Remove dc2 for towhee1.0

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>

README.md

@@ -20,7 +20,7 @@ to generate a text embedding for the sentence "This is a sentence.".
 *Write a pipeline with explicit inputs/outputs name specifications:*
 
 ```python
-from towhee.dc2 import pipe, ops, DataCollection
+from towhee import pipe, ops, DataCollection
 
 p = (
     pipe.input('sentence')
@@ -140,4 +140,4 @@ new_op = towhee.ops.sentence_embedding.sbert(model_name=model_path).get_op()
 
 ### Dive deep and customize your training
 You can change the [training script](https://towhee.io/sentence-embedding/sbert/src/branch/main/train_sts_task.py) in your customer way. 
-Or your can refer to the original [sbert training guide](https://www.sbert.net/docs/training/overview.html) and [code example](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training) for more information.
+Or your can refer to the original [sbert training guide](https://www.sbert.net/docs/training/overview.html) and [code example](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training) for more information.

s_bert.py

@@ -85,6 +85,8 @@ class STransformers(NNOperator):
         else:
             sentences = txt
         inputs = self.tokenize(sentences)
+	for k, v in inputs.items():
+		inputs[k] = v.to(self.device)
         embs = self.model(**inputs).cpu().detach().numpy()
         if isinstance(txt, str):
             embs = embs.squeeze(0)
@@ -271,4 +273,4 @@ if __name__ == '__main__':
         'num_epochs': 4,
         'model_save_path': './output'
     }
-    op.train(training_config)
+    op.train(training_config)