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:
- 668c39479322e89718cf5ef0f2870dcf39182b6e00635b655a11d57eac68d4c7
- Size of remote file:
- 14.6 kB
- SHA256:
- a9332ac53b4803d8c803cf4c7438a0624967cd99dc46a1a2397b3b99dafe17d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.