Modify output format

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

README.md

@@ -329,6 +329,13 @@ If None, the operator will use default tokenizer by `model_name` from Huggingfac
 
 <br />
 
+***return_sentence_emb***: *bool*
+
+The flag to output a sentence embedding for each text, defaults to True.
+If False, the operator returns token embeddings for each text.
+
+<br />
+
 ## Interface
 
 The operator takes a piece of text in string as input.
@@ -339,9 +346,11 @@ and then return text embedding in ndarray.
 
 **Parameters:**
 
-***txt***: *str*
+***data***: *Union[str, list]*
 
-​	The text in string.
+​	The text in string or a list of texts.
+If data is string, the operator returns embedding(s) in ndarray.
+If data is a list, the operator returns embedding(s) in a list.
 
 
 **Returns**:
@@ -350,9 +359,6 @@ and then return text embedding in ndarray.
 
 ​	The text embedding extracted by model.
 
-***return_sentence_emb***: *bool*
-
-The flag to output sentence embedding instead of token embeddings, defaults to False.
 
 <br />
 

auto_transformers.py

@@ -17,6 +17,7 @@ import os
 import torch
 import shutil
 from pathlib import Path
+from typing import Union
 
 from transformers import AutoTokenizer, AutoModel, AutoModelForMaskedLM, AutoModelForCausalLM
 
@@ -26,13 +27,15 @@ from towhee import register
 
 import warnings
 import logging
+from transformers import logging as t_logging
 
 from .train_mlm_with_hf_trainer import train_mlm_with_hf_trainer
 from .train_clm_with_hf_trainer import train_clm_with_hf_trainer
 
 log = logging.getLogger('run_op')
-
 warnings.filterwarnings('ignore')
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+t_logging.set_verbosity_error()
 
 
 # @accelerate
@@ -76,12 +79,14 @@ class AutoTransformers(NNOperator):
                  checkpoint_path: str = None,
                  tokenizer: object = None,
                  device: str = None,
+                 return_sentence_emb: bool = True
                  ):
         super().__init__()
         if device is None:
             device = 'cuda' if torch.cuda.is_available() else 'cpu'
         self.device = device
         self.model_name = model_name
+        self.return_sentence_emb = return_sentence_emb
 
         if self.model_name:
             model_list = self.supported_model_names()
@@ -102,7 +107,11 @@ class AutoTransformers(NNOperator):
             log.warning('The operator is initialized without specified model.')
             pass
 
-    def __call__(self, txt: str, return_sentence_emb: bool = False) -> numpy.ndarray:
+    def __call__(self, data: Union[str, list]) -> numpy.ndarray:
+        if isinstance(data, str):
+            txt = [data]
+        else:
+            txt = data
         try:
             inputs = self.tokenizer(txt, padding=True, truncation=True, return_tensors="pt").to(self.device)
         except Exception as e:
@@ -113,11 +122,14 @@ class AutoTransformers(NNOperator):
         except Exception as e:
             log.error(f'Invalid input for the model: {self.model_name}')
             raise e
-        if return_sentence_emb:
+        if self.return_sentence_emb:
             outs = self.post_proc(outs, inputs)
-        features = outs.squeeze(0)
-        vec = features.cpu().detach().numpy()
-        return vec
+        features = outs.cpu().detach().numpy()
+        if isinstance(data, str):
+            features = features.squeeze(0)
+        else:
+            features = list(features)
+        return features
 
     @property
     def _model(self):