Refactor operator

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

README.md

@@ -1,2 +1,74 @@
-# transformers
+# Text Embedding with Transformers
+
+*author: Jael Gu*
+
+
+
+## Desription
+
+A text embedding operator implemented with pretrained models from [Huggingface Transformers](https://huggingface.co/docs/transformers).
+
+
+
+```python
+from towhee import ops
+
+text_encoder = ops.text_embedding.transformers("bert-base-cased")
+text_embedding = text_encoder("Hello, world.")
+```
+
+## Factory Constructor
+
+Create the operator via the following factory method
+
+***ops.text_embedding.transformers(model_name)***
+
+
+
+## Interface
+
+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.
+
+
+**Parameters:**
+
+​	***text***: *str*
+
+​	The text in string.
+
+
+
+**Returns**: *numpy.ndarray*
+
+​	The text embedding extracted by model.
+
+
+
+## Code Example
+
+Use the pretrained Bert-Base-Cased model ('bert-base-cased')
+to generate a text embedding for the sentence "Hello, world.". 
+
+ *Write the pipeline in simplified style*:
+
+```python
+import towhee.DataCollection as dc
+
+dc.glob("Hello, world.")
+  .text_embedding.transformers('bert-base-cased')
+  .show()
+```
+
+*Write a same pipeline with explicit inputs/outputs name specifications:*
+
+```python
+from towhee import DataCollection as dc
+
+dc.glob['text']('Hello, world.')
+  .text_embedding.transformers['text', 'vec']('bert-base-cased')
+  .select('vec')
+  .show()
+```
+
 

__init__.py

@@ -0,0 +1,19 @@
+# Copyright 2021 Zilliz. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .auto_transformers import AutoTransformers
+
+
+def transformers():
+    return AutoTransformers()

auto_transformers.py

@@ -0,0 +1,70 @@
+# Copyright 2021 Zilliz. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import numpy
+
+from transformers import AutoTokenizer, AutoModel
+
+from towhee.operator import NNOperator
+from towhee import register
+
+import warnings
+
+warnings.filterwarnings('ignore')
+log = logging.getLogger()
+
+
+@register(output_schema=['vec'])
+class AutoTransformers(NNOperator):
+    """
+    NLP embedding operator that uses the pretrained transformers model gathered by huggingface.
+    Args:
+        model_name (`str`):
+            Which model to use for the embeddings.
+    """
+
+    def __init__(self, model_name: str) -> None:
+        super().__init__()
+        self.model_name = model_name
+        try:
+            self.model = AutoModel.from_pretrained(model_name)
+        except Exception as e:
+            log.error(f'Fail to load model by name: {self.model_name}')
+            raise e
+        try:
+            self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        except Exception as e:
+            log.error(f'Fail to load tokenizer by name: {self.model_name}')
+            raise e
+
+    def __call__(self, txt: str) -> numpy.ndarray:
+        try:
+            inputs = self.tokenizer(txt, return_tensors="pt")
+        except Exception as e:
+            log.error(f'Invalid input for the tokenizer: {self.model_name}')
+            raise e
+        try:
+            outs = self.model(**inputs)
+        except Exception as e:
+            log.error(f'Invalid input for the model: {self.model_name}')
+            raise e
+        try:
+            features = outs.last_hidden_state.squeeze(0)
+        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
+

requirements.txt

@@ -0,0 +1,4 @@
+numpy
+transformers
+sentencepiece
+protobuf