added confidence

generic_nel.py

@@ -111,26 +111,25 @@ class NelPipeline(Pipeline):
             num_beams=1,
             num_return_sequences=1,
             max_new_tokens=30,
-            # return_dict_in_generate=True,
-            # output_scores=True,
+            return_dict_in_generate=True,
+            output_scores=True,
         )
-        # print(outputs)
-        # token_ids, scores = outputs.sequences, outputs.scores
+        print(outputs.keys(), "!!!!")
+        token_ids, scores = outputs.sequences, outputs.scores
         # Decode the predictions into readable text
         wikipedia_predictions = self.tokenizer.batch_decode(
-            outputs, skip_special_tokens=True
+            outputs.sequences, skip_special_tokens=True
         )
         # Process the scores for each token
 
         # print(scores)
         # print(type(scores), len(scores), type(list(scores)))
-        # scores = torch.cat(scores, dim=0)
+        scores = torch.cat(scores, dim=0)
         #
         # # scores_tensor = scores.clone().detach()
-        # probabilities = torch.exp(scores)
+        probabilities = torch.exp(scores)
         # percentages = (probabilities * 100.0).cpu().numpy().tolist()
 
-        percentages = [0, 1, 2, 3, 3, 5]
         # # Process each score (logits for the generated tokens)
         # for i, score in enumerate(scores):
         #     # Apply softmax to convert logits into probabilities