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language:
  - am
license: mit
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:245876
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: rasyosef/roberta-base-amharic
widget:
  - source_sentence: በኢትዮጵያ ለመጀመሪያ ጊዜ ወታደራዊ ስልጠና የወሰዱ ዕጩ ዲፕሎማቶች ተመረቁ
    sentences:
      - "የውጭ ጉዳይ ሚኒስቴር ከሜጀር ጄነራል ሀየሎም አርአያ ወታደራዊ አካዳሚ ጋር በመተባበር በኢትዮጵያ ለመጀመሪያ ጊዜ ወታደራዊ ስልጠና የወሰዱ ዲፕሎማቶችን \_አስመረቀ፡፡በወታደራዊ አካዳሚው ትላንት በተካሄደ የምርቃት ሥነ- ስርዓት ስልጠናውን ላገኙ 89 ዕጩ ድፕሎማቶች የምስክር ወረቀት ተበረክቷል።"
      - >-
        አዲስ አበባ፣ የካቲት 19፣ 2012 (ኤፍ.ቢ.ሲ) የኢፌዴሪ አየር ኃይል ለከፍተኛ መኮንኖች የማዕረግ እድገት
        ሰጥቷል።አየር ኃይሉ በዛሬው እለት በቢሾፍቱ በሚገኘው የኢፌዴሪ አየር ኃይል ጠቅላይ መምሪያ ባካሄደው ስነ ስርዓት
        ላይ የኢፌዴሪ ጦር ኃይሎች ምክተል ኤታማዦር ሹም ጄኔራል ብርሃኑ ጁላ እና የኢፌዴሪ አየር ኃይል ዋና አዛዥ ሜጀር
        ጄኔራል ይልማ መርዳሳን ጨምሮ ከፍተኛ አመራሮች ተገኝተዋል።በስነ ስርዓቱ ላይ 106 ለሚሆኑ መኮንኖች በአየር ኃይል
        ዋና አዛዥ ሜጀር ጄኔራል ይልማ መርዳሳ የተለያዩ የማዕረግ እድገቶችን ሰጥተዋል።
  - source_sentence: ኢትዮጵያ ኢንተርኔትን በመዝጋቷ ከ130 ሚሊዮን ዶላር በላይ አጣች
    sentences:
      - >-
        የአሜሪካ ድምፅ ባለፉት ሰባ አምስት ዓመታት ውስጥ በዓለም ዙሪያ ያሉ የተለያዩ አድማጮችና ተመልካቾች ከሌሎች
        ምንጮች ሊያገኟቸው የማይችሏቸውን መረጃዎች ለዓለም ሲያደርስ መቆየቱን ዋና ዳይሬክተሯ አማንዳ ቤኔት ገልፀዋል።
      - >-
        የተቋሙ ጥናት የኢንተርኔን መዘጋት በሃገራት ምጣኔ ሐብት ላይ ያደረሰውን ጉዳት በተለያዩ መለኪያዎች የገመተ ሲሆን፤
        በዚህም መሰረት ኢትዮጵያ ለ36 ቀናት ያህል ኢንተርኔትን በዘጋችበት እንዲሁም ለሰባት ቀናት ያህል በነበረው
        የማኅበራዊ ሚዲያ መናወጥ\ ወቅት በጥቅሉ ከ130 ሚሊዮን ዶላር በላይ አጥታለች ይላል።
pipeline_tag: text-retrieval
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: RoBERTa Amharic Embed Base
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_recall@5
            value: 0.869800820152314
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9050966608084359
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8036666074756674
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7707977655033881
            name: Cosine Mrr@10
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_recall@5
            value: 0.8646748681898067
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9020210896309314
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7977610383416281
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.764035577128722
            name: Cosine Mrr@10
datasets:
  - rasyosef/Amharic-Passage-Retrieval-Dataset-V2

Embedding-Amharic-Base

This is a sentence-transformers model finetuned from rasyosef/roberta-base-amharic. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

It was introduced in the paper The Multilingual Curse at the Retrieval Layer: Evidence from Amharic.

Code: GitHub Repository
Paper: The Multilingual Curse at the Retrieval Layer: Evidence from Amharic

Model Details

Model Description

Model Type: Sentence Transformer
Base model: rasyosef/roberta-base-amharic
Maximum Sequence Length: 510 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Language: am
License: mit

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 510, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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 SentenceTransformer

model = SentenceTransformer("rasyosef/embedding-amharic-base")

# What is the capital of Ethiopia? / France
queries = ['የኢትዮጵያ ዋና ከተማ ማናት?', 'የፈረንሳይ ዋና ከተማ ማናት?'] 

# Addis Ababa, Gondar, Paris, London, Washington D.C.
documents = ['አዲስ አበባ', 'ጎንደር', 'ፓሪስ', 'ለንደን', 'ዋሽንግተን ዲሲ'] 

# Compute embeddings
query_embeddings = model.encode_query(queries) # [2, 768]
document_embeddings = model.encode_document(documents) # [5, 768]

# Calculate semantic similarity
similarities = model.similarity(
    query_embeddings, 
    document_embeddings
)

print(similarities)
# tensor([[0.5075, 0.3114, 0.0798, 0.1967, 0.1340],
#         [0.1777, 0.0770, 0.5714, 0.2596, 0.1076]])

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 768
}
```

Metric	Value
cosine_recall@5	0.8698
cosine_recall@10	0.9051
cosine_ndcg@10	0.8037
cosine_mrr@10	0.7708

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 256
}
```

Metric	Value
cosine_recall@5	0.8647
cosine_recall@10	0.902
cosine_ndcg@10	0.7978
cosine_mrr@10	0.764

Training Details

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
gradient_accumulation_steps: 2
learning_rate: 6e-05
num_train_epochs: 6
lr_scheduler_type: cosine
warmup_ratio: 0.025
fp16: True
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_256_cosine_ndcg@10
-1	-1	-	0.0735	0.0582
1.0	1921	0.6769	0.7826	0.7751
2.0	3842	0.07	0.7894	0.7829
3.0	5763	0.0254	0.8030	0.7953
4.0	7684	0.0139	0.8037	0.7978

Framework Versions

Python: 3.11.13
Sentence Transformers: 4.1.0
Transformers: 4.52.4
PyTorch: 2.7.1+cu126
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},
}