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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7fb1f5e2daed647b0b53ba0d96f35b7b82d2171f508323674906b9e61dce087
- Size of remote file:
- 8 MB
- SHA256:
- 56051a885496c4505443ffe4fff8db3bebded0793ba7d5a88bdc150694c2779f
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