Update README.md

README.md

@@ -5,6 +5,82 @@ tags:
 - transformers
 - sentence-transformers
 - text-embeddings-inference
+language:
+- af
+- ar
+- az
+- be
+- bg
+- bn
+- ca
+- ceb
+- cs
+- cy
+- da
+- de
+- el
+- en
+- es
+- et
+- eu
+- fa
+- fi
+- fr
+- gl
+- gu
+- he
+- hi
+- hr
+- ht
+- hu
+- hy
+- id
+- is
+- it
+- ja
+- jv
+- ka
+- kk
+- km
+- kn
+- ko
+- ky
+- lo
+- lt
+- lv
+- mk
+- ml
+- mn
+- mr
+- ms
+- my
+- ne
+- nl
+- 'no'
+- pa
+- pl
+- pt
+- qu
+- ro
+- ru
+- si
+- sk
+- sl
+- so
+- sq
+- sr
+- sv
+- sw
+- ta
+- te
+- th
+- tl
+- tr
+- uk
+- ur
+- vi
+- yo
+- zh
 ---
 
 ## gte-multilingual-reranker-base
@@ -22,6 +98,10 @@ The **gte-multilingual-reranker-base** model is the first reranker model in the
 
 
 ### Usage
+- **It is recommended to install xformers and enable unpadding for acceleration,
+refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).**
+- **How to use it offline: [new-impl/discussions/2](https://huggingface.co/Alibaba-NLP/new-impl/discussions/2#662b08d04d8c3d0a09c88fa3)**
+
 
 Using Huggingface transformers (transformers>=4.36.0)
 ```
@@ -31,7 +111,10 @@ from transformers import AutoModelForSequenceClassification, AutoTokenizer
 model_name_or_path = "Alibaba-NLP/gte-multilingual-reranker-base"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
-model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, trust_remote_code=True)
+model = AutoModelForSequenceClassification.from_pretrained(
+    model_name_or_path, trust_remote_code=True,
+    torch_dtype=torch.float16
+)
 model.eval()
 
 pairs = [["中国的首都在哪儿"，"北京"], ["what is the capital of China?", "北京"], ["how to implement quick sort in python?","Introduction of quick sort"]]
@@ -43,8 +126,6 @@ with torch.no_grad():
 # tensor([1.2315, 0.5923, 0.3041])
 ```
 
-### How to use it offline
-Refer to [Disable trust_remote_code](https://huggingface.co/Alibaba-NLP/new-impl/discussions/2#662b08d04d8c3d0a09c88fa3)
 
 ## Evaluation