app modified

app.py

@@ -269,16 +269,16 @@ class ModeloDataset:
                    
             self.model = RobertaForTokenClassification.from_pretrained("BSC-LT/roberta_model_for_anonimization")
             with torch.no_grad():
-             logits = model(input_ids).logits
+             logits = self.model(input_ids).logits
             predicted_token_class_ids = logits.argmax(-1)
             i=0
             _predicted_tokens_classes=[]
             for a in predicted_token_class_ids:
                     #_predicted_tokens_classes[i]=[model.config.id2label[t.item()] for t in predicted_token_class_ids[i]]
-                    _predicted_tokens_classes.append([model.config.id2label[t.item()] for t in predicted_token_class_ids[i]])
+                    _predicted_tokens_classes.append([self.model.config.id2label[t.item()] for t in predicted_token_class_ids[i]])
                     i=i+1  
             labels = predicted_token_class_ids
-            loss = model(input_ids, labels=labels).loss
+            loss = self.model(input_ids, labels=labels).loss
             #print(round(loss.item(), 2))
         else:
             
@@ -296,16 +296,16 @@ class ModeloDataset:
                     
             self.model = AutoModelForTokenClassification.from_pretrained("FacebookAI/xlm-roberta-large-finetuned-conll03-english")
             with torch.no_grad():
-             logits = model(input_ids).logits
+             logits = self.model(input_ids).logits
             predicted_token_class_ids = logits.argmax(-1)
             i=0
             _predicted_tokens_classes=[]
             for a in predicted_token_class_ids:
                     #_predicted_tokens_classes[i]=[model.config.id2label[t.item()] for t in predicted_token_class_ids[i]]
-                    _predicted_tokens_classes.append([model.config.id2label[t.item()] for t in predicted_token_class_ids[i]])
+                    _predicted_tokens_classes.append([self.model.config.id2label[t.item()] for t in predicted_token_class_ids[i]])
                     i=i+1  
             labels = predicted_token_class_ids
-            loss = model(input_ids, labels=labels).loss
+            loss = self.model(input_ids, labels=labels).loss
             #print(round(loss.item(), 2))
         return ids, _predicted_tokens_classes
     def salida_texto( self,ids,pre_tokens):