Sentence Similarity
Safetensors
sentence-transformers
English
PyLate
bert
multi-vector
ColBERT
feature-extraction
Generated from Trainer
loss:Distillation
text-embeddings-inference
Instructions to use NeuML/biomedbert-base-colbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/biomedbert-base-colbert with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="NeuML/biomedbert-base-colbert") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Download added_tokens.json from NeuML/biomedbert-base-colbert: direct link, hf CLI and curl.
- Browser
- Download file 37 Bytes
-
https://huggingface.co/NeuML/biomedbert-base-colbert/resolve/55d520162eef76d6ea301e7cda403d946919b3f4/added_tokens.json
- Command line
-
hf download hf://NeuML/biomedbert-base-colbert@55d520162eef76d6ea301e7cda403d946919b3f4/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/NeuML/biomedbert-base-colbert/resolve/55d520162eef76d6ea301e7cda403d946919b3f4/added_tokens.json
37 Bytes
| { | |
| "[D] ": 30523, | |
| "[Q] ": 30522 | |
| } | |