Text Retrieval
Safetensors
sentence-transformers
PyLate
xlm-roberta
ColBERT
feature-extraction
Generated from Trainer
dataset_size:118938
loss:Contrastive
Eval Results (legacy)
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
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README.md
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- dataset_size:118938
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- loss:Contrastive
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dataset:
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name: Amharic Passage Retrieval Dataset V2
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type: rasyosef/Amharic-Passage-Retrieval-Dataset-V2
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metrics:
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---
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# ColBERT-Amharic-Medium
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## Evaluation
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| Metric | Value |
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| **accuracy** | **0.9817** |
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## Training Details
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- dataset_size:118938
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- loss:Contrastive
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model-index:
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- name: ColBERT-Amharic-Medium
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results:
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type: information-retrieval
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name: Information Retrieval
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dataset:
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name: Amharic Passage Retrieval Dataset V2
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type: rasyosef/Amharic-Passage-Retrieval-Dataset-V2
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metrics:
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- type: cosine_recall@5
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value: 0.882
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name: Cosine Recall@5
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value: 0.913
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name: Cosine Recall@10
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- type: cosine_ndcg@10
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value: 0.811
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name: Cosine Ndcg@10
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- type: cosine_mrr@10
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value: 0.778
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name: Cosine Mrr@10
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---
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# ColBERT-Amharic-Medium
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## Evaluation
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| Metric | Value |
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| cosine_recall@5 | 0.882 |
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| cosine_recall@10 | 0.913 |
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| **cosine_ndcg@10** | **0.811** |
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| cosine_mrr@10 | 0.778 |
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## Training Details
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