Sentence Similarity
Safetensors
sentence-transformers
PyLate
modernbert
ColBERT
feature-extraction
Generated from Trainer
dataset_size:640000
loss:Distillation
Eval Results (legacy)
text-embeddings-inference
Instructions to use patrick-358/my-gte-colbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use patrick-358/my-gte-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="patrick-358/my-gte-colbert") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from patrick-358/my-gte-colbert: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/patrick-358/my-gte-colbert/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://patrick-358/my-gte-colbert/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/patrick-358/my-gte-colbert/resolve/main/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 299, | |
| "do_lower_case": false | |
| } |