Refactor

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

README.md

@@ -23,11 +23,20 @@ where embeddings are learned from a small number of questions and passages by a
 
 [2].https://arxiv.org/abs/2004.04906
 
+## Code Example
+
+Use the pretrained model "facebook/dpr-ctx_encoder-single-nq-base"
+to generate a text embedding for the sentence "Hello, world.". 
+
+ *Write the pipeline*:
+
 ```python
-from towhee import ops
+from towhee import dc
+
 
-text_encoder = ops.text_embedding.dpr(model_name="allenai/longformer-base-4096")
-text_embedding = text_encoder("Hello, world.")
+dc.stream(["Hello, world."])
+  .text_embedding.dpr("facebook/dpr-ctx_encoder-single-nq-base")
+  .show()
 ```
 
 ## Factory Constructor
@@ -38,48 +47,37 @@ Create the operator via the following factory method
 
 
 
-## Interface
+## Factory Constructor
 
-A text embedding operator takes a sentence, paragraph, or document in string as an input
-and output an embedding vector in ndarray which captures the input's core semantic elements.
+Create the operator via the following factory method
 
+***text_embedding.dpr(model_name="facebook/dpr-ctx_encoder-single-nq-base")***
 
 **Parameters:**
 
-​	***text***: *str*
+​	***model_name***: *str*
 
-​	The text in string.
+​	The model name in string.
+The default value is "facebook/dpr-ctx_encoder-single-nq-base".
+You can get the list of supported model names by calling `get_model_list` from [auto_transformers.py](https://towhee.io/text-embedding/transformers/src/branch/main/auto_transformers.py).
 
 
+## Interface
 
-**Returns**: *numpy.ndarray*
-
-​	The text embedding extracted by model.
-
-
+The operator takes a text in string as input.
+It loads tokenizer and pre-trained model using model name.
+and then return text embedding in ndarray.
 
-## Code Example
+**Parameters:**
 
-Use the pretrained model ('allenai/longformer-base-4096')
-to generate a text embedding for the sentence "Hello, world.". 
+​	***text***: *str*
 
- *Write the pipeline in simplified style*:
+​	The text in string.
 
-```python
-import towhee.DataCollection as dc
 
-dc.glob("Hello, world.")
-  .text_embedding.dpr('longformer-base-4096')
-  .show()
-```
 
-*Write a same pipeline with explicit inputs/outputs name specifications:*
+**Returns**:
 
-```python
-from towhee import DataCollection as dc
+​    *numpy.ndarray*
 
-dc.glob['text']('Hello, world.')
-  .text_embedding.dpr['text', 'vec']('longformer-base-4096')
-  .select('vec')
-  .show()
-```
+​	The text embedding extracted by model.

__init__.py

@@ -15,5 +15,5 @@
 from .dpr import Dpr
 
 
-def dpr(model_name: str):
-    return Dpr(model_name)
+def dpr(**kwargs):
+    return Dpr(**kwargs)

dpr.py

@@ -1,12 +1,15 @@
 import numpy
-import logging
 from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
 
 from towhee import register
 from towhee.operator import NNOperator
 
 import warnings
+import logging
+
+
 warnings.filterwarnings('ignore')
+logging.getLogger("transformers").setLevel(logging.ERROR)
 log = logging.getLogger()
 
 
@@ -23,7 +26,7 @@ class Dpr(NNOperator):
         model_name (`str`):
             Which model to use for the embeddings.
     """
-    def __init__(self, model_name: str) -> None:
+    def __init__(self, model_name: str = "facebook/dpr-ctx_encoder-single-nq-base") -> None:
         self.model_name = model_name
         try:
             self.tokenizer = DPRContextEncoderTokenizer.from_pretrained(model_name)
@@ -47,5 +50,14 @@ class Dpr(NNOperator):
         except Exception as e:
             log.error(f'Invalid input for the model: {self.model_name}')
             raise e
-        feature_vector = embeddings.detach().numpy()
-        return feature_vector
+        vec = embeddings.detach().numpy()
+        return vec
+
+
+def get_model_list():
+    full_list = [
+        "facebook/dpr-ctx_encoder-single-nq-base",
+        "facebook/dpr-ctx_encoder-multiset-base",
+    ]
+    full_list.sort()
+    return full_list