modeling_xlm_roberta.py

# coding=utf-8
# Copyright 2019 Facebook AI Research and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""PyTorch XLM-RoBERTa model. """


import logging

from .configuration_xlm_roberta import XLMRobertaConfig
from .file_utils import add_start_docstrings
from .modeling_roberta import (
    RobertaForMaskedLM,
    RobertaForMultipleChoice,
    RobertaForQuestionAnswering,
    RobertaForSequenceClassification,
    RobertaForTokenClassification,
    RobertaModel,
)


logger = logging.getLogger(__name__)

XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "xlm-roberta-base",
    "xlm-roberta-large",
    "xlm-roberta-large-finetuned-conll02-dutch",
    "xlm-roberta-large-finetuned-conll02-spanish",
    "xlm-roberta-large-finetuned-conll03-english",
    "xlm-roberta-large-finetuned-conll03-german",
    # See all XLM-RoBERTa models at https://huggingface.co/models?filter=xlm-roberta
]


XLM_ROBERTA_START_DOCSTRING = r"""

    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
    usage and behavior.

    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.
"""


@add_start_docstrings(
    "The bare XLM-RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
    XLM_ROBERTA_START_DOCSTRING,
)
class XLMRobertaModel(RobertaModel):
    """
    This class overrides :class:`~transformers.RobertaModel`. Please check the
    superclass for the appropriate documentation alongside usage examples.
    """

    config_class = XLMRobertaConfig


@add_start_docstrings(
    """XLM-RoBERTa Model with a `language modeling` head on top. """, XLM_ROBERTA_START_DOCSTRING,
)
class XLMRobertaForMaskedLM(RobertaForMaskedLM):
    """
    This class overrides :class:`~transformers.RobertaForMaskedLM`. Please check the
    superclass for the appropriate documentation alongside usage examples.
    """

    config_class = XLMRobertaConfig


@add_start_docstrings(
    """XLM-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. """,
    XLM_ROBERTA_START_DOCSTRING,
)
class XLMRobertaForSequenceClassification(RobertaForSequenceClassification):
    """
    This class overrides :class:`~transformers.RobertaForSequenceClassification`. Please check the
    superclass for the appropriate documentation alongside usage examples.
    """

    config_class = XLMRobertaConfig


@add_start_docstrings(
    """XLM-RoBERTa Model with a multiple choice classification head on top (a linear layer on top of
    the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
    XLM_ROBERTA_START_DOCSTRING,
)
class XLMRobertaForMultipleChoice(RobertaForMultipleChoice):
    """
    This class overrides :class:`~transformers.RobertaForMultipleChoice`. Please check the
    superclass for the appropriate documentation alongside usage examples.
    """

    config_class = XLMRobertaConfig


@add_start_docstrings(
    """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. """,
    XLM_ROBERTA_START_DOCSTRING,
)
class XLMRobertaForTokenClassification(RobertaForTokenClassification):
    """
    This class overrides :class:`~transformers.RobertaForTokenClassification`. Please check the
    superclass for the appropriate documentation alongside usage examples.
    """

    config_class = XLMRobertaConfig


@add_start_docstrings(
    """XLM-RoBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
    linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).""",
    XLM_ROBERTA_START_DOCSTRING,
)
class XLMRobertaForQuestionAnswering(RobertaForQuestionAnswering):
    """
    This class overrides :class:`~transformers.RobertaForQuestionAnswering`. Please check the
    superclass for the appropriate documentation alongside usage examples.
    """

    config_class = XLMRobertaConfig