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
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# Linq Research Team.
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- [Junseong Kim](https://huggingface.co/Junseong)
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- Seolhwa Lee
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- [Jihoon Kwon](https://huggingface.co/Mayfull)
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- Sangmo Gu
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- Yejin Kim
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# Linq Research Team.
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- [Junseong Kim](https://huggingface.co/Junseong)
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- [Seolhwa Lee] (https://huggingface.co/Seolhwa)
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- [Jihoon Kwon](https://huggingface.co/Mayfull)
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| 1875 |
- Sangmo Gu
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- Yejin Kim
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