# coding=utf-8 # Copyright 2018 XXX Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch XXX model. """ #################################################### # In this template, replace all the XXX (various casings) with your model name #################################################### from __future__ import absolute_import, division, print_function, unicode_literals import json import logging import math import os import sys from io import open import torch from torch import nn from torch.nn import CrossEntropyLoss, MSELoss from .modeling_utils import PreTrainedModel, prune_linear_layer from .configuration_xxx import XxxConfig from .file_utils import add_start_docstrings logger = logging.getLogger(__name__) #################################################### # This dict contrains shortcut names and associated url # for the pretrained weights provided with the models #################################################### XXX_PRETRAINED_MODEL_ARCHIVE_MAP = { 'xxx-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/xxx-base-uncased-pytorch_model.bin", 'xxx-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/xxx-large-uncased-pytorch_model.bin", } #################################################### # This is a conversion method from TF 1.0 to PyTorch # More details: https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28 #################################################### def load_tf_weights_in_xxx(model, config, tf_checkpoint_path): """ Load tf checkpoints in a pytorch model. """ try: import re import numpy as np import tensorflow as tf except ImportError: logger.error("Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions.") raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info("Converting TensorFlow checkpoint from {}".format(tf_path)) # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info("Loading TF weight {} with shape {}".format(name, shape)) array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name.split('/') # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model if any(n in ["adam_v", "adam_m", "global_step"] for n in name): logger.info("Skipping {}".format("/".join(name))) continue pointer = model for m_name in name: if re.fullmatch(r'[A-Za-z]+_\d+', m_name): l = re.split(r'_(\d+)', m_name) else: l = [m_name] if l[0] == 'kernel' or l[0] == 'gamma': pointer = getattr(pointer, 'weight') elif l[0] == 'output_bias' or l[0] == 'beta': pointer = getattr(pointer, 'bias') elif l[0] == 'output_weights': pointer = getattr(pointer, 'weight') elif l[0] == 'squad': pointer = getattr(pointer, 'classifier') else: try: pointer = getattr(pointer, l[0]) except AttributeError: logger.info("Skipping {}".format("/".join(name))) continue if len(l) >= 2: num = int(l[1]) pointer = pointer[num] if m_name[-11:] == '_embeddings': pointer = getattr(pointer, 'weight') elif m_name == 'kernel': array = np.transpose(array) try: assert pointer.shape == array.shape except AssertionError as e: e.args += (pointer.shape, array.shape) raise logger.info("Initialize PyTorch weight {}".format(name)) pointer.data = torch.from_numpy(array) return model #################################################### # PyTorch Models are constructed by sub-classing # - torch.nn.Module for the layers and # - PreTrainedModel for the models (itself a sub-class of torch.nn.Module) #################################################### #################################################### # Here is an example of typical layer in a PyTorch model of the library # The classes are usually identical to the TF 2.0 ones without the 'TF' prefix. # # See the conversion methods in modeling_tf_pytorch_utils.py for more details #################################################### class XxxLayer(nn.Module): def __init__(self, config): super(XxxLayer, self).__init__() self.attention = XxxAttention(config) self.intermediate = XxxIntermediate(config) self.output = XxxOutput(config) def forward(self, hidden_states, attention_mask=None, head_mask=None): attention_outputs = self.attention(hidden_states, attention_mask, head_mask) attention_output = attention_outputs[0] intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them return outputs #################################################### # PreTrainedModel is a sub-class of torch.nn.Module # which take care of loading and saving pretrained weights # and various common utilities. # # Here you just need to specify a few (self-explanatory) # pointers for your model and the weights initialization # method if its not fully covered by PreTrainedModel's default method #################################################### class XxxPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for dowloading and loading pretrained models. """ config_class = XxxConfig pretrained_model_archive_map = XXX_PRETRAINED_MODEL_ARCHIVE_MAP load_tf_weights = load_tf_weights_in_xxx base_model_prefix = "transformer" def _init_weights(self, module): """ Initialize the weights """ if isinstance(module, (nn.Linear, nn.Embedding)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) elif isinstance(module, XxxLayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() XXX_START_DOCSTRING = r""" The XXX model was proposed in `XXX: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a bidirectional transformer pre-trained using a combination of masked language modeling objective and next sentence prediction on a large corpus comprising the Toronto Book Corpus and Wikipedia. This model is a PyTorch `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. .. _`XXX: Pre-training of Deep Bidirectional Transformers for Language Understanding`: https://arxiv.org/abs/1810.04805 .. _`torch.nn.Module`: https://pytorch.org/docs/stable/nn.html#module Parameters: config (:class:`~transformers.XxxConfig`): 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. """ XXX_INPUTS_DOCSTRING = r""" Inputs: **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: Indices of input sequence tokens in the vocabulary. To match pre-training, XXX input sequence should be formatted with [CLS] and [SEP] tokens as follows: (a) For sequence pairs: ``tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]`` ``token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1`` (b) For single sequences: ``tokens: [CLS] the dog is hairy . [SEP]`` ``token_type_ids: 0 0 0 0 0 0 0`` Xxx is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than the left. Indices can be obtained using :class:`transformers.XxxTokenizer`. See :func:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details. **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``: 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. **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: 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 (see `XXX: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details). **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0, config.max_position_embeddings - 1]``. **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``: Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``: ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**. **inputs_embeds**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``: Optionally, instead of passing ``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. """ @add_start_docstrings("The bare Xxx Model transformer outputting raw hidden-states without any specific head on top.", XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING) class XxxModel(XxxPreTrainedModel): r""" Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)`` Sequence of hidden-states at the output of the last layer of the model. **pooler_output**: ``torch.FloatTensor`` of shape ``(batch_size, hidden_size)`` Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during Xxx pretraining. This output is usually *not* a good summary of the semantic content of the input, you're often better with averaging or pooling the sequence of hidden-states for the whole input sequence. **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) list of ``torch.FloatTensor`` (one for each layer) of shape ``(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:: tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased') model = XxxModel.from_pretrained('xxx-base-uncased') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 outputs = model(input_ids) last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple """ def __init__(self, config): super(XxxModel, self).__init__(config) self.embeddings = XxxEmbeddings(config) self.encoder = XxxEncoder(config) self.pooler = XxxPooler(config) self.init_weights() @property def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, new_embeddings): self.embeddings.word_embeddings = new_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None): if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] if head_mask is not None: if head_mask.dim() == 1: head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1) head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1) elif head_mask.dim() == 2: head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility else: head_mask = [None] * self.config.num_hidden_layers ################################## # Replace this with your model code embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds) encoder_outputs = self.encoder(embedding_output, extended_attention_mask, head_mask=head_mask) sequence_output = encoder_outputs[0] outputs = (sequence_output,) + encoder_outputs[1:] # add hidden_states and attentions if they are here return outputs # sequence_output, (hidden_states), (attentions) @add_start_docstrings("""Xxx Model with a `language modeling` head on top. """, XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING) class XxxForMaskedLM(XxxPreTrainedModel): r""" **masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: Labels for computing the masked language modeling loss. Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]`` Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: Masked language modeling loss. **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)`` Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) list of ``torch.FloatTensor`` (one for each layer) of shape ``(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:: tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased') model = XxxForMaskedLM.from_pretrained('xxx-base-uncased') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 outputs = model(input_ids, masked_lm_labels=input_ids) loss, prediction_scores = outputs[:2] """ def __init__(self, config): super(XxxForMaskedLM, self).__init__(config) self.transformer = XxxModel(config) self.lm_head = nn.Linear(config.n_embd, config.vocab_size) self.init_weights() def get_output_embeddings(self): return self.lm_head 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): outputs = self.transformer(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.cls(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(ignore_index=-1) 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) @add_start_docstrings("""Xxx Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING) class XxxForSequenceClassification(XxxPreTrainedModel): r""" **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: Labels for computing the sequence classification/regression loss. Indices should be in ``[0, ..., config.num_labels - 1]``. If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss), If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy). Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: Classification (or regression if config.num_labels==1) loss. **logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)`` Classification (or regression if config.num_labels==1) scores (before SoftMax). **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) list of ``torch.FloatTensor`` (one for each layer) of shape ``(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:: tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased') model = XxxForSequenceClassification.from_pretrained('xxx-base-uncased') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 outputs = model(input_ids, labels=labels) loss, logits = outputs[:2] """ def __init__(self, config): super(XxxForSequenceClassification, self).__init__(config) self.num_labels = config.num_labels self.transformer = XxxModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, self.config.num_labels) self.init_weights() def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None): outputs = self.transformer(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here 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("""Xxx 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. """, XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING) class XxxForTokenClassification(XxxPreTrainedModel): r""" **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels - 1]``. Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: Classification loss. **scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.num_labels)`` Classification scores (before SoftMax). **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) list of ``torch.FloatTensor`` (one for each layer) of shape ``(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:: tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased') model = XxxForTokenClassification.from_pretrained('xxx-base-uncased') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).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] """ def __init__(self, config): super(XxxForTokenClassification, self).__init__(config) self.num_labels = config.num_labels self.transformer = XxxModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) self.init_weights() def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None): outputs = self.transformer(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) @add_start_docstrings("""Xxx 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`). """, XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING) class XxxForQuestionAnswering(XxxPreTrainedModel): r""" **start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: 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**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: 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. Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. **start_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)`` Span-start scores (before SoftMax). **end_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)`` Span-end scores (before SoftMax). **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings) of shape ``(batch_size, sequence_length, hidden_size)``: Hidden-states of the model at the output of each layer plus the initial embedding outputs. **attentions**: (`optional`, returned when ``config.output_attentions=True``) list of ``torch.FloatTensor`` (one for each layer) of shape ``(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:: tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased') model = XxxForQuestionAnswering.from_pretrained('xxx-large-uncased-whole-word-masking-finetuned-squad') question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" input_text = "[CLS] " + question + " [SEP] " + text + " [SEP]" input_ids = tokenizer.encode(input_text) token_type_ids = [0 if i <= input_ids.index(102) else 1 for i in range(len(input_ids))] start_scores, end_scores = model(torch.tensor([input_ids]), token_type_ids=torch.tensor([token_type_ids])) all_tokens = tokenizer.convert_ids_to_tokens(input_ids) print(' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])) # a nice puppet """ def __init__(self, config): super(XxxForQuestionAnswering, self).__init__(config) self.num_labels = config.num_labels self.transformer = XxxModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) self.init_weights() def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None): outputs = self.transformer(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)