U &ºcÛã@sºdZddlZddlmZddlmZddlmZmZm Z m Z e  e ¡Z iZdZedeƒGd d „d e ƒƒZed eƒGd d „d eƒƒZedeƒGdd„deƒƒZedeƒGdd„de ƒƒZdS)z TF 2.0 XLM-RoBERTa model. éNé)ÚXLMRobertaConfig)Úadd_start_docstrings)ÚTFRobertaForMaskedLMÚ"TFRobertaForSequenceClassificationÚTFRobertaForTokenClassificationÚTFRobertaModelaÓ .. 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.XLMRobertaConfig`): 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. zeThe bare XLM-RoBERTa Model transformer outputting raw hidden-states without any specific head on top.c@seZdZdZeZeZdS)ÚTFXLMRobertaModelz¡ 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_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAPZpretrained_model_archive_map©rrúH/tmp/pip-unpacked-wheel-ymerj3tt/transformers/modeling_tf_xlm_roberta.pyr @sr z:XLM-RoBERTa Model with a `language modeling` head on top. c@seZdZdZeZeZdS)ÚTFXLMRobertaForMaskedLMz§ This class overrides :class:`~transformers.TFRobertaForMaskedLM`. Please check the superclass for the appropriate documentation alongside usage examples. Nr rrrrrNsrzšXLM-RoBERTa 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%TFXLMRobertaForSequenceClassificationzµ This class overrides :class:`~transformers.TFRobertaForSequenceClassification`. Please check the superclass for the appropriate documentation alongside usage examples. Nr rrrrr[srz¡XLM-RoBERTa 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"TFXLMRobertaForTokenClassificationz² This class overrides :class:`~transformers.TFRobertaForTokenClassification`. Please check the superclass for the appropriate documentation alongside usage examples. Nr rrrrrjsr)rÚloggingZconfiguration_xlm_robertarZ file_utilsrZmodeling_tf_robertarrrrÚ getLoggerr ÚloggerrZXLM_ROBERTA_START_DOCSTRINGr rrrrrrrÚs6   þ ÿ ý ý