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  4. Natural Language Processing

AutoModelForMaskedLM

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Last updated 1 year ago

AutoModelForMaskedLM

class transformers.AutoModelForMaskedLM

( *args**kwargs )

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 class method or the class method.

This class cannot be instantiated directly using __init__() (throws an error).

from_config

( **kwargs )

Parameters

  • config () — The model class to instantiate is selected based on the configuration class:

    • configuration class: (ALBERT model)

    • configuration class: (BART model)

    • configuration class: (BERT model)

    • configuration class: (BigBird model)

    • configuration class: (CamemBERT model)

    • configuration class: (ConvBERT model)

    • configuration class: (Data2VecText model)

    • configuration class: (DeBERTa model)

    • configuration class: (DeBERTa-v2 model)

    • configuration class: (DistilBERT model)

    • configuration class: (ELECTRA model)

    • configuration class: (ERNIE model)

    • configuration class: (ESM model)

    • configuration class: (FNet model)

    • configuration class: (FlauBERT model)

    • configuration class: (Funnel Transformer model)

    • configuration class: (I-BERT model)

    • configuration class: (LayoutLM model)

    • configuration class: (Longformer model)

    • configuration class: (LUKE model)

    • configuration class: (mBART model)

    • configuration class: (MPNet model)

    • configuration class: (MEGA model)

    • configuration class: (Megatron-BERT model)

    • configuration class: (MobileBERT model)

    • configuration class: (MRA model)

    • configuration class: (MVP model)

    • configuration class: (Nezha model)

    • configuration class: (Nyströmformer model)

    • configuration class: (Perceiver model)

    • configuration class: (QDQBert model)

    • configuration class: (Reformer model)

    • configuration class: (RemBERT model)

    • configuration class: (RoCBert model)

    • configuration class: (RoFormer model)

    • configuration class: (RoBERTa model)

    • configuration class: (RoBERTa-PreLayerNorm model)

    • configuration class: (SqueezeBERT model)

    • configuration class: (TAPAS model)

    • configuration class: Wav2Vec2ForMaskedLM (Wav2Vec2 model)

    • configuration class: (XLM model)

    • configuration class: (XLM-RoBERTa model)

    • configuration class: (XLM-RoBERTa-XL model)

    • configuration class: (X-MOD model)

    • configuration class: (YOSO model)

Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.

Examples:

Copied

>>> from transformers import AutoConfig, AutoModelForMaskedLM

>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained("bert-base-cased")
>>> model = AutoModelForMaskedLM.from_config(config)

from_pretrained

( *model_args**kwargs )

Parameters

  • pretrained_model_name_or_path (str or os.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, like dbmdz/bert-base-german-cased.

    • A path or url to a tensorflow index checkpoint file (e.g, ./tf_model/model.ckpt.index). In this case, from_tf should be set to True and a configuration object should be provided as config argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.

  • model_args (additional positional arguments, optional) — Will be passed along to the underlying model __init__() method.

    • The model is a model provided by the library (loaded with the model id string of a pretrained model).

    • The model is loaded by supplying a local directory as pretrained_model_name_or_path and 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.

  • cache_dir (str or os.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.

  • from_tf (bool, optional, defaults to False) — Load the model weights from a TensorFlow checkpoint save file (see docstring of pretrained_model_name_or_path argument).

  • force_download (bool, optional, defaults to False) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.

  • resume_download (bool, optional, defaults to False) — 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 to False) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.

  • local_files_only(bool, optional, defaults to False) — 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, so revision can be any identifier allowed by git.

  • trust_remote_code (bool, optional, defaults to False) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to True for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.

  • code_revision (str, optional, defaults to "main") — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so revision can be any identifier allowed by git.

  • 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 a config is provided or automatically loaded:

    • If a configuration is provided with config, **kwargs will be directly passed to the underlying model’s __init__ method (we assume all relevant updates to the configuration have already been done)

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:

  • wav2vec2 — Wav2Vec2ForMaskedLM (Wav2Vec2 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:

Copied

>>> 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
... )

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use to load the model weights.

A path to a directory containing model weights saved using , e.g., ./my_model_directory/.

config (, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:

The model was saved using and is reloaded by supplying the save directory.

This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using and is not a simpler option.

If a configuration is not provided, kwargs will be first passed to the configuration class initialization function (). Each key of kwargs that corresponds to a configuration attribute will be used to override said attribute with the supplied kwargs value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s __init__ function.

albert — (ALBERT model)

bart — (BART model)

bert — (BERT model)

big_bird — (BigBird model)

camembert — (CamemBERT model)

convbert — (ConvBERT model)

data2vec-text — (Data2VecText model)

deberta — (DeBERTa model)

deberta-v2 — (DeBERTa-v2 model)

distilbert — (DistilBERT model)

electra — (ELECTRA model)

ernie — (ERNIE model)

esm — (ESM model)

flaubert — (FlauBERT model)

fnet — (FNet model)

funnel — (Funnel Transformer model)

ibert — (I-BERT model)

layoutlm — (LayoutLM model)

longformer — (Longformer model)

luke — (LUKE model)

mbart — (mBART model)

mega — (MEGA model)

megatron-bert — (Megatron-BERT model)

mobilebert — (MobileBERT model)

mpnet — (MPNet model)

mra — (MRA model)

mvp — (MVP model)

nezha — (Nezha model)

nystromformer — (Nyströmformer model)

perceiver — (Perceiver model)

qdqbert — (QDQBert model)

reformer — (Reformer model)

rembert — (RemBERT model)

roberta — (RoBERTa model)

roberta-prelayernorm — (RoBERTa-PreLayerNorm model)

roc_bert — (RoCBert model)

roformer — (RoFormer model)

squeezebert — (SqueezeBERT model)

tapas — (TAPAS model)

xlm — (XLM model)

xlm-roberta — (XLM-RoBERTa model)

xlm-roberta-xl — (XLM-RoBERTa-XL model)

xmod — (X-MOD model)

yoso — (YOSO model)

🌍
🌍
🌍
<source>
from_pretrained()
from_config()
<source>
PretrainedConfig
AlbertConfig
AlbertForMaskedLM
BartConfig
BartForConditionalGeneration
BertConfig
BertForMaskedLM
BigBirdConfig
BigBirdForMaskedLM
CamembertConfig
CamembertForMaskedLM
ConvBertConfig
ConvBertForMaskedLM
Data2VecTextConfig
Data2VecTextForMaskedLM
DebertaConfig
DebertaForMaskedLM
DebertaV2Config
DebertaV2ForMaskedLM
DistilBertConfig
DistilBertForMaskedLM
ElectraConfig
ElectraForMaskedLM
ErnieConfig
ErnieForMaskedLM
EsmConfig
EsmForMaskedLM
FNetConfig
FNetForMaskedLM
FlaubertConfig
FlaubertWithLMHeadModel
FunnelConfig
FunnelForMaskedLM
IBertConfig
IBertForMaskedLM
LayoutLMConfig
LayoutLMForMaskedLM
LongformerConfig
LongformerForMaskedLM
LukeConfig
LukeForMaskedLM
MBartConfig
MBartForConditionalGeneration
MPNetConfig
MPNetForMaskedLM
MegaConfig
MegaForMaskedLM
MegatronBertConfig
MegatronBertForMaskedLM
MobileBertConfig
MobileBertForMaskedLM
MraConfig
MraForMaskedLM
MvpConfig
MvpForConditionalGeneration
NezhaConfig
NezhaForMaskedLM
NystromformerConfig
NystromformerForMaskedLM
PerceiverConfig
PerceiverForMaskedLM
QDQBertConfig
QDQBertForMaskedLM
ReformerConfig
ReformerForMaskedLM
RemBertConfig
RemBertForMaskedLM
RoCBertConfig
RoCBertForMaskedLM
RoFormerConfig
RoFormerForMaskedLM
RobertaConfig
RobertaForMaskedLM
RobertaPreLayerNormConfig
RobertaPreLayerNormForMaskedLM
SqueezeBertConfig
SqueezeBertForMaskedLM
TapasConfig
TapasForMaskedLM
Wav2Vec2Config
XLMConfig
XLMWithLMHeadModel
XLMRobertaConfig
XLMRobertaForMaskedLM
XLMRobertaXLConfig
XLMRobertaXLForMaskedLM
XmodConfig
XmodForMaskedLM
YosoConfig
YosoForMaskedLM
from_pretrained()
<source>
save_pretrained()
PretrainedConfig
save_pretrained()
save_pretrained()
from_pretrained()
from_pretrained()
AlbertForMaskedLM
BartForConditionalGeneration
BertForMaskedLM
BigBirdForMaskedLM
CamembertForMaskedLM
ConvBertForMaskedLM
Data2VecTextForMaskedLM
DebertaForMaskedLM
DebertaV2ForMaskedLM
DistilBertForMaskedLM
ElectraForMaskedLM
ErnieForMaskedLM
EsmForMaskedLM
FlaubertWithLMHeadModel
FNetForMaskedLM
FunnelForMaskedLM
IBertForMaskedLM
LayoutLMForMaskedLM
LongformerForMaskedLM
LukeForMaskedLM
MBartForConditionalGeneration
MegaForMaskedLM
MegatronBertForMaskedLM
MobileBertForMaskedLM
MPNetForMaskedLM
MraForMaskedLM
MvpForConditionalGeneration
NezhaForMaskedLM
NystromformerForMaskedLM
PerceiverForMaskedLM
QDQBertForMaskedLM
ReformerForMaskedLM
RemBertForMaskedLM
RobertaForMaskedLM
RobertaPreLayerNormForMaskedLM
RoCBertForMaskedLM
RoFormerForMaskedLM
SqueezeBertForMaskedLM
TapasForMaskedLM
XLMWithLMHeadModel
XLMRobertaForMaskedLM
XLMRobertaXLForMaskedLM
XmodForMaskedLM
YosoForMaskedLM