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Arabic-STS-Matryoshka

Sentence Similarity
sentence-transformers
Safetensors
Arabic
xlm-roberta
feature-extraction
mteb
Eval Results
text-embeddings-inference
Inference Endpoints
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Arabic-STS-Matryoshka / README.md
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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
model-index:
  - name: omarelshehy/Arabic-STS-Matryoshka
    results:
      - dataset:
          config: ar-ar
          name: MTEB STS17 (ar-ar)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cosine_pearson
            value: 81.88865368687937
          - type: cosine_spearman
            value: 82.90236782891859
          - type: euclidean_pearson
            value: 81.21254869664341
          - type: euclidean_spearman
            value: 82.28002933909444
          - type: main_score
            value: 82.90236782891859
          - type: manhattan_pearson
            value: 81.26482951395201
          - type: manhattan_spearman
            value: 82.36146806563059
          - type: pearson
            value: 81.88865526924
          - type: spearman
            value: 82.89304993265725
        task:
          type: STS
license: apache-2.0
language:
  - ar

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:

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-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

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 

@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}
}