README.md

---
inference: false
language:
- ar
library_name: sentence-transformers
tags:
- mteb
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/LaBSE
datasets:
- Omartificial-Intelligence-Space/Arabic-NLi-Triplet
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: ذكر متوازن بعناية يقف على قدم واحدة بالقرب من منطقة شاطئ المحيط النظيفة
  sentences:
  - رجل يقدم عرضاً
  - هناك رجل بالخارج قرب الشاطئ
  - رجل يجلس على أريكه
- source_sentence: رجل يقفز إلى سريره القذر
  sentences:
  - السرير قذر.
  - رجل يضحك أثناء غسيل الملابس
  - الرجل على القمر
- source_sentence: الفتيات بالخارج
  sentences:
  - امرأة تلف الخيط إلى كرات بجانب كومة من الكرات
  - فتيان يركبان في جولة متعة
  - >-
    ثلاث فتيات يقفون سوية في غرفة واحدة تستمع وواحدة تكتب على الحائط والثالثة
    تتحدث إليهن
- source_sentence: الرجل يرتدي قميصاً أزرق.
  sentences:
  - >-
    رجل يرتدي قميصاً أزرق يميل إلى الجدار بجانب الطريق مع شاحنة زرقاء وسيارة
    حمراء مع الماء في الخلفية.
  - كتاب القصص مفتوح
  - رجل يرتدي قميص أسود يعزف على الجيتار.
- source_sentence: يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.
  sentences:
  - ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه
  - رجل يستلقي على وجهه على مقعد في الحديقة.
  - الشاب نائم بينما الأم تقود ابنتها إلى الحديقة
pipeline_tag: sentence-similarity
model-index:
- name: Omartificial-Intelligence-Space/Arabic-labse-Matryoshka
  results:
  - dataset:
      config: default
      name: MTEB BIOSSES (default)
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
      split: test
      type: mteb/biosses-sts
    metrics:
    - type: cosine_pearson
      value: 76.46793440999714
    - type: cosine_spearman
      value: 76.66439745271298
    - type: euclidean_pearson
      value: 76.52075972347127
    - type: euclidean_spearman
      value: 76.66439745271298
    - type: main_score
      value: 76.66439745271298
    - type: manhattan_pearson
      value: 76.68001857069733
    - type: manhattan_spearman
      value: 76.73066402288269
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB SICK-R (default)
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
      split: test
      type: mteb/sickr-sts
    metrics:
    - type: cosine_pearson
      value: 79.67657890693198
    - type: cosine_spearman
      value: 77.03286420274621
    - type: euclidean_pearson
      value: 78.1960735272073
    - type: euclidean_spearman
      value: 77.032855497919
    - type: main_score
      value: 77.03286420274621
    - type: manhattan_pearson
      value: 78.25627275994229
    - type: manhattan_spearman
      value: 77.00430810589081
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS12 (default)
      revision: a0d554a64d88156834ff5ae9920b964011b16384
      split: test
      type: mteb/sts12-sts
    metrics:
    - type: cosine_pearson
      value: 83.94288954523996
    - type: cosine_spearman
      value: 79.21432176112556
    - type: euclidean_pearson
      value: 81.21333251943913
    - type: euclidean_spearman
      value: 79.2152067330468
    - type: main_score
      value: 79.21432176112556
    - type: manhattan_pearson
      value: 81.16910737482634
    - type: manhattan_spearman
      value: 79.08756466301445
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS13 (default)
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
      split: test
      type: mteb/sts13-sts
    metrics:
    - type: cosine_pearson
      value: 77.48393909963059
    - type: cosine_spearman
      value: 79.54963868861196
    - type: euclidean_pearson
      value: 79.28416002197451
    - type: euclidean_spearman
      value: 79.54963861790114
    - type: main_score
      value: 79.54963868861196
    - type: manhattan_pearson
      value: 79.18653917582513
    - type: manhattan_spearman
      value: 79.46713533414295
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS14 (default)
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
      split: test
      type: mteb/sts14-sts
    metrics:
    - type: cosine_pearson
      value: 78.51596313692846
    - type: cosine_spearman
      value: 78.84601702652395
    - type: euclidean_pearson
      value: 78.55199809961427
    - type: euclidean_spearman
      value: 78.84603362286225
    - type: main_score
      value: 78.84601702652395
    - type: manhattan_pearson
      value: 78.52780170677605
    - type: manhattan_spearman
      value: 78.77744294039178
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS15 (default)
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
      split: test
      type: mteb/sts15-sts
    metrics:
    - type: cosine_pearson
      value: 84.53393478889929
    - type: cosine_spearman
      value: 85.60821849381648
    - type: euclidean_pearson
      value: 85.32813923250558
    - type: euclidean_spearman
      value: 85.6081835456016
    - type: main_score
      value: 85.60821849381648
    - type: manhattan_pearson
      value: 85.32782097916476
    - type: manhattan_spearman
      value: 85.58098670898562
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS16 (default)
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
      split: test
      type: mteb/sts16-sts
    metrics:
    - type: cosine_pearson
      value: 77.00196998325856
    - type: cosine_spearman
      value: 79.930951699069
    - type: euclidean_pearson
      value: 79.43196738390897
    - type: euclidean_spearman
      value: 79.93095112410258
    - type: main_score
      value: 79.930951699069
    - type: manhattan_pearson
      value: 79.33744358111427
    - type: manhattan_spearman
      value: 79.82939266539601
    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: 81.60289529424327
    - type: cosine_spearman
      value: 82.46806381979653
    - type: euclidean_pearson
      value: 81.32235058296072
    - type: euclidean_spearman
      value: 82.46676890643914
    - type: main_score
      value: 82.46806381979653
    - type: manhattan_pearson
      value: 81.43885277175312
    - type: manhattan_spearman
      value: 82.38955952718666
    task:
      type: STS
  - dataset:
      config: ar
      name: MTEB STS22 (ar)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 49.58293768761314
    - type: cosine_spearman
      value: 57.261888789832874
    - type: euclidean_pearson
      value: 53.36549109538782
    - type: euclidean_spearman
      value: 57.261888789832874
    - type: main_score
      value: 57.261888789832874
    - type: manhattan_pearson
      value: 53.06640323833928
    - type: manhattan_spearman
      value: 57.05837935512948
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STSBenchmark (default)
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
      split: test
      type: mteb/stsbenchmark-sts
    metrics:
    - type: cosine_pearson
      value: 81.43997935928729
    - type: cosine_spearman
      value: 82.04996129795596
    - type: euclidean_pearson
      value: 82.01917866996972
    - type: euclidean_spearman
      value: 82.04996129795596
    - type: main_score
      value: 82.04996129795596
    - type: manhattan_pearson
      value: 82.03487112040936
    - type: manhattan_spearman
      value: 82.03774605775651
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB SummEval (default)
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
      split: test
      type: mteb/summeval
    metrics:
    - type: cosine_pearson
      value: 32.113475997147674
    - type: cosine_spearman
      value: 32.17194233764879
    - type: dot_pearson
      value: 32.113469728827255
    - type: dot_spearman
      value: 32.174771315355386
    - type: main_score
      value: 32.17194233764879
    - type: pearson
      value: 32.113475997147674
    - type: spearman
      value: 32.17194233764879
    task:
      type: Summarization
- name: SentenceTransformer based on sentence-transformers/LaBSE
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.7269177710249681
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7225258779395222
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7259261785622463
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7210463582530393
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7259567884235211
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.722525823788783
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7269177712136122
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7225258771129475
      name: Spearman Dot
    - type: pearson_max
      value: 0.7269177712136122
      name: Pearson Max
    - type: spearman_max
      value: 0.7225258779395222
      name: Spearman Max
    - type: pearson_cosine
      value: 0.8143867576376295
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8205044914629483
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8203365887013151
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8203816698535976
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8201809453496319
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8205044914629483
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8143867541070537
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8205044914629483
      name: Spearman Dot
    - type: pearson_max
      value: 0.8203365887013151
      name: Pearson Max
    - type: spearman_max
      value: 0.8205044914629483
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.7268389724271859
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7224359411000278
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7241418669615103
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7195408311833029
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7248184919191593
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7212936866178097
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7252522928016701
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7205040482865328
      name: Spearman Dot
    - type: pearson_max
      value: 0.7268389724271859
      name: Pearson Max
    - type: spearman_max
      value: 0.7224359411000278
      name: Spearman Max
    - type: pearson_cosine
      value: 0.8143448965624136
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8211700903453509
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8217448619823571
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8216016599665544
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8216413349390971
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.82188122418776
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8097020064483653
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8147306090545295
      name: Spearman Dot
    - type: pearson_max
      value: 0.8217448619823571
      name: Pearson Max
    - type: spearman_max
      value: 0.82188122418776
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.7283468617741852
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7264294106954872
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7227711798003426
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.718067982079232
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7251492361775083
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7215068115809131
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7243396991648858
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7221390873398206
      name: Spearman Dot
    - type: pearson_max
      value: 0.7283468617741852
      name: Pearson Max
    - type: spearman_max
      value: 0.7264294106954872
      name: Spearman Max
    - type: pearson_cosine
      value: 0.8075613785257986
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8159258089804861
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8208711370091426
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8196747601014518
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8210210137439432
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8203004500356083
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7870611647231145
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7874848213991118
      name: Spearman Dot
    - type: pearson_max
      value: 0.8210210137439432
      name: Pearson Max
    - type: spearman_max
      value: 0.8203004500356083
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.7102082520621849
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7103917869311991
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7134729607181519
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.708895102058259
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7171545288118942
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7130380237150746
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6777774738547628
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6746474823963989
      name: Spearman Dot
    - type: pearson_max
      value: 0.7171545288118942
      name: Pearson Max
    - type: spearman_max
      value: 0.7130380237150746
      name: Spearman Max
    - type: pearson_cosine
      value: 0.8024378358145556
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8117561815472325
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.818920309459774
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8180515365910205
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8198346073356603
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8185162896024369
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7513270537478935
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7427542871546953
      name: Spearman Dot
    - type: pearson_max
      value: 0.8198346073356603
      name: Pearson Max
    - type: spearman_max
      value: 0.8185162896024369
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.6930745722517785
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6982194042238953
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6971382079778946
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6942362764367931
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7012627015062325
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6986972295835788
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6376735798940838
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6344835722310429
      name: Spearman Dot
    - type: pearson_max
      value: 0.7012627015062325
      name: Pearson Max
    - type: spearman_max
      value: 0.6986972295835788
      name: Spearman Max
    - type: pearson_cosine
      value: 0.7855080652087961
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7948979371698327
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8060407473462375
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8041199691999044
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8088262858195556
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8060483394849104
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.677754045289596
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6616232873061395
      name: Spearman Dot
    - type: pearson_max
      value: 0.8088262858195556
      name: Pearson Max
    - type: spearman_max
      value: 0.8060483394849104
      name: Spearman Max
license: apache-2.0
---

# SentenceTransformer based on sentence-transformers/LaBSE

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) on the Omartificial-Intelligence-Space/arabic-n_li-triplet dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision e34fab64a3011d2176c99545a93d5cbddc9a91b7 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - Omartificial-Intelligence-Space/arabic-n_li-triplet
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  (3): Normalize()
)
```

## 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
model = SentenceTransformer("Omartificial-Intelligence-Space/Arabic-labse")
# Run inference
sentences = [
    'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.',
    'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه',
    'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7269     |
| **spearman_cosine** | **0.7225** |
| pearson_manhattan   | 0.7259     |
| spearman_manhattan  | 0.721      |
| pearson_euclidean   | 0.726      |
| spearman_euclidean  | 0.7225     |
| pearson_dot         | 0.7269     |
| spearman_dot        | 0.7225     |
| pearson_max         | 0.7269     |
| spearman_max        | 0.7225     |

#### Semantic Similarity
* Dataset: `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7268     |
| **spearman_cosine** | **0.7224** |
| pearson_manhattan   | 0.7241     |
| spearman_manhattan  | 0.7195     |
| pearson_euclidean   | 0.7248     |
| spearman_euclidean  | 0.7213     |
| pearson_dot         | 0.7253     |
| spearman_dot        | 0.7205     |
| pearson_max         | 0.7268     |
| spearman_max        | 0.7224     |

#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7283     |
| **spearman_cosine** | **0.7264** |
| pearson_manhattan   | 0.7228     |
| spearman_manhattan  | 0.7181     |
| pearson_euclidean   | 0.7251     |
| spearman_euclidean  | 0.7215     |
| pearson_dot         | 0.7243     |
| spearman_dot        | 0.7221     |
| pearson_max         | 0.7283     |
| spearman_max        | 0.7264     |

#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7102     |
| **spearman_cosine** | **0.7104** |
| pearson_manhattan   | 0.7135     |
| spearman_manhattan  | 0.7089     |
| pearson_euclidean   | 0.7172     |
| spearman_euclidean  | 0.713      |
| pearson_dot         | 0.6778     |
| spearman_dot        | 0.6746     |
| pearson_max         | 0.7172     |
| spearman_max        | 0.713      |

#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6931     |
| **spearman_cosine** | **0.6982** |
| pearson_manhattan   | 0.6971     |
| spearman_manhattan  | 0.6942     |
| pearson_euclidean   | 0.7013     |
| spearman_euclidean  | 0.6987     |
| pearson_dot         | 0.6377     |
| spearman_dot        | 0.6345     |
| pearson_max         | 0.7013     |
| spearman_max        | 0.6987     |

#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8144     |
| **spearman_cosine** | **0.8205** |
| pearson_manhattan   | 0.8203     |
| spearman_manhattan  | 0.8204     |
| pearson_euclidean   | 0.8202     |
| spearman_euclidean  | 0.8205     |
| pearson_dot         | 0.8144     |
| spearman_dot        | 0.8205     |
| pearson_max         | 0.8203     |
| spearman_max        | 0.8205     |

#### Semantic Similarity
* Dataset: `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8143     |
| **spearman_cosine** | **0.8212** |
| pearson_manhattan   | 0.8217     |
| spearman_manhattan  | 0.8216     |
| pearson_euclidean   | 0.8216     |
| spearman_euclidean  | 0.8219     |
| pearson_dot         | 0.8097     |
| spearman_dot        | 0.8147     |
| pearson_max         | 0.8217     |
| spearman_max        | 0.8219     |

#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8076     |
| **spearman_cosine** | **0.8159** |
| pearson_manhattan   | 0.8209     |
| spearman_manhattan  | 0.8197     |
| pearson_euclidean   | 0.821      |
| spearman_euclidean  | 0.8203     |
| pearson_dot         | 0.7871     |
| spearman_dot        | 0.7875     |
| pearson_max         | 0.821      |
| spearman_max        | 0.8203     |

#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8024     |
| **spearman_cosine** | **0.8118** |
| pearson_manhattan   | 0.8189     |
| spearman_manhattan  | 0.8181     |
| pearson_euclidean   | 0.8198     |
| spearman_euclidean  | 0.8185     |
| pearson_dot         | 0.7513     |
| spearman_dot        | 0.7428     |
| pearson_max         | 0.8198     |
| spearman_max        | 0.8185     |

#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7855     |
| **spearman_cosine** | **0.7949** |
| pearson_manhattan   | 0.806      |
| spearman_manhattan  | 0.8041     |
| pearson_euclidean   | 0.8088     |
| spearman_euclidean  | 0.806      |
| pearson_dot         | 0.6778     |
| spearman_dot        | 0.6616     |
| pearson_max         | 0.8088     |
| spearman_max        | 0.806      |

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## Training Details

### Training Dataset

#### Omartificial-Intelligence-Space/arabic-n_li-triplet

* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                          | negative                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 9.99 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.44 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.82 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
  | anchor                                                      | positive                                    | negative                            |
  |:------------------------------------------------------------|:--------------------------------------------|:------------------------------------|
  | <code>شخص على حصان يقفز فوق طائرة معطلة</code>              | <code>شخص في الهواء الطلق، على حصان.</code> | <code>شخص في مطعم، يطلب عجة.</code> |
  | <code>أطفال يبتسمون و يلوحون للكاميرا</code>                | <code>هناك أطفال حاضرون</code>              | <code>الاطفال يتجهمون</code>        |
  | <code>صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.</code> | <code>الفتى يقوم بخدعة التزلج</code>        | <code>الصبي يتزلج على الرصيف</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Evaluation Dataset

#### Omartificial-Intelligence-Space/arabic-n_li-triplet

* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
* Size: 6,584 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                         | negative                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                           | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 19.71 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.37 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.49 tokens</li><li>max: 34 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                               | positive                                               | negative                                           |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:---------------------------------------------------|
  | <code>امرأتان يتعانقان بينما يحملان حزمة</code>                                                                                                      | <code>إمرأتان يحملان حزمة</code>                       | <code>الرجال يتشاجرون خارج مطعم</code>             |
  | <code>طفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة.</code> | <code>طفلين يرتديان قميصاً مرقماً يغسلون أيديهم</code> | <code>طفلين يرتديان سترة يذهبان إلى المدرسة</code> |
  | <code>رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس</code>                                                                             | <code>رجل يبيع الدونات لعميل</code>                    | <code>امرأة تشرب قهوتها في مقهى صغير</code>        |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| None   | 0    | -             | 0.7104                       | 0.7264                       | 0.7224                       | 0.6982                      | 0.7225                       |
| 0.0229 | 200  | 13.1738       | -                            | -                            | -                            | -                           | -                            |
| 0.0459 | 400  | 8.8127        | -                            | -                            | -                            | -                           | -                            |
| 0.0688 | 600  | 8.0984        | -                            | -                            | -                            | -                           | -                            |
| 0.0918 | 800  | 7.2984        | -                            | -                            | -                            | -                           | -                            |
| 0.1147 | 1000 | 7.5749        | -                            | -                            | -                            | -                           | -                            |
| 0.1377 | 1200 | 7.1292        | -                            | -                            | -                            | -                           | -                            |
| 0.1606 | 1400 | 6.6146        | -                            | -                            | -                            | -                           | -                            |
| 0.1835 | 1600 | 6.6523        | -                            | -                            | -                            | -                           | -                            |
| 0.2065 | 1800 | 6.1095        | -                            | -                            | -                            | -                           | -                            |
| 0.2294 | 2000 | 6.0841        | -                            | -                            | -                            | -                           | -                            |
| 0.2524 | 2200 | 6.3024        | -                            | -                            | -                            | -                           | -                            |
| 0.2753 | 2400 | 6.1941        | -                            | -                            | -                            | -                           | -                            |
| 0.2983 | 2600 | 6.1686        | -                            | -                            | -                            | -                           | -                            |
| 0.3212 | 2800 | 5.8317        | -                            | -                            | -                            | -                           | -                            |
| 0.3442 | 3000 | 6.0597        | -                            | -                            | -                            | -                           | -                            |
| 0.3671 | 3200 | 5.7832        | -                            | -                            | -                            | -                           | -                            |
| 0.3900 | 3400 | 5.7088        | -                            | -                            | -                            | -                           | -                            |
| 0.4130 | 3600 | 5.6988        | -                            | -                            | -                            | -                           | -                            |
| 0.4359 | 3800 | 5.5268        | -                            | -                            | -                            | -                           | -                            |
| 0.4589 | 4000 | 5.5543        | -                            | -                            | -                            | -                           | -                            |
| 0.4818 | 4200 | 5.3152        | -                            | -                            | -                            | -                           | -                            |
| 0.5048 | 4400 | 5.2894        | -                            | -                            | -                            | -                           | -                            |
| 0.5277 | 4600 | 5.1805        | -                            | -                            | -                            | -                           | -                            |
| 0.5506 | 4800 | 5.4559        | -                            | -                            | -                            | -                           | -                            |
| 0.5736 | 5000 | 5.3836        | -                            | -                            | -                            | -                           | -                            |
| 0.5965 | 5200 | 5.2626        | -                            | -                            | -                            | -                           | -                            |
| 0.6195 | 5400 | 5.2511        | -                            | -                            | -                            | -                           | -                            |
| 0.6424 | 5600 | 5.3308        | -                            | -                            | -                            | -                           | -                            |
| 0.6654 | 5800 | 5.2264        | -                            | -                            | -                            | -                           | -                            |
| 0.6883 | 6000 | 5.2881        | -                            | -                            | -                            | -                           | -                            |
| 0.7113 | 6200 | 5.1349        | -                            | -                            | -                            | -                           | -                            |
| 0.7342 | 6400 | 5.0872        | -                            | -                            | -                            | -                           | -                            |
| 0.7571 | 6600 | 4.5515        | -                            | -                            | -                            | -                           | -                            |
| 0.7801 | 6800 | 3.4312        | -                            | -                            | -                            | -                           | -                            |
| 0.8030 | 7000 | 3.1008        | -                            | -                            | -                            | -                           | -                            |
| 0.8260 | 7200 | 2.9582        | -                            | -                            | -                            | -                           | -                            |
| 0.8489 | 7400 | 2.8153        | -                            | -                            | -                            | -                           | -                            |
| 0.8719 | 7600 | 2.7214        | -                            | -                            | -                            | -                           | -                            |
| 0.8948 | 7800 | 2.5392        | -                            | -                            | -                            | -                           | -                            |
| 0.9177 | 8000 | 2.584         | -                            | -                            | -                            | -                           | -                            |
| 0.9407 | 8200 | 2.5384        | -                            | -                            | -                            | -                           | -                            |
| 0.9636 | 8400 | 2.4937        | -                            | -                            | -                            | -                           | -                            |
| 0.9866 | 8600 | 2.4155        | -                            | -                            | -                            | -                           | -                            |
| 1.0    | 8717 | -             | 0.8118                       | 0.8159                       | 0.8212                       | 0.7949                      | 0.8205                       |


### Framework Versions
- Python: 3.9.18
- Sentence Transformers: 3.0.1
- Transformers: 4.40.0
- PyTorch: 2.2.2+cu121
- Accelerate: 0.26.1
- Datasets: 2.19.0
- Tokenizers: 0.19.1

## 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}
}
```

## <span style="color:blue">Acknowledgments</span>

The author would like to thank Prince Sultan University for their invaluable support in this project. Their contributions and resources have been instrumental in the development and fine-tuning of these models.



```markdown
## Citation

If you use the Arabic Matryoshka Embeddings Model, please cite it as follows:

@misc{nacar2024enhancingsemanticsimilarityunderstanding,
      title={Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning}, 
      author={Omer Nacar and Anis Koubaa},
      year={2024},
      eprint={2407.21139},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.21139}, 
}