apply ns

.gitignore

@@ -7,4 +7,3 @@ dataset/FB15k-237/masked_*.txt
 dataset/FB15k-237/cached_*.pkl
 **/__pycache__/
 **/.DS_Store
-nohup.out

.vscode/launch.json

@@ -12,18 +12,17 @@
             "console": "integratedTerminal",
             "justMyCode": true,
             "args": [
-                "--gpus", "1", 
+                "--gpus", "1,", 
                 "--max_epochs=16",  
                 "--num_workers=32", 
                 "--model_name_or_path",  "bert-base-uncased",
                 "--accumulate_grad_batches", "1", 
                 "--model_class", "BertKGC",
-                "--batch_size", "64",
-                "--checkpoint", "/kg_374/Relphormer/pretrain/output/FB15k-237/epoch=15-step=19299-Eval/hits10=0.96.ckpt",
+                "--batch_size", "32",
+                "--checkpoint", "/root/kg_374/Relphormer/pretrain/output/FB15k-237/epoch=15-step=38899-Eval/hits10=0.96.ckpt",
                 "--pretrain", "0",
                 "--bce", "0",
                 "--check_val_every_n_epoch", "1",
-                "--overwrite_cache",
                 "--data_dir", "dataset/FB15k-237", 
                 "--eval_batch_size", "128",
                 "--max_seq_length", "128",
@@ -31,7 +30,6 @@
                 "--max_triplet", "64",
                 "--add_attn_bias", "True",
                 "--use_global_node", "True",
-                "--fast_dev_run", "True",
             ]
         }
     ]

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, getNegativeEntityId
+from .processor import KGProcessor, get_dataset
 import transformers
 transformers.logging.set_verbosity_error()
 
@@ -79,7 +79,6 @@ class DataCollatorForSeq2Seq:
     label_pad_token_id: int = -100
     return_tensors: str = "pt"
     num_labels: int = 0
-    args: Any = None
 
     def __call__(self, features, return_tensors=None):
 
@@ -106,7 +105,6 @@ class DataCollatorForSeq2Seq:
                 if isinstance(l, int): 
                     new_labels[i][l] = 1
                 else:
-                    if (l[0] != getNegativeEntityId(self.args)):
                     for j in l:
                         new_labels[i][j] = 1
             labels = new_labels
@@ -143,7 +141,6 @@ class KGC(BaseDataModule):
             padding="longest",
             max_length=self.args.max_seq_length,
             num_labels = len(entity_list),
-            args=args
         )
         relations_tokens = self.processor.get_relations(args.data_dir)
         self.num_relations = len(relations_tokens)

data/processor.py

@@ -5,7 +5,7 @@ import contextlib
 import sys
 
 from collections import Counter
-from multiprocessing import Pool, synchronize
+from multiprocessing import Pool
 from torch._C import HOIST_CONV_PACKED_PARAMS
 from torch.utils.data import Dataset, Sampler, IterableDataset
 from collections import defaultdict
@@ -110,7 +110,7 @@ def cache_results(_cache_fp, _refresh=False, _verbose=1):
             
             model_name = my_args.model_name_or_path.split("/")[-1]
             is_pretrain = my_args.pretrain
-            cache_filepath = os.path.join(my_args.data_dir, f"cached_{func.__name__}_{mode}_features{model_name}_pretrain{is_pretrain}_faiss{my_args.faiss_init}_seqlength{my_args.max_seq_length}_{my_args.litmodel_class}.pkl")
+            cache_filepath = os.path.join(my_args.data_dir, f"cached_{mode}_features{model_name}_pretrain{is_pretrain}_faiss{my_args.faiss_init}_seqlength{my_args.max_seq_length}_{my_args.litmodel_class}.pkl")
             refresh = my_args.overwrite_cache
 
             if cache_filepath is not None and refresh is False:
@@ -137,116 +137,6 @@ def cache_results(_cache_fp, _refresh=False, _verbose=1):
 
     return wrapper_
 
-def cache_results_load_once(_cache_fp, _refresh=False, _verbose=1, _global_var=None):
-    r"""
-    === USE IN TRAINING MODE ONLY ===
-    cache_results是fastNLP中用于cache数据的装饰器。通过下面的例子看一下如何使用::
-
-        import time
-        import numpy as np
-        from fastNLP import cache_results
-        
-        @cache_results('cache.pkl')
-        def process_data():
-            # 一些比较耗时的工作，比如读取数据，预处理数据等，这里用time.sleep()代替耗时
-            time.sleep(1)
-            return np.random.randint(10, size=(5,))
-        
-        start_time = time.time()
-        print("res =",process_data())
-        print(time.time() - start_time)
-        
-        start_time = time.time()
-        print("res =",process_data())
-        print(time.time() - start_time)
-        
-        # 输出内容如下，可以看到两次结果相同，且第二次几乎没有花费时间
-        # Save cache to cache.pkl.
-        # res = [5 4 9 1 8]
-        # 1.0042750835418701
-        # Read cache from cache.pkl.
-        # res = [5 4 9 1 8]
-        # 0.0040721893310546875
-
-    可以看到第二次运行的时候，只用了0.0001s左右，是由于第二次运行将直接从cache.pkl这个文件读取数据，而不会经过再次预处理::
-
-        # 还是以上面的例子为例，如果需要重新生成另一个cache，比如另一个数据集的内容，通过如下的方式调用即可
-        process_data(_cache_fp='cache2.pkl')  # 完全不影响之前的‘cache.pkl'
-
-    上面的_cache_fp是cache_results会识别的参数，它将从'cache2.pkl'这里缓存/读取数据，即这里的'cache2.pkl'覆盖默认的
-    'cache.pkl'。如果在你的函数前面加上了@cache_results()则你的函数会增加三个参数[_cache_fp, _refresh, _verbose]。
-    上面的例子即为使用_cache_fp的情况，这三个参数不会传入到你的函数中，当然你写的函数参数名也不可能包含这三个名称::
-
-        process_data(_cache_fp='cache2.pkl', _refresh=True)  # 这里强制重新生成一份对预处理的cache。
-        #  _verbose是用于控制输出信息的，如果为0,则不输出任何内容;如果为1,则会提醒当前步骤是读取的cache还是生成了新的cache
-
-    :param str _cache_fp: 将返回结果缓存到什么位置;或从什么位置读取缓存。如果为None，cache_results没有任何效用，除非在
-        函数调用的时候传入_cache_fp这个参数。
-    :param bool _refresh: 是否重新生成cache。
-    :param int _verbose: 是否打印cache的信息。
-    :return:
-    """
-
-    def wrapper_(func):
-        signature = inspect.signature(func)
-        for key, _ in signature.parameters.items():
-            if key in ('_cache_fp', '_refresh', '_verbose', '_global_var'):
-                raise RuntimeError("The function decorated by cache_results cannot have keyword `{}`.".format(key))
-        v = globals().get(_global_var, None)
-        if (v is not None):
-            return v
-
-        def wrapper(*args, **kwargs):
-            
-            my_args = args[0]
-            mode = "train"
-            if '_cache_fp' in kwargs:
-                cache_filepath = kwargs.pop('_cache_fp')
-                assert isinstance(cache_filepath, str), "_cache_fp can only be str."
-            else:
-                cache_filepath = _cache_fp
-            if '_refresh' in kwargs:
-                refresh = kwargs.pop('_refresh')
-                assert isinstance(refresh, bool), "_refresh can only be bool."
-            else:
-                refresh = _refresh
-            if '_verbose' in kwargs:
-                verbose = kwargs.pop('_verbose')
-                assert isinstance(verbose, int), "_verbose can only be integer."
-            else:
-                verbose = _verbose
-            refresh_flag = True
-            
-            model_name = my_args.model_name_or_path.split("/")[-1]
-            is_pretrain = my_args.pretrain
-            cache_filepath = os.path.join(my_args.data_dir, f"cached_{func.__name__}_{mode}_features{model_name}_pretrain{is_pretrain}.pkl")
-            refresh = my_args.overwrite_cache
-
-            if cache_filepath is not None and refresh is False:
-                # load data
-                if os.path.exists(cache_filepath):
-                    with open(cache_filepath, 'rb') as f:
-                        results = pickle.load(f)
-                        globals()[_global_var] = results
-                    if verbose == 1:
-                        logger.info("Read cache from {}.".format(cache_filepath))
-                    refresh_flag = False
-
-            if refresh_flag:
-                results = func(*args, **kwargs)
-                if cache_filepath is not None:
-                    if results is None:
-                        raise RuntimeError("The return value is None. Delete the decorator.")
-                    with open(cache_filepath, 'wb') as f:
-                        pickle.dump(results, f)
-                    logger.info("Save cache to {}.".format(cache_filepath))
-
-            return results
-
-        return wrapper
-
-    return wrapper_
-
 
 import argparse
 import csv
@@ -345,31 +235,6 @@ class DataProcessor(object):
 
 import copy
 
-from collections import deque
-import threading
-
-
-class _LiveState(type):
-    _instances = {}
-    _lock = threading.Lock()
-
-    def __call__(cls, *args, **kwargs):
-        if cls not in cls._instances:
-            with cls._lock:
-                if cls not in cls._instances:
-                    cls._instances[cls] = super(_LiveState, cls).__call__(*args, **kwargs)
-        
-        return cls._instances[cls]
-
-class LiveState(metaclass=_LiveState):
-
-    def __init__(self):
-        self._pool_size = 4
-        self._deq = deque(maxlen=self._pool_size)
-    def put(self, item):
-        self._deq.append(item)
-    def get(self):
-        return list(self._deq)
 
 def solve_get_knowledge_store(line, set_type="train", pretrain=1):
     """
@@ -499,58 +364,6 @@ def solve(line,  set_type="train", pretrain=1, max_triplet=32):
             InputExample(guid=guid, text_a="[MASK]", text_b="[PAD]", text_c = "[PAD]", text_d = list(masked_head_seq), label=lmap(lambda x: ent2id[x], b), real_label=ent2id[line[0]], en=[line[1], line[2]], en_id = [rel2id[line[1]], ent2id[line[2]]], rel=rel2id[line[1]], text_d_id = list(masked_head_seq_id), graph_inf = masked_head_graph_list))
         examples.append(
             InputExample(guid=guid, text_a="[PAD]", text_b="[PAD]", text_c = "[MASK]", text_d = list(masked_tail_seq), label=lmap(lambda x: ent2id[x], a), real_label=ent2id[line[2]], en=[line[0], line[1]], en_id = [ent2id[line[0]], rel2id[line[1]]], rel=rel2id[line[1]], text_d_id = list(masked_tail_seq_id), graph_inf = masked_tail_graph_list))
-        
-        liveState = LiveState()
-        _prev = liveState.get()
-
-        if (set_type == "train" and len(_prev) > 0):
-
-            for prev_ent in _prev:
-
-                if (prev_ent == line[0] or prev_ent == line[2]):
-                    continue
-
-                z = head_filter_entities["\t".join([prev_ent,line[1]])]
-                if (len(z) == 0):
-                    z.append('[NEG]')
-                    z.append(line[2])
-                    z.append(line[0])
-                masked_neg_seq = set()
-                masked_neg_seq_id = set()
-
-                masked_neg_graph_list = masked_tail_neighbor.get("\t".join([prev_ent, line[1]]), []) if len(masked_tail_neighbor.get("\t".join([prev_ent, line[1]]), [])) < max_triplet else \
-                    random.sample(masked_tail_neighbor["\t".join([prev_ent, line[1]])], max_triplet)
-                if (len(masked_head_graph_list) == 0):
-                    masked_head_graph_list.append(['[NEG]', line[1], '[NEG]'])
-
-                for item in masked_neg_graph_list:
-                    masked_neg_seq.add(item[0])
-                    masked_neg_seq.add(item[1])
-                    masked_neg_seq.add(item[2])
-                    masked_neg_seq_id.add(ent2id[item[0]])
-                    masked_neg_seq_id.add(rel2id[item[1]])
-                    masked_neg_seq_id.add(ent2id[item[2]])
-                    
-                masked_neg_seq = masked_neg_seq.difference({line[0]})
-                masked_neg_seq = masked_neg_seq.difference({line[2]})
-                masked_neg_seq = masked_neg_seq.difference({line[1]})
-                masked_neg_seq = masked_neg_seq.difference({prev_ent})
-                masked_neg_seq_id = masked_neg_seq_id.difference({ent2id[line[0]]})
-                masked_neg_seq_id = masked_neg_seq_id.difference({rel2id[line[1]]})
-                masked_neg_seq_id = masked_neg_seq_id.difference({ent2id[line[2]]})
-                masked_neg_seq_id = masked_neg_seq_id.difference({ent2id[prev_ent]})
-                # examples.append(
-                #     InputExample(guid=guid, text_a="[MASK]", text_b=' '.join(text_b.split(' ')[:16]) + " [PAD]", text_c = "[PAD]" + " " + ' '.join(text_c.split(' ')[:16]), text_d = masked_head_seq, label=lmap(lambda x: ent2id[x], b), real_label=ent2id[line[0]], en=[rel2id[line[1]], ent2id[line[2]]], rel=rel2id[line[1]]))
-                # examples.append(
-                #     InputExample(guid=guid, text_a="[PAD] ", text_b=' '.join(text_b.split(' ')[:16]) + " [PAD]", text_c = "[MASK]" +" " + ' '.join(text_a.split(' ')[:16]), text_d = masked_tail_seq, label=lmap(lambda x: ent2id[x], a), real_label=ent2id[line[2]], en=[ent2id[line[0]], rel2id[line[1]]], rel=rel2id[line[1]]))
-                examples.append(
-                    InputExample(guid=guid, text_a="[MASK]", text_b="[PAD]", text_c = "[PAD]", text_d = list(masked_neg_seq), label=lmap(lambda x: ent2id[x], z), real_label=ent2id[line[0]], en=[line[1], prev_ent], en_id = [rel2id[line[1]], ent2id[prev_ent]], rel=rel2id[line[1]], text_d_id = list(masked_neg_seq_id), graph_inf = masked_neg_graph_list))
-                examples.append(    
-                    InputExample(guid=guid, text_a="[MASK]", text_b="[PAD]", text_c = "[PAD]", text_d = list(masked_neg_seq), label=lmap(lambda x: ent2id[x], z), real_label=ent2id[line[2]], en=[line[1], prev_ent], en_id = [rel2id[line[1]], ent2id[prev_ent]], rel=rel2id[line[1]], text_d_id = list(masked_neg_seq_id), graph_inf = masked_neg_graph_list))
-        
-
-        liveState.put(line[0])
-        liveState.put(line[2])
     return examples
 
 def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_, masked_head_neighbor_, masked_tail_neighbor_, rel2token_):
@@ -564,7 +377,6 @@ def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_, masked_head_nei
     global masked_head_neighbor
     global masked_tail_neighbor
     global rel2token
-    # global negativeEntity
 
     head_filter_entities = head
     tail_filter_entities = tail
@@ -576,23 +388,11 @@ def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_, masked_head_nei
     masked_head_neighbor = masked_head_neighbor_
     masked_tail_neighbor = masked_tail_neighbor_
     rel2token = rel2token_
-    # negativeEntity = ent2id['[NEG]']
-    print("Initialized negative entity ID")
 
 def delete_init(ent2text_):
     global ent2text
     ent2text = ent2text_
 
-def getEntityIdByName(name):
-    global ent2id
-    return ent2id[name]
-
-@cache_results_load_once(_cache_fp="./dataset", _global_var='negativeEntity')
-def getNegativeEntityId(args):
-    global negativeEntity
-    negativeEntity = ent2id['[NEG]']
-    return negativeEntity
-
 
 class KGProcessor(DataProcessor):
     """Processor for knowledge graph data set."""
@@ -643,7 +443,6 @@ 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])
@@ -670,7 +469,6 @@ 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:
@@ -772,8 +570,6 @@ class KGProcessor(DataProcessor):
         threads = min(1, cpu_count())
         filter_init(head_filter_entities, tail_filter_entities,ent2text, rel2text, ent2id, ent2token, rel2id, masked_head_neighbor, masked_tail_neighbor, rel2token
             )
-        # cache this
-        getNegativeEntityId(args)
         
         if hasattr(args, "faiss_init") and args.faiss_init:
             annotate_ = partial(
@@ -783,7 +579,6 @@ class KGProcessor(DataProcessor):
         else:
             annotate_ = partial(
                 solve,
-                set_type=set_type,
                 pretrain=self.args.pretrain,
                 max_triplet=self.args.max_triplet
             )

lit_models/transformer.py

@@ -73,21 +73,63 @@ class TransformerLitModel(BaseLitModel):
     def forward(self, x):
         return self.model(x)
 
+    def create_negatives(self, batch):
+        negativeBatches = []
+        label = batch['label']
+
+        for i in range(label.shape[0]):
+            newBatch = {}
+            newBatch['attention_mask'] = None
+            newBatch['input_ids'] = torch.clone(batch['input_ids'])
+            newBatch['label'] = torch.zeros_like(batch['label'])
+            negativeBatches.append(newBatch)
+
+        entity_idx = []
+        self_label = []
+        for idx, l in enumerate(label):
+            decoded = self.decode([batch['input_ids'][idx]])[0].split(' ')
+            for j in range(1, len(decoded)):
+                if (decoded[j].startswith("[ENTITY_")):
+                    entity_idx.append(j)
+                    self_label.append(batch['input_ids'][idx][j])   
+                    break
+        
+        for idx, lbl in enumerate(label):
+            for i in range(label.shape[0]):
+                if (negativeBatches[idx]['input_ids'][i][entity_idx[i]] != lbl):
+                    negativeBatches[idx]['input_ids'][i][entity_idx[i]] = lbl
+                else:
+                    negativeBatches[idx]['input_ids'][i][entity_idx[i]] = self_label[i]
+                
+        return negativeBatches
+
     def training_step(self, batch, batch_idx):  # pylint: disable=unused-argument
         # embed();exit()
         # print(self.optimizers().param_groups[1]['lr'])
+
+        negativeBatches = self.create_negatives(batch)
+
+        loss = 0
+
+        for negativeBatch in negativeBatches:
+            label = negativeBatch.pop("label")
+            input_ids = batch['input_ids']
+            
+            logits = self.model(**negativeBatch, return_dict=True, distance_attention=None).logits
+            _, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True)
+            bs = input_ids.shape[0]
+            mask_logits = logits[torch.arange(bs), mask_idx][:, self.entity_id_st:self.entity_id_ed]
+            loss += self.loss_fn(mask_logits, label)
+
         labels = batch.pop("labels")
         label = batch.pop("label")
         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")
@@ -113,9 +155,9 @@ class TransformerLitModel(BaseLitModel):
 
         assert mask_idx.shape[0] == bs, "only one mask in sequence!"
         if self.args.bce:
-            loss = self.loss_fn(mask_logits, labels)
+            loss += self.loss_fn(mask_logits, labels)
         else:
-            loss = self.loss_fn(mask_logits, label)
+            loss += self.loss_fn(mask_logits, label)
 
         if batch_idx == 0:
             print('\n'.join(self.decode(batch['input_ids'][:4])))
@@ -385,17 +427,13 @@ class CombineEntityEmbeddingLitModel(UseEntityEmbeddingLitModel):
         self.id2entity = {}
         with open("./dataset/FB15k-237/entity2textlong.txt", 'r') as file:
             cnt = 0
-            lines = file.readlines()
-            lines.append('[NEG]\t')
-            for line in lines:
+            for line in file.readlines():
                 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:
-            lines = file.readlines()
-            lines.append('[NEG]\t')
-            for line in lines:
+            for line in file.readlines():
                 e, d = line.strip().split("\t")
                 self.id2entity_t[e] = d
         for k, v in self.id2entity.items():

main.py

@@ -98,7 +98,6 @@ def main():
     tokenizer = data.tokenizer
 
     lit_model = litmodel_class(args=args, model=model, tokenizer=tokenizer, data_config=data.get_config())
-    print("__DEBUG__: Initialized")
     if args.checkpoint:
         lit_model.load_state_dict(torch.load(args.checkpoint, map_location="cpu")["state_dict"], strict=False)
 
@@ -123,10 +122,7 @@ def main():
     # args.weights_summary = "full"  # Print full summary of the model
     trainer = pl.Trainer.from_argparse_args(args, callbacks=callbacks, logger=logger, default_root_dir="training/logs")
     
-    print('__DEBUG__: Init trainer')
-
     if "EntityEmbedding" not in lit_model.__class__.__name__:
-        print('__DEBUG__: Fit trainer')
         trainer.fit(lit_model, datamodule=data)
         path = model_checkpoint.best_model_path
         lit_model.load_state_dict(torch.load(path)["state_dict"], strict=False)

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, getNegativeEntityId
+from .processor import KGProcessor, get_dataset
 import transformers
 transformers.logging.set_verbosity_error()
 
@@ -106,7 +106,6 @@ class DataCollatorForSeq2Seq:
                 if isinstance(l, int): 
                     new_labels[i][l] = 1
                 else:
-                    if (l[0] != getNegativeEntityId()):
                     for j in l:
                         new_labels[i][j] = 1
             labels = new_labels

pretrain/data/processor.py

@@ -314,7 +314,6 @@ 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
@@ -323,19 +322,11 @@ 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."""
@@ -386,7 +377,6 @@ 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])
@@ -413,7 +403,6 @@ 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

@@ -364,17 +364,13 @@ class CombineEntityEmbeddingLitModel(UseEntityEmbeddingLitModel):
         self.id2entity = {}
         with open("./dataset/FB15k-237/entity2textlong.txt", 'r') as file:
             cnt = 0
-            lines = file.readlines()
-            lines.append('[NEG]\t')
-            for line in lines:
+            for line in file.readlines():
                 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:
-            lines = file.readlines()
-            lines.append('[NEG]\t')
-            for line in lines:
+            for line in file.readlines():
                 e, d = line.strip().split("\t")
                 self.id2entity_t[e] = d
         for k, v in self.id2entity.items():

pretrain/scripts/pretrain_fb15k-237.sh

@@ -1,13 +1,13 @@
-nohup python -u main.py --gpus "1" --max_epochs=16  --num_workers=32 \
+nohup python -u main.py --gpus "1," --max_epochs=16  --num_workers=32 \
    --model_name_or_path  bert-base-uncased \
    --accumulate_grad_batches 1 \
    --model_class BertKGC \
-   --batch_size 128 \
+   --batch_size 64 \
    --pretrain 1 \
    --bce 0 \
    --check_val_every_n_epoch 1 \
    --overwrite_cache \
-   --data_dir /kg_374/Relphormer/dataset/FB15k-237 \
+   --data_dir /root/kg_374/Relphormer/dataset/FB15k-237 \
    --eval_batch_size 256 \
    --max_seq_length 64 \
    --lr 1e-4 \

scripts/fb15k-237/fb15k-237.sh

@@ -1,9 +1,9 @@
-nohup python -u main.py --gpus "2," --max_epochs=16  --num_workers=32 \
+nohup python -u main.py --gpus "1," --max_epochs=16  --num_workers=32 \
    --model_name_or_path  bert-base-uncased \
    --accumulate_grad_batches 1 \
    --model_class BertKGC \
-   --batch_size 64 \
-   --checkpoint /kg_374/Relphormer/output/FB15k-237/epoch=1-Eval/hits10=Eval/hits1=0.47-Eval/hits1=0.22.ckpt \
+   --batch_size 16 \
+   --checkpoint /root/kg_374/Relphormer/pretrain/output/FB15k-237/epoch\=15-step\=38899-Eval/hits10=0.96.ckpt \
    --pretrain 0 \
    --bce 0 \
    --check_val_every_n_epoch 1 \