Sentence Similarity
Safetensors
sentence-transformers
English
PyLate
modernbert
ColBERT
multi-vector
feature-extraction
late-interaction
ModernBERT
text-embeddings-inference
Instructions to use DataScience-UIBK/uibk-embedding-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DataScience-UIBK/uibk-embedding-v1 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="DataScience-UIBK/uibk-embedding-v1") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Download modules.json from DataScience-UIBK/uibk-embedding-v1: direct link, hf CLI and curl.
- Browser
- Download file 224 Bytes
-
https://huggingface.co/DataScience-UIBK/uibk-embedding-v1/resolve/main/modules.json
- Command line
-
hf download hf://DataScience-UIBK/uibk-embedding-v1/modules.json
-
curl -L -o modules.json https://huggingface.co/DataScience-UIBK/uibk-embedding-v1/resolve/main/modules.json
224 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "branched_transformer.BranchedTransformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_HybridTokens", | |
| "type": "hybrid_tokens.HybridTokens" | |
| } | |
| ] |