README.md

---
base_model: FacebookAI/xlm-roberta-large
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- mteb
- bilingual
model-index:
- name: omarelshehy/arabic-english-sts-matryoshka
  results:
  - dataset:
      config: en-en
      name: MTEB STS17 (en-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 87.17053120821998
    - type: cosine_spearman
      value: 87.05959159411456
    - type: euclidean_pearson
      value: 87.63706739480517
    - type: euclidean_spearman
      value: 87.7675347222274
    - type: main_score
      value: 87.05959159411456
    - type: manhattan_pearson
      value: 87.7006832512623
    - type: manhattan_spearman
      value: 87.80128473941168
    - type: pearson
      value: 87.17053012311975
    - type: spearman
      value: 87.05959159411456
    task:
      type: STS
  - dataset:
      config: ar-ar
      name: MTEB STS17 (ar-ar)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 82.22889478671283
    - type: cosine_spearman
      value: 83.0533648934447
    - type: euclidean_pearson
      value: 81.15891941165452
    - type: euclidean_spearman
      value: 82.14034597386936
    - type: main_score
      value: 83.0533648934447
    - type: manhattan_pearson
      value: 81.17463976232014
    - type: manhattan_spearman
      value: 82.09804987736345
    - type: pearson
      value: 82.22889389569819
    - type: spearman
      value: 83.0529662284269
    task:
      type: STS
  - dataset:
      config: en-ar
      name: MTEB STS17 (en-ar)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 79.79480510851795
    - type: cosine_spearman
      value: 79.67609346073252
    - type: euclidean_pearson
      value: 81.64087935350051
    - type: euclidean_spearman
      value: 80.52588414802709
    - type: main_score
      value: 79.67609346073252
    - type: manhattan_pearson
      value: 81.57042957417305
    - type: manhattan_spearman
      value: 80.44331526051143
    - type: pearson
      value: 79.79480418294698
    - type: spearman
      value: 79.67609346073252
    task:
      type: STS
language:
- ar
- en
license: apache-2.0
---

# SentenceTransformer based on FacebookAI/xlm-roberta-large

This is a **Bilingual** (Arabic-English) [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for **semantic textual similarity, semantic search, paraphrase mining, text classification, clustering**, and more.

The model handles both languages separately 🌐, but also **interchangeably**, which unlocks flexible applications for developers and researchers who want to further build on Arabic models! 💡

📊 Metrics from MTEB are promising, but don't just rely on them — test the model yourself and see if it fits your needs! ✅

## Matryoshka Embeddings 🪆 

This model supports Matryoshka embeddings, allowing you to truncate embeddings into smaller sizes to optimize performance and memory usage, based on your task requirements. Available truncation sizes include: **1024, 768, 512, 256, 128, and 64**

You can select the appropriate embedding size for your use case, ensuring flexibility in resource management.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) <!-- at revision c23d21b0620b635a76227c604d44e43a9f0ee389 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->



## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
matryoshka_dim = 786
model = SentenceTransformer("omarelshehy/arabic-english-sts-matryoshka", truncate_dim=matryoshka_dim)
# Run inference
sentences = [
    "She enjoyed reading books by the window as the rain poured outside.",
    "كانت تستمتع بقراءة الكتب بجانب النافذة بينما كانت الأمطار تتساقط في الخارج.",
    "Reading by the window was her favorite thing, especially during rainy days."
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->


## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```