fix num classes in unsup.

examples/training_unsup_text_matching_model_en.py

@@ -59,7 +59,7 @@ def load_en_stsb_dataset(stsb_file):
     return train_samples, valid_samples, test_samples
 
 
-def load_en_nli_dataset(nli_file, limit_size=200000):
+def load_en_nli_dataset(nli_file, limit_size=100000):
     # Load NLI train dataset
     if not os.path.exists(nli_file):
         http_get('https://sbert.net/datasets/AllNLI.tsv.gz', nli_file)
@@ -104,7 +104,7 @@ def main():
     parser.add_argument('--num_epochs', default=10, type=int, help='Number of training epochs')
     parser.add_argument('--batch_size', default=64, type=int, help='Batch size')
     parser.add_argument('--learning_rate', default=2e-5, type=float, help='Learning rate')
-    parser.add_argument('--nli_limit_size', default=200000, type=float, help='Learning rate')
+    parser.add_argument('--nli_limit_size', default=100000, type=float, help='Learning rate')
     parser.add_argument('--encoder_type', default='FIRST_LAST_AVG', type=lambda t: EncoderType[t],
                         choices=list(EncoderType), help='Encoder type, string name of EncoderType')
     args = parser.parse_args()
@@ -124,11 +124,11 @@ def main():
             train_dataset = CosentTrainDataset(model.tokenizer, train_samples, args.max_seq_length)
         elif args.model_arch == 'sentencebert':
             model = SentenceBertModel(model_name_or_path=args.model_name, encoder_type=args.encoder_type,
-                                      max_seq_length=args.max_seq_length)
+                                      max_seq_length=args.max_seq_length, num_classes=3)
             train_dataset = TextMatchingTrainDataset(model.tokenizer, train_samples, args.max_seq_length)
         else:
             model = BertMatchModel(model_name_or_path=args.model_name, encoder_type=args.encoder_type,
-                                   max_seq_length=args.max_seq_length)
+                                   max_seq_length=args.max_seq_length, num_classes=3)
             train_dataset = TextMatchingTrainDataset(model.tokenizer, train_samples, args.max_seq_length)
         valid_dataset = TextMatchingTestDataset(model.tokenizer, valid_samples, args.max_seq_length)