rasyosef/embedding-amharic-base

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 <!-- at revision b1a3d2c267262e2b82c83be9d4e59db762a5e931 -->
• —Maximum Sequence Length: 510 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown -->
• —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]])

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Evaluation

Metrics

Information Retrieval

• —Dataset: dim_768
• —Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "truncate_dim": 768
  }

Information Retrieval

• —Dataset: dim_256
• —Evaluated with <code>InformationRetrievalEvaluator</code> with these parameters:

  {
      "truncate_dim": 256
  }

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Training Details

<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: adamwtorchfused
• —batch_sampler: no_duplicates

Training Logs

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

</details>

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},
}

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