mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-22 11:09:21 +01:00
112 lines
3.8 KiB
Python
112 lines
3.8 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
|
|
from typing import Any, Dict
|
|
|
|
import torch
|
|
from torch import nn
|
|
from torch.nn import CrossEntropyLoss
|
|
from transformers.modeling_outputs import TokenClassifierOutput
|
|
from transformers.models.bert.modeling_bert import (BertModel,
|
|
BertPreTrainedModel)
|
|
|
|
from modelscope.metainfo import Models
|
|
from modelscope.models.base import Model
|
|
from modelscope.models.builder import MODELS
|
|
from modelscope.utils.constant import Tasks
|
|
|
|
__all__ = ['BertForDocumentSegmentation']
|
|
|
|
|
|
@MODELS.register_module(
|
|
Tasks.document_segmentation, module_name=Models.bert_for_ds)
|
|
class BertForDocumentSegmentation(Model):
|
|
|
|
def __init__(self, model_dir: str, model_config: Dict[str, Any], *args,
|
|
**kwargs):
|
|
super().__init__(model_dir, model_config, *args, **kwargs)
|
|
self.model_cfg = model_config
|
|
|
|
def build_with_config(self, config):
|
|
self.bert_model = BertForDocumentSegmentationBase.from_pretrained(
|
|
self.model_dir, from_tf=False, config=config)
|
|
return self.bert_model
|
|
|
|
def forward(self) -> Dict[str, Any]:
|
|
return self.model_cfg
|
|
|
|
|
|
class BertForDocumentSegmentationBase(BertPreTrainedModel):
|
|
|
|
_keys_to_ignore_on_load_unexpected = [r'pooler']
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.sentence_pooler_type = None
|
|
self.bert = BertModel(config, add_pooling_layer=False)
|
|
|
|
classifier_dropout = config.hidden_dropout_prob
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
self.class_weights = None
|
|
self.init_weights()
|
|
|
|
def forward(self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
sentence_attention_mask=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None):
|
|
|
|
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,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
if self.sentence_pooler_type is not None:
|
|
raise NotImplementedError
|
|
else:
|
|
sequence_output = self.dropout(sequence_output)
|
|
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss(weight=self.class_weights)
|
|
if sentence_attention_mask is not None:
|
|
active_loss = sentence_attention_mask.view(-1) == 1
|
|
active_logits = logits.view(-1, self.num_labels)
|
|
active_labels = torch.where(
|
|
active_loss, labels.view(-1),
|
|
torch.tensor(loss_fct.ignore_index).type_as(labels))
|
|
loss = loss_fct(active_logits, active_labels)
|
|
else:
|
|
loss = loss_fct(
|
|
logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits, ) + outputs[2:]
|
|
return ((loss, ) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|