Feature Extraction
sentence-transformers
Safetensors
modernbert
multi-vector
colbert
late-interaction
Generated from Trainer
dataset_size:1000000
loss:CachedMultiVectorMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use multi-vector-encoder/mLateOn-medical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use multi-vector-encoder/mLateOn-medical with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("multi-vector-encoder/mLateOn-medical") 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
| { | |
| "in_features": 1536, | |
| "out_features": 768, | |
| "bias": false, | |
| "activation_function": "torch.nn.modules.linear.Identity", | |
| "module_input_name": "token_embeddings", | |
| "module_output_name": "token_embeddings", | |
| "use_residual": true | |
| } |