---
dataset_info:
  features:
  - name: query_id
    dtype: string
  - name: passage_id
    dtype: string
  - name: query
    dtype: string
  - name: passage
    dtype: string
  - name: category
    dtype: string
  - name: link
    dtype: string
  splits:
  - name: train
    num_bytes: 165304734
    num_examples: 44708
  download_size: 76505983
  dataset_size: 165304734
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- sentence-similarity
language:
- am
size_categories:
- 10K<n<100K
---

# Amharic Passage Retrieval Dataset

This dataset is a version of [amharic-news-category-classification](https://huggingface.co/datasets/rasyosef/amharic-news-category-classification) that has been filtered, deduplicated, and formatted for passage retrieval.

This dataset can be used directly with Sentence Transformers to train Amharic embedding models.

### Source Datasets:

- https://huggingface.co/datasets/rasyosef/amharic-news-category-classification

### Models and Code
The following Text Embedding and ColBERT late-interaction retrieval models were trained using this dataset as part of our **ACL 2025 Findings** paper: ***Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval***.

- Models Collection: https://huggingface.co/collections/rasyosef/amharic-text-embedding-models-679cb55eae1d498e3ac5bdc5
- Code: https://github.com/kidist-amde/amharic-ir-benchmarks

## Citation
```
@inproceedings{mekonnen2025amharic,
  title={Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval},
  author={Kidist Amde Mekonnen, Yosef Worku Alemneh, Maarten de Rijke },
  booktitle={Findings of ACL},
  year={2025}
}
```

```
@misc{https://doi.org/10.48550/arxiv.2103.05639,
  doi = {10.48550/ARXIV.2103.05639},
  url = {https://arxiv.org/abs/2103.05639},
  author = {Azime, Israel Abebe and Mohammed, Nebil},
  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {An Amharic News Text classification Dataset},
  publisher = {arXiv},
  year = {2021},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
```