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
| { | |
| "__version__": { | |
| "pytorch": "2.10.0+cu128", | |
| "sentence_transformers": "6.1.0.dev0", | |
| "transformers": "5.16.1" | |
| }, | |
| "default_prompt_name": null, | |
| "model_type": "MultiVectorEncoder", | |
| "prompts": { | |
| "document": "", | |
| "query": "" | |
| }, | |
| "similarity_fn_name": "maxsim" | |
| } |