commit the model file.

model.py

@@ -0,0 +1,90 @@
+import torch 
+from transformers import AutoModelForTokenClassification, AutoModelForSequenceClassification, AdamW, get_linear_schedule_with_warmup, AutoModel
+from transformers import BertForTokenClassification, BertForSequenceClassification,BertPreTrainedModel, BertModel
+import torch.nn as nn
+from .utils import *
+import torch.nn.functional as F
+
+from ekphrasis.classes.preprocessor import TextPreProcessor
+from ekphrasis.classes.tokenizer import SocialTokenizer
+from ekphrasis.dicts.emoticons import emoticons
+import re
+from transformers import AutoTokenizer, AutoModelForTokenClassification, AdamW, get_linear_schedule_with_warmup
+from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
+
+
+class Model_Rational_Label(BertPreTrainedModel):
+     def __init__(self,config,params):
+        super().__init__(config)
+        self.num_labels=params['num_classes']
+        self.num_targets=params['targets_num']
+        self.impact_factor=params['rationale_impact']
+        self.target_factor=params['target_impact']
+        self.bert = BertModel(config,add_pooling_layer=False)
+        self.pooler=BertPooler(config)
+        self.token_dropout = nn.Dropout(0.2)
+        self.token_classifier = nn.Linear(config.hidden_size, 2)
+        self.dropout = nn.Dropout(0.2)
+        self.classifier = nn.Linear(config.hidden_size, self.num_labels)
+        self.target_dropout = nn.Dropout(0.2)
+        self.target_classifier = nn.Linear(config.hidden_size, self.num_targets)
+        self.init_weights()        
+#         self.embeddings = AutoModelForTokenClassification.from_pretrained(params['model_path'], cache_dir=params['cache_path'])
+        
+     def forward(self, input_ids=None, mask=None, attn=None, labels=None, targets=None):
+        outputs = self.bert(input_ids, mask)
+        # out = outputs.last_hidden_state
+        out=outputs[0]
+        logits = self.token_classifier(self.token_dropout(out))
+        
+        
+#         mean_pooling = torch.mean(out, 1)
+#         max_pooling, _ = torch.max(out, 1)
+#         embed = torch.cat((mean_pooling, max_pooling), 1)
+        embed=self.pooler(outputs[0])
+        y_pred = self.classifier(self.dropout(embed))
+        y_pred_target = torch.sigmoid(self.target_classifier(self.target_dropout(embed)))
+        
+        loss_token = None
+        loss_target= None
+        loss_label = None
+        loss_total = None
+        
+        if attn is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            ### Adding weighted
+            
+            # Only keep active parts of the loss
+            if mask is not None:
+                class_weights=torch.tensor([1.0,1.0],dtype=torch.float).to(input_ids.device)
+                loss_funct = nn.CrossEntropyLoss(class_weights)
+                active_loss = mask.view(-1) == 1
+                active_logits = logits.view(-1, 2)
+                active_labels = torch.where(
+                    active_loss, attn.view(-1), torch.tensor(loss_fct.ignore_index).type_as(attn)
+                )
+                loss_token = loss_funct(active_logits, active_labels)
+            else:
+                loss_token = loss_funct(logits.view(-1, 2), attn.view(-1))
+            
+            loss_total=self.impact_factor*loss_token
+            
+        if targets is not None:
+            loss_funct = nn.BCELoss()
+            loss_logits =  loss_funct(y_pred_target.view(-1, self.num_targets), targets.view(-1, self.num_targets))
+            loss_targets= loss_logits
+            loss_total+=self.target_factor*loss_targets
+            
+            
+        if labels is not None:
+            loss_funct = nn.CrossEntropyLoss()
+            loss_logits =  loss_funct(y_pred.view(-1, self.num_labels), labels.view(-1))
+            loss_label= loss_logits
+            if(loss_total is not None):
+                loss_total+=loss_label
+            else:
+                loss_total=loss_label
+        if(loss_total is not None):
+            return y_pred,y_pred_target,logits, loss_total
+        else:
+            return y_pred,y_pred_target,logits