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:
- ac8a5b88549f307ba36d1dcd9fe9861c351826843e2ac473746ddf3a826df335
- Size of remote file:
- 65.6 kB
- SHA256:
- dff3a9b4a56a4e6adb29807bab01900d8465f490ad23cf6934a7e0b2536ade2c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.