Update train.py

train.py

@@ -92,9 +92,9 @@ def _get_train_sampler(self) :
             )
 
     def compute_loss(self, model, inputs, return_outputs=False):
-        for task_id in inputs['task_name']:
-            assert task_id==inputs['task_name'][0],f"Examples in the same batch should come from the same task, " \
-                                                 f"but task {task_id} and task {inputs['task_name'][0]} are found"
+        for task_id in inputs['task_id']:
+            assert task_id==inputs['task_id'][0],f"Examples in the same batch should come from the same task, " \
+                                                 f"but task {task_id} and task {inputs['task_id'][0]} are found"
         cur_results = {}
         for k in ['query', 'pos', 'neg']:
             cur_inputs = {
@@ -447,12 +447,12 @@ def main():
     def get_examples_raw(old_examples_raw, total_n, real_batch_size):
         examples_raw = []
         for idx in range(0, total_n, real_batch_size):
-            local_task_name = old_examples_raw[idx]['task_name']
+            local_task_name = old_examples_raw[idx]['task_id']
             cur_batch = []
             include_batch = True
             for idx1 in range(idx, min(idx + real_batch_size, total_n)):
-                if not old_examples_raw[idx1]['task_name'] == local_task_name:
-                    print(f'one batch in task {old_examples_raw[idx1]["task_name"]} is skipped')
+                if not old_examples_raw[idx1]['task_id'] == local_task_name:
+                    print(f'one batch in task {old_examples_raw[idx1]["task_id"]} is skipped')
                     include_batch = False
                     break
                 else:
@@ -478,7 +478,7 @@ def get_examples_raw(old_examples_raw, total_n, real_batch_size):
         train_examples_raw = train_examples_raw[:int(data_args.debug_mode)]
 
     def get_dataset(examples_raw):
-        examples = {'query':[],'pos':[],'neg':[],'task_name':[]}
+        examples = {'query':[],'pos':[],'neg':[],'task_id':[]}
         task_name_map = {}
         total_num = len(examples_raw)
         task_count = 0
@@ -492,10 +492,10 @@ def get_dataset(examples_raw):
                     cur_e[k][0] = ''
                 assert cur_e[k][0].startswith('Represent ') or cur_e[k][0]==''
                 examples[k].append('!@#$%^&**!@#$%^&**'.join(cur_e[k]))
-            if not cur_e['task_name'] in task_name_map:
-                task_name_map[cur_e['task_name']] = task_count
+            if not cur_e['task_id'] in task_name_map:
+                task_name_map[cur_e['task_id']] = task_count
                 task_count += 1
-            examples['task_name'].append(task_name_map[cur_e['task_name']])
+            examples['task_id'].append(task_name_map[cur_e['task_id']])
         return examples
 
     train_raw_datasets = DatasetDict({'train':Dataset.from_dict(get_dataset(train_examples_raw))})
@@ -530,7 +530,7 @@ def preprocess_function(examples):
                     all_tokenized[k] = all_tokenized[k].tolist()
             for k in keys:
                 all_tokenized[f'{key}_{k}'] = tokenized[k].tolist()
-        all_tokenized['task_name'] = examples['task_name']
+        all_tokenized['task_id'] = examples['task_id']
         return all_tokenized
 
     train_dataset = train_raw_datasets["train"]