refine train readme and script

README.md

@@ -96,7 +96,7 @@ If None, it will return a full list of supported model names.
 ```python
 from towhee import ops
 
-op = ops.sentence_embedding.sentence_transformers().get_op()
+op = ops.sentence_embedding.sbert().get_op()
 full_list = op.supported_model_names()
 ```
 
@@ -113,22 +113,31 @@ import towhee
 import os
 from sentence_transformers import util
 
-op = towhee.ops.sentence_embedding.sentence_transformers(model_name='nli-distilroberta-base-v2').get_op()
+op = towhee.ops.sentence_embedding.sbert(model_name='nli-distilroberta-base-v2').get_op()
 
 sts_dataset_path = 'datasets/stsbenchmark.tsv.gz'
 
 if not os.path.exists(sts_dataset_path):
     util.http_get('https://sbert.net/datasets/stsbenchmark.tsv.gz', sts_dataset_path)
 
+    
+model_save_path = './output'
 training_config = {
     'sts_dataset_path': sts_dataset_path,
     'train_batch_size': 16,
     'num_epochs': 4,
-    'model_save_path': './output'
+    'model_save_path': model_save_path
 }
 op.train(training_config)
+
+
+### Load trained weights
+### You just need to init a new operator with the trained folder under `model_save_path`.
+model_path = os.path.join(model_save_path, os.listdir(model_save_path)[-1]) 
+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/sentence_transformers/src/branch/main/train_sts_task.py) in your customer way. 
+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.

train_sts_task.py

@@ -27,7 +27,7 @@ def train_sts(model, training_config):
     model_save_path = training_config['model_save_path']
     if not os.path.exists(model_save_path):
         os.mkdir(model_save_path)
-    model_save_path = os.path.join('training_stsbenchmark_continue_training-' + datetime.now().strftime(
+    model_save_path = os.path.join(model_save_path, 'training_stsbenchmark_continue_training-' + datetime.now().strftime(
         "%Y-%m-%d_%H-%M-%S"))