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README.md

@@ -5,7 +5,7 @@ tags:
 - 奇虎360
 - RAG-reranking
 model-index:
-- name: 360Zhinao-1.8B-Reranking
+- name: 360Zhinao-1_8B-reranking
   results:
   - task:
       type: Reranking
@@ -70,7 +70,7 @@ We have validated the performance of our model on the [mteb-chinese-reranking le
 
 | Model | T2Reranking | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |  
 |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|  
-| **360Zhinao-1.8B-Reranking** | **68.55** | **37.29** | **86.75** | **87.92** | **70.13** |
+| **360Zhinao-1_8B-Reranking** | **68.55** | **37.29** | **86.75** | **87.92** | **70.13** |
 | piccolo-large-zh-v2 | 67.15 | 33.39 | 90.14 | 89.31 | 70 |
 | Baichuan-text-embedding | 67.85 | 34.3 | 88.46 | 88.06 | 69.67 |
 | stella-mrl-large-zh-v3.5-1792d | 66.43 | 28.85 | 89.18 | 89.33 | 68.45 |
@@ -95,6 +95,10 @@ cd flash-attention && pip install .
 # No need to install the following if the flash-attn version is above 2.1.1.
 # pip install csrc/rotary
 ```
+You can also use the following command to install flash-attention.
+```bash
+FLASH_ATTENTION_FORCE_BUILD=TRUE ./miniconda3/bin/python -m pip install flash-attn==2.3.6
+```
 
 # Model Introduction
 
@@ -262,7 +266,7 @@ class FlagRerankerCustom:
 
         all_scores = []
         for start_index in tqdm(range(0, len(sentence_pairs), batch_size), desc="Compute Scores",
-                                disable=len(sentence_pairs) < 128):
+                                disable=False):
             sentences_batch = sentence_pairs[start_index:start_index + batch_size] # [[q,ans],[q, ans]...]
             inputs = preprocess(sources=sentences_batch, tokenizer=self.tokenizer,max_len=1024,device=self.device)
             scores = self.model(**inputs, return_dict=True).logits.view(-1, ).float()
@@ -274,7 +278,7 @@ class FlagRerankerCustom:
 
 
 if __name__ == "__main__":
-    model_name_or_path = "360Zhinao-1.8B-Reranking"
+    model_name_or_path = "360Zhinao-1_8B-Reranking"
     model = FlagRerankerCustom(model_name_or_path, use_fp16=False)
     inputs=[["What Color Is the Sky","Blue"], ["What Color Is the Sky","Pink"],]
     ret = model.compute_score(inputs)