Update

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

README.md

@@ -1,74 +1,70 @@
 # Text Embedding with Transformers
 
-*author: Jael Gu and David Wang*
+*author: Jael Gu*
 
 
 
 ## Desription
 
-A REALM[1] text embedding operator implemented with pretrained models from [Huggingface Transformers](https://huggingface.co/docs/transformers).
+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.
+This operator uses the REALM model, which is a retrieval-augmented language model that firstly retrieves documents from a textual knowledge corpus and then utilizes retrieved documents to process question answering tasks. [1]
+The original model was proposed in REALM: Retrieval-Augmented Language Model Pre-Training by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.[2]
 
+### Reference
 
+[1].https://huggingface.co/docs/transformers/model_doc/realm
 
-```python
-from towhee import ops
+[2].https://arxiv.org/abs/2002.08909
 
-text_encoder = ops.text_embedding.realm('google/realm-cc-news-pretrained-encoder')
-text_embedding = text_encoder("Hello, world.")
-```
+## Code Example
 
-## Factory Constructor
+Use the pretrained model "google/realm-cc-news-pretrained-embedder"
+to generate a text embedding for the sentence "Hello, world.". 
 
-Create the operator via the following factory method
+ *Write the pipeline*:
 
-***ops.text_embedding.realm(model_name)***
+```python
+from towhee import dc
 
 
+dc.stream(["Hello, world."])
+  .text_embedding.realm(model_name="google/realm-cc-news-pretrained-embedder")
+  .show()
+```
 
-## 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.transformers(model_name="google/realm-cc-news-pretrained-embedder")***
 
 **Parameters:**
 
-​	***text***: *str*
-
-​	The text in string.
-
+​	***model_name***: *str*
 
+​	The model name in string.
+You can get the list of supported model names by calling `get_model_list` from [realm.py](https://towhee.io/text-embedding/realm/src/branch/main/realm.py).
 
-**Returns**: *numpy.ndarray*
 
-​	The text embedding extracted by model.
+## Interface
 
+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 ('google/realm-cc-news-pretrained-encoder')
-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.realm('google/realm-cc-news-pretrained-encoder')
-  .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.realm['text', 'vec']('bert-base-cased')
-  .select('vec')
-  .show()
-```
+​	The text embedding extracted by model.
 
-[1] https://arxiv.org/abs/2002.08909

__init__.py

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

realm.py

@@ -23,6 +23,7 @@ from towhee import register
 import warnings
 
 warnings.filterwarnings('ignore')
+logging.getLogger("transformers").setLevel(logging.ERROR)
 log = logging.getLogger()
 
 
@@ -35,7 +36,7 @@ class Realm(NNOperator):
             Which model to use for the embeddings.
     """
 
-    def __init__(self, model_name: str) -> None:
+    def __init__(self, model_name: str = "google/realm-cc-news-pretrained-embedder") -> None:
         super().__init__()
         self.model_name = model_name
         try:
@@ -65,6 +66,13 @@ class Realm(NNOperator):
         except Exception as e:
             log.error(f'Fail to extract features by model: {self.model_name}')
             raise e
-        feature_vector = features.detach().numpy()
-        return feature_vector
+        vec = features.detach().numpy()
+        return vec
 
+
+def get_model_list():
+    full_list = [
+        "google/realm-cc-news-pretrained-embedder"
+    ]
+    full_list.sort()
+    return full_list