| --- |
| language: |
| - ar |
| library_name: sentence-transformers |
| tags: |
| - mteb |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:557850 |
| - loss:MatryoshkaLoss |
| - loss:MultipleNegativesRankingLoss |
| base_model: tomaarsen/mpnet-base-all-nli-triplet |
| 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-mpnet-base-all-nli-triplet |
| results: |
| - dataset: |
| config: ar |
| name: MTEB MIRACLRetrieval (ar) |
| revision: main |
| split: dev |
| type: miracl/mmteb-miracl |
| metrics: |
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| - type: ndcg_at_3 |
| value: 2.461 |
| - type: ndcg_at_5 |
| value: 2.907 |
| - type: ndcg_at_10 |
| value: 3.581 |
| - type: ndcg_at_20 |
| value: 4.041 |
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| value: 5.669 |
| - type: ndcg_at_1000 |
| value: 8.247 |
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| - type: map_at_3 |
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| - type: map_at_20 |
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| - type: map_at_100 |
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| - type: map_at_1000 |
| value: 2.939 |
| - type: recall_at_1 |
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| - type: recall_at_3 |
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| - type: recall_at_5 |
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| - type: recall_at_10 |
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| - type: recall_at_20 |
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| - type: recall_at_100 |
| value: 14.999 |
| - type: recall_at_1000 |
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| task: |
| type: Retrieval |
| - dataset: |
| config: ar |
| name: MTEB MIRACLRetrievalHardNegatives (ar) |
| revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
| split: dev |
| type: mteb/miracl-hard-negatives |
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| value: 4.223 |
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| value: 4.941 |
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| value: 4.662 |
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| - type: map_at_1000 |
| value: 5.288 |
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| value: 4.941 |
| - type: recall_at_5 |
| value: 6.641 |
| - type: recall_at_10 |
| value: 9.998 |
| - type: recall_at_20 |
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| - type: recall_at_100 |
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| - type: recall_at_1000 |
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| - type: precision_at_10 |
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| revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f |
| split: test |
| type: jinaai/xpqa |
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| task: |
| type: Retrieval |
| - dataset: |
| config: eng-ara |
| name: MTEB XPQARetrieval (eng-ara) |
| revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f |
| split: test |
| type: jinaai/xpqa |
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| task: |
| type: Retrieval |
| - dataset: |
| config: ara-eng |
| name: MTEB XPQARetrieval (ara-eng) |
| revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f |
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| - type: map_at_1000 |
| value: 2.259 |
| - type: recall_at_1 |
| value: 0.903 |
| - type: recall_at_3 |
| value: 1.923 |
| - type: recall_at_5 |
| value: 2.4330000000000003 |
| - type: recall_at_10 |
| value: 3.4819999999999998 |
| - type: recall_at_20 |
| value: 4.5440000000000005 |
| - type: recall_at_100 |
| value: 11.846 |
| - type: recall_at_1000 |
| value: 74.371 |
| - type: precision_at_1 |
| value: 1.617 |
| - type: precision_at_3 |
| value: 1.168 |
| - type: precision_at_5 |
| value: 0.889 |
| - type: precision_at_10 |
| value: 0.647 |
| - type: precision_at_20 |
| value: 0.438 |
| - type: precision_at_100 |
| value: 0.244 |
| - type: precision_at_1000 |
| value: 0.146 |
| - type: mrr_at_1 |
| value: 1.6173 |
| - type: mrr_at_3 |
| value: 2.2686 |
| - type: mrr_at_5 |
| value: 2.3899 |
| - type: mrr_at_10 |
| value: 2.5806 |
| - type: mrr_at_20 |
| value: 2.7121 |
| - type: mrr_at_100 |
| value: 2.9324 |
| - type: mrr_at_1000 |
| value: 3.1441 |
| - type: nauc_ndcg_at_1_max |
| value: 41.4733 |
| - type: nauc_ndcg_at_1_std |
| value: 34.5204 |
| - type: nauc_ndcg_at_1_diff1 |
| value: 38.8662 |
| - type: nauc_ndcg_at_3_max |
| value: 41.3135 |
| - type: nauc_ndcg_at_3_std |
| value: 40.0385 |
| - type: nauc_ndcg_at_3_diff1 |
| value: 36.750899999999994 |
| - type: nauc_ndcg_at_5_max |
| value: 42.9281 |
| - type: nauc_ndcg_at_5_std |
| value: 39.9347 |
| - type: nauc_ndcg_at_5_diff1 |
| value: 35.3783 |
| - type: nauc_ndcg_at_10_max |
| value: 42.743900000000004 |
| - type: nauc_ndcg_at_10_std |
| value: 41.6663 |
| - type: nauc_ndcg_at_10_diff1 |
| value: 31.0463 |
| - type: nauc_ndcg_at_20_max |
| value: 43.5237 |
| - type: nauc_ndcg_at_20_std |
| value: 39.6809 |
| - type: nauc_ndcg_at_20_diff1 |
| value: 32.651 |
| - type: nauc_ndcg_at_100_max |
| value: 33.3655 |
| - type: nauc_ndcg_at_100_std |
| value: 32.0311 |
| - type: nauc_ndcg_at_100_diff1 |
| value: 28.723399999999998 |
| - type: nauc_ndcg_at_1000_max |
| value: 31.1311 |
| - type: nauc_ndcg_at_1000_std |
| value: 28.838900000000002 |
| - type: nauc_ndcg_at_1000_diff1 |
| value: 26.2104 |
| - type: nauc_map_at_1_max |
| value: 34.202 |
| - type: nauc_map_at_1_std |
| value: 33.9772 |
| - type: nauc_map_at_1_diff1 |
| value: 44.6104 |
| - type: nauc_map_at_3_max |
| value: 39.6785 |
| - type: nauc_map_at_3_std |
| value: 39.4152 |
| - type: nauc_map_at_3_diff1 |
| value: 37.6022 |
| - type: nauc_map_at_5_max |
| value: 41.2645 |
| - type: nauc_map_at_5_std |
| value: 38.6109 |
| - type: nauc_map_at_5_diff1 |
| value: 37.3159 |
| - type: nauc_map_at_10_max |
| value: 41.9172 |
| - type: nauc_map_at_10_std |
| value: 40.3848 |
| - type: nauc_map_at_10_diff1 |
| value: 35.2489 |
| - type: nauc_map_at_20_max |
| value: 42.0995 |
| - type: nauc_map_at_20_std |
| value: 39.6004 |
| - type: nauc_map_at_20_diff1 |
| value: 35.4418 |
| - type: nauc_map_at_100_max |
| value: 39.7447 |
| - type: nauc_map_at_100_std |
| value: 37.819599999999994 |
| - type: nauc_map_at_100_diff1 |
| value: 34.1062 |
| - type: nauc_map_at_1000_max |
| value: 39.2917 |
| - type: nauc_map_at_1000_std |
| value: 37.1777 |
| - type: nauc_map_at_1000_diff1 |
| value: 33.6102 |
| - type: nauc_recall_at_1_max |
| value: 34.202 |
| - type: nauc_recall_at_1_std |
| value: 33.9772 |
| - type: nauc_recall_at_1_diff1 |
| value: 44.6104 |
| - type: nauc_recall_at_3_max |
| value: 39.048 |
| - type: nauc_recall_at_3_std |
| value: 39.7222 |
| - type: nauc_recall_at_3_diff1 |
| value: 33.0168 |
| - type: nauc_recall_at_5_max |
| value: 42.954100000000004 |
| - type: nauc_recall_at_5_std |
| value: 39.4149 |
| - type: nauc_recall_at_5_diff1 |
| value: 31.6088 |
| - type: nauc_recall_at_10_max |
| value: 41.2203 |
| - type: nauc_recall_at_10_std |
| value: 41.7063 |
| - type: nauc_recall_at_10_diff1 |
| value: 24.0288 |
| - type: nauc_recall_at_20_max |
| value: 44.0757 |
| - type: nauc_recall_at_20_std |
| value: 38.6803 |
| - type: nauc_recall_at_20_diff1 |
| value: 29.157899999999998 |
| - type: nauc_recall_at_100_max |
| value: 24.6526 |
| - type: nauc_recall_at_100_std |
| value: 24.0066 |
| - type: nauc_recall_at_100_diff1 |
| value: 23.8347 |
| - type: nauc_recall_at_1000_max |
| value: 22.596 |
| - type: nauc_recall_at_1000_std |
| value: 21.290799999999997 |
| - type: nauc_recall_at_1000_diff1 |
| value: 21.012700000000002 |
| - type: nauc_precision_at_1_max |
| value: 41.4733 |
| - type: nauc_precision_at_1_std |
| value: 34.5204 |
| - type: nauc_precision_at_1_diff1 |
| value: 38.8662 |
| - type: nauc_precision_at_3_max |
| value: 48.1229 |
| - type: nauc_precision_at_3_std |
| value: 47.712500000000006 |
| - type: nauc_precision_at_3_diff1 |
| value: 35.7151 |
| - type: nauc_precision_at_5_max |
| value: 50.8463 |
| - type: nauc_precision_at_5_std |
| value: 46.9867 |
| - type: nauc_precision_at_5_diff1 |
| value: 33.0426 |
| - type: nauc_precision_at_10_max |
| value: 50.7306 |
| - type: nauc_precision_at_10_std |
| value: 49.5174 |
| - type: nauc_precision_at_10_diff1 |
| value: 28.2889 |
| - type: nauc_precision_at_20_max |
| value: 49.6035 |
| - type: nauc_precision_at_20_std |
| value: 42.9794 |
| - type: nauc_precision_at_20_diff1 |
| value: 32.3811 |
| - type: nauc_precision_at_100_max |
| value: 30.7262 |
| - type: nauc_precision_at_100_std |
| value: 29.2314 |
| - type: nauc_precision_at_100_diff1 |
| value: 25.7678 |
| - type: nauc_precision_at_1000_max |
| value: 13.3632 |
| - type: nauc_precision_at_1000_std |
| value: 11.4093 |
| - type: nauc_precision_at_1000_diff1 |
| value: 11.015 |
| - type: nauc_mrr_at_1_max |
| value: 41.4733 |
| - type: nauc_mrr_at_1_std |
| value: 34.5204 |
| - type: nauc_mrr_at_1_diff1 |
| value: 38.8662 |
| - type: nauc_mrr_at_3_max |
| value: 43.217299999999994 |
| - type: nauc_mrr_at_3_std |
| value: 39.5736 |
| - type: nauc_mrr_at_3_diff1 |
| value: 38.129999999999995 |
| - type: nauc_mrr_at_5_max |
| value: 44.241 |
| - type: nauc_mrr_at_5_std |
| value: 40.646100000000004 |
| - type: nauc_mrr_at_5_diff1 |
| value: 36.2331 |
| - type: nauc_mrr_at_10_max |
| value: 43.6115 |
| - type: nauc_mrr_at_10_std |
| value: 40.7157 |
| - type: nauc_mrr_at_10_diff1 |
| value: 33.1217 |
| - type: nauc_mrr_at_20_max |
| value: 43.3382 |
| - type: nauc_mrr_at_20_std |
| value: 39.4582 |
| - type: nauc_mrr_at_20_diff1 |
| value: 33.6253 |
| - type: nauc_mrr_at_100_max |
| value: 40.780100000000004 |
| - type: nauc_mrr_at_100_std |
| value: 37.9242 |
| - type: nauc_mrr_at_100_diff1 |
| value: 32.8418 |
| - type: nauc_mrr_at_1000_max |
| value: 40.5963 |
| - type: nauc_mrr_at_1000_std |
| value: 37.5467 |
| - type: nauc_mrr_at_1000_diff1 |
| value: 32.542 |
| - type: main_score |
| value: 2.394 |
| task: |
| type: Retrieval |
| - dataset: |
| config: default |
| name: MTEB BIOSSES (default) |
| revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| split: test |
| type: mteb/biosses-sts |
| metrics: |
| - type: cosine_pearson |
| value: 69.84925402371587 |
| - type: cosine_spearman |
| value: 67.12261377163864 |
| - type: euclidean_pearson |
| value: 68.77931734192 |
| - type: euclidean_spearman |
| value: 67.10454107068325 |
| - type: main_score |
| value: 67.12261377163864 |
| - type: manhattan_pearson |
| value: 69.39988076793398 |
| - type: manhattan_spearman |
| value: 67.68708446481159 |
| task: |
| type: STS |
| - dataset: |
| config: default |
| name: MTEB SICK-R (default) |
| revision: 20a6d6f312dd54037fe07a32d58e5e168867909d |
| split: test |
| type: mteb/sickr-sts |
| metrics: |
| - type: cosine_pearson |
| value: 72.71925116055804 |
| - type: cosine_spearman |
| value: 68.9386835022992 |
| - type: euclidean_pearson |
| value: 71.00708266525079 |
| - type: euclidean_spearman |
| value: 69.07087906196487 |
| - type: main_score |
| value: 68.9386835022992 |
| - type: manhattan_pearson |
| value: 70.95266060047263 |
| - type: manhattan_spearman |
| value: 69.11051988196195 |
| task: |
| type: STS |
| - dataset: |
| config: default |
| name: MTEB STS12 (default) |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| split: test |
| type: mteb/sts12-sts |
| metrics: |
| - type: cosine_pearson |
| value: 71.67274455692545 |
| - type: cosine_spearman |
| value: 68.71669873972587 |
| - type: euclidean_pearson |
| value: 69.79037485042406 |
| - type: euclidean_spearman |
| value: 68.80550150752252 |
| - type: main_score |
| value: 68.71669873972587 |
| - type: manhattan_pearson |
| value: 69.7571283034187 |
| - type: manhattan_spearman |
| value: 68.58306466019968 |
| task: |
| type: STS |
| - dataset: |
| config: default |
| name: MTEB STS13 (default) |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| split: test |
| type: mteb/sts13-sts |
| metrics: |
| - type: cosine_pearson |
| value: 54.172888286882504 |
| - type: cosine_spearman |
| value: 56.04247097489131 |
| - type: euclidean_pearson |
| value: 57.88587934777827 |
| - type: euclidean_spearman |
| value: 57.6139294630564 |
| - type: main_score |
| value: 56.04247097489131 |
| - type: manhattan_pearson |
| value: 57.616116618991185 |
| - type: manhattan_spearman |
| value: 57.23150380799801 |
| task: |
| type: STS |
| - dataset: |
| config: default |
| name: MTEB STS14 (default) |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| split: test |
| type: mteb/sts14-sts |
| metrics: |
| - type: cosine_pearson |
| value: 59.58820914531488 |
| - type: cosine_spearman |
| value: 58.80575077741524 |
| - type: euclidean_pearson |
| value: 61.1884427988923 |
| - type: euclidean_spearman |
| value: 60.661625936116124 |
| - type: main_score |
| value: 58.80575077741524 |
| - type: manhattan_pearson |
| value: 60.800157410891885 |
| - type: manhattan_spearman |
| value: 60.29447727072491 |
| task: |
| type: STS |
| - dataset: |
| config: default |
| name: MTEB STS15 (default) |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| split: test |
| type: mteb/sts15-sts |
| metrics: |
| - type: cosine_pearson |
| value: 73.45220638967554 |
| - type: cosine_spearman |
| value: 73.74453589715445 |
| - type: euclidean_pearson |
| value: 73.8887071337604 |
| - type: euclidean_spearman |
| value: 73.51752094057372 |
| - type: main_score |
| value: 73.74453589715445 |
| - type: manhattan_pearson |
| value: 73.45961523235827 |
| - type: manhattan_spearman |
| value: 73.07675481848841 |
| task: |
| type: STS |
| - dataset: |
| config: default |
| name: MTEB STS16 (default) |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| split: test |
| type: mteb/sts16-sts |
| metrics: |
| - type: cosine_pearson |
| value: 66.84132105540075 |
| - type: cosine_spearman |
| value: 68.24735989887876 |
| - type: euclidean_pearson |
| value: 68.2712231484699 |
| - type: euclidean_spearman |
| value: 68.02365271737838 |
| - type: main_score |
| value: 68.24735989887876 |
| - type: manhattan_pearson |
| value: 67.87379902773417 |
| - type: manhattan_spearman |
| value: 67.65342499070456 |
| 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: 79.2987412566616 |
| - type: cosine_spearman |
| value: 79.93275889323859 |
| - type: euclidean_pearson |
| value: 77.90301430319637 |
| - type: euclidean_spearman |
| value: 79.12169562085792 |
| - type: main_score |
| value: 79.93275889323859 |
| - type: manhattan_pearson |
| value: 77.93298637610417 |
| - type: manhattan_spearman |
| value: 79.38516109229111 |
| task: |
| type: STS |
| - dataset: |
| config: ar |
| name: MTEB STS22 (ar) |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
| split: test |
| type: mteb/sts22-crosslingual-sts |
| metrics: |
| - type: cosine_pearson |
| value: 46.955019830396445 |
| - type: cosine_spearman |
| value: 52.44226852669887 |
| - type: euclidean_pearson |
| value: 42.80891863181744 |
| - type: euclidean_spearman |
| value: 53.175461247693704 |
| - type: main_score |
| value: 52.44226852669887 |
| - type: manhattan_pearson |
| value: 42.97005510727849 |
| - type: manhattan_spearman |
| value: 53.158087426369825 |
| task: |
| type: STS |
| - dataset: |
| config: default |
| name: MTEB STSBenchmark (default) |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| split: test |
| type: mteb/stsbenchmark-sts |
| metrics: |
| - type: cosine_pearson |
| value: 66.99025999216197 |
| - type: cosine_spearman |
| value: 67.56341643518167 |
| - type: euclidean_pearson |
| value: 69.73441598964332 |
| - type: euclidean_spearman |
| value: 68.72541136876826 |
| - type: main_score |
| value: 67.56341643518167 |
| - type: manhattan_pearson |
| value: 69.43492004000674 |
| - type: manhattan_spearman |
| value: 68.39614969063062 |
| task: |
| type: STS |
| - dataset: |
| config: default |
| name: MTEB SummEval (default) |
| revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| split: test |
| type: mteb/summeval |
| metrics: |
| - type: cosine_pearson |
| value: 30.13248188083236 |
| - type: cosine_spearman |
| value: 28.78575545661001 |
| - type: dot_pearson |
| value: 30.934754821379464 |
| - type: dot_spearman |
| value: 29.730792596057093 |
| - type: main_score |
| value: 28.78575545661001 |
| - type: pearson |
| value: 30.13248188083236 |
| - type: spearman |
| value: 28.78575545661001 |
| task: |
| type: Summarization |
| - name: SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts test 768 |
| type: sts-test-768 |
| metrics: |
| - type: pearson_cosine |
| value: 0.66986244175229 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.675651628513557 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.6943200977280434 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.6839707658313092 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.6973190148612566 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.6872926092972673 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.5534197296097646 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.5421965591416092 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.6973190148612566 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.6872926092972673 |
| name: Spearman Max |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts test 512 |
| type: sts-test-512 |
| metrics: |
| - type: pearson_cosine |
| value: 0.6628171358537143 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.670314701212355 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.6916567677127377 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.6815748132707206 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.6948756461188812 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.685329042213794 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.5229142840207227 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.5113740757424073 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.6948756461188812 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.685329042213794 |
| name: Spearman Max |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts test 256 |
| type: sts-test-256 |
| metrics: |
| - type: pearson_cosine |
| value: 0.6368313837029833 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.6512526280069127 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.6832129716443456 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.674638334774044 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.6843664039671002 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.6760040651639672 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.4266095536126992 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.4179376458107888 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.6843664039671002 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.6760040651639672 |
| name: Spearman Max |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts test 128 |
| type: sts-test-128 |
| metrics: |
| - type: pearson_cosine |
| value: 0.6147896744901056 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.6354730852658397 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.6730782159165468 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.6652649799789521 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.676407799774529 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.6691409653459247 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.35130869784942953 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.3445374275232203 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.676407799774529 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.6691409653459247 |
| name: Spearman Max |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: sts test 64 |
| type: sts-test-64 |
| metrics: |
| - type: pearson_cosine |
| value: 0.5789158725954748 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.6081197115891086 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.6578631744829946 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.6518503436513217 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.6629734628760299 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.6570510967281272 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.24034366392620327 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.2331392769925126 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.6629734628760299 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.6570510967281272 |
| name: Spearman Max |
| license: apache-2.0 |
| --- |
| |
| # SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [tomaarsen/mpnet-base-all-nli-triplet](https://huggingface.co/tomaarsen/mpnet-base-all-nli-triplet) 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:** [tomaarsen/mpnet-base-all-nli-triplet](https://huggingface.co/tomaarsen/mpnet-base-all-nli-triplet) <!-- at revision e88732e5620f3592bf6566604be9a6a5cad814ec --> |
| - **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: MPNetModel |
| (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/mpnet-base-all-nli-triplet-Arabic-mpnet_base") |
| # 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] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## 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 | 0.6699 | |
| | **spearman_cosine** | **0.6757** | |
| | pearson_manhattan | 0.6943 | |
| | spearman_manhattan | 0.684 | |
| | pearson_euclidean | 0.6973 | |
| | spearman_euclidean | 0.6873 | |
| | pearson_dot | 0.5534 | |
| | spearman_dot | 0.5422 | |
| | pearson_max | 0.6973 | |
| | spearman_max | 0.6873 | |
| |
| #### 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 | 0.6628 | |
| | **spearman_cosine** | **0.6703** | |
| | pearson_manhattan | 0.6917 | |
| | spearman_manhattan | 0.6816 | |
| | pearson_euclidean | 0.6949 | |
| | spearman_euclidean | 0.6853 | |
| | pearson_dot | 0.5229 | |
| | spearman_dot | 0.5114 | |
| | pearson_max | 0.6949 | |
| | spearman_max | 0.6853 | |
| |
| #### 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 | 0.6368 | |
| | **spearman_cosine** | **0.6513** | |
| | pearson_manhattan | 0.6832 | |
| | spearman_manhattan | 0.6746 | |
| | pearson_euclidean | 0.6844 | |
| | spearman_euclidean | 0.676 | |
| | pearson_dot | 0.4266 | |
| | spearman_dot | 0.4179 | |
| | pearson_max | 0.6844 | |
| | spearman_max | 0.676 | |
| |
| #### 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 | 0.6148 | |
| | **spearman_cosine** | **0.6355** | |
| | pearson_manhattan | 0.6731 | |
| | spearman_manhattan | 0.6653 | |
| | pearson_euclidean | 0.6764 | |
| | spearman_euclidean | 0.6691 | |
| | pearson_dot | 0.3513 | |
| | spearman_dot | 0.3445 | |
| | pearson_max | 0.6764 | |
| | spearman_max | 0.6691 | |
| |
| #### 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 | 0.5789 | |
| | **spearman_cosine** | **0.6081** | |
| | pearson_manhattan | 0.6579 | |
| | spearman_manhattan | 0.6519 | |
| | pearson_euclidean | 0.663 | |
| | spearman_euclidean | 0.6571 | |
| | pearson_dot | 0.2403 | |
| | spearman_dot | 0.2331 | |
| | pearson_max | 0.663 | |
| | spearman_max | 0.6571 | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## 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: 12 tokens</li><li>mean: 23.93 tokens</li><li>max: 155 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 29.62 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 33.95 tokens</li><li>max: 149 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: 12 tokens</li><li>mean: 49.5 tokens</li><li>max: 246 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 23.66 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 25.33 tokens</li><li>max: 82 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 |
| - `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`: 3 |
| - `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 |
| <details><summary>Click to expand</summary> |
|
|
| | 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 | 21.5318 | - | - | - | - | - | |
| | 0.0459 | 400 | 17.2344 | - | - | - | - | - | |
| | 0.0688 | 600 | 15.393 | - | - | - | - | - | |
| | 0.0918 | 800 | 13.7897 | - | - | - | - | - | |
| | 0.1147 | 1000 | 13.534 | - | - | - | - | - | |
| | 0.1377 | 1200 | 12.2683 | - | - | - | - | - | |
| | 0.1606 | 1400 | 10.9271 | - | - | - | - | - | |
| | 0.1835 | 1600 | 11.071 | - | - | - | - | - | |
| | 0.2065 | 1800 | 10.0153 | - | - | - | - | - | |
| | 0.2294 | 2000 | 9.8463 | - | - | - | - | - | |
| | 0.2524 | 2200 | 10.0194 | - | - | - | - | - | |
| | 0.2753 | 2400 | 9.8371 | - | - | - | - | - | |
| | 0.2983 | 2600 | 9.6315 | - | - | - | - | - | |
| | 0.3212 | 2800 | 8.9858 | - | - | - | - | - | |
| | 0.3442 | 3000 | 9.1876 | - | - | - | - | - | |
| | 0.3671 | 3200 | 8.8028 | - | - | - | - | - | |
| | 0.3900 | 3400 | 8.6075 | - | - | - | - | - | |
| | 0.4130 | 3600 | 8.4285 | - | - | - | - | - | |
| | 0.4359 | 3800 | 8.1258 | - | - | - | - | - | |
| | 0.4589 | 4000 | 8.2508 | - | - | - | - | - | |
| | 0.4818 | 4200 | 7.8037 | - | - | - | - | - | |
| | 0.5048 | 4400 | 7.7133 | - | - | - | - | - | |
| | 0.5277 | 4600 | 7.5006 | - | - | - | - | - | |
| | 0.5506 | 4800 | 7.7025 | - | - | - | - | - | |
| | 0.5736 | 5000 | 7.7593 | - | - | - | - | - | |
| | 0.5965 | 5200 | 7.6305 | - | - | - | - | - | |
| | 0.6195 | 5400 | 7.7502 | - | - | - | - | - | |
| | 0.6424 | 5600 | 7.5624 | - | - | - | - | - | |
| | 0.6654 | 5800 | 7.5287 | - | - | - | - | - | |
| | 0.6883 | 6000 | 7.4261 | - | - | - | - | - | |
| | 0.7113 | 6200 | 7.239 | - | - | - | - | - | |
| | 0.7342 | 6400 | 7.1631 | - | - | - | - | - | |
| | 0.7571 | 6600 | 7.6865 | - | - | - | - | - | |
| | 0.7801 | 6800 | 7.6124 | - | - | - | - | - | |
| | 0.8030 | 7000 | 6.9936 | - | - | - | - | - | |
| | 0.8260 | 7200 | 6.7331 | - | - | - | - | - | |
| | 0.8489 | 7400 | 6.4542 | - | - | - | - | - | |
| | 0.8719 | 7600 | 6.1994 | - | - | - | - | - | |
| | 0.8948 | 7800 | 5.9798 | - | - | - | - | - | |
| | 0.9177 | 8000 | 5.7808 | - | - | - | - | - | |
| | 0.9407 | 8200 | 5.6952 | - | - | - | - | - | |
| | 0.9636 | 8400 | 5.5082 | - | - | - | - | - | |
| | 0.9866 | 8600 | 5.4421 | - | - | - | - | - | |
| | 1.0095 | 8800 | 3.0309 | - | - | - | - | - | |
| | 1.0026 | 9000 | 1.1835 | - | - | - | - | - | |
| | 1.0256 | 9200 | 8.1196 | - | - | - | - | - | |
| | 1.0485 | 9400 | 8.0326 | - | - | - | - | - | |
| | 1.0715 | 9600 | 8.5028 | - | - | - | - | - | |
| | 1.0944 | 9800 | 7.6923 | - | - | - | - | - | |
| | 1.1174 | 10000 | 8.029 | - | - | - | - | - | |
| | 1.1403 | 10200 | 7.5052 | - | - | - | - | - | |
| | 1.1632 | 10400 | 7.1177 | - | - | - | - | - | |
| | 1.1862 | 10600 | 6.9594 | - | - | - | - | - | |
| | 1.2091 | 10800 | 6.6662 | - | - | - | - | - | |
| | 1.2321 | 11000 | 6.6903 | - | - | - | - | - | |
| | 1.2550 | 11200 | 6.9523 | - | - | - | - | - | |
| | 1.2780 | 11400 | 6.676 | - | - | - | - | - | |
| | 1.3009 | 11600 | 6.7141 | - | - | - | - | - | |
| | 1.3238 | 11800 | 6.568 | - | - | - | - | - | |
| | 1.3468 | 12000 | 6.8938 | - | - | - | - | - | |
| | 1.3697 | 12200 | 6.3745 | - | - | - | - | - | |
| | 1.3927 | 12400 | 6.2513 | - | - | - | - | - | |
| | 1.4156 | 12600 | 6.2589 | - | - | - | - | - | |
| | 1.4386 | 12800 | 6.1388 | - | - | - | - | - | |
| | 1.4615 | 13000 | 6.1835 | - | - | - | - | - | |
| | 1.4845 | 13200 | 5.9004 | - | - | - | - | - | |
| | 1.5074 | 13400 | 5.7891 | - | - | - | - | - | |
| | 1.5303 | 13600 | 5.6184 | - | - | - | - | - | |
| | 1.5533 | 13800 | 5.9762 | - | - | - | - | - | |
| | 1.5762 | 14000 | 5.9737 | - | - | - | - | - | |
| | 1.5992 | 14200 | 5.8563 | - | - | - | - | - | |
| | 1.6221 | 14400 | 5.8904 | - | - | - | - | - | |
| | 1.6451 | 14600 | 5.8484 | - | - | - | - | - | |
| | 1.6680 | 14800 | 5.8906 | - | - | - | - | - | |
| | 1.6909 | 15000 | 5.7613 | - | - | - | - | - | |
| | 1.7139 | 15200 | 5.5744 | - | - | - | - | - | |
| | 1.7368 | 15400 | 5.6569 | - | - | - | - | - | |
| | 1.7598 | 15600 | 5.7439 | - | - | - | - | - | |
| | 1.7827 | 15800 | 5.5593 | - | - | - | - | - | |
| | 1.8057 | 16000 | 5.2935 | - | - | - | - | - | |
| | 1.8286 | 16200 | 5.088 | - | - | - | - | - | |
| | 1.8516 | 16400 | 5.0167 | - | - | - | - | - | |
| | 1.8745 | 16600 | 4.84 | - | - | - | - | - | |
| | 1.8974 | 16800 | 4.6731 | - | - | - | - | - | |
| | 1.9204 | 17000 | 4.6404 | - | - | - | - | - | |
| | 1.9433 | 17200 | 4.6413 | - | - | - | - | - | |
| | 1.9663 | 17400 | 4.4495 | - | - | - | - | - | |
| | 1.9892 | 17600 | 4.4262 | - | - | - | - | - | |
| | 2.0122 | 17800 | 2.01 | - | - | - | - | - | |
| | 2.0053 | 18000 | 1.8418 | - | - | - | - | - | |
| | 2.0282 | 18200 | 6.2714 | - | - | - | - | - | |
| | 2.0512 | 18400 | 6.1742 | - | - | - | - | - | |
| | 2.0741 | 18600 | 6.5996 | - | - | - | - | - | |
| | 2.0971 | 18800 | 6.0907 | - | - | - | - | - | |
| | 2.1200 | 19000 | 6.2418 | - | - | - | - | - | |
| | 2.1429 | 19200 | 5.7817 | - | - | - | - | - | |
| | 2.1659 | 19400 | 5.7073 | - | - | - | - | - | |
| | 2.1888 | 19600 | 5.2645 | - | - | - | - | - | |
| | 2.2118 | 19800 | 5.3451 | - | - | - | - | - | |
| | 2.2347 | 20000 | 5.2453 | - | - | - | - | - | |
| | 2.2577 | 20200 | 5.6161 | - | - | - | - | - | |
| | 2.2806 | 20400 | 5.2289 | - | - | - | - | - | |
| | 2.3035 | 20600 | 5.3888 | - | - | - | - | - | |
| | 2.3265 | 20800 | 5.2483 | - | - | - | - | - | |
| | 2.3494 | 21000 | 5.5791 | - | - | - | - | - | |
| | 2.3724 | 21200 | 5.1643 | - | - | - | - | - | |
| | 2.3953 | 21400 | 5.1231 | - | - | - | - | - | |
| | 2.4183 | 21600 | 5.1055 | - | - | - | - | - | |
| | 2.4412 | 21800 | 5.1778 | - | - | - | - | - | |
| | 2.4642 | 22000 | 5.0466 | - | - | - | - | - | |
| | 2.4871 | 22200 | 4.8321 | - | - | - | - | - | |
| | 2.5100 | 22400 | 4.7056 | - | - | - | - | - | |
| | 2.5330 | 22600 | 4.6858 | - | - | - | - | - | |
| | 2.5559 | 22800 | 4.9189 | - | - | - | - | - | |
| | 2.5789 | 23000 | 4.912 | - | - | - | - | - | |
| | 2.6018 | 23200 | 4.8289 | - | - | - | - | - | |
| | 2.6248 | 23400 | 4.8959 | - | - | - | - | - | |
| | 2.6477 | 23600 | 4.9441 | - | - | - | - | - | |
| | 2.6706 | 23800 | 4.9334 | - | - | - | - | - | |
| | 2.6936 | 24000 | 4.8328 | - | - | - | - | - | |
| | 2.7165 | 24200 | 4.601 | - | - | - | - | - | |
| | 2.7395 | 24400 | 4.834 | - | - | - | - | - | |
| | 2.7624 | 24600 | 5.152 | - | - | - | - | - | |
| | 2.7854 | 24800 | 4.9232 | - | - | - | - | - | |
| | 2.8083 | 25000 | 4.6556 | - | - | - | - | - | |
| | 2.8312 | 25200 | 4.6229 | - | - | - | - | - | |
| | 2.8542 | 25400 | 4.5768 | - | - | - | - | - | |
| | 2.8771 | 25600 | 4.3619 | - | - | - | - | - | |
| | 2.9001 | 25800 | 4.3608 | - | - | - | - | - | |
| | 2.9230 | 26000 | 4.2834 | - | - | - | - | - | |
| | 2.9403 | 26151 | - | 0.6355 | 0.6513 | 0.6703 | 0.6081 | 0.6757 | |
|
|
| </details> |
|
|
| ### 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}, |
| } |
