Sentence Similarity
Safetensors
sentence-transformers
PyLate
xlm-roberta
ColBERT
feature-extraction
Generated from Trainer
dataset_size:256886
loss:Contrastive
custom_code
text-embeddings-inference
Instructions to use Bheri/ithasa-jina-colbertv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Bheri/ithasa-jina-colbertv2 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="Bheri/ithasa-jina-colbertv2") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 879e74f8df86c6203409cd86fc9ee0f3cdde56663cf6d22ca2ee58c13e418c69
- Size of remote file:
- 1.38 kB
- SHA256:
- b21c5349d5e7d02de630ebc1cb53ade1d9c6079eeb8594d223bb786011a0428b
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