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--- |
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inference: false |
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language: |
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- ar |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:557850 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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- mteb |
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base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
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datasets: |
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- Omartificial-Intelligence-Space/Arabic-NLi-Triplet |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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widget: |
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- source_sentence: ذكر متوازن بعناية يقف على قدم واحدة بالقرب من منطقة شاطئ المحيط النظيفة |
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sentences: |
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- رجل يقدم عرضاً |
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- هناك رجل بالخارج قرب الشاطئ |
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- رجل يجلس على أريكه |
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- source_sentence: رجل يقفز إلى سريره القذر |
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sentences: |
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- السرير قذر. |
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- رجل يضحك أثناء غسيل الملابس |
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- الرجل على القمر |
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- source_sentence: الفتيات بالخارج |
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sentences: |
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- امرأة تلف الخيط إلى كرات بجانب كومة من الكرات |
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- فتيان يركبان في جولة متعة |
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- >- |
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ثلاث فتيات يقفون سوية في غرفة واحدة تستمع وواحدة تكتب على الحائط والثالثة |
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تتحدث إليهن |
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- source_sentence: الرجل يرتدي قميصاً أزرق. |
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sentences: |
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- >- |
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رجل يرتدي قميصاً أزرق يميل إلى الجدار بجانب الطريق مع شاحنة زرقاء وسيارة |
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حمراء مع الماء في الخلفية. |
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- كتاب القصص مفتوح |
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- رجل يرتدي قميص أسود يعزف على الجيتار. |
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- source_sentence: يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة. |
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sentences: |
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- ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه |
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- رجل يستلقي على وجهه على مقعد في الحديقة. |
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- الشاب نائم بينما الأم تقود ابنتها إلى الحديقة |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka |
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results: |
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- dataset: |
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config: ar |
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name: MTEB MintakaRetrieval (ar) |
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revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e |
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split: test |
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type: mintaka/mmteb-mintaka |
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metrics: |
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- type: main_score |
|
value: 16.1 |
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- type: map_at_1 |
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value: 8.761 |
|
- type: map_at_3 |
|
value: 11.855 |
|
- type: map_at_5 |
|
value: 12.661 |
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- type: map_at_10 |
|
value: 13.396 |
|
- type: ndcg_at_1 |
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value: 8.761 |
|
- type: ndcg_at_3 |
|
value: 12.867 |
|
- type: ndcg_at_5 |
|
value: 14.322 |
|
- type: ndcg_at_10 |
|
value: 16.1 |
|
- type: recall_at_1 |
|
value: 8.761 |
|
- type: recall_at_3 |
|
value: 15.797 |
|
- type: recall_at_5 |
|
value: 19.337 |
|
- type: recall_at_10 |
|
value: 24.83 |
|
- type: precision_at_1 |
|
value: 8.761 |
|
- type: precision_at_3 |
|
value: 5.266 |
|
- type: precision_at_5 |
|
value: 3.867 |
|
- type: precision_at_10 |
|
value: 2.483 |
|
- type: mrr_at_1 |
|
value: 8.7608 |
|
- type: mrr_at_3 |
|
value: 11.855 |
|
- type: mrr_at_5 |
|
value: 12.6608 |
|
- type: mrr_at_10 |
|
value: 13.3959 |
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task: |
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type: Retrieval |
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- dataset: |
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config: ar |
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name: MTEB MIRACLRetrievalHardNegatives (ar) |
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revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
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split: dev |
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type: miracl/mmteb-miracl-hardnegatives |
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metrics: |
|
- type: main_score |
|
value: 30.152 |
|
- 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: ndcg_at_1 |
|
value: 20.7 |
|
- type: ndcg_at_3 |
|
value: 23.766 |
|
- type: ndcg_at_5 |
|
value: 26.479 |
|
- type: ndcg_at_10 |
|
value: 30.152 |
|
- type: recall_at_1 |
|
value: 13.067 |
|
- type: recall_at_3 |
|
value: 25.663 |
|
- type: recall_at_5 |
|
value: 32.707 |
|
- type: recall_at_10 |
|
value: 42.458 |
|
- type: precision_at_1 |
|
value: 20.7 |
|
- type: precision_at_3 |
|
value: 14.367 |
|
- type: precision_at_5 |
|
value: 11.36 |
|
- type: precision_at_10 |
|
value: 7.68 |
|
- type: mrr_at_1 |
|
value: 20.7 |
|
- type: mrr_at_3 |
|
value: 27.75 |
|
- type: mrr_at_5 |
|
value: 29.66 |
|
- type: mrr_at_10 |
|
value: 31.0725 |
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task: |
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type: Retrieval |
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- dataset: |
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config: ar |
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name: MTEB MLQARetrieval (ar) |
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revision: 397ed406c1a7902140303e7faf60fff35b58d285 |
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split: validation |
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type: mlqa/mmteb-mlqa |
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metrics: |
|
- type: main_score |
|
value: 63.541 |
|
- type: map_at_1 |
|
value: 51.451 |
|
- type: map_at_3 |
|
value: 58.027 |
|
- type: map_at_5 |
|
value: 59.197 |
|
- type: map_at_10 |
|
value: 59.644 |
|
- type: ndcg_at_1 |
|
value: 51.451 |
|
- type: ndcg_at_3 |
|
value: 60.302 |
|
- type: ndcg_at_5 |
|
value: 62.432 |
|
- type: ndcg_at_10 |
|
value: 63.541 |
|
- type: recall_at_1 |
|
value: 51.451 |
|
- type: recall_at_3 |
|
value: 66.925 |
|
- type: recall_at_5 |
|
value: 72.147 |
|
- type: recall_at_10 |
|
value: 75.629 |
|
- type: precision_at_1 |
|
value: 51.451 |
|
- type: precision_at_3 |
|
value: 22.308 |
|
- type: precision_at_5 |
|
value: 14.429 |
|
- type: precision_at_10 |
|
value: 7.563 |
|
- type: mrr_at_1 |
|
value: 51.4507 |
|
- type: mrr_at_3 |
|
value: 58.0271 |
|
- type: mrr_at_5 |
|
value: 59.1973 |
|
- type: mrr_at_10 |
|
value: 59.6441 |
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task: |
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type: Retrieval |
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- dataset: |
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config: default |
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name: MTEB SadeemQuestionRetrieval (ar) |
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revision: 3cb0752b182e5d5d740df547748b06663c8e0bd9 |
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split: test |
|
type: sadeem/mmteb-sadeem |
|
metrics: |
|
- type: main_score |
|
value: 58.884 |
|
- type: map_at_1 |
|
value: 25.945 |
|
- type: map_at_3 |
|
value: 47.981 |
|
- type: map_at_5 |
|
value: 49.051 |
|
- type: map_at_10 |
|
value: 49.536 |
|
- type: ndcg_at_1 |
|
value: 25.945 |
|
- type: ndcg_at_3 |
|
value: 55.796 |
|
- type: ndcg_at_5 |
|
value: 57.726 |
|
- type: ndcg_at_10 |
|
value: 58.884 |
|
- type: recall_at_1 |
|
value: 25.945 |
|
- type: recall_at_3 |
|
value: 78.602 |
|
- type: recall_at_5 |
|
value: 83.293 |
|
- type: recall_at_10 |
|
value: 86.836 |
|
- type: precision_at_1 |
|
value: 25.945 |
|
- type: precision_at_3 |
|
value: 26.201 |
|
- type: precision_at_5 |
|
value: 16.659 |
|
- type: precision_at_10 |
|
value: 8.684 |
|
- type: mrr_at_1 |
|
value: 24.3179 |
|
- type: mrr_at_3 |
|
value: 46.8566 |
|
- type: mrr_at_5 |
|
value: 47.9288 |
|
- type: mrr_at_10 |
|
value: 48.4848 |
|
task: |
|
type: Retrieval |
|
- dataset: |
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config: default |
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name: MTEB BIOSSES (default) |
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
split: test |
|
type: mteb/biosses-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 81.20578037912223 |
|
- type: cosine_spearman |
|
value: 77.43670420687278 |
|
- type: euclidean_pearson |
|
value: 74.60444698819703 |
|
- type: euclidean_spearman |
|
value: 72.25767053642666 |
|
- type: main_score |
|
value: 77.43670420687278 |
|
- type: manhattan_pearson |
|
value: 73.86951335383257 |
|
- type: manhattan_spearman |
|
value: 71.41608509527123 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB SICK-R (default) |
|
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d |
|
split: test |
|
type: mteb/sickr-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 83.11155556919923 |
|
- type: cosine_spearman |
|
value: 79.39435627520159 |
|
- type: euclidean_pearson |
|
value: 81.05225024180342 |
|
- type: euclidean_spearman |
|
value: 79.09926890001618 |
|
- type: main_score |
|
value: 79.39435627520159 |
|
- type: manhattan_pearson |
|
value: 80.74351302609706 |
|
- type: manhattan_spearman |
|
value: 78.826254748334 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB STS12 (default) |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
split: test |
|
type: mteb/sts12-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 85.10074960888633 |
|
- type: cosine_spearman |
|
value: 78.93043293576132 |
|
- type: euclidean_pearson |
|
value: 84.1168219787408 |
|
- type: euclidean_spearman |
|
value: 78.44739559202252 |
|
- type: main_score |
|
value: 78.93043293576132 |
|
- type: manhattan_pearson |
|
value: 83.79447841594396 |
|
- type: manhattan_spearman |
|
value: 77.94028171700384 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB STS13 (default) |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
split: test |
|
type: mteb/sts13-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 81.34459901517775 |
|
- type: cosine_spearman |
|
value: 82.73032633919925 |
|
- type: euclidean_pearson |
|
value: 82.83546499367434 |
|
- type: euclidean_spearman |
|
value: 83.29701673615389 |
|
- type: main_score |
|
value: 82.73032633919925 |
|
- type: manhattan_pearson |
|
value: 82.63480502797324 |
|
- type: manhattan_spearman |
|
value: 83.05016589615636 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB STS14 (default) |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
split: test |
|
type: mteb/sts14-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 82.53179983763488 |
|
- type: cosine_spearman |
|
value: 81.64974497557361 |
|
- type: euclidean_pearson |
|
value: 83.03981070806898 |
|
- type: euclidean_spearman |
|
value: 82.65556168300631 |
|
- type: main_score |
|
value: 81.64974497557361 |
|
- type: manhattan_pearson |
|
value: 82.83722360191446 |
|
- type: manhattan_spearman |
|
value: 82.4164264119 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB STS15 (default) |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
split: test |
|
type: mteb/sts15-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 86.5684162475647 |
|
- type: cosine_spearman |
|
value: 87.62163215009723 |
|
- type: euclidean_pearson |
|
value: 87.3068288651339 |
|
- type: euclidean_spearman |
|
value: 88.03508640722863 |
|
- type: main_score |
|
value: 87.62163215009723 |
|
- type: manhattan_pearson |
|
value: 87.21818681800193 |
|
- type: manhattan_spearman |
|
value: 87.94690511382603 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB STS16 (default) |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
split: test |
|
type: mteb/sts16-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 81.70518105237446 |
|
- type: cosine_spearman |
|
value: 83.66083698795428 |
|
- type: euclidean_pearson |
|
value: 82.80400684544435 |
|
- type: euclidean_spearman |
|
value: 83.39926895275799 |
|
- type: main_score |
|
value: 83.66083698795428 |
|
- type: manhattan_pearson |
|
value: 82.44430538731845 |
|
- type: manhattan_spearman |
|
value: 82.99600783826028 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: ar-ar |
|
name: MTEB STS17 (ar-ar) |
|
revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
|
split: test |
|
type: mteb/sts17-crosslingual-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 82.23229967696153 |
|
- type: cosine_spearman |
|
value: 82.40039006538706 |
|
- type: euclidean_pearson |
|
value: 79.21322872573518 |
|
- type: euclidean_spearman |
|
value: 79.14230529579783 |
|
- type: main_score |
|
value: 82.40039006538706 |
|
- type: manhattan_pearson |
|
value: 79.1476348987964 |
|
- type: manhattan_spearman |
|
value: 78.82381660638143 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: ar |
|
name: MTEB STS22 (ar) |
|
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
|
split: test |
|
type: mteb/sts22-crosslingual-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 45.95767124518871 |
|
- type: cosine_spearman |
|
value: 51.37922888872568 |
|
- type: euclidean_pearson |
|
value: 45.519471121310126 |
|
- type: euclidean_spearman |
|
value: 51.45605803385654 |
|
- type: main_score |
|
value: 51.37922888872568 |
|
- type: manhattan_pearson |
|
value: 45.98761117909666 |
|
- type: manhattan_spearman |
|
value: 51.48451973989366 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB STSBenchmark (default) |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
split: test |
|
type: mteb/stsbenchmark-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 85.38916827757183 |
|
- type: cosine_spearman |
|
value: 86.16303183485594 |
|
- type: euclidean_pearson |
|
value: 85.16406897245115 |
|
- type: euclidean_spearman |
|
value: 85.40364087457081 |
|
- type: main_score |
|
value: 86.16303183485594 |
|
- type: manhattan_pearson |
|
value: 84.96853193915084 |
|
- type: manhattan_spearman |
|
value: 85.13238442843544 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB SummEval (default) |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
split: test |
|
type: mteb/summeval |
|
metrics: |
|
- type: cosine_pearson |
|
value: 30.077426987171158 |
|
- type: cosine_spearman |
|
value: 30.163682020271608 |
|
- type: dot_pearson |
|
value: 27.31125295906803 |
|
- type: dot_spearman |
|
value: 29.138235153208193 |
|
- type: main_score |
|
value: 30.163682020271608 |
|
- type: pearson |
|
value: 30.077426987171158 |
|
- type: spearman |
|
value: 30.163682020271608 |
|
task: |
|
type: Summarization |
|
- name: >- |
|
SentenceTransformer based on |
|
sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
|
results: |
|
- task: |
|
type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: sts test 768 |
|
type: sts-test-768 |
|
metrics: |
|
- type: pearson_cosine |
|
value: 0.8538831619509135 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.861625750018802 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8496745674597512 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8513333417508545 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8516261261374778 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8540549341060195 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.7281308266536204 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.7230282720855726 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.8538831619509135 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.861625750018802 |
|
name: Spearman Max |
|
- task: |
|
type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: sts test 512 |
|
type: sts-test-512 |
|
metrics: |
|
- type: pearson_cosine |
|
value: 0.8542379189261009 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.8609329396560859 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8486657899695456 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8512120732504748 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8505249483849495 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8538738365440234 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.7075618032859148 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.7028728329509918 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.8542379189261009 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.8609329396560859 |
|
name: Spearman Max |
|
- task: |
|
type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: sts test 256 |
|
type: sts-test-256 |
|
metrics: |
|
- type: pearson_cosine |
|
value: 0.8486308733045101 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.8578681811996274 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8404506123980291 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.845565163232125 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8414758099131773 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8471566121478254 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.6668664182302968 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.6651222481800894 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.8486308733045101 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.8578681811996274 |
|
name: Spearman Max |
|
- task: |
|
type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: sts test 128 |
|
type: sts-test-128 |
|
metrics: |
|
- type: pearson_cosine |
|
value: 0.8389761445410956 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.8499312736457453 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8287388421834582 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8353046807483782 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8297699263897746 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8371843253238523 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.5855876200722326 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.5834920267418124 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.8389761445410956 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.8499312736457453 |
|
name: Spearman Max |
|
- task: |
|
type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: sts test 64 |
|
type: sts-test-64 |
|
metrics: |
|
- type: pearson_cosine |
|
value: 0.8290685425698586 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.8429054799136109 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8100968316314205 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8221121550434057 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8129044863346081 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8255133471709527 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.5067257944655903 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.5109761436588146 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.8290685425698586 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.8429054799136109 |
|
name: Spearman Max |
|
license: apache-2.0 |
|
--- |
|
|
|
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the Omartificial-Intelligence-Space/arabic-n_li-triplet dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 79f2382ceacceacdf38563d7c5d16b9ff8d725d6 --> |
|
- **Maximum Sequence Length:** 128 tokens |
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- **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( |
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(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 |
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from sentence_transformers import SentenceTransformer |
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|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("Omartificial-Intelligence-Space/Arabic-Nli-Matryoshka") |
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# Run inference |
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sentences = [ |
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'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.', |
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'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه', |
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'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة', |
|
] |
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embeddings = model.encode(sentences) |
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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) |
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|
|
<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 | 0.8539 | |
|
| **spearman_cosine** | **0.8616** | |
|
| pearson_manhattan | 0.8497 | |
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| spearman_manhattan | 0.8513 | |
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| pearson_euclidean | 0.8516 | |
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| spearman_euclidean | 0.8541 | |
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| pearson_dot | 0.7281 | |
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| spearman_dot | 0.723 | |
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| pearson_max | 0.8539 | |
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| 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 | |
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| spearman_manhattan | 0.8512 | |
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| pearson_euclidean | 0.8505 | |
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| spearman_euclidean | 0.8539 | |
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| pearson_dot | 0.7076 | |
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| spearman_dot | 0.7029 | |
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| pearson_max | 0.8542 | |
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| 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 | |
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| spearman_manhattan | 0.8456 | |
|
| pearson_euclidean | 0.8415 | |
|
| spearman_euclidean | 0.8472 | |
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| 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 | |
|
|
|
<!-- |
|
## 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: 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}, |
|
} |
|
|