update reranker v2

Author: 545999961

FlagEmbedding/llm_reranker/README.md

@@ -65,7 +65,8 @@ print(scores) # [0.00027803096387751553, 0.9948403768236574]
 
 ```python
 from FlagEmbedding import FlagLLMReranker
-reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_bf16=True) # Setting use_bf16 to True speeds up computation with a slight performance degradation
+reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+# reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_fp16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation
 
 score = reranker.compute_score(['query', 'passage'])
 print(score)
@@ -78,7 +79,8 @@ print(scores)
 
 ```python
 from FlagEmbedding import LayerWiseFlagLLMReranker
-reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_bf16=True) # Setting use_bf16 to True speeds up computation with a slight performance degradation
+reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+# reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation
 
 score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.
 print(score)
@@ -218,8 +220,8 @@ def get_inputs(pairs, tokenizer, prompt=None, max_length=1024):
             return_tensors='pt',
     )
 
-tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.bfloat16)
-model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.float16)
 model = model.to('cuda')
 model.eval()
 
@@ -331,7 +333,7 @@ Our rerankers are initialized from [google/gemma-2b](https://huggingface.co/goog
 
 rereank the top 100 results from bge-en-v1.5 large.
 
-![image-20240318152741276](./evaluation/BEIR-bge-en-v1.5.png)
+![image-20240319140555921](./evaluation/BEIR-bge-en-v1.5.png)
 
 rereank the top 100 results from e5 mistral 7b instruct.