879e1d3234
* Add TF2 version of FlauBERT * Add TF2 version of FlauBERT * Add documentation * Apply style and quality * Apply style once again Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
119 lines
4.7 KiB
Python
119 lines
4.7 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" TF 2.0 CamemBERT model. """
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import logging
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from .configuration_camembert import CamembertConfig
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from .file_utils import add_start_docstrings
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from .modeling_tf_roberta import (
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TFRobertaForMaskedLM,
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TFRobertaForSequenceClassification,
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TFRobertaForTokenClassification,
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TFRobertaModel,
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)
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logger = logging.getLogger(__name__)
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TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {}
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CAMEMBERT_START_DOCSTRING = r"""
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.. note::
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TF 2.0 models accepts two formats as inputs:
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- having all inputs as keyword arguments (like PyTorch models), or
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- having all inputs as a list, tuple or dict in the first positional arguments.
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This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having
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all the tensors in the first argument of the model call function: :obj:`model(inputs)`.
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If you choose this second option, there are three possibilities you can use to gather all the input Tensors
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in the first positional argument :
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- a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)`
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- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
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:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
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- a dictionary with one or several input Tensors associated to the input names given in the docstring:
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:obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
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Parameters:
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config (:class:`~transformers.CamembertConfig`): Model configuration class with all the parameters of the
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model. Initializing with a config file does not load the weights associated with the model, only the configuration.
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Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
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"""
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@add_start_docstrings(
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"The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.",
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CAMEMBERT_START_DOCSTRING,
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)
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class TFCamembertModel(TFRobertaModel):
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"""
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This class overrides :class:`~transformers.TFRobertaModel`. Please check the
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superclass for the appropriate documentation alongside usage examples.
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"""
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config_class = CamembertConfig
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pretrained_model_archive_map = TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
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@add_start_docstrings(
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"""CamemBERT Model with a `language modeling` head on top. """, CAMEMBERT_START_DOCSTRING,
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)
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class TFCamembertForMaskedLM(TFRobertaForMaskedLM):
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"""
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This class overrides :class:`~transformers.TFRobertaForMaskedLM`. Please check the
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superclass for the appropriate documentation alongside usage examples.
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"""
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config_class = CamembertConfig
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pretrained_model_archive_map = TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
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@add_start_docstrings(
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"""CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer
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on top of the pooled output) e.g. for GLUE tasks. """,
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CAMEMBERT_START_DOCSTRING,
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)
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class TFCamembertForSequenceClassification(TFRobertaForSequenceClassification):
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"""
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This class overrides :class:`~transformers.TFRobertaForSequenceClassification`. Please check the
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superclass for the appropriate documentation alongside usage examples.
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"""
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config_class = CamembertConfig
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pretrained_model_archive_map = TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
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@add_start_docstrings(
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"""CamemBERT Model with a token classification head on top (a linear layer on top of
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the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
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CAMEMBERT_START_DOCSTRING,
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)
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class TFCamembertForTokenClassification(TFRobertaForTokenClassification):
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"""
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This class overrides :class:`~transformers.TFRobertaForTokenClassification`. Please check the
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superclass for the appropriate documentation alongside usage examples.
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"""
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config_class = CamembertConfig
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pretrained_model_archive_map = TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
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