metadata
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 model finetuned from 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
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 tokens
- Similarity Function: Cosine Similarity
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
# 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]
Citation
BibTeX
Sentence Transformers
@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
@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
@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}
}