Feature Extraction
sentence-transformers
Safetensors
Transformers
English
mistral
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Linq-AI-Research/Linq-Embed-Mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Linq-AI-Research/Linq-Embed-Mistral with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Linq-AI-Research/Linq-Embed-Mistral") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use Linq-AI-Research/Linq-Embed-Mistral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Linq-AI-Research/Linq-Embed-Mistral")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Linq-AI-Research/Linq-Embed-Mistral") model = AutoModel.from_pretrained("Linq-AI-Research/Linq-Embed-Mistral", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -73,7 +73,7 @@ embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_ma
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embeddings = F.normalize(embeddings, p=2, dim=1)
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scores = (embeddings[:2] @ embeddings[2:].T) * 100
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print(scores.tolist())
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# [[
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```
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### MTEB Benchmark Evaluation
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embeddings = F.normalize(embeddings, p=2, dim=1)
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scores = (embeddings[:2] @ embeddings[2:].T) * 100
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print(scores.tolist())
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# [[73.72909545898438, 30.122783660888672], [29.155078887939453, 79.25374603271484]]
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```
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### MTEB Benchmark Evaluation
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