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README.md

@@ -95,6 +95,7 @@ We provide detailed parameters and environment configurations so that you can ru
 - Accelerate: 1.3.0
 - Datasets: 3.2.0
 - Tokenizers: 0.21.2
+- mteb: 1.38.30
 #### Transformers model load arguments
 torch_dtype=torch.bfloat16<br>
 attn_implementation='sdpa'<br>
@@ -133,11 +134,11 @@ This is a general script that can be used to evaluate other huggingface embeddin
 ```
 from sentence_transformers import SentenceTransformer
 
-model = SentenceTransformer("QZhou-Embedding")
+model = SentenceTransformer("Kingsoft-LLM/QZhou-Embedding")
 
 model = SentenceTransformer(
-    "QZhou-Embedding",
-    model_kwargs={"device_map": "auto", "trust_remote_code": True},
+    "Kingsoft-LLM/QZhou-Embedding",
+    model_kwargs={"device_map": "cuda", "trust_remote_code": True},
     tokenizer_kwargs={"padding_side": "left", "trust_remote_code": True},
     trust_remote_code=True
 )
@@ -195,8 +196,8 @@ documents = [
 
 input_texts = queries + documents
 
-tokenizer = AutoTokenizer.from_pretrained('QZhou-Embedding', padding_side='left', trust_remote_code=True)
-model = AutoModel.from_pretrained('QZhou-Embedding', trust_remote_code=True, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained('/home/yupeng5/yupeng/output_models/output/publish/QZhou-Embedding', padding_side='left', trust_remote_code=True)
+model = AutoModel.from_pretrained('/home/yupeng5/yupeng/output_models/output/publish/QZhou-Embedding', trust_remote_code=True, device_map='cuda')
 
 batch_dict = tokenizer(
     input_texts,