Sentence Similarity
Safetensors
sentence-transformers
Turkish
PyLate
modernbert
ColBERT
feature-extraction
Generated from Trainer
dataset_size:910904
loss:Contrastive
Eval Results (legacy)
text-embeddings-inference
Instructions to use MElHuseyni/mxbai-edge-colbert-v0-17mv3-tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MElHuseyni/mxbai-edge-colbert-v0-17mv3-tr with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="MElHuseyni/mxbai-edge-colbert-v0-17mv3-tr") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 984b11978df7194b64a8fa6cab82b9f4d028f64865ebf54801272902af1f908b
- Size of remote file:
- 1.47 kB
- SHA256:
- 8b198d63af5c6563399a525397894e9b3d1958651f2304ed8bae0db0183a0164
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