305 lines
11 KiB
Markdown
305 lines
11 KiB
Markdown
---
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language:
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- am
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license: mit
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:245876
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- loss:MatryoshkaLoss
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- loss:MultipleNegativesRankingLoss
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base_model: rasyosef/roberta-base-amharic
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widget:
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- source_sentence: በኢትዮጵያ ለመጀመሪያ ጊዜ ወታደራዊ ስልጠና የወሰዱ ዕጩ ዲፕሎማቶች ተመረቁ
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sentences:
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- የውጭ ጉዳይ ሚኒስቴር ከሜጀር ጄነራል ሀየሎም አርአያ ወታደራዊ አካዳሚ ጋር በመተባበር በኢትዮጵያ ለመጀመሪያ ጊዜ ወታደራዊ
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ስልጠና የወሰዱ ዲፕሎማቶችን አስመረቀ፡፡በወታደራዊ አካዳሚው ትላንት በተካሄደ የምርቃት ሥነ- ስርዓት ስልጠናውን ላገኙ 89
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ዕጩ ድፕሎማቶች የምስክር ወረቀት ተበረክቷል።
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- አዲስ አበባ፣ የካቲት 19፣ 2012 (ኤፍ.ቢ.ሲ) የኢፌዴሪ አየር ኃይል ለከፍተኛ መኮንኖች የማዕረግ እድገት ሰጥቷል።አየር
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ኃይሉ በዛሬው እለት በቢሾፍቱ በሚገኘው የኢፌዴሪ አየር ኃይል ጠቅላይ መምሪያ ባካሄደው ስነ ስርዓት ላይ የኢፌዴሪ ጦር ኃይሎች
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ምክተል ኤታማዦር ሹም ጄኔራል ብርሃኑ ጁላ እና የኢፌዴሪ አየር ኃይል ዋና አዛዥ ሜጀር ጄኔራል ይልማ መርዳሳን ጨምሮ ከፍተኛ
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አመራሮች ተገኝተዋል።በስነ ስርዓቱ ላይ 106 ለሚሆኑ መኮንኖች በአየር ኃይል ዋና አዛዥ ሜጀር ጄኔራል ይልማ መርዳሳ የተለያዩ
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የማዕረግ እድገቶችን ሰጥተዋል።
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- source_sentence: ኢትዮጵያ ኢንተርኔትን በመዝጋቷ ከ130 ሚሊዮን ዶላር በላይ አጣች
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sentences:
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- የአሜሪካ ድምፅ ባለፉት ሰባ አምስት ዓመታት ውስጥ በዓለም ዙሪያ ያሉ የተለያዩ አድማጮችና ተመልካቾች ከሌሎች ምንጮች ሊያገኟቸው
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የማይችሏቸውን መረጃዎች ለዓለም ሲያደርስ መቆየቱን ዋና ዳይሬክተሯ አማንዳ ቤኔት ገልፀዋል።
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- የተቋሙ ጥናት የኢንተርኔን መዘጋት በሃገራት ምጣኔ ሐብት ላይ ያደረሰውን ጉዳት በተለያዩ መለኪያዎች የገመተ ሲሆን፤ በዚህም
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መሰረት ኢትዮጵያ ለ36 ቀናት ያህል ኢንተርኔትን በዘጋችበት እንዲሁም ለሰባት ቀናት ያህል በነበረው የማኅበራዊ ሚዲያ መናወጥ\
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ወቅት በጥቅሉ ከ130 ሚሊዮን ዶላር በላይ አጥታለች ይላል።
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pipeline_tag: text-retrieval
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library_name: sentence-transformers
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metrics:
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- cosine_accuracy@1
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- cosine_accuracy@3
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- cosine_accuracy@5
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- cosine_accuracy@10
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- cosine_precision@1
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- cosine_precision@3
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- cosine_precision@5
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- cosine_precision@10
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- cosine_recall@1
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- cosine_recall@3
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- cosine_recall@5
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- cosine_recall@10
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- cosine_ndcg@10
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- cosine_mrr@10
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- cosine_map@100
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model-index:
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- name: RoBERTa Amharic Embed Base
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results:
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- task:
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type: information-retrieval
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name: Information Retrieval
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dataset:
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name: dim 768
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type: dim_768
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metrics:
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- type: cosine_recall@5
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value: 0.869800820152314
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name: Cosine Recall@5
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- type: cosine_recall@10
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value: 0.9050966608084359
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name: Cosine Recall@10
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- type: cosine_ndcg@10
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value: 0.8036666074756674
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name: Cosine Ndcg@10
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- type: cosine_mrr@10
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value: 0.7707977655033881
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name: Cosine Mrr@10
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- task:
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type: information-retrieval
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name: Information Retrieval
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dataset:
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name: dim 256
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type: dim_256
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metrics:
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- type: cosine_recall@5
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value: 0.8646748681898067
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name: Cosine Recall@5
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- type: cosine_recall@10
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value: 0.9020210896309314
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name: Cosine Recall@10
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- type: cosine_ndcg@10
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value: 0.7977610383416281
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name: Cosine Ndcg@10
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- type: cosine_mrr@10
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value: 0.764035577128722
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name: Cosine Mrr@10
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datasets:
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- rasyosef/Amharic-Passage-Retrieval-Dataset-V2
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---
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# Embedding-Amharic-Base
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [rasyosef/roberta-base-amharic](https://huggingface.co/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.
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It was introduced in the paper [The Multilingual Curse at the Retrieval Layer: Evidence from Amharic](https://huggingface.co/papers/2605.24556).
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- **Code:** [GitHub Repository](https://github.com/rasyosef/amharic-neural-ir)
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- **Paper:** [The Multilingual Curse at the Retrieval Layer: Evidence from Amharic](https://huggingface.co/papers/2605.24556)
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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [rasyosef/roberta-base-amharic](https://huggingface.co/rasyosef/roberta-base-amharic) <!-- at revision b1a3d2c267262e2b82c83be9d4e59db762a5e931 -->
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- **Maximum Sequence Length:** 510 tokens
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- **Output Dimensionality:** 768 dimensions
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- **Similarity Function:** Cosine Similarity
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<!-- - **Training Dataset:** Unknown -->
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- **Language:** am
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- **License:** mit
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### Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 510, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
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(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})
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(2): Normalize()
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)
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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("rasyosef/embedding-amharic-base")
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# What is the capital of Ethiopia? / France
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queries = ['የኢትዮጵያ ዋና ከተማ ማናት?', 'የፈረንሳይ ዋና ከተማ ማናት?']
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# Addis Ababa, Gondar, Paris, London, Washington D.C.
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documents = ['አዲስ አበባ', 'ጎንደር', 'ፓሪስ', 'ለንደን', 'ዋሽንግተን ዲሲ']
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# Compute embeddings
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query_embeddings = model.encode_query(queries) # [2, 768]
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document_embeddings = model.encode_document(documents) # [5, 768]
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# Calculate semantic similarity
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similarities = model.similarity(
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query_embeddings,
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document_embeddings
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)
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print(similarities)
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# tensor([[0.5075, 0.3114, 0.0798, 0.1967, 0.1340],
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# [0.1777, 0.0770, 0.5714, 0.2596, 0.1076]])
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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## Evaluation
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### Metrics
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#### Information Retrieval
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* Dataset: `dim_768`
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
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```json
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{
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"truncate_dim": 768
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}
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```
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| Metric | Value |
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|:--------------------|:-----------|
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| cosine_recall@5 | 0.8698 |
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| cosine_recall@10 | 0.9051 |
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| **cosine_ndcg@10** | **0.8037** |
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| cosine_mrr@10 | 0.7708 |
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#### Information Retrieval
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* Dataset: `dim_256`
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
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```json
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{
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"truncate_dim": 256
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}
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```
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| Metric | Value |
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|:--------------------|:-----------|
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| cosine_recall@5 | 0.8647 |
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| cosine_recall@10 | 0.902 |
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| **cosine_ndcg@10** | **0.7978** |
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| cosine_mrr@10 | 0.764 |
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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<details>
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `eval_strategy`: epoch
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- `per_device_train_batch_size`: 64
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- `per_device_eval_batch_size`: 64
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- `gradient_accumulation_steps`: 2
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- `learning_rate`: 6e-05
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- `num_train_epochs`: 6
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- `lr_scheduler_type`: cosine
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- `warmup_ratio`: 0.025
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- `fp16`: True
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- `load_best_model_at_end`: True
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- `optim`: adamw_torch_fused
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- `batch_sampler`: no_duplicates
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### Training Logs
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| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_256_cosine_ndcg@10 |
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|:-----:|:----:|:-------------:|:----------------------:|:----------------------:|
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| -1 | -1 | - | 0.0735 | 0.0582 |
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| 1.0 | 1921 | 0.6769 | 0.7826 | 0.7751 |
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| 2.0 | 3842 | 0.07 | 0.7894 | 0.7829 |
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| 3.0 | 5763 | 0.0254 | 0.8030 | 0.7953 |
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| 4.0 | 7684 | 0.0139 | 0.8037 | 0.7978 |
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### Framework Versions
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- Python: 3.11.13
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- Sentence Transformers: 4.1.0
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- Transformers: 4.52.4
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- PyTorch: 2.7.1+cu126
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- Accelerate: 1.7.0
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- Datasets: 3.6.0
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- Tokenizers: 0.21.1
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</details>
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## Citation
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```bibtex
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@inproceedings{alemneh2026amharicir,
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title = {The Multilingual Curse at the Retrieval Layer: Evidence from Amharic},
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author = {Alemneh, Yosef Worku and Mekonnen, Kidist Amde and de Rijke, Maarten},
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booktitle = {Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM), ACL 2026},
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year = {2026},
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}
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```
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<!--
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## Glossary
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*Clearly define terms in order to be accessible across audiences.*
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-->
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## Model Card Authors
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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-->
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## Model Card Contact
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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