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import torch
import torch.nn as nn
from transformers import (
BertPreTrainedModel,
BertModel,
AutoModelForSequenceClassification,
BertConfig,
)
from transformers.modeling_outputs import SequenceClassifierOutput
class BertForRelationExtraction(BertPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
def __init__(self, config):
super().__init__(config)
self.num_labels = len(config.label2id)
self.config = config
self.bert = BertModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(
config.classifier_dropout
if config.classifier_dropout is not None
else config.hidden_dropout_prob
)
self.layer_norm = nn.LayerNorm(2 * config.hidden_size)
self.classifier = nn.Linear(2 * config.hidden_size, self.num_labels)
self.post_init()
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,
):
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]
sequence_output = self.dropout(sequence_output)
e1_start = torch.where(input_ids == self.config.e1_start_token_id)
e2_start = torch.where(input_ids == self.config.e2_start_token_id)
e1_hidden_states = sequence_output[e1_start[0], e1_start[1]]
e2_hidden_states = sequence_output[e2_start[0], e2_start[1]]
h = torch.cat((e1_hidden_states, e2_hidden_states), dim=-1)
logits = self.classifier(self.layer_norm(h))
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:] # Need to check outputs shape
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
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