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import torch |
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from transformers import AutoModelForTokenClassification, AutoModelForSequenceClassification, AdamW, get_linear_schedule_with_warmup, AutoModel |
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from transformers import BertForTokenClassification, BertForSequenceClassification,BertPreTrainedModel, BertModel |
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import torch.nn as nn |
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from .utils import * |
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import torch.nn.functional as F |
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from ekphrasis.classes.preprocessor import TextPreProcessor |
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from ekphrasis.classes.tokenizer import SocialTokenizer |
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from ekphrasis.dicts.emoticons import emoticons |
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import re |
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from transformers import AutoTokenizer, AutoModelForTokenClassification, AdamW, get_linear_schedule_with_warmup |
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from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler |
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class Model_Rational_Label(BertPreTrainedModel): |
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def __init__(self,config,params): |
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super().__init__(config) |
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self.num_labels=params['num_classes'] |
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self.num_targets=params['targets_num'] |
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self.impact_factor=params['rationale_impact'] |
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self.target_factor=params['target_impact'] |
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self.bert = BertModel(config,add_pooling_layer=False) |
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self.pooler=BertPooler(config) |
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self.token_dropout = nn.Dropout(0.2) |
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self.token_classifier = nn.Linear(config.hidden_size, 2) |
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self.dropout = nn.Dropout(0.2) |
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self.classifier = nn.Linear(config.hidden_size, self.num_labels) |
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self.target_dropout = nn.Dropout(0.2) |
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self.target_classifier = nn.Linear(config.hidden_size, self.num_targets) |
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self.init_weights() |
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def forward(self, input_ids=None, mask=None, attn=None, labels=None, targets=None): |
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outputs = self.bert(input_ids, mask) |
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out=outputs[0] |
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logits = self.token_classifier(self.token_dropout(out)) |
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embed=self.pooler(outputs[0]) |
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y_pred = self.classifier(self.dropout(embed)) |
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y_pred_target = torch.sigmoid(self.target_classifier(self.target_dropout(embed))) |
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loss_token = None |
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loss_target= None |
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loss_label = None |
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loss_total = None |
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if attn is not None: |
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loss_fct = nn.CrossEntropyLoss() |
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if mask is not None: |
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class_weights=torch.tensor([1.0,1.0],dtype=torch.float).to(input_ids.device) |
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loss_funct = nn.CrossEntropyLoss(class_weights) |
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active_loss = mask.view(-1) == 1 |
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active_logits = logits.view(-1, 2) |
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active_labels = torch.where( |
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active_loss, attn.view(-1), torch.tensor(loss_fct.ignore_index).type_as(attn) |
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) |
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loss_token = loss_funct(active_logits, active_labels) |
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else: |
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loss_token = loss_funct(logits.view(-1, 2), attn.view(-1)) |
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loss_total=self.impact_factor*loss_token |
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if targets is not None: |
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loss_funct = nn.BCELoss() |
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loss_logits = loss_funct(y_pred_target.view(-1, self.num_targets), targets.view(-1, self.num_targets)) |
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loss_targets= loss_logits |
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loss_total+=self.target_factor*loss_targets |
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if labels is not None: |
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loss_funct = nn.CrossEntropyLoss() |
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loss_logits = loss_funct(y_pred.view(-1, self.num_labels), labels.view(-1)) |
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loss_label= loss_logits |
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if(loss_total is not None): |
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loss_total+=loss_label |
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else: |
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loss_total=loss_label |
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if(loss_total is not None): |
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return y_pred,y_pred_target,logits, loss_total |
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else: |
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return y_pred,y_pred_target,logits |
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