cache in run_classifier + various fixes to the examples

examples/bertology.py

@@ -20,8 +20,6 @@ def run_model():
     parser.add_argument('--model_name_or_path', type=str, default='bert-base-uncased',
                                                 help='pretrained model name or path to local checkpoint')
     parser.add_argument("--seed", type=int, default=42)
-    parser.add_argument("--batch_size", type=int, default=-1)
-    parser.add_argument('--unconditional', action='store_true', help='If true, unconditional generation.')
     args = parser.parse_args()
     print(args)
 
@@ -34,57 +32,12 @@ def run_model():
     torch.cuda.manual_seed(args.seed)
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    enc = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
-    model = GPT2LMHeadModel.from_pretrained(args.model_name_or_path)
+    tokenizer = BertTokenizer.from_pretrained(args.model_name_or_path)
+    model = BertModel.from_pretrained(args.model_name_or_path)
     model.to(device)
     model.eval()
 
-    if args.length == -1:
-        args.length = model.config.n_ctx // 2
-    elif args.length > model.config.n_ctx:
-        raise ValueError("Can't get samples longer than window size: %s" % model.config.n_ctx)
-
-    while True:
-        context_tokens = []
-        if not args.unconditional:
-            raw_text = input("Model prompt >>> ")
-            while not raw_text:
-                print('Prompt should not be empty!')
-                raw_text = input("Model prompt >>> ")
-            context_tokens = enc.encode(raw_text)
-            generated = 0
-            for _ in range(args.nsamples // args.batch_size):
-                out = sample_sequence(
-                    model=model, length=args.length,
-                    context=context_tokens,
-                    start_token=None,
-                    batch_size=args.batch_size,
-                    temperature=args.temperature, top_k=args.top_k, device=device
-                )
-                out = out[:, len(context_tokens):].tolist()
-                for i in range(args.batch_size):
-                    generated += 1
-                    text = enc.decode(out[i])
-                    print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
-                    print(text)
-            print("=" * 80)
-        else:
-            generated = 0
-            for _ in range(args.nsamples // args.batch_size):
-                out = sample_sequence(
-                    model=model, length=args.length,
-                    context=None,
-                    start_token=enc.encoder['<|endoftext|>'],
-                    batch_size=args.batch_size,
-                    temperature=args.temperature, top_k=args.top_k, device=device
-                )
-                out = out[:,1:].tolist()
-                for i in range(args.batch_size):
-                    generated += 1
-                    text = enc.decode(out[i])
-                    print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
-                    print(text)
-            print("=" * 80)
+    
 
 if __name__ == '__main__':
     run_model()