U &ºc¿ã@sºdZddlZddlmZddlmZddlmZmZm Z m Z e  e ¡Z iZdZedeƒGd d „d e ƒƒZed eƒGd d „d eƒƒZedeƒGdd„deƒƒZedeƒGdd„de ƒƒZdS)z TF 2.0 CamemBERT model. éNé)ÚCamembertConfig)Úadd_start_docstrings)ÚTFRobertaForMaskedLMÚ"TFRobertaForSequenceClassificationÚTFRobertaForTokenClassificationÚTFRobertaModelaÒ .. note:: TF 2.0 models accepts two formats as inputs: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional arguments. This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having all the tensors in the first argument of the model call function: :obj:`model(inputs)`. If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument : - a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: :obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: :obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})` Parameters: config (:class:`~transformers.CamembertConfig`): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights. zcThe bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.c@seZdZdZeZeZdS)ÚTFCamembertModelz¡ This class overrides :class:`~transformers.TFRobertaModel`. Please check the superclass for the appropriate documentation alongside usage examples. N©Ú__name__Ú __module__Ú __qualname__Ú__doc__rZ config_classÚ)TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAPZpretrained_model_archive_map©rrúF/tmp/pip-unpacked-wheel-ymerj3tt/transformers/modeling_tf_camembert.pyr @sr z8CamemBERT Model with a `language modeling` head on top. c@seZdZdZeZeZdS)ÚTFCamembertForMaskedLMz§ This class overrides :class:`~transformers.TFRobertaForMaskedLM`. Please check the superclass for the appropriate documentation alongside usage examples. Nr rrrrrNsrz˜CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. c@seZdZdZeZeZdS)Ú$TFCamembertForSequenceClassificationzµ This class overrides :class:`~transformers.TFRobertaForSequenceClassification`. Please check the superclass for the appropriate documentation alongside usage examples. Nr rrrrr[srzŸCamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. c@seZdZdZeZeZdS)Ú!TFCamembertForTokenClassificationz² This class overrides :class:`~transformers.TFRobertaForTokenClassification`. Please check the superclass for the appropriate documentation alongside usage examples. Nr rrrrrjsr)rÚloggingZconfiguration_camembertrZ file_utilsrZmodeling_tf_robertarrrrÚ getLoggerr ÚloggerrZCAMEMBERT_START_DOCSTRINGr rrrrrrrÚs6   þ ÿ ý ý