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Linq-Embed-Mistral

Feature Extraction
sentence-transformers
Safetensors
Transformers
English
mistral
mteb
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
 Community
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Update README.md

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  1. README.md +2 -2
README.md CHANGED
@@ -56,10 +56,10 @@ passages = [
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  # Load model and tokenizer
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  tokenizer = AutoTokenizer.from_pretrained('Salesforce/SFR-Embedding-Mistral')
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  model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-Mistral')
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-
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  max_length = 4096
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  # Tokenize the input texts
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- batch_dict = tokenizer([*queries, *passages], max_length=max_length, padding=True, truncation=True, return_tensors="pt")
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  outputs = model(**batch_dict)
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  embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
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  # Load model and tokenizer
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  tokenizer = AutoTokenizer.from_pretrained('Salesforce/SFR-Embedding-Mistral')
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  model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-Mistral')
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+ input_texts = [*queries, *passages]
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  max_length = 4096
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  # Tokenize the input texts
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+ batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors="pt")
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  outputs = model(**batch_dict)
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  embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
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