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from typing import Optional, Tuple, Union |
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import torch |
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from torch import nn |
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from torch.nn import CrossEntropyLoss, BCEWithLogitsLoss, MSELoss |
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from transformers import BertPreTrainedModel, BertModel, BertForSequenceClassification |
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from transformers.modeling_outputs import SequenceClassifierOutput |
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from trc_model.temporal_relation_classification_config import TemporalRelationClassificationConfig |
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class TokenPooler(nn.Module): |
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def __init__(self, config): |
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super().__init__() |
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self.dense = nn.Linear(config.hidden_size, config.hidden_size) |
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self.activation = nn.Tanh() |
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def forward(self, token_tensor: torch.Tensor) -> torch.Tensor: |
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pooled_output = self.dense(token_tensor) |
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pooled_output = self.activation(pooled_output) |
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return pooled_output |
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class TemporalRelationClassification(BertForSequenceClassification): |
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config_class = TemporalRelationClassificationConfig |
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def __init__(self, config): |
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super().__init__(config) |
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self.num_labels = config.num_labels |
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self.special_markers = config.special_markers |
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self.pool_tokens = config.pool_tokens |
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self.ES_ID = config.ES_ID |
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self.EMS1 = config.EMS1 |
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self.EMS2 = config.EMS2 |
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self.architecture = config.architecture |
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self.config = config |
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self.bert = BertModel.from_pretrained(config.base_lm) |
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if self.bert.config.vocab_size != config.vocab_size: |
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self.bert.resize_token_embeddings(config.vocab_size) |
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classifier_dropout = ( |
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config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob |
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) |
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if config.pool_tokens: |
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self.ems_1_pooler = TokenPooler(config) |
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self.ems_2_pooler = TokenPooler(config) |
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self.e_1_pooler = TokenPooler(config) |
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self.e_2_pooler = TokenPooler(config) |
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self.dropout = nn.Dropout(classifier_dropout) |
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self.classification_layers = None |
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if self.architecture == 'SEQ_CLS': |
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self.classification_layers = nn.Sequential( |
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nn.Linear(config.hidden_size, config.num_labels) |
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) |
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if self.architecture == 'EMP': |
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self.e_1_linear = nn.Linear(config.hidden_size * 2, config.hidden_size) |
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self.e_2_linear = nn.Linear(config.hidden_size * 2, config.hidden_size) |
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if self.architecture in ['ESS', 'EF', 'EMP']: |
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self.classification_layers = nn.Sequential( |
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nn.Linear(config.hidden_size * 2, config.hidden_size), |
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nn.Linear(config.hidden_size, config.num_labels) |
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) |
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def _get_entities_and_start_markers_indices(self, input_ids): |
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if not self.special_markers: |
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event_1_start, event_2_start = torch.tensor( |
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[(ids == self.ES_ID).nonzero().squeeze().tolist() for ids in input_ids]).T |
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return event_1_start, event_1_start + 1, event_2_start, event_2_start + 1 |
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em1_s = torch.tensor([(ids == self.EMS1).nonzero().item() for ids in input_ids], device=self.device) |
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entity_1 = em1_s + 1 |
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em2_s = torch.tensor([(ids == self.EMS2).nonzero().item() for ids in input_ids], device=self.device) |
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entity_2 = em2_s + 1 |
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return em1_s, entity_1, em2_s, entity_2 |
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def forward( |
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self, |
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input_ids: Optional[torch.Tensor] = None, |
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attention_mask: Optional[torch.Tensor] = None, |
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token_type_ids: Optional[torch.Tensor] = None, |
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position_ids: Optional[torch.Tensor] = None, |
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head_mask: Optional[torch.Tensor] = None, |
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inputs_embeds: Optional[torch.Tensor] = None, |
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labels: Optional[torch.Tensor] = None, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: |
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r""" |
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labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): |
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Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., |
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config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If |
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`config.num_labels > 1` a classification loss is computed (Cross-Entropy). |
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""" |
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
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outputs = self.bert( |
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input_ids, |
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attention_mask=attention_mask, |
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token_type_ids=token_type_ids, |
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position_ids=position_ids, |
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head_mask=head_mask, |
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inputs_embeds=inputs_embeds, |
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output_attentions=output_attentions, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict, |
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) |
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logits = None |
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if self.architecture == 'SEQ_CLS': |
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pooled_output = outputs[1] |
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pooled_output = self.dropout(pooled_output) |
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logits = self.classification_layers(pooled_output) |
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else: |
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sequence_output = outputs[0] |
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sequence_output = self.dropout(sequence_output) |
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entity_mark_1_s, entity_1, entity_mark_2_s, entity_2 = self._get_entities_and_start_markers_indices( |
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input_ids) |
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e1_start_mark_tensors = sequence_output[torch.arange(sequence_output.size(0)), entity_mark_1_s] |
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e2_start_mark_tensors = sequence_output[torch.arange(sequence_output.size(0)), entity_mark_2_s] |
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e1_tensor = sequence_output[torch.arange(sequence_output.size(0)), entity_1] |
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e2_tensor = sequence_output[torch.arange(sequence_output.size(0)), entity_2] |
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if self.pool_tokens: |
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e1_start_mark_tensors = self.ems_1_pooler(e1_start_mark_tensors) |
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e2_start_mark_tensors = self.ems_2_pooler(e2_start_mark_tensors) |
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e1_tensor = self.e_1_pooler(e1_tensor) |
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e2_tensor = self.e_2_pooler(e2_tensor) |
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if self.architecture == 'ESS': |
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e_start_markers_cat = torch.cat((e1_start_mark_tensors, e2_start_mark_tensors), 1) |
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logits = self.classification_layers(e_start_markers_cat) |
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if self.architecture == 'EF': |
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events_cat = torch.cat((e1_tensor, e2_tensor), 1) |
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logits = self.classification_layers(events_cat) |
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if self.architecture == 'EMP': |
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e1_and_start_mark = self.e_1_linear(torch.cat((e1_start_mark_tensors, e1_tensor), 1)) |
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e2_and_start_mark = self.e_2_linear(torch.cat((e2_start_mark_tensors, e2_tensor), 1)) |
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both_e_cat = torch.cat((e1_and_start_mark, e2_and_start_mark), 1) |
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logits = self.classification_layers(both_e_cat) |
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loss = None |
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if labels is not None: |
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loss_fct = CrossEntropyLoss() |
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loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) |
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if not return_dict: |
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output = (logits,) + outputs[2:] |
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return ((loss,) + output) if loss is not None else output |
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return SequenceClassifierOutput( |
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loss=loss, |
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logits=logits, |
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hidden_states=outputs.hidden_states, |
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attentions=outputs.attentions, |
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) |
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