Modelpublic

omarelshehy/Arabic-STS-Matryoshka

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

2likes107downloads

Model Card

SentenceTransformer based on FacebookAI/xlm-roberta-large

This is an Arabic only 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 is trained using the MatryoshkaLoss for embeddings of size 1024, 786, 512, 128, and 64 for storage optimization (See Evaluation).

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:

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-STS-Matryoshka", truncate_dim=matryoshka_dim)
# Run inference
sentences = [
    'أحب قراءة الكتب في أوقات فراغي.',
    'أستمتع بقراءة القصص في المساء قبل النوم.',
    'القراءة تعزز معرفتي وتفتح أمامي آفاق جديدة.',
]
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]

Evaluation

Metrics

Semantic Similarity

—Dataset: sts-dev
—Evaluated with <code>EmbeddingSimilarityEvaluator</code>

Metric	Value
pearson_cosine	0.8256
spearman_cosine	0.8275
pearson_manhattan	0.8228
spearman_manhattan	0.8284
pearson_euclidean	0.8232
spearman_euclidean	0.8289
pearson_dot	0.8017
spearman_dot	0.8004
pearson_max	0.8256
spearman_max	0.8289

Embedding Size and Performance

This plot shows the slight degradation of performance qith smaller embedding sizes (worth investigating for your case since the benefits are huge compared to the slight loss in performance)

Plot

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}
}

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