HIT-TMG
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KaLM-embedding-multilingual-mini-instruct-v2

Feature Extraction

sentence-transformers

text-embeddings-inference

Model card Files Files and versions

Yuki131 commited on Jun 24

Commit

feadc03

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verified ·

1 Parent(s): 7fa63d0

Update README.md

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README.md +2 -2

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@@ -65,7 +65,7 @@ from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
-model = SentenceTransformer("{MODEL_NAME_OR_PATH}", trust_remote_code=True, model_kwargs={"torch_dtype": torch.bfloat16, "attn_implementation": "flash_attention_2"})
 model.max_seq_length = 512
 embeddings = model.encode(
@@ -87,7 +87,7 @@ from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
-model = SentenceTransformer("{MODEL_NAME_OR_PATH}", trust_remote_code=True, model_kwargs={"torch_dtype": torch.bfloat16, "attn_implementation": "flash_attention_2"})
 model.max_seq_length = 512
 prompt = "Instruct: Classifying the category of french news. \n Query: "

 sentences = ["This is an example sentence", "Each sentence is converted"]
+model = SentenceTransformer("{MODEL_NAME_OR_PATH}", trust_remote_code=True, truncate_dim=None, model_kwargs={"torch_dtype": torch.bfloat16, "attn_implementation": "flash_attention_2"})
 model.max_seq_length = 512
 embeddings = model.encode(
 sentences = ["This is an example sentence", "Each sentence is converted"]
+model = SentenceTransformer("{MODEL_NAME_OR_PATH}", trust_remote_code=True, truncate_dim=None, model_kwargs={"torch_dtype": torch.bfloat16, "attn_implementation": "flash_attention_2"})
 model.max_seq_length = 512
 prompt = "Instruct: Classifying the category of french news. \n Query: "