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
Update sentence bert config to 4096
#7
by michaelfeil - opened
By default many users will not have the sliding window installed. This setting sets a preferred cix windows of 4096