sentence-transformers safetensors xlm-roberta feature-extraction text-retrieval sentence-similarity semantic-search amharic text-embedding-inference transformers license:mit model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
library_name: sentence-transformers
license: mit
metrics:
- cosine_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- text-retrieval
- sentence-similarity
- feature-extraction
- semantic-search
- amharic
- text-embedding-inference
- transformers
model-index:
- name: SentenceTransformer
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: TestTripletEvaluator
      type: TestTripletEvaluator
    metrics:
    - type: cosine_accuracy
      value: 0.875
      name: Cosine Accuracy
---

# SentenceTransformer Fine-Tuned for Amharic Retrieval

This model is a [sentence-transformers](https://www.sbert.net) model finetuned on Amharic QA triplets. It maps sentences and 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.

## Model Details

- **Model Type:** Sentence Transformer
- **Base Model:** `sentence-transformers/paraphrase-xlm-r-multilingual-v1`
- **Training Task:** Triplet loss with Matryoshka loss
- **Language:** Amharic
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity

## Training Overview

- **Training Data:** Custom Amharic QA triplets (with positive and negative examples)
- **Training Strategy:**  
  The model was finetuned using a combination of triplet loss and a Matryoshka loss, with evaluation performed using a `TripletEvaluator`.
- **Hyperparameters:**  
  - Epochs: 3  
  - Batch Size: 16  
  - Learning Rate: 1e-6  
  - Warmup Ratio: 0.08  
  - Weight Decay: 0.05

## Evaluation

The model was evaluated on a held-out test set using cosine similarity as the metric:

| Metric              | Value  |
|---------------------|--------|
| **Cosine Accuracy** | 0.875  |

## Usage

To use the model in your own project:

1. **Install Sentence Transformers:**

   ```bash
   pip install -U sentence-transformers
   ```

2. **Load the Model:**

   ```python
   from sentence_transformers import SentenceTransformer

   model = SentenceTransformer("abdulmunimjemal/xlm-r-retrieval-am-v5")
   sentences = [
       "ሰማይ ምን አይነት ቀለም ነው?",
       "ሰማይ ሰማያዊ ቀለም አለው።" ,
       "እኔ ምሳ እንጀራ በላሁ።" ,
       "ባሕር ምን አይነት ቀለም ነው?",
       "አየር በምድር ዙሪያ ያለ ነው።"
   ]
   embeddings = model.encode(sentences)
   print(embeddings.shape)  # Expected output: (5, 768)
   ```

3. **Compute Similarity:**

   ```python
   from sklearn.metrics.pairwise import cosine_similarity
   similarities = cosine_similarity(embeddings, embeddings)
   print(similarities.shape)  # Expected output: (5, 5)
   ```

## Model Architecture

Below is an outline of the model architecture:

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_mean_tokens': True, ...})
)
```

## Training Environment

- **Python:** 3.11.11
- **Sentence Transformers:** 3.3.1
- **Transformers:** 4.47.1
- **PyTorch:** 2.5.1+cu124
- **Accelerate:** 1.2.1
- **Datasets:** 3.2.0
- **Tokenizers:** 0.21.0

## Citation

If you use this model in your research, please cite it appropriately.

```bibtex
@misc{your_model,
  title = {SentenceTransformer Fine-Tuned for Amharic Retrieval},
  author = {Abdulmunim J. Jemal},
  year = {2025},
  howpublished = {Hugging Face Model Hub, \url{https://huggingface.co/abdulmunimjemal/xlm-r-retrieval-am-v1}}
}
```