--- 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-english-sts-matryoshka-v2-checkpoint-375k results: - dataset: config: en-en name: MTEB STS17 (en-en) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics: - type: cosine_pearson value: 87.38302667611983 - type: cosine_spearman value: 86.87900209442004 - type: euclidean_pearson value: 87.57406800102012 - type: euclidean_spearman value: 86.86643232719993 - type: main_score value: 86.87900209442004 - type: manhattan_pearson value: 87.67669085683242 - type: manhattan_spearman value: 86.75687931014386 - type: pearson value: 87.383027901324 - type: spearman value: 86.87900209442004 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: 83.63516310524058 - type: cosine_spearman value: 83.77655124170212 - type: euclidean_pearson value: 82.4202692817126 - type: euclidean_spearman value: 83.45140961256212 - type: main_score value: 83.77655124170212 - type: manhattan_pearson value: 82.46545160293968 - type: manhattan_spearman value: 83.44641098297507 - type: pearson value: 83.6351624999596 - type: spearman value: 83.76918950829455 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: 82.29919720659755 - type: cosine_spearman value: 82.18717939041626 - type: euclidean_pearson value: 83.49181602363565 - type: euclidean_spearman value: 82.66998443101066 - type: main_score value: 82.18717939041626 - type: manhattan_pearson value: 83.50361267643626 - type: manhattan_spearman value: 82.68143951875724 - type: pearson value: 82.29919479978703 - type: spearman value: 82.18717939041626 task: type: STS language: - ar - en --- # SentenceTransformer based on FacebookAI/xlm-roberta-large 🚀 This **v2.0** from the previously released version of [omarelshehy/arabic-english-sts-matryoshka](https://huggingface.co/omarelshehy/arabic-english-sts-matryoshka) 📊 Metrics (MTEB) in this version are better especially on **ar-en** metrics, but again don't just rely on them — test the model yourself and see if it fits your needs! ✅ # Model description 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! 💡 - **Model Type:** Sentence Transformer - **Base model:** [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity ## 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. ## 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-v2.0", 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 ```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} } ```