README.md

---
language:
- ar
library_name: sentence-transformers
tags:
- mteb
- transformers
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
model-index:
- name: Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka
  results:
  - dataset:
      config: ar
      name: MTEB MIRACLRetrievalHardNegatives (ar)
      revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
      split: dev
      type: miracl/mmteb-miracl-hardnegatives
    metrics:
      - type: main_score
        value: 22.583
      - type: map_at_1
        value: 8.211
      - type: map_at_3
        value: 12.913
      - type: map_at_5
        value: 14.622
      - type: map_at_10
        value: 16.315
      - type: ndcg_at_1
        value: 12.5
      - type: ndcg_at_3
        value: 16.058
      - type: ndcg_at_5
        value: 18.833
      - type: ndcg_at_10
        value: 22.583
      - type: recall_at_1
        value: 8.211
      - type: recall_at_3
        value: 18.474
      - type: recall_at_5
        value: 24.969
      - type: recall_at_10
        value: 34.894
      - type: precision_at_1
        value: 12.5
      - type: precision_at_3
        value: 9.7
      - type: precision_at_5
        value: 8.24
      - type: precision_at_10
        value: 6.07
      - type: mrr_at_1
        value: 12.5
      - type: mrr_at_3
        value: 18.5333
      - type: mrr_at_5
        value: 20.5983
      - type: mrr_at_10
        value: 22.165
    task:
      type: Retrieval
  - dataset:
      config: ar
      name: MTEB MintakaRetrieval (ar)
      revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
      split: test
      type: mintaka/mmteb-mintaka
    metrics:
      - type: main_score
        value: 18.23
      - type: map_at_1
        value: 9.941
      - type: map_at_3
        value: 13.277
      - type: map_at_5
        value: 14.33
      - type: map_at_10
        value: 15.12
      - type: ndcg_at_1
        value: 9.941
      - type: ndcg_at_3
        value: 14.41
      - type: ndcg_at_5
        value: 16.303
      - type: ndcg_at_10
        value: 18.23
      - type: recall_at_1
        value: 9.941
      - type: recall_at_3
        value: 17.703
      - type: recall_at_5
        value: 22.288
      - type: recall_at_10
        value: 28.28
      - type: precision_at_1
        value: 9.941
      - type: precision_at_3
        value: 5.901
      - type: precision_at_5
        value: 4.458
      - type: precision_at_10
        value: 2.828
      - type: mrr_at_1
        value: 9.941
      - type: mrr_at_3
        value: 13.2773
      - type: mrr_at_5
        value: 14.3305
      - type: mrr_at_10
        value: 15.1196
    task:
      type: Retrieval
  - dataset:
      config: ar
      name: MTEB MLQARetrieval (ar)
      revision: 397ed406c1a7902140303e7faf60fff35b58d285
      split: validation
      type: mlqa/mmteb-mlqa
    metrics:
      - type: main_score
        value: 65.234
      - type: map_at_1
        value: 50.29
      - type: map_at_3
        value: 58.317
      - type: map_at_5
        value: 59.487
      - type: map_at_10
        value: 60.37
      - type: ndcg_at_1
        value: 50.29
      - type: ndcg_at_3
        value: 60.972
      - type: ndcg_at_5
        value: 63.102
      - type: ndcg_at_10
        value: 65.234
      - type: recall_at_1
        value: 50.29
      - type: recall_at_3
        value: 68.665
      - type: recall_at_5
        value: 73.888
      - type: recall_at_10
        value: 80.464
      - type: precision_at_1
        value: 50.29
      - type: precision_at_3
        value: 22.888
      - type: precision_at_5
        value: 14.778
      - type: precision_at_10
        value: 8.046
      - type: mrr_at_1
        value: 50.2901
      - type: mrr_at_3
        value: 58.3172
      - type: mrr_at_5
        value: 59.4874
      - type: mrr_at_10
        value: 60.3699
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB SadeemQuestionRetrieval (ar)
      revision: 3cb0752b182e5d5d740df547748b06663c8e0bd9
      split: test
      type: sadeem/mmteb-sadeem
    metrics:
      - type: main_score
        value: 62.241
      - type: map_at_1
        value: 28.147
      - type: map_at_3
        value: 50.989
      - type: map_at_5
        value: 52.059
      - type: map_at_10
        value: 52.553
      - type: ndcg_at_1
        value: 28.147
      - type: ndcg_at_3
        value: 59.156
      - type: ndcg_at_5
        value: 61.066
      - type: ndcg_at_10
        value: 62.241
      - type: recall_at_1
        value: 28.147
      - type: recall_at_3
        value: 83.006
      - type: recall_at_5
        value: 87.602
      - type: recall_at_10
        value: 91.192
      - type: precision_at_1
        value: 28.147
      - type: precision_at_3
        value: 27.669
      - type: precision_at_5
        value: 17.52
      - type: precision_at_10
        value: 9.119
      - type: mrr_at_1
        value: 26.9507
      - type: mrr_at_3
        value: 50.1675
      - type: mrr_at_5
        value: 51.2207
      - type: mrr_at_10
        value: 51.7396
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB BIOSSES (default)
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
      split: test
      type: mteb/biosses-sts
    metrics:
    - type: cosine_pearson
      value: 67.88078975738149
    - type: cosine_spearman
      value: 67.36900492799694
    - type: euclidean_pearson
      value: 66.00402957388015
    - type: euclidean_spearman
      value: 65.70270189991112
    - type: main_score
      value: 67.36900492799694
    - type: manhattan_pearson
      value: 66.54937895501651
    - type: manhattan_spearman
      value: 66.12198856207587
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB SICK-R (default)
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
      split: test
      type: mteb/sickr-sts
    metrics:
    - type: cosine_pearson
      value: 62.931439439697044
    - type: cosine_spearman
      value: 57.64441663261227
    - type: euclidean_pearson
      value: 61.119408834167835
    - type: euclidean_spearman
      value: 57.42332323654558
    - type: main_score
      value: 57.64441663261227
    - type: manhattan_pearson
      value: 60.692516462749204
    - type: manhattan_spearman
      value: 56.99349446063643
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS12 (default)
      revision: a0d554a64d88156834ff5ae9920b964011b16384
      split: test
      type: mteb/sts12-sts
    metrics:
    - type: cosine_pearson
      value: 70.42631404785132
    - type: cosine_spearman
      value: 69.67060431422327
    - type: euclidean_pearson
      value: 68.70261457119209
    - type: euclidean_spearman
      value: 68.99597672902992
    - type: main_score
      value: 69.67060431422327
    - type: manhattan_pearson
      value: 67.99048393745159
    - type: manhattan_spearman
      value: 68.1853179140009
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS13 (default)
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
      split: test
      type: mteb/sts13-sts
    metrics:
    - type: cosine_pearson
      value: 49.46916157874787
    - type: cosine_spearman
      value: 51.95037157769884
    - type: euclidean_pearson
      value: 55.17336596392549
    - type: euclidean_spearman
      value: 54.312304378478835
    - type: main_score
      value: 51.95037157769884
    - type: manhattan_pearson
      value: 55.09060773902408
    - type: manhattan_spearman
      value: 53.96813218977611
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS14 (default)
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
      split: test
      type: mteb/sts14-sts
    metrics:
    - type: cosine_pearson
      value: 54.37699141667456
    - type: cosine_spearman
      value: 57.36607721958864
    - type: euclidean_pearson
      value: 57.98000825695592
    - type: euclidean_spearman
      value: 59.08844527739818
    - type: main_score
      value: 57.36607721958864
    - type: manhattan_pearson
      value: 57.588062173142106
    - type: manhattan_spearman
      value: 58.35590953779109
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS15 (default)
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
      split: test
      type: mteb/sts15-sts
    metrics:
    - type: cosine_pearson
      value: 67.37948361289261
    - type: cosine_spearman
      value: 70.0994395240558
    - type: euclidean_pearson
      value: 70.28341277052768
    - type: euclidean_spearman
      value: 70.11050982217422
    - type: main_score
      value: 70.0994395240558
    - type: manhattan_pearson
      value: 70.66000566140171
    - type: manhattan_spearman
      value: 70.41742785288693
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS16 (default)
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
      split: test
      type: mteb/sts16-sts
    metrics:
    - type: cosine_pearson
      value: 61.559501698409434
    - type: cosine_spearman
      value: 65.04903130808405
    - type: euclidean_pearson
      value: 63.92021058086694
    - type: euclidean_spearman
      value: 64.22673046991633
    - type: main_score
      value: 65.04903130808405
    - type: manhattan_pearson
      value: 63.958100692077956
    - type: manhattan_spearman
      value: 64.15057001708075
    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: 82.35377320218275
    - type: cosine_spearman
      value: 83.15514468203664
    - type: euclidean_pearson
      value: 80.56116685008965
    - type: euclidean_spearman
      value: 82.38252301503367
    - type: main_score
      value: 83.15514468203664
    - type: manhattan_pearson
      value: 80.74794586574093
    - type: manhattan_spearman
      value: 82.54224799581789
    task:
      type: STS
  - dataset:
      config: ar
      name: MTEB STS22 (ar)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 48.22154847597003
    - type: cosine_spearman
      value: 58.29235719729918
    - type: euclidean_pearson
      value: 51.54481297728728
    - type: euclidean_spearman
      value: 58.990627664376674
    - type: main_score
      value: 58.29235719729918
    - type: manhattan_pearson
      value: 52.195039627338126
    - type: manhattan_spearman
      value: 59.12018922641005
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STSBenchmark (default)
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
      split: test
      type: mteb/stsbenchmark-sts
    metrics:
    - type: cosine_pearson
      value: 59.50286436994106
    - type: cosine_spearman
      value: 61.592426810014366
    - type: euclidean_pearson
      value: 63.268627193788916
    - type: euclidean_spearman
      value: 63.16239630067321
    - type: main_score
      value: 61.592426810014366
    - type: manhattan_pearson
      value: 62.95949714767757
    - type: manhattan_spearman
      value: 62.687737378385364
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB SummEval (default)
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
      split: test
      type: mteb/summeval
    metrics:
    - type: cosine_pearson
      value: 31.1427099547469
    - type: cosine_spearman
      value: 31.32880594576111
    - type: dot_pearson
      value: 25.98395652985614
    - type: dot_spearman
      value: 25.30831374828529
    - type: main_score
      value: 31.32880594576111
    - type: pearson
      value: 31.1427099547469
    - type: spearman
      value: 31.32880594576111
    task:
      type: Summarization
- name: SentenceTransformer based on aubmindlab/bert-base-arabertv02
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.5949906740977448
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6159750250469712
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6295622269205102
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6269654283099967
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6326526932327604
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6317081341785673
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.42816790752358297
      name: Pearson Dot
    - type: spearman_dot
      value: 0.4295282086669423
      name: Spearman Dot
    - type: pearson_max
      value: 0.6326526932327604
      name: Pearson Max
    - type: spearman_max
      value: 0.6317081341785673
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.5846223235167534
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6064092420664184
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6287774004727389
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6263546541183983
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.631267664308041
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6301778108727977
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.3788565672017437
      name: Pearson Dot
    - type: spearman_dot
      value: 0.37680551461721923
      name: Spearman Dot
    - type: pearson_max
      value: 0.631267664308041
      name: Pearson Max
    - type: spearman_max
      value: 0.6301778108727977
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.5778623383989389
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.5959667709300495
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6242980982402613
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6217473192873829
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6237908608463304
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6215304658549996
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.35968442092444003
      name: Pearson Dot
    - type: spearman_dot
      value: 0.35304547874806785
      name: Spearman Dot
    - type: pearson_max
      value: 0.6242980982402613
      name: Pearson Max
    - type: spearman_max
      value: 0.6217473192873829
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.5830782075122916
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6022044167653756
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6151866925343435
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6121950064533626
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6162225316000448
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.615301209345362
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.40438461342780957
      name: Pearson Dot
    - type: spearman_dot
      value: 0.40153111017443666
      name: Spearman Dot
    - type: pearson_max
      value: 0.6162225316000448
      name: Pearson Max
    - type: spearman_max
      value: 0.615301209345362
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.5724838823862283
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.5914127847098
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6023812283389073
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.5967205030284914
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6069294574719372
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6041440553344074
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.36315938245739166
      name: Pearson Dot
    - type: spearman_dot
      value: 0.358512645020771
      name: Spearman Dot
    - type: pearson_max
      value: 0.6069294574719372
      name: Pearson Max
    - type: spearman_max
      value: 0.6041440553344074
      name: Spearman Max
base_model: aubmindlab/bert-base-arabertv02
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
license: apache-2.0
---

# Arabert All NLI Triplet Matryoshka Model

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) 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:** [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) <!-- at revision 016fb9d6768f522a59c6e0d2d5d5d43a4e1bff60 -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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-arabert-all-nli-triplet")
# 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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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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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      | 0.595     |
| **spearman_cosine** | **0.616** |
| pearson_manhattan   | 0.6296    |
| spearman_manhattan  | 0.627     |
| pearson_euclidean   | 0.6327    |
| spearman_euclidean  | 0.6317    |
| pearson_dot         | 0.4282    |
| spearman_dot        | 0.4295    |
| pearson_max         | 0.6327    |
| spearman_max        | 0.6317    |

#### 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      | 0.5846     |
| **spearman_cosine** | **0.6064** |
| pearson_manhattan   | 0.6288     |
| spearman_manhattan  | 0.6264     |
| pearson_euclidean   | 0.6313     |
| spearman_euclidean  | 0.6302     |
| pearson_dot         | 0.3789     |
| spearman_dot        | 0.3768     |
| pearson_max         | 0.6313     |
| spearman_max        | 0.6302     |

#### 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      | 0.5779    |
| **spearman_cosine** | **0.596** |
| pearson_manhattan   | 0.6243    |
| spearman_manhattan  | 0.6217    |
| pearson_euclidean   | 0.6238    |
| spearman_euclidean  | 0.6215    |
| pearson_dot         | 0.3597    |
| spearman_dot        | 0.353     |
| pearson_max         | 0.6243    |
| spearman_max        | 0.6217    |

#### 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      | 0.5831     |
| **spearman_cosine** | **0.6022** |
| pearson_manhattan   | 0.6152     |
| spearman_manhattan  | 0.6122     |
| pearson_euclidean   | 0.6162     |
| spearman_euclidean  | 0.6153     |
| pearson_dot         | 0.4044     |
| spearman_dot        | 0.4015     |
| pearson_max         | 0.6162     |
| spearman_max        | 0.6153     |

#### 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      | 0.5725     |
| **spearman_cosine** | **0.5914** |
| pearson_manhattan   | 0.6024     |
| spearman_manhattan  | 0.5967     |
| pearson_euclidean   | 0.6069     |
| spearman_euclidean  | 0.6041     |
| pearson_dot         | 0.3632     |
| spearman_dot        | 0.3585     |
| pearson_max         | 0.6069     |
| spearman_max        | 0.6041     |

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### Recommendations

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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: 8.02 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.03 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.72 tokens</li><li>max: 38 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: 14.87 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.54 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.14 tokens</li><li>max: 23 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 |
|:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0229 | 200  | 14.4811       | -                            | -                            | -                            | -                           | -                            |
| 0.0459 | 400  | 9.0389        | -                            | -                            | -                            | -                           | -                            |
| 0.0688 | 600  | 8.1478        | -                            | -                            | -                            | -                           | -                            |
| 0.0918 | 800  | 7.168         | -                            | -                            | -                            | -                           | -                            |
| 0.1147 | 1000 | 7.1998        | -                            | -                            | -                            | -                           | -                            |
| 0.1377 | 1200 | 6.7985        | -                            | -                            | -                            | -                           | -                            |
| 0.1606 | 1400 | 6.3754        | -                            | -                            | -                            | -                           | -                            |
| 0.1835 | 1600 | 6.3202        | -                            | -                            | -                            | -                           | -                            |
| 0.2065 | 1800 | 5.9186        | -                            | -                            | -                            | -                           | -                            |
| 0.2294 | 2000 | 5.9594        | -                            | -                            | -                            | -                           | -                            |
| 0.2524 | 2200 | 6.0211        | -                            | -                            | -                            | -                           | -                            |
| 0.2753 | 2400 | 5.9984        | -                            | -                            | -                            | -                           | -                            |
| 0.2983 | 2600 | 5.8321        | -                            | -                            | -                            | -                           | -                            |
| 0.3212 | 2800 | 5.621         | -                            | -                            | -                            | -                           | -                            |
| 0.3442 | 3000 | 5.9004        | -                            | -                            | -                            | -                           | -                            |
| 0.3671 | 3200 | 5.562         | -                            | -                            | -                            | -                           | -                            |
| 0.3900 | 3400 | 5.5125        | -                            | -                            | -                            | -                           | -                            |
| 0.4130 | 3600 | 5.4922        | -                            | -                            | -                            | -                           | -                            |
| 0.4359 | 3800 | 5.3023        | -                            | -                            | -                            | -                           | -                            |
| 0.4589 | 4000 | 5.4376        | -                            | -                            | -                            | -                           | -                            |
| 0.4818 | 4200 | 5.1048        | -                            | -                            | -                            | -                           | -                            |
| 0.5048 | 4400 | 5.0605        | -                            | -                            | -                            | -                           | -                            |
| 0.5277 | 4600 | 4.9985        | -                            | -                            | -                            | -                           | -                            |
| 0.5506 | 4800 | 5.2594        | -                            | -                            | -                            | -                           | -                            |
| 0.5736 | 5000 | 5.2183        | -                            | -                            | -                            | -                           | -                            |
| 0.5965 | 5200 | 5.1621        | -                            | -                            | -                            | -                           | -                            |
| 0.6195 | 5400 | 5.166         | -                            | -                            | -                            | -                           | -                            |
| 0.6424 | 5600 | 5.2241        | -                            | -                            | -                            | -                           | -                            |
| 0.6654 | 5800 | 5.1342        | -                            | -                            | -                            | -                           | -                            |
| 0.6883 | 6000 | 5.2267        | -                            | -                            | -                            | -                           | -                            |
| 0.7113 | 6200 | 5.1083        | -                            | -                            | -                            | -                           | -                            |
| 0.7342 | 6400 | 5.0119        | -                            | -                            | -                            | -                           | -                            |
| 0.7571 | 6600 | 4.6471        | -                            | -                            | -                            | -                           | -                            |
| 0.7801 | 6800 | 3.6699        | -                            | -                            | -                            | -                           | -                            |
| 0.8030 | 7000 | 3.2954        | -                            | -                            | -                            | -                           | -                            |
| 0.8260 | 7200 | 3.1039        | -                            | -                            | -                            | -                           | -                            |
| 0.8489 | 7400 | 3.001         | -                            | -                            | -                            | -                           | -                            |
| 0.8719 | 7600 | 2.8992        | -                            | -                            | -                            | -                           | -                            |
| 0.8948 | 7800 | 2.7504        | -                            | -                            | -                            | -                           | -                            |
| 0.9177 | 8000 | 2.7891        | -                            | -                            | -                            | -                           | -                            |
| 0.9407 | 8200 | 2.7157        | -                            | -                            | -                            | -                           | -                            |
| 0.9636 | 8400 | 2.6795        | -                            | -                            | -                            | -                           | -                            |
| 0.9866 | 8600 | 2.6278        | -                            | -                            | -                            | -                           | -                            |
| 1.0    | 8717 | -             | 0.6022                       | 0.5960                       | 0.6064                       | 0.5914                      | 0.6160                       |


### 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}, 
}