first version of negative sampling

lit_models/transformer.py

@@ -81,10 +81,13 @@ class TransformerLitModel(BaseLitModel):
         pos = batch.pop("pos")
         try:
             en = batch.pop("en")
+            self.print("__DEBUG__: en", en)
             rel = batch.pop("rel")
+            self.print("__DEBUG__: rel", rel)
         except KeyError:
             pass
         input_ids = batch['input_ids']
+        self.print("__DEBUG__: input_ids", input_ids)
 
         distance_attention = torch.stack([pad_distance(len(input_ids[i]) - len(distance) - 1, distance) for i, distance in enumerate(batch['distance_attention'])])
         distance = batch.pop("distance_attention")
@@ -382,13 +385,17 @@ class CombineEntityEmbeddingLitModel(UseEntityEmbeddingLitModel):
         self.id2entity = {}
         with open("./dataset/FB15k-237/entity2textlong.txt", 'r') as file:
             cnt = 0
-            for line in file.readlines():
+            lines = file.readlines()
+            lines.append('[NEG]\t')
+            for line in lines:
                 e, d = line.strip().split("\t")
                 self.id2entity[cnt] = e
                 cnt += 1
         self.id2entity_t = {}
         with open("./dataset/FB15k-237/entity2text.txt", 'r') as file:
-            for line in file.readlines():
+            lines = file.readlines()
+            lines.append('[NEG]\t')
+            for line in lines:
                 e, d = line.strip().split("\t")
                 self.id2entity_t[e] = d
         for k, v in self.id2entity.items():