use logger

FlagEmbedding/BGE_M3/modeling.py

@@ -82,10 +82,10 @@ class BGEM3Model(nn.Module):
 
         if os.path.exists(os.path.join(model_name, 'colbert_linear.pt')) and os.path.exists(
                 os.path.join(model_name, 'sparse_linear.pt')):
-            print('loading existing colbert_linear and sparse_linear---------')
+            logger.info('loading existing colbert_linear and sparse_linear---------')
             self.load_pooler(model_dir=model_name)
         else:
-            print(
+            logger.info(
                 'The parameters of colbert_linear and sparse linear is new initialize. Make sure the model is loaded for training, not inferencing')
 
     def gradient_checkpointing_enable(self, **kwargs):

examples/unified_finetune/README.md

@@ -15,14 +15,10 @@ pip install -U FlagEmbedding
 ```
 git clone https://github.com/FlagOpen/FlagEmbedding.git
 cd FlagEmbedding
-pip install .
-```
-For development, install as editable:
-
-```
 pip install -e .
 ```
 
+
 ## 2. Data format
 
 Training data should be a jsonl file, where each line is a dict like this:

setup.py

@@ -5,7 +5,7 @@ with open("README.md", mode="r", encoding="utf-8") as readme_file:
 
 setup(
     name='FlagEmbedding',
-    version='1.2.5',
+    version='1.2.6',
     description='FlagEmbedding',
     long_description=readme,
     long_description_content_type="text/markdown",