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
| { | |
| "skiplist_words": [ | |
| "!", | |
| "\"", | |
| "#", | |
| "$", | |
| "%", | |
| "&", | |
| "'", | |
| "(", | |
| ")", | |
| "*", | |
| "+", | |
| ",", | |
| "-", | |
| ".", | |
| "/", | |
| ":", | |
| ";", | |
| "<", | |
| "=", | |
| ">", | |
| "?", | |
| "@", | |
| "[", | |
| "\\", | |
| "]", | |
| "^", | |
| "_", | |
| "`", | |
| "{", | |
| "|", | |
| "}", | |
| "~" | |
| ], | |
| "skiplist_tasks": [ | |
| "document" | |
| ], | |
| "keep_only_token_ids": null | |
| } |