|
""" |
|
Implementation borrowed from transformers package and extended to support multiple prediction heads: |
|
|
|
https://github.com/huggingface/transformers/blob/master/src/transformers/models/bert/modeling_bert.py |
|
""" |
|
|
|
import torch |
|
from torch import nn |
|
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
|
|
|
import transformers |
|
from transformers import BertTokenizer |
|
from transformers import models |
|
from transformers.modeling_outputs import SequenceClassifierOutput |
|
|
|
from transformers.models.bert.configuration_bert import BertConfig |
|
from transformers.models.bert.modeling_bert import ( |
|
BertPreTrainedModel, |
|
BERT_INPUTS_DOCSTRING, |
|
_TOKENIZER_FOR_DOC, |
|
_CHECKPOINT_FOR_DOC, |
|
_CONFIG_FOR_DOC, |
|
BertModel, |
|
) |
|
|
|
from transformers.file_utils import ( |
|
add_code_sample_docstrings, |
|
add_start_docstrings_to_model_forward, |
|
) |
|
|
|
|
|
class BertForSequenceClassification(BertPreTrainedModel): |
|
def __init__(self, config, **kwargs): |
|
super().__init__(transformers.PretrainedConfig()) |
|
self.num_labels = kwargs.get("task_labels_map", {}) |
|
self.config = config |
|
|
|
self.bert = BertModel(config) |
|
classifier_dropout = ( |
|
config.classifier_dropout |
|
if config.classifier_dropout is not None |
|
else config.hidden_dropout_prob |
|
) |
|
self.dropout = nn.Dropout(classifier_dropout) |
|
|
|
self.classifier1 = nn.Linear( |
|
config.hidden_size, list(self.num_labels.values())[0] |
|
) |
|
self.classifier2 = nn.Linear( |
|
config.hidden_size, list(self.num_labels.values())[1] |
|
) |
|
|
|
self.init_weights() |
|
|
|
@add_start_docstrings_to_model_forward( |
|
BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length") |
|
) |
|
@add_code_sample_docstrings( |
|
tokenizer_class=_TOKENIZER_FOR_DOC, |
|
checkpoint=_CHECKPOINT_FOR_DOC, |
|
output_type=SequenceClassifierOutput, |
|
config_class=_CONFIG_FOR_DOC, |
|
) |
|
def forward( |
|
self, |
|
input_ids=None, |
|
attention_mask=None, |
|
token_type_ids=None, |
|
position_ids=None, |
|
head_mask=None, |
|
inputs_embeds=None, |
|
labels=None, |
|
output_attentions=None, |
|
output_hidden_states=None, |
|
return_dict=None, |
|
task_name=None, |
|
): |
|
r""" |
|
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): |
|
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). |
|
""" |
|
return_dict = ( |
|
return_dict if return_dict is not None else self.config.use_return_dict |
|
) |
|
|
|
outputs = self.bert( |
|
input_ids, |
|
attention_mask=attention_mask, |
|
token_type_ids=token_type_ids, |
|
position_ids=position_ids, |
|
head_mask=head_mask, |
|
inputs_embeds=inputs_embeds, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
pooled_output = outputs[1] |
|
|
|
pooled_output = self.dropout(pooled_output) |
|
logits = None |
|
if task_name == list(self.num_labels.keys())[0]: |
|
logits = self.classifier1(pooled_output) |
|
elif task_name == list(self.num_labels.keys())[1]: |
|
logits = self.classifier2(pooled_output) |
|
|
|
loss = None |
|
if labels is not None: |
|
if self.config.problem_type is None: |
|
if self.num_labels[task_name] == 1: |
|
self.config.problem_type = "regression" |
|
elif self.num_labels[task_name] > 1 and ( |
|
labels.dtype == torch.long or labels.dtype == torch.int |
|
): |
|
self.config.problem_type = "single_label_classification" |
|
else: |
|
self.config.problem_type = "multi_label_classification" |
|
|
|
if self.config.problem_type == "regression": |
|
loss_fct = MSELoss() |
|
if self.num_labels[task_name] == 1: |
|
loss = loss_fct(logits.squeeze(), labels.squeeze()) |
|
else: |
|
loss = loss_fct(logits, labels) |
|
elif self.config.problem_type == "single_label_classification": |
|
loss_fct = CrossEntropyLoss() |
|
loss = loss_fct( |
|
logits.view(-1, self.num_labels[task_name]), labels.view(-1) |
|
) |
|
elif self.config.problem_type == "multi_label_classification": |
|
loss_fct = BCEWithLogitsLoss() |
|
loss = loss_fct(logits, labels) |
|
if not return_dict: |
|
output = (logits,) + outputs[2:] |
|
return ((loss,) + output) if loss is not None else output |
|
|
|
return SequenceClassifierOutput( |
|
loss=loss, |
|
logits=logits, |
|
hidden_states=outputs.hidden_states, |
|
attentions=outputs.attentions, |
|
) |
|
|
