sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:557850 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss mteb ar dataset:Omartificial-Intelligence-Space/Arabic-NLi-Triplet arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 arxiv:2407.21139 base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference region:us
---
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
datasets:
- Omartificial-Intelligence-Space/Arabic-NLi-Triplet
language:
- ar
library_name: sentence-transformers
license: apache-2.0
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
- mteb
inference: false
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
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
  results:
  - task:
      type: Retrieval
    dataset:
      name: MTEB MIRACLRetrieval (ar)
      type: miracl/mmteb-miracl
      config: ar
      split: dev
      revision: main
    metrics:
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      value: 19.233
    - type: ndcg_at_3
      value: 21.393
    - type: ndcg_at_5
      value: 23.347
    - type: ndcg_at_10
      value: 26.273999999999997
    - type: ndcg_at_20
      value: 28.591
    - type: ndcg_at_100
      value: 32.098
    - type: ndcg_at_1000
      value: 34.971000000000004
    - type: map_at_1
      value: 12.555
    - type: map_at_3
      value: 17.763
    - type: map_at_5
      value: 19.317
    - type: map_at_10
      value: 20.748
    - type: map_at_20
      value: 21.535
    - type: map_at_100
      value: 22.147
    - type: map_at_1000
      value: 22.275
    - type: recall_at_1
      value: 12.555
    - type: recall_at_3
      value: 22.576
    - type: recall_at_5
      value: 27.681
    - type: recall_at_10
      value: 35.461
    - type: recall_at_20
      value: 43.097
    - type: recall_at_100
      value: 58.902
    - type: recall_at_1000
      value: 78.33099999999999
    - type: precision_at_1
      value: 19.233
    - type: precision_at_3
      value: 12.65
    - type: precision_at_5
      value: 9.626999999999999
    - type: precision_at_10
      value: 6.35
    - type: precision_at_20
      value: 3.961
    - type: precision_at_100
      value: 1.118
    - type: precision_at_1000
      value: 0.152
    - type: mrr_at_1
      value: 19.2334
    - type: mrr_at_3
      value: 25.1266
    - type: mrr_at_5
      value: 26.4681
    - type: mrr_at_10
      value: 27.6315
    - type: mrr_at_20
      value: 28.1315
    - type: mrr_at_100
      value: 28.4874
    - type: mrr_at_1000
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  - task:
      type: Retrieval
    dataset:
      name: MTEB MIRACLRetrievalHardNegatives (ar)
      type: mteb/miracl-hard-negatives
      config: ar
      split: dev
      revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
    metrics:
    - type: ndcg_at_1
      value: 20.7
    - type: ndcg_at_3
      value: 23.766000000000002
    - type: ndcg_at_5
      value: 26.479000000000003
    - type: ndcg_at_10
      value: 30.152
    - type: ndcg_at_20
      value: 33.123000000000005
    - type: ndcg_at_100
      value: 37.721
    - type: ndcg_at_1000
      value: 40.469
    - type: map_at_1
      value: 13.067
    - type: map_at_3
      value: 19.303
    - type: map_at_5
      value: 21.406
    - type: map_at_10
      value: 23.195
    - type: map_at_20
      value: 24.256
    - type: map_at_100
      value: 25.115
    - type: map_at_1000
      value: 25.257
    - type: recall_at_1
      value: 13.067
    - type: recall_at_3
      value: 25.663000000000004
    - type: recall_at_5
      value: 32.707
    - type: recall_at_10
      value: 42.458
    - type: recall_at_20
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    - type: recall_at_100
      value: 72.509
    - type: recall_at_1000
      value: 90.62400000000001
    - type: precision_at_1
      value: 20.7
    - type: precision_at_3
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    - type: precision_at_5
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    - type: precision_at_10
      value: 7.68
    - type: precision_at_20
      value: 4.88
    - type: precision_at_100
      value: 1.413
    - type: precision_at_1000
      value: 0.179
    - type: mrr_at_1
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      value: 27.750000000000004
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  - task:
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      type: facebook/mlqa
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      revision: 397ed406c1a7902140303e7faf60fff35b58d285
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  - task:
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      type: facebook/mlqa
      config: ara-eng
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      revision: 397ed406c1a7902140303e7faf60fff35b58d285
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    - type: recall_at_1000
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  - task:
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      revision: 397ed406c1a7902140303e7faf60fff35b58d285
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  - task:
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    dataset:
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      type: facebook/mlqa
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      revision: 397ed406c1a7902140303e7faf60fff35b58d285
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  - task:
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      type: facebook/mlqa
      config: ara-vie
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      revision: 397ed406c1a7902140303e7faf60fff35b58d285
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  - task:
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    dataset:
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      type: facebook/mlqa
      config: zho-ara
      split: test
      revision: 397ed406c1a7902140303e7faf60fff35b58d285
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  - task:
      type: Retrieval
    dataset:
      name: MTEB MintakaRetrieval (ar)
      type: jinaai/mintakaqa
      config: ar
      split: test
      revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
    metrics:
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      value: 8.761
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      value: 12.867
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      value: 14.322
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    - type: map_at_1000
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    - type: recall_at_5
      value: 19.337
    - type: recall_at_10
      value: 24.83
    - type: recall_at_20
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    - type: recall_at_1000
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    - type: precision_at_1
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    - type: precision_at_3
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    - type: precision_at_10
      value: 2.483
    - type: precision_at_20
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    - type: precision_at_100
      value: 0.464
    - type: precision_at_1000
      value: 0.09
    - type: mrr_at_1
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    - type: mrr_at_3
      value: 11.855
    - type: mrr_at_5
      value: 12.6608
    - type: mrr_at_10
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    - type: mrr_at_20
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    - type: mrr_at_1000
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  - task:
      type: Retrieval
    dataset:
      name: MTEB MrTidyRetrieval (arabic)
      type: mteb/mrtidy
      config: arabic
      split: test
      revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
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    - type: map_at_1000
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    - type: recall_at_1
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    - type: recall_at_10
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    - type: recall_at_20
      value: 46.546
    - type: recall_at_100
      value: 61.949
    - type: recall_at_1000
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    - type: precision_at_1
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    - type: precision_at_3
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    - type: precision_at_5
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    - type: precision_at_10
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    - type: precision_at_100
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    - type: precision_at_1000
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    - type: mrr_at_1
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    - type: mrr_at_3
      value: 19.5652
    - type: mrr_at_5
      value: 21.1517
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      value: 28.503
    - type: nauc_precision_at_3_max
      value: 18.2643
    - type: nauc_precision_at_3_std
      value: -7.6172
    - type: nauc_precision_at_3_diff1
      value: 20.1407
    - type: nauc_precision_at_5_max
      value: 23.6899
    - type: nauc_precision_at_5_std
      value: -5.0431
    - type: nauc_precision_at_5_diff1
      value: 19.3496
    - type: nauc_precision_at_10_max
      value: 23.7744
    - type: nauc_precision_at_10_std
      value: -2.9978000000000002
    - type: nauc_precision_at_10_diff1
      value: 15.9886
    - type: nauc_precision_at_20_max
      value: 23.9516
    - type: nauc_precision_at_20_std
      value: -1.881
    - type: nauc_precision_at_20_diff1
      value: 13.858
    - type: nauc_precision_at_100_max
      value: 22.0491
    - type: nauc_precision_at_100_std
      value: 3.9923
    - type: nauc_precision_at_100_diff1
      value: 10.8588
    - type: nauc_precision_at_1000_max
      value: 15.2248
    - type: nauc_precision_at_1000_std
      value: 2.2651
    - type: nauc_precision_at_1000_diff1
      value: 8.451500000000001
    - type: nauc_mrr_at_1_max
      value: 13.658600000000002
    - type: nauc_mrr_at_1_std
      value: -10.888399999999999
    - type: nauc_mrr_at_1_diff1
      value: 28.503
    - type: nauc_mrr_at_3_max
      value: 12.0131
    - type: nauc_mrr_at_3_std
      value: -9.0483
    - type: nauc_mrr_at_3_diff1
      value: 25.1263
    - type: nauc_mrr_at_5_max
      value: 14.2408
    - type: nauc_mrr_at_5_std
      value: -7.324400000000001
    - type: nauc_mrr_at_5_diff1
      value: 24.4894
    - type: nauc_mrr_at_10_max
      value: 15.1286
    - type: nauc_mrr_at_10_std
      value: -6.958
    - type: nauc_mrr_at_10_diff1
      value: 24.5045
    - type: nauc_mrr_at_20_max
      value: 15.3281
    - type: nauc_mrr_at_20_std
      value: -6.8811
    - type: nauc_mrr_at_20_diff1
      value: 24.4511
    - type: nauc_mrr_at_100_max
      value: 15.237700000000002
    - type: nauc_mrr_at_100_std
      value: -6.6511000000000005
    - type: nauc_mrr_at_100_diff1
      value: 24.4441
    - type: nauc_mrr_at_1000_max
      value: 15.2116
    - type: nauc_mrr_at_1000_std
      value: -6.6709000000000005
    - type: nauc_mrr_at_1000_diff1
      value: 24.4846
    - type: main_score
      value: 26.913999999999998
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES (default)
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    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:
      name: MTEB SICK-R (default)
      type: mteb/sickr-sts
      config: default
      split: test
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
    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:
      name: MTEB STS12 (default)
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    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:
      name: MTEB STS13 (default)
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    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:
      name: MTEB STS14 (default)
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    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:
      name: MTEB STS15 (default)
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    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:
      name: MTEB STS16 (default)
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    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:
      name: MTEB STS17 (ar-ar)
      type: mteb/sts17-crosslingual-sts
      config: ar-ar
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    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:
      name: MTEB STS22 (ar)
      type: mteb/sts22-crosslingual-sts
      config: ar
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    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:
      name: MTEB STSBenchmark (default)
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    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: Summarization
    dataset:
      name: MTEB SummEval (default)
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    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: 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
---

# 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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</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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### Out-of-Scope Use

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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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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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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}, 
}
