Source code for transformers.modeling_roberta

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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"""PyTorch RoBERTa model. """


import logging

import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss

from .configuration_roberta import RobertaConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_bert import BertEmbeddings, BertLayerNorm, BertModel, BertPreTrainedModel, gelu


logger = logging.getLogger(__name__)

ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
    "roberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-pytorch_model.bin",
    "roberta-large": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-pytorch_model.bin",
    "roberta-large-mnli": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-pytorch_model.bin",
    "distilroberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-pytorch_model.bin",
    "roberta-base-openai-detector": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-openai-detector-pytorch_model.bin",
    "roberta-large-openai-detector": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-openai-detector-pytorch_model.bin",
}


class RobertaEmbeddings(BertEmbeddings):
    """
    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
    """

    def __init__(self, config):
        super().__init__(config)
        self.padding_idx = 1
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=self.padding_idx)
        self.position_embeddings = nn.Embedding(
            config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
        )

    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
        if position_ids is None:
            if input_ids is not None:
                # Create the position ids from the input token ids. Any padded tokens remain padded.
                position_ids = self.create_position_ids_from_input_ids(input_ids).to(input_ids.device)
            else:
                position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)

        return super().forward(
            input_ids, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds
        )

    def create_position_ids_from_input_ids(self, x):
        """ Replace non-padding symbols with their position numbers. Position numbers begin at
        padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
        `utils.make_positions`.

        :param torch.Tensor x:
        :return torch.Tensor:
        """
        mask = x.ne(self.padding_idx).long()
        incremental_indicies = torch.cumsum(mask, dim=1) * mask
        return incremental_indicies + self.padding_idx

    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
        """ We are provided embeddings directly. We cannot infer which are padded so just generate
        sequential position ids.

        :param torch.Tensor inputs_embeds:
        :return torch.Tensor:
        """
        input_shape = inputs_embeds.size()[:-1]
        sequence_length = input_shape[1]

        position_ids = torch.arange(
            self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
        )
        return position_ids.unsqueeze(0).expand(input_shape)


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.RobertaConfig`): 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.
"""

ROBERTA_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using :class:`transformers.RobertaTokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.encode_plus` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.

            `What are attention masks? <../glossary.html#attention-mask>`__
        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Segment token indices to indicate first and second portions of the inputs.
            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
            corresponds to a `sentence B` token

            `What are token type IDs? <../glossary.html#token-type-ids>`_
        position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Indices of positions of each input sequence tokens in the position embeddings.
            Selected in the range ``[0, config.max_position_embeddings - 1]``.

            `What are position IDs? <../glossary.html#position-ids>`_
        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
            Mask to nullify selected heads of the self-attention modules.
            Mask values selected in ``[0, 1]``:
            :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
"""


[docs]@add_start_docstrings( "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.", ROBERTA_START_DOCSTRING, ) class RobertaModel(BertModel): """ This class overrides :class:`~transformers.BertModel`. Please check the superclass for the appropriate documentation alongside usage examples. """ config_class = RobertaConfig pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP base_model_prefix = "roberta" def __init__(self, config): super().__init__(config) self.embeddings = RobertaEmbeddings(config) self.init_weights()
[docs] def get_input_embeddings(self): return self.embeddings.word_embeddings
[docs] def set_input_embeddings(self, value): self.embeddings.word_embeddings = value
[docs]@add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING) class RobertaForMaskedLM(BertPreTrainedModel): config_class = RobertaConfig pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP base_model_prefix = "roberta" def __init__(self, config): super().__init__(config) self.roberta = RobertaModel(config) self.lm_head = RobertaLMHead(config) self.init_weights()
[docs] def get_output_embeddings(self): return self.lm_head.decoder
[docs] @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, masked_lm_labels=None, ): r""" masked_lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]`` Returns: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs: masked_lm_loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: Masked language modeling loss. prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: from transformers import RobertaTokenizer, RobertaForMaskedLM import torch tokenizer = RobertaTokenizer.from_pretrained('roberta-base') model = RobertaForMaskedLM.from_pretrained('roberta-base') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 outputs = model(input_ids, masked_lm_labels=input_ids) loss, prediction_scores = outputs[:2] """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here if masked_lm_labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1)) outputs = (masked_lm_loss,) + outputs return outputs # (masked_lm_loss), prediction_scores, (hidden_states), (attentions)
class RobertaLMHead(nn.Module): """Roberta Head for masked language modeling.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.layer_norm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, features, **kwargs): x = self.dense(features) x = gelu(x) x = self.layer_norm(x) # project back to size of vocabulary with bias x = self.decoder(x) + self.bias return x
[docs]@add_start_docstrings( """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. """, ROBERTA_START_DOCSTRING, ) class RobertaForSequenceClassification(BertPreTrainedModel): config_class = RobertaConfig pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP base_model_prefix = "roberta" def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = RobertaModel(config) self.classifier = RobertaClassificationHead(config)
[docs] @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ..., config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss), If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs: loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided): Classification (or regression if config.num_labels==1) loss. logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: from transformers import RobertaTokenizer, RobertaForSequenceClassification import torch tokenizer = RobertaTokenizer.from_pretrained('roberta-base') model = RobertaForSequenceClassification.from_pretrained('roberta-base') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 outputs = model(input_ids, labels=labels) loss, logits = outputs[:2] """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) outputs = (logits,) + outputs[2:] if labels is not None: if self.num_labels == 1: # We are doing regression loss_fct = MSELoss() loss = loss_fct(logits.view(-1), labels.view(-1)) else: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) outputs = (loss,) + outputs return outputs # (loss), logits, (hidden_states), (attentions)
@add_start_docstrings( """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. """, ROBERTA_START_DOCSTRING, ) class RobertaForMultipleChoice(BertPreTrainedModel): config_class = RobertaConfig pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP base_model_prefix = "roberta" def __init__(self, config): super().__init__(config) self.roberta = RobertaModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) self.init_weights() @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING) def forward( self, input_ids=None, token_type_ids=None, attention_mask=None, labels=None, position_ids=None, head_mask=None, inputs_embeds=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): Labels for computing the multiple choice classification loss. Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above) Returns: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs: loss (:obj:`torch.FloatTensor`` of shape ``(1,)`, `optional`, returned when :obj:`labels` is provided): Classification loss. classification_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`): `num_choices` is the second dimension of the input tensors. (see `input_ids` above). Classification scores (before SoftMax). hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: from transformers import RobertaTokenizer, RobertaForMultipleChoice import torch tokenizer = RobertaTokenizer.from_pretrained('roberta-base') model = RobertaForMultipleChoice.from_pretrained('roberta-base') choices = ["Hello, my dog is cute", "Hello, my cat is amazing"] input_ids = torch.tensor([tokenizer.encode(s, add_special_tokens=True) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices labels = torch.tensor(1).unsqueeze(0) # Batch size 1 outputs = model(input_ids, labels=labels) loss, classification_scores = outputs[:2] """ num_choices = input_ids.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None outputs = self.roberta( flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, head_mask=head_mask, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) outputs = (reshaped_logits,) + outputs[2:] # add hidden states and attention if they are here if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) outputs = (loss,) + outputs return outputs # (loss), reshaped_logits, (hidden_states), (attentions)
[docs]@add_start_docstrings( """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. """, ROBERTA_START_DOCSTRING, ) class RobertaForTokenClassification(BertPreTrainedModel): config_class = RobertaConfig pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP base_model_prefix = "roberta" def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = RobertaModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) self.init_weights()
[docs] @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels - 1]``. Returns: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs: loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) : Classification loss. scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`) Classification scores (before SoftMax). hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: from transformers import RobertaTokenizer, RobertaForTokenClassification import torch tokenizer = RobertaTokenizer.from_pretrained('roberta-base') model = RobertaForTokenClassification.from_pretrained('roberta-base') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0) # Batch size 1 outputs = model(input_ids, labels=labels) loss, scores = outputs[:2] """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here if labels is not None: loss_fct = CrossEntropyLoss() # Only keep active parts of the loss if attention_mask is not None: active_loss = attention_mask.view(-1) == 1 active_logits = logits.view(-1, self.num_labels)[active_loss] active_labels = labels.view(-1)[active_loss] loss = loss_fct(active_logits, active_labels) else: loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) outputs = (loss,) + outputs return outputs # (loss), scores, (hidden_states), (attentions)
class RobertaClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) def forward(self, features, **kwargs): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = torch.tanh(x) x = self.dropout(x) x = self.out_proj(x) return x @add_start_docstrings( """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`). """, ROBERTA_START_DOCSTRING, ) class RobertaForQuestionAnswering(BertPreTrainedModel): config_class = RobertaConfig pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP base_model_prefix = "roberta" def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roberta = RobertaModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) self.init_weights() @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING) def forward( self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None, ): r""" start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. Returns: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs: loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided): Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`): Span-start scores (before SoftMax). end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`): Span-end scores (before SoftMax). hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: # The checkpoint roberta-large is not fine-tuned for question answering. Please see the # examples/run_squad.py example to see how to fine-tune a model to a question answering task. from transformers import RobertaTokenizer, RobertaForQuestionAnswering import torch tokenizer = RobertaTokenizer.from_pretrained('roberta-base') model = RobertaForQuestionAnswering.from_pretrained('roberta-base') question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" input_ids = tokenizer.encode(question, text) start_scores, end_scores = model(torch.tensor([input_ids])) all_tokens = tokenizer.convert_ids_to_tokens(input_ids) answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1]) """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) outputs = (start_logits, end_logits,) + outputs[2:] if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions.clamp_(0, ignored_index) end_positions.clamp_(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 outputs = (total_loss,) + outputs return outputs # (loss), start_logits, end_logits, (hidden_states), (attentions)