Instructions to use rasyosef/colbert-amharic-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rasyosef/colbert-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-amharic-medium") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Improve model card: update pipeline tag and add paper/code links
Browse filesHi! I'm Niels, part of the community science team at Hugging Face.
I'm opening this PR to improve the model card for `colbert-amharic-medium`. Based on the associated research, I've made the following updates:
- Changed the `pipeline_tag` to `text-retrieval` to improve discoverability for retrieval tasks.
- Added a link to the research paper: [The Multilingual Curse at the Retrieval Layer: Evidence from Amharic](https://huggingface.co/papers/2605.24556).
- Added a link to the official [GitHub repository](https://github.com/rasyosef/amharic-neural-ir).
- Updated model details (Language: Amharic, License: MIT) that were previously marked as unknown.
- Added a formal citation section.
These changes help document the model's provenance and make it easier for researchers to use and cite.
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