Feature Extraction
sentence-transformers
Safetensors
English
bert
multi-vector
colbert
late-interaction
Generated from Trainer
dataset_size:501907
loss:MultiVectorMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use multi-vector-encoder-testing/bert-tiny-multi-vector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use multi-vector-encoder-testing/bert-tiny-multi-vector with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("multi-vector-encoder-testing/bert-tiny-multi-vector") 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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95d5a09365bb9721a9eedf61a475ead1c272981e4e4ab9d1e6812bf8c0d7e0a9
- Size of remote file:
- 17.5 MB
- SHA256:
- bd69e90a72afaa8fdbb8fb3d00955479f81dcaf325842b33a6405eca4550002d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.