TFAutoModelForTokenClassification

TFAutoModelForTokenClassification

class transformers.TFAutoModelForTokenClassification

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( *args**kwargs )

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

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( **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)

  • RobertaPreLayerNormConfig configuration class: TFRobertaPreLayerNormForTokenClassification (RoBERTa-PreLayerNorm model)

  • XLMConfig configuration class: TFXLMForTokenClassification (XLM model)

  • XLMRobertaConfig configuration class: TFXLMRobertaForTokenClassification (XLM-RoBERTa model)

  • XLNetConfig configuration class: TFXLNetForTokenClassification (XLNet model)

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.

Examples:

Copied

>>> from transformers import AutoConfig, TFAutoModelForTokenClassification

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

from_pretrained

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( *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 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_pt should be set to True and a configuration object should be provided as config argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.

• 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_path and a configuration JSON file named config.json is found in the directory.

• 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_pt (bool, optional, defaults to False) — Load the model weights from a PyTorch 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)

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

Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.

The model class to instantiate is selected based on the model_type property of the config object (either passed as an argument or loaded from pretrained_model_name_or_path if possible), or when it’s missing, by falling back to using pattern matching on pretrained_model_name_or_path:

• 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)

• roberta-prelayernorm — TFRobertaPreLayerNormForTokenClassification (RoBERTa-PreLayerNorm model)

• roformer — TFRoFormerForTokenClassification (RoFormer model)

• xlm — TFXLMForTokenClassification (XLM model)

• xlm-roberta — TFXLMRobertaForTokenClassification (XLM-RoBERTa model)

• xlnet — TFXLNetForTokenClassification (XLNet model)

Examples:

Copied

>>> 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
... )