Sentence Similarity
Safetensors
sentence-transformers
Amharic
PyLate
xlm-roberta
ColBERT
feature-extraction
Generated from Trainer
dataset_size:76474
loss:Contrastive
Eval Results (legacy)
text-embeddings-inference
Instructions to use rasyosef/colbert-roberta-amharic-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rasyosef/colbert-roberta-amharic-medium 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="rasyosef/colbert-roberta-amharic-medium") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from rasyosef/colbert-roberta-amharic-medium: direct link, hf CLI and curl.
- Browser
- Download file 2.56 MB
-
https://huggingface.co/rasyosef/colbert-roberta-amharic-medium/resolve/main/tokenizer.json
- Command line
-
hf download hf://rasyosef/colbert-roberta-amharic-medium/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/rasyosef/colbert-roberta-amharic-medium/resolve/main/tokenizer.json
2.56 MB
File too large to display, you can check the raw version instead.