Sentence Similarity
Safetensors
sentence-transformers
Turkish
PyLate
bert_hash
ColBERT
feature-extraction
Generated from Trainer
dataset_size:910904
loss:Contrastive
custom_code
Instructions to use newmindai/colbert-hash-femto-tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use newmindai/colbert-hash-femto-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="newmindai/colbert-hash-femto-tr") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc49468668ebb0dfa5b7cfcdf3d9dfbfe9eb680f56a0788b96424163f1b1b53c
- Size of remote file:
- 2.01 MB
- SHA256:
- 7c09e3cc52d63528ef77ed4feb9760d394ec7fb47b740edffcc85fa30eea1036
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