMForQuestionAnswering (LayoutLM 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
... )TFAutoModelForTokenClassification
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: TFAlbertForTokenClassification (ALBERT model)
- BertConfig configuration class: TFBertForTokenClassification (BERT model)
- CamembertConfig configuration class: TFCamembertForTokenClassification (CamemBERT model)
- ConvBertConfig configuration class: TFConvBertForTokenClassification (ConvBERT model)
- DebertaConfig configuration class: TFDebertaForTokenClassification (DeBERTa model)
- DebertaV2Config configuration class: TFDebertaV2ForTokenClassification (DeBERTa-v2 model)
- 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)
- XLMConfig configuration class: TFXLMForTokenClassification (XLM model)
- XLMRobertaConfig configuration class: TFXLMRobertaForTokenClassification (XLM-RoBERTa model)
- XLNetConfig configuration class: TFXLNetForTokenClassification (XLNet model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — TFAlbertForTokenClassification (ALBERT model)
- bert — TFBertForTokenClassification (BERT model)
- camembert — TFCamembertForTokenClassification (CamemBERT model)
- convbert — TFConvBertForTokenClassification (ConvBERT model)
- deberta — TFDebertaForTokenClassification (DeBERTa model)
- deberta-v2 — TFDebertaV2ForTokenClassification (DeBERTa-v2 model)
- distilbert — TFDistilBertForTokenClassification (DistilBERT model)
- electra — TFElectraForTokenClassification (ELECTRA model)
- esm — TFEsmForTokenClassification (ESM model)
- flaubert — TFFlaubertForTokenClassification (FlauBERT model)
- funnel — TFFunnelForTokenClassification (Funnel Transformer model)
- layoutlm — TFLayoutLMForTokenClassification (LayoutLM model)
- layoutlmv3 — TFLayoutLMv3ForTokenClassification (LayoutLMv3 model)
- longformer — TFLongformerForTokenClassification (Longformer model)
- mobilebert — TFMobileBertForTokenClassification (MobileBERT model)
- mpnet — TFMPNetForTokenClassification (MPNet model)
- rembert — TFRemBertForTokenClassification (RemBERT model)
- roberta — TFRobertaForTokenClassification (RoBERTa model)
- roformer — TFRoFormerForTokenClassification (RoFormer model)
- xlm — TFXLMForTokenClassification (XLM model)
- xlm-roberta — TFXLMRobertaForTokenClassification (XLM-RoBERTa model)
- xlnet — TFXLNetForTokenClassification (XLNet model)
Examples:
>>> from transformers import AutoConfig, TFAutoModelForTokenClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForTokenClassification.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForTokenClassification.from_pretrained("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
... )TFAutoModelForQuestionAnswering
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: TFAlbertForQuestionAnswering (ALBERT model)
- BertConfig configuration class: TFBertForQuestionAnswering (BERT model)
- CamembertConfig configuration class: TFCamembertForQuestionAnswering (CamemBERT model)
- ConvBertConfig configuration class: TFConvBertForQuestionAnswering (ConvBERT model)
- DebertaConfig configuration class: TFDebertaForQuestionAnswering (DeBERTa model)
- DebertaV2Config configuration class: TFDebertaV2ForQuestionAnswering (DeBERTa-v2 model)
- 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)
- XLMConfig configuration class: TFXLMForQuestionAnsweringSimple (XLM model)
- XLMRobertaConfig configuration class: TFXLMRobertaForQuestionAnswering (XLM-RoBERTa model)
- XLNetConfig configuration class: TFXLNetForQuestionAnsweringSimple (XLNet model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — TFAlbertForQuestionAnswering (ALBERT model)
- bert — TFBertForQuestionAnswering (BERT model)
- camembert — TFCamembertForQuestionAnswering (CamemBERT model)
- convbert — TFConvBertForQuestionAnswering (ConvBERT model)
- deberta — TFDebertaForQuestionAnswering (DeBERTa model)
- deberta-v2 — TFDebertaV2ForQuestionAnswering (DeBERTa-v2 model)
- distilbert — TFDistilBertForQuestionAnswering (DistilBERT model)
- electra — TFElectraForQuestionAnswering (ELECTRA model)
- flaubert — TFFlaubertForQuestionAnsweringSimple (FlauBERT model)
- funnel — TFFunnelForQuestionAnswering (Funnel Transformer model)
- gptj — TFGPTJForQuestionAnswering (GPT-J model)
- layoutlmv3 — TFLayoutLMv3ForQuestionAnswering (LayoutLMv3 model)
- longformer — TFLongformerForQuestionAnswering (Longformer model)
- mobilebert — TFMobileBertForQuestionAnswering (MobileBERT model)
- mpnet — TFMPNetForQuestionAnswering (MPNet model)
- rembert — TFRemBertForQuestionAnswering (RemBERT model)
- roberta — TFRobertaForQuestionAnswering (RoBERTa model)
- roformer — TFRoFormerForQuestionAnswering (RoFormer model)
- xlm — TFXLMForQuestionAnsweringSimple (XLM model)
- xlm-roberta — TFXLMRobertaForQuestionAnswering (XLM-RoBERTa model)
- xlnet — TFXLNetForQuestionAnsweringSimple (XLNet model)
Examples:
>>> from transformers import AutoConfig, TFAutoModelForQuestionAnswering
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForQuestionAnswering.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForQuestionAnswering.from_pretrained("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
... )TFAutoModelForVision2Seq
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- VisionEncoderDecoderConfig configuration class: TFVisionEncoderDecoderModel (Vision Encoder decoder model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- vision-encoder-decoder — TFVisionEncoderDecoderModel (Vision Encoder decoder model)
Examples:
>>> from transformers import AutoConfig, TFAutoModelForVision2Seq
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForVision2Seq.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForVision2Seq.from_pretrained("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
... )TFAutoModelForSpeechSeq2Seq
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- Speech2TextConfig configuration class: TFSpeech2TextForConditionalGeneration (Speech2Text model)
- WhisperConfig configuration class: TFWhisperForConditionalGeneration (Whisper model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- speech_to_text — TFSpeech2TextForConditionalGeneration (Speech2Text model)
- whisper — TFWhisperForConditionalGeneration (Whisper model)
Examples:
>>> from transformers import AutoConfig, TFAutoModelForSpeechSeq2Seq
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFAutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = TFAutoModelForSpeechSeq2Seq.from_pretrained("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
... )FlaxAutoModel
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: FlaxAlbertModel (ALBERT model)
- BartConfig configuration class: FlaxBartModel (BART model)
- BeitConfig configuration class: FlaxBeitModel (BEiT model)
- BertConfig configuration class: FlaxBertModel (BERT model)
- BigBirdConfig configuration class: FlaxBigBirdModel (BigBird model)
- BlenderbotConfig configuration class: FlaxBlenderbotModel (Blenderbot model)
- BlenderbotSmallConfig configuration class: FlaxBlenderbotSmallModel (BlenderbotSmall model)
- CLIPConfig configuration class: FlaxCLIPModel (CLIP model)
- 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)
- LongT5Config configuration class: FlaxLongT5Model (LongT5 model)
- MBartConfig configuration class: FlaxMBartModel (mBART model)
- MT5Config configuration class: FlaxMT5Model (MT5 model)
- MarianConfig configuration class: FlaxMarianModel (Marian model)
- OPTConfig configuration class: FlaxOPTModel (OPT model)
- PegasusConfig configuration class: FlaxPegasusModel (Pegasus model)
- RoFormerConfig configuration class: FlaxRoFormerModel (RoFormer model)
- RobertaConfig configuration class: FlaxRobertaModel (RoBERTa 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)
- XGLMConfig configuration class: FlaxXGLMModel (XGLM model)
- XLMRobertaConfig configuration class: FlaxXLMRobertaModel (XLM-RoBERTa model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — FlaxAlbertModel (ALBERT model)
- bart — FlaxBartModel (BART model)
- beit — FlaxBeitModel (BEiT model)
- bert — FlaxBertModel (BERT model)
- big_bird — FlaxBigBirdModel (BigBird model)
- blenderbot — FlaxBlenderbotModel (Blenderbot model)
- blenderbot-small — FlaxBlenderbotSmallModel (BlenderbotSmall model)
- clip — FlaxCLIPModel (CLIP model)
- distilbert — FlaxDistilBertModel (DistilBERT model)
- electra — FlaxElectraModel (ELECTRA model)
- gpt2 — FlaxGPT2Model (OpenAI GPT-2 model)
- gpt_neo — FlaxGPTNeoModel (GPT Neo model)
- gptj — FlaxGPTJModel (GPT-J model)
- longt5 — FlaxLongT5Model (LongT5 model)
- marian — FlaxMarianModel (Marian model)
- mbart — FlaxMBartModel (mBART model)
- mt5 — FlaxMT5Model (MT5 model)
- opt — FlaxOPTModel (OPT model)
- pegasus — FlaxPegasusModel (Pegasus model)
- roberta — FlaxRobertaModel (RoBERTa model)
- roformer — FlaxRoFormerModel (RoFormer model)
- t5 — FlaxT5Model (T5 model)
- vision-text-dual-encoder — FlaxVisionTextDualEncoderModel (VisionTextDualEncoder model)
- vit — FlaxViTModel (ViT model)
- wav2vec2 — FlaxWav2Vec2Model (Wav2Vec2 model)
- xglm — FlaxXGLMModel (XGLM model)
- xlm-roberta — FlaxXLMRobertaModel (XLM-RoBERTa model)
Examples:
>>> from transformers import AutoConfig, FlaxAutoModel
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModel.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModel.from_pretrained("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
... )FlaxAutoModelForCausalLM
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- BartConfig configuration class: FlaxBartForCausalLM (BART model)
- BertConfig configuration class: FlaxBertForCausalLM (BERT model)
- BigBirdConfig configuration class: FlaxBigBirdForCausalLM (BigBird model)
- 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)
- OPTConfig configuration class: FlaxOPTForCausalLM (OPT model)
- RobertaConfig configuration class: FlaxRobertaForCausalLM (RoBERTa model)
- XGLMConfig configuration class: FlaxXGLMForCausalLM (XGLM model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- bart — FlaxBartForCausalLM (BART model)
- bert — FlaxBertForCausalLM (BERT model)
- big_bird — FlaxBigBirdForCausalLM (BigBird model)
- electra — FlaxElectraForCausalLM (ELECTRA model)
- gpt2 — FlaxGPT2LMHeadModel (OpenAI GPT-2 model)
- gpt_neo — FlaxGPTNeoForCausalLM (GPT Neo model)
- gptj — FlaxGPTJForCausalLM (GPT-J model)
- opt — FlaxOPTForCausalLM (OPT model)
- roberta — FlaxRobertaForCausalLM (RoBERTa model)
- xglm — FlaxXGLMForCausalLM (XGLM model)
Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForCausalLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForCausalLM.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForCausalLM.from_pretrained("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
... )FlaxAutoModelForPreTraining
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: FlaxAlbertForPreTraining (ALBERT model)
- BartConfig configuration class: FlaxBartForConditionalGeneration (BART model)
- BertConfig configuration class: FlaxBertForPreTraining (BERT model)
- BigBirdConfig configuration class: FlaxBigBirdForPreTraining (BigBird model)
- 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)
- T5Config configuration class: FlaxT5ForConditionalGeneration (T5 model)
- Wav2Vec2Config configuration class: FlaxWav2Vec2ForPreTraining (Wav2Vec2 model)
- XLMRobertaConfig configuration class: FlaxXLMRobertaForMaskedLM (XLM-RoBERTa model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — FlaxAlbertForPreTraining (ALBERT model)
- bart — FlaxBartForConditionalGeneration (BART model)
- bert — FlaxBertForPreTraining (BERT model)
- big_bird — FlaxBigBirdForPreTraining (BigBird model)
- electra — FlaxElectraForPreTraining (ELECTRA model)
- longt5 — FlaxLongT5ForConditionalGeneration (LongT5 model)
- mbart — FlaxMBartForConditionalGeneration (mBART model)
- mt5 — FlaxMT5ForConditionalGeneration (MT5 model)
- roberta — FlaxRobertaForMaskedLM (RoBERTa model)
- roformer — FlaxRoFormerForMaskedLM (RoFormer model)
- t5 — FlaxT5ForConditionalGeneration (T5 model)
- wav2vec2 — FlaxWav2Vec2ForPreTraining (Wav2Vec2 model)
- xlm-roberta — FlaxXLMRobertaForMaskedLM (XLM-RoBERTa model)
Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForPreTraining
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForPreTraining.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForPreTraining.from_pretrained("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
... )FlaxAutoModelForMaskedLM
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: FlaxAlbertForMaskedLM (ALBERT model)
- BartConfig configuration class: FlaxBartForConditionalGeneration (BART model)
- BertConfig configuration class: FlaxBertForMaskedLM (BERT model)
- BigBirdConfig configuration class: FlaxBigBirdForMaskedLM (BigBird model)
- 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)
- XLMRobertaConfig configuration class: FlaxXLMRobertaForMaskedLM (XLM-RoBERTa model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — FlaxAlbertForMaskedLM (ALBERT model)
- bart — FlaxBartForConditionalGeneration (BART model)
- bert — FlaxBertForMaskedLM (BERT model)
- big_bird — FlaxBigBirdForMaskedLM (BigBird model)
- distilbert — FlaxDistilBertForMaskedLM (DistilBERT model)
- electra — FlaxElectraForMaskedLM (ELECTRA model)
- mbart — FlaxMBartForConditionalGeneration (mBART model)
- roberta — FlaxRobertaForMaskedLM (RoBERTa model)
- roformer — FlaxRoFormerForMaskedLM (RoFormer model)
- xlm-roberta — FlaxXLMRobertaForMaskedLM (XLM-RoBERTa model)
Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForMaskedLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForMaskedLM.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForMaskedLM.from_pretrained("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
... )FlaxAutoModelForSeq2SeqLM
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- BartConfig configuration class: FlaxBartForConditionalGeneration (BART model)
- BlenderbotConfig configuration class: FlaxBlenderbotForConditionalGeneration (Blenderbot model)
- BlenderbotSmallConfig configuration class: FlaxBlenderbotSmallForConditionalGeneration (BlenderbotSmall model)
- 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)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- bart — FlaxBartForConditionalGeneration (BART model)
- blenderbot — FlaxBlenderbotForConditionalGeneration (Blenderbot model)
- blenderbot-small — FlaxBlenderbotSmallForConditionalGeneration (BlenderbotSmall model)
- encoder-decoder — FlaxEncoderDecoderModel (Encoder decoder model)
- longt5 — FlaxLongT5ForConditionalGeneration (LongT5 model)
- marian — FlaxMarianMTModel (Marian model)
- mbart — FlaxMBartForConditionalGeneration (mBART model)
- mt5 — FlaxMT5ForConditionalGeneration (MT5 model)
- pegasus — FlaxPegasusForConditionalGeneration (Pegasus model)
- t5 — FlaxT5ForConditionalGeneration (T5 model)
Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForSeq2SeqLM.from_pretrained("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
... )FlaxAutoModelForSequenceClassification
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: FlaxAlbertForSequenceClassification (ALBERT model)
- BartConfig configuration class: FlaxBartForSequenceClassification (BART model)
- BertConfig configuration class: FlaxBertForSequenceClassification (BERT model)
- BigBirdConfig configuration class: FlaxBigBirdForSequenceClassification (BigBird model)
- 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)
- XLMRobertaConfig configuration class: FlaxXLMRobertaForSequenceClassification (XLM-RoBERTa model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — FlaxAlbertForSequenceClassification (ALBERT model)
- bart — FlaxBartForSequenceClassification (BART model)
- bert — FlaxBertForSequenceClassification (BERT model)
- big_bird — FlaxBigBirdForSequenceClassification (BigBird model)
- distilbert — FlaxDistilBertForSequenceClassification (DistilBERT model)
- electra — FlaxElectraForSequenceClassification (ELECTRA model)
- mbart — FlaxMBartForSequenceClassification (mBART model)
- roberta — FlaxRobertaForSequenceClassification (RoBERTa model)
- roformer — FlaxRoFormerForSequenceClassification (RoFormer model)
- xlm-roberta — FlaxXLMRobertaForSequenceClassification (XLM-RoBERTa model)
Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForSequenceClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForSequenceClassification.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForSequenceClassification.from_pretrained("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
... )FlaxAutoModelForQuestionAnswering
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: FlaxAlbertForQuestionAnswering (ALBERT model)
- BartConfig configuration class: FlaxBartForQuestionAnswering (BART model)
- BertConfig configuration class: FlaxBertForQuestionAnswering (BERT model)
- BigBirdConfig configuration class: FlaxBigBirdForQuestionAnswering (BigBird model)
- 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)
- XLMRobertaConfig configuration class: FlaxXLMRobertaForQuestionAnswering (XLM-RoBERTa model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — FlaxAlbertForQuestionAnswering (ALBERT model)
- bart — FlaxBartForQuestionAnswering (BART model)
- bert — FlaxBertForQuestionAnswering (BERT model)
- big_bird — FlaxBigBirdForQuestionAnswering (BigBird model)
- distilbert — FlaxDistilBertForQuestionAnswering (DistilBERT model)
- electra — FlaxElectraForQuestionAnswering (ELECTRA model)
- mbart — FlaxMBartForQuestionAnswering (mBART model)
- roberta — FlaxRobertaForQuestionAnswering (RoBERTa model)
- roformer — FlaxRoFormerForQuestionAnswering (RoFormer model)
- xlm-roberta — FlaxXLMRobertaForQuestionAnswering (XLM-RoBERTa model)
Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForQuestionAnswering
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForQuestionAnswering.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForQuestionAnswering.from_pretrained("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
... )FlaxAutoModelForTokenClassification
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: FlaxAlbertForTokenClassification (ALBERT model)
- BertConfig configuration class: FlaxBertForTokenClassification (BERT model)
- BigBirdConfig configuration class: FlaxBigBirdForTokenClassification (BigBird model)
- DistilBertConfig configuration class: FlaxDistilBertForTokenClassification (DistilBERT model)
- ElectraConfig configuration class: FlaxElectraForTokenClassification (ELECTRA model)
- RoFormerConfig configuration class: FlaxRoFormerForTokenClassification (RoFormer model)
- RobertaConfig configuration class: FlaxRobertaForTokenClassification (RoBERTa model)
- XLMRobertaConfig configuration class: FlaxXLMRobertaForTokenClassification (XLM-RoBERTa model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — FlaxAlbertForTokenClassification (ALBERT model)
- bert — FlaxBertForTokenClassification (BERT model)
- big_bird — FlaxBigBirdForTokenClassification (BigBird model)
- distilbert — FlaxDistilBertForTokenClassification (DistilBERT model)
- electra — FlaxElectraForTokenClassification (ELECTRA model)
- roberta — FlaxRobertaForTokenClassification (RoBERTa model)
- roformer — FlaxRoFormerForTokenClassification (RoFormer model)
- xlm-roberta — FlaxXLMRobertaForTokenClassification (XLM-RoBERTa model)
Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForTokenClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForTokenClassification.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForTokenClassification.from_pretrained("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
... )FlaxAutoModelForMultipleChoice
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- AlbertConfig configuration class: FlaxAlbertForMultipleChoice (ALBERT model)
- BertConfig configuration class: FlaxBertForMultipleChoice (BERT model)
- BigBirdConfig configuration class: FlaxBigBirdForMultipleChoice (BigBird model)
- DistilBertConfig configuration class: FlaxDistilBertForMultipleChoice (DistilBERT model)
- ElectraConfig configuration class: FlaxElectraForMultipleChoice (ELECTRA model)
- RoFormerConfig configuration class: FlaxRoFormerForMultipleChoice (RoFormer model)
- RobertaConfig configuration class: FlaxRobertaForMultipleChoice (RoBERTa model)
- XLMRobertaConfig configuration class: FlaxXLMRobertaForMultipleChoice (XLM-RoBERTa model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- albert — FlaxAlbertForMultipleChoice (ALBERT model)
- bert — FlaxBertForMultipleChoice (BERT model)
- big_bird — FlaxBigBirdForMultipleChoice (BigBird model)
- distilbert — FlaxDistilBertForMultipleChoice (DistilBERT model)
- electra — FlaxElectraForMultipleChoice (ELECTRA model)
- roberta — FlaxRobertaForMultipleChoice (RoBERTa model)
- roformer — FlaxRoFormerForMultipleChoice (RoFormer model)
- xlm-roberta — FlaxXLMRobertaForMultipleChoice (XLM-RoBERTa model)
Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForMultipleChoice
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForMultipleChoice.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForMultipleChoice.from_pretrained("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
... )FlaxAutoModelForNextSentencePrediction
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- BertConfig configuration class: FlaxBertForNextSentencePrediction (BERT model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- bert — FlaxBertForNextSentencePrediction (BERT model)
Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForNextSentencePrediction
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForNextSentencePrediction.from_pretrained("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
... )FlaxAutoModelForImageClassification
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- BeitConfig configuration class: FlaxBeitForImageClassification (BEiT model)
- ViTConfig configuration class: FlaxViTForImageClassification (ViT model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- beit — FlaxBeitForImageClassification (BEiT model)
- vit — FlaxViTForImageClassification (ViT model)
Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForImageClassification
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForImageClassification.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForImageClassification.from_pretrained("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
... )FlaxAutoModelForVision2Seq
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).
from_config
< source >( **kwargs )
Parameters
-
config (PretrainedConfig) —
The model class to instantiate is selected based on the configuration class:
- VisionEncoderDecoderConfig configuration class: FlaxVisionEncoderDecoderModel (Vision Encoder decoder model)
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.
from_pretrained
< source >( *model_args **kwargs )
Parameters
-
pretrained_model_name_or_path (
stroros.PathLike) — Can be either:- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
bert-base-uncased, or namespaced under a user or organization name, likedbmdz/bert-base-german-cased. - A path to a directory containing model weights saved using
save_pretrained(), e.g.,
./my_model_directory/. - A path or url to a PyTorch state_dict save file (e.g,
./pt_model/pytorch_model.bin). In this case,from_ptshould be set toTrueand a configuration object should be provided asconfigargument. 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.
- A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like
-
model_args (additional positional arguments, optional) —
Will be passed along to the underlying model
__init__()method. -
config (PretrainedConfig, optional) —
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the model id string of a pretrained model).
- The model was saved using save_pretrained() and is reloaded by supplying the save directory.
- The model is loaded by supplying a local directory as
pretrained_model_name_or_pathand a configuration JSON file named config.json is found in the directory.
-
cache_dir (
stroros.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. -
from_pt (
bool, optional, defaults toFalse) — Load the model weights from a PyTorch checkpoint save file (see docstring ofpretrained_model_name_or_pathargument). -
force_download (
bool, optional, defaults toFalse) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. -
resume_download (
bool, optional, defaults toFalse) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. -
proxies (
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. -
output_loading_info(
bool, optional, defaults toFalse) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only(
bool, optional, defaults toFalse) — Whether or not to only look at local files (e.g., not try downloading the model). -
revision (
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, sorevisioncan be any identifier allowed by git. -
trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. -
kwargs (additional keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True). Behaves differently depending on whether aconfigis provided or automatically loaded:- If a configuration is provided with
config,**kwargswill be directly passed to the underlying model’s__init__method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided,
kwargswill be first passed to the configuration class initialization function (from_pretrained()). Each key ofkwargsthat corresponds to a configuration attribute will be used to override said attribute with the suppliedkwargsvalue. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s__init__function.
- If a configuration is provided with
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:
- vision-encoder-decoder — FlaxVisionEncoderDecoderModel (Vision Encoder decoder model)
Examples:
>>> from transformers import AutoConfig, FlaxAutoModelForVision2Seq
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxAutoModelForVision2Seq.from_pretrained("bert-base-cased")
>>> # Update configuration during loading
>>> model = FlaxAutoModelForVision2Seq.from_pretrained("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
... )