first version of negative sampling

pretrain/data/data_module.py

@@ -10,7 +10,7 @@ from transformers.tokenization_utils_base import (BatchEncoding,
                                                   PreTrainedTokenizerBase)
 
 from .base_data_module import BaseDataModule
-from .processor import KGProcessor, get_dataset
+from .processor import KGProcessor, get_dataset, getNegativeEntityId
 import transformers
 transformers.logging.set_verbosity_error()
 
@@ -106,8 +106,9 @@ class DataCollatorForSeq2Seq:
                 if isinstance(l, int): 
                     new_labels[i][l] = 1
                 else:
-                    for j in l:
-                        new_labels[i][j] = 1
+                    if (l[0] != getNegativeEntityId()):
+                        for j in l:
+                            new_labels[i][j] = 1
             labels = new_labels
 
         features = self.tokenizer.pad(

pretrain/data/processor.py

@@ -314,6 +314,7 @@ def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_):
     global ent2id
     global ent2token
     global rel2id
+    global negativeEntity
 
     head_filter_entities = head
     tail_filter_entities = tail
@@ -322,11 +323,19 @@ def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_):
     ent2id = ent2id_
     ent2token = ent2token_
     rel2id = rel2id_
+    negativeEntity = ent2id['[NEG]']
 
 def delete_init(ent2text_):
     global ent2text
     ent2text = ent2text_
 
+def getEntityIdByName(name):
+    global ent2id
+    return ent2id[name]
+
+def getNegativeEntityId():
+    global negativeEntity
+    return negativeEntity
 
 class KGProcessor(DataProcessor):
     """Processor for knowledge graph data set."""
@@ -377,6 +386,7 @@ class KGProcessor(DataProcessor):
         """Gets all entities in the knowledge graph."""
         with open(self.entity_path, 'r') as f:
             lines = f.readlines()
+            lines.append('[NEG]\t')
             entities = []
             for line in lines:
                 entities.append(line.strip().split("\t")[0])
@@ -403,6 +413,7 @@ class KGProcessor(DataProcessor):
         ent2text_with_type = {}
         with open(self.entity_path, 'r') as f:
             ent_lines = f.readlines()
+            ent_lines.append('[NEG]\t')
             for line in ent_lines:
                 temp = line.strip().split('\t')
                 try:

pretrain/lit_models/transformer.py

@@ -364,13 +364,17 @@ 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():