Text Ranking
sentence-transformers
Safetensors
Amharic
xlm-roberta
cross-encoder
Generated from Trainer
dataset_size:491752
loss:BinaryCrossEntropyLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use rasyosef/reranker-amharic-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rasyosef/reranker-amharic-medium with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("rasyosef/reranker-amharic-medium") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
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# Cross-Encoder: roberta-amharic-reranker-medium
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The model can be used for Information Retrieval: Given a query, encode the query with all possible passages (e.g. retrieved with BM25 and/or an Embedding Model). Then sort the passages in a decreasing order.
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# How to Use
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First, You need to have [sentence-transformers](https://www.sbert.net/) installed.
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# Cross-Encoder: roberta-amharic-reranker-medium
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The model can be used for Information Retrieval: Given a query, encode the query with all possible passages (e.g. retrieved with BM25 and/or an Embedding Model). Then sort the passages in a decreasing order of scores.
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# How to Use
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First, You need to have [sentence-transformers](https://www.sbert.net/) installed.
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