reranker-amharic-medium

This is a Cross Encoder model finetuned from rasyosef/roberta-medium-amharic using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

This model is part of the research presented in the paper "The Multilingual Curse at the Retrieval Layer: Evidence from Amharic".

• Paper: The Multilingual Curse at the Retrieval Layer: Evidence from Amharic
• Code: https://github.com/rasyosef/amharic-neural-ir

Model Details

Model Description

• Model Type: Cross Encoder
• Base model: rasyosef/roberta-medium-amharic
• Maximum Sequence Length: 510 tokens
• Number of Output Labels: 1 label
• Language: Amharic (am)
• License: MIT

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Cross Encoder Documentation
• Repository: Sentence Transformers on GitHub

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("rasyosef/reranker-amharic-medium")

# Get scores for pairs of texts
pairs = [
    ['ለውጭ ገበያ በሚቀርበው የኢትዮጵያ ቡና ላይ የተጋረጠው ፈተና', 'የኢትዮጵያ ዋነኛ የውጭ ምንዛሬ ምንጭ የሆነው ወደ ውጭ የሚላክ ቡና ዘርፍ በአሁኑ ጊዜ ከፍተኛ ውጥረት ውስጥ ገብቷል።'],
    ['ለውጭ ገበያ በሚቀርበው የኢትዮጵያ ቡና ላይ የተጋረጠው ፈተና', 'የቻይናው ፕሬዝዳንት ዚ ጂንፒንግ ከትራምፕ ጋር ባደረጉት ጉባኤ ትኩረታቸው በሁለቱ ሀገራት መካከል ለወራት ከተፈጠረ ውጥረት እና የንግድ ጦርነት በኋላ የተረገጋጋ ግንኙነትን ማስቀጠል ነበር።']
]
scores = model.predict(pairs)
print(scores.shape)
# (2,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'ለውጭ ገበያ በሚቀርበው የኢትዮጵያ ቡና ላይ የተጋረጠው ፈተና',
    [
        'የኢትዮጵያ ዋነኛ የውጭ ምንዛሬ ምንጭ የሆነው ወደ ውጭ የሚላክ ቡና ዘርፍ በአሁኑ ጊዜ ከፍተኛ ውጥረት ውስጥ ገብቷል።',
        'የቻይናው ፕሬዝዳንት ዚ ጂንፒንግ ከትራምፕ ጋር ባደረጉት ጉባኤ ትኩረታቸው በሁለቱ ሀገራት መካከል ለወራት ከተፈጠረ ውጥረት እና የንግድ ጦርነት በኋላ የተረገጋጋ ግንኙነትን ማስቀጠል ነበር።',
    ]
)
print(ranks)
# [{'corpus_id': 0, 'score': ...}, {'corpus_id': 1, 'score': ...}]

Evaluation

Metrics

Cross Encoder Reranking

• Dataset: amh-passage-retrieval-dev
• Evaluated with CrossEncoderRerankingEvaluator with these parameters:

  {
      "at_k": 10
  }
  

Metric	Value
mrr@10	0.805
ndcg@10	0.835

Training Details

Training Dataset

Amharic Passage Retrieval Dataset V2

• Size: 491,752 training samples
• Columns: query, passage, and label
• Loss: BinaryCrossEntropyLoss with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": 7
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 4e-05
• num_train_epochs: 4
• lr_scheduler_type: cosine
• warmup_ratio: 0.05
• fp16: True
• dataloader_num_workers: 2
• load_best_model_at_end: True
• batch_sampler: no_duplicates

Training Logs

Epoch	Step	Training Loss	amh-passage-retrieval-dev_ndcg@10
1.0	7684	0.4048	0.8289
2.0	15368	0.2366	0.8546
3.0	23052	0.1588	0.8353
4.0	30736	0.1024	0.8551

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.11.13
• Sentence Transformers: 4.1.0
• Transformers: 4.52.4
• PyTorch: 2.6.0+cu124
• Accelerate: 1.7.0
• Datasets: 3.6.0
• Tokenizers: 0.21.1

Citation

@inproceedings{alemneh2026amharicir,
  title     = {The Multilingual Curse at the Retrieval Layer: Evidence from Amharic},
  author    = {Alemneh, Yosef Worku and Mekonnen, Kidist Amde and de Rijke, Maarten},
  booktitle = {Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM), ACL 2026},
  year      = {2026},
}