File size: 34,991 Bytes

---
inference: false
language:
- ar
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
- mteb
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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-all-nli-triplet-Matryoshka
  results:
  - dataset:
      config: default
      name: MTEB BIOSSES (default)
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
      split: test
      type: mteb/biosses-sts
    metrics:
    - type: cosine_pearson
      value: 81.20578037912223
    - type: cosine_spearman
      value: 77.43670420687278
    - type: euclidean_pearson
      value: 74.60444698819703
    - type: euclidean_spearman
      value: 72.25767053642666
    - type: main_score
      value: 77.43670420687278
    - type: manhattan_pearson
      value: 73.86951335383257
    - type: manhattan_spearman
      value: 71.41608509527123
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB SICK-R (default)
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
      split: test
      type: mteb/sickr-sts
    metrics:
    - type: cosine_pearson
      value: 83.11155556919923
    - type: cosine_spearman
      value: 79.39435627520159
    - type: euclidean_pearson
      value: 81.05225024180342
    - type: euclidean_spearman
      value: 79.09926890001618
    - type: main_score
      value: 79.39435627520159
    - type: manhattan_pearson
      value: 80.74351302609706
    - type: manhattan_spearman
      value: 78.826254748334
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS12 (default)
      revision: a0d554a64d88156834ff5ae9920b964011b16384
      split: test
      type: mteb/sts12-sts
    metrics:
    - type: cosine_pearson
      value: 85.10074960888633
    - type: cosine_spearman
      value: 78.93043293576132
    - type: euclidean_pearson
      value: 84.1168219787408
    - type: euclidean_spearman
      value: 78.44739559202252
    - type: main_score
      value: 78.93043293576132
    - type: manhattan_pearson
      value: 83.79447841594396
    - type: manhattan_spearman
      value: 77.94028171700384
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS13 (default)
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
      split: test
      type: mteb/sts13-sts
    metrics:
    - type: cosine_pearson
      value: 81.34459901517775
    - type: cosine_spearman
      value: 82.73032633919925
    - type: euclidean_pearson
      value: 82.83546499367434
    - type: euclidean_spearman
      value: 83.29701673615389
    - type: main_score
      value: 82.73032633919925
    - type: manhattan_pearson
      value: 82.63480502797324
    - type: manhattan_spearman
      value: 83.05016589615636
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS14 (default)
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
      split: test
      type: mteb/sts14-sts
    metrics:
    - type: cosine_pearson
      value: 82.53179983763488
    - type: cosine_spearman
      value: 81.64974497557361
    - type: euclidean_pearson
      value: 83.03981070806898
    - type: euclidean_spearman
      value: 82.65556168300631
    - type: main_score
      value: 81.64974497557361
    - type: manhattan_pearson
      value: 82.83722360191446
    - type: manhattan_spearman
      value: 82.4164264119
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS15 (default)
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
      split: test
      type: mteb/sts15-sts
    metrics:
    - type: cosine_pearson
      value: 86.5684162475647
    - type: cosine_spearman
      value: 87.62163215009723
    - type: euclidean_pearson
      value: 87.3068288651339
    - type: euclidean_spearman
      value: 88.03508640722863
    - type: main_score
      value: 87.62163215009723
    - type: manhattan_pearson
      value: 87.21818681800193
    - type: manhattan_spearman
      value: 87.94690511382603
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS16 (default)
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
      split: test
      type: mteb/sts16-sts
    metrics:
    - type: cosine_pearson
      value: 81.70518105237446
    - type: cosine_spearman
      value: 83.66083698795428
    - type: euclidean_pearson
      value: 82.80400684544435
    - type: euclidean_spearman
      value: 83.39926895275799
    - type: main_score
      value: 83.66083698795428
    - type: manhattan_pearson
      value: 82.44430538731845
    - type: manhattan_spearman
      value: 82.99600783826028
    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: 82.23229967696153
    - type: cosine_spearman
      value: 82.40039006538706
    - type: euclidean_pearson
      value: 79.21322872573518
    - type: euclidean_spearman
      value: 79.14230529579783
    - type: main_score
      value: 82.40039006538706
    - type: manhattan_pearson
      value: 79.1476348987964
    - type: manhattan_spearman
      value: 78.82381660638143
    task:
      type: STS
  - dataset:
      config: ar
      name: MTEB STS22 (ar)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 45.95767124518871
    - type: cosine_spearman
      value: 51.37922888872568
    - type: euclidean_pearson
      value: 45.519471121310126
    - type: euclidean_spearman
      value: 51.45605803385654
    - type: main_score
      value: 51.37922888872568
    - type: manhattan_pearson
      value: 45.98761117909666
    - type: manhattan_spearman
      value: 51.48451973989366
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STSBenchmark (default)
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
      split: test
      type: mteb/stsbenchmark-sts
    metrics:
    - type: cosine_pearson
      value: 85.38916827757183
    - type: cosine_spearman
      value: 86.16303183485594
    - type: euclidean_pearson
      value: 85.16406897245115
    - type: euclidean_spearman
      value: 85.40364087457081
    - type: main_score
      value: 86.16303183485594
    - type: manhattan_pearson
      value: 84.96853193915084
    - type: manhattan_spearman
      value: 85.13238442843544
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB SummEval (default)
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
      split: test
      type: mteb/summeval
    metrics:
    - type: cosine_pearson
      value: 30.077426987171158
    - type: cosine_spearman
      value: 30.163682020271608
    - type: dot_pearson
      value: 27.31125295906803
    - type: dot_spearman
      value: 29.138235153208193
    - type: main_score
      value: 30.163682020271608
    - type: pearson
      value: 30.077426987171158
    - type: spearman
      value: 30.163682020271608
    task:
      type: Summarization
- name: >-
    SentenceTransformer based on
    sentence-transformers/paraphrase-multilingual-mpnet-base-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.8538831619509135
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.861625750018802
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8496745674597512
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8513333417508545
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8516261261374778
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8540549341060195
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7281308266536204
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7230282720855726
      name: Spearman Dot
    - type: pearson_max
      value: 0.8538831619509135
      name: Pearson Max
    - type: spearman_max
      value: 0.861625750018802
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.8542379189261009
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8609329396560859
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8486657899695456
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8512120732504748
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8505249483849495
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8538738365440234
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7075618032859148
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7028728329509918
      name: Spearman Dot
    - type: pearson_max
      value: 0.8542379189261009
      name: Pearson Max
    - type: spearman_max
      value: 0.8609329396560859
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.8486308733045101
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8578681811996274
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8404506123980291
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.845565163232125
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8414758099131773
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8471566121478254
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6668664182302968
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6651222481800894
      name: Spearman Dot
    - type: pearson_max
      value: 0.8486308733045101
      name: Pearson Max
    - type: spearman_max
      value: 0.8578681811996274
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.8389761445410956
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8499312736457453
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8287388421834582
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8353046807483782
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8297699263897746
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8371843253238523
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5855876200722326
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5834920267418124
      name: Spearman Dot
    - type: pearson_max
      value: 0.8389761445410956
      name: Pearson Max
    - type: spearman_max
      value: 0.8499312736457453
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.8290685425698586
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8429054799136109
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8100968316314205
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8221121550434057
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8129044863346081
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8255133471709527
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5067257944655903
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5109761436588146
      name: Spearman Dot
    - type: pearson_max
      value: 0.8290685425698586
      name: Pearson Max
    - type: spearman_max
      value: 0.8429054799136109
      name: Spearman Max
license: apache-2.0
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) 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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 79f2382ceacceacdf38563d7c5d16b9ff8d725d6 -->
- **Maximum Sequence Length:** 128 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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 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-Nli-Matryoshka")
# 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      | 0.8539     |
| **spearman_cosine** | **0.8616** |
| pearson_manhattan   | 0.8497     |
| spearman_manhattan  | 0.8513     |
| pearson_euclidean   | 0.8516     |
| spearman_euclidean  | 0.8541     |
| pearson_dot         | 0.7281     |
| spearman_dot        | 0.723      |
| pearson_max         | 0.8539     |
| spearman_max        | 0.8616     |

#### 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      | 0.8542     |
| **spearman_cosine** | **0.8609** |
| pearson_manhattan   | 0.8487     |
| spearman_manhattan  | 0.8512     |
| pearson_euclidean   | 0.8505     |
| spearman_euclidean  | 0.8539     |
| pearson_dot         | 0.7076     |
| spearman_dot        | 0.7029     |
| pearson_max         | 0.8542     |
| spearman_max        | 0.8609     |

#### 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      | 0.8486     |
| **spearman_cosine** | **0.8579** |
| pearson_manhattan   | 0.8405     |
| spearman_manhattan  | 0.8456     |
| pearson_euclidean   | 0.8415     |
| spearman_euclidean  | 0.8472     |
| pearson_dot         | 0.6669     |
| spearman_dot        | 0.6651     |
| pearson_max         | 0.8486     |
| spearman_max        | 0.8579     |

#### 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      | 0.839      |
| **spearman_cosine** | **0.8499** |
| pearson_manhattan   | 0.8287     |
| spearman_manhattan  | 0.8353     |
| pearson_euclidean   | 0.8298     |
| spearman_euclidean  | 0.8372     |
| pearson_dot         | 0.5856     |
| spearman_dot        | 0.5835     |
| pearson_max         | 0.839      |
| spearman_max        | 0.8499     |

#### 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      | 0.8291     |
| **spearman_cosine** | **0.8429** |
| pearson_manhattan   | 0.8101     |
| spearman_manhattan  | 0.8221     |
| pearson_euclidean   | 0.8129     |
| spearman_euclidean  | 0.8255     |
| pearson_dot         | 0.5067     |
| spearman_dot        | 0.511      |
| pearson_max         | 0.8291     |
| spearman_max        | 0.8429     |

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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: 5 tokens</li><li>mean: 10.33 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.21 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.32 tokens</li><li>max: 53 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: 5 tokens</li><li>mean: 21.86 tokens</li><li>max: 105 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.22 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.2 tokens</li><li>max: 33 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`: 256
- `per_device_eval_batch_size`: 256
- `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`: 256
- `per_device_eval_batch_size`: 256
- `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 |
|:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.2294 | 500  | 10.1279       | -                            | -                            | -                            | -                           | -                            |
| 0.4587 | 1000 | 8.0384        | -                            | -                            | -                            | -                           | -                            |
| 0.6881 | 1500 | 7.3484        | -                            | -                            | -                            | -                           | -                            |
| 0.9174 | 2000 | 4.2216        | -                            | -                            | -                            | -                           | -                            |
| 1.0    | 2180 | -             | 0.8499                       | 0.8579                       | 0.8609                       | 0.8429                      | 0.8616                       |


### 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}, 
}