metadata
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
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value: 2.461
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value: 2.907
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value: 3.581
- type: ndcg_at_20
value: 4.041
- type: ndcg_at_100
value: 5.669
- type: ndcg_at_1000
value: 8.247
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- type: map_at_3
value: 1.974
- type: map_at_5
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- type: recall_at_100
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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_20
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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
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- type: mrr_at_5
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- 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:
config: ar
name: MTEB MIRACLRetrievalHardNegatives (ar)
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
split: dev
type: mteb/miracl-hard-negatives
metrics:
- type: ndcg_at_1
value: 3.2
- type: ndcg_at_3
value: 4.223
- type: ndcg_at_5
value: 4.941
- type: ndcg_at_10
value: 6.198
- type: ndcg_at_20
value: 7.405
- type: ndcg_at_100
value: 10.586
- type: ndcg_at_1000
value: 14.695
- type: map_at_1
value: 2.083
- type: map_at_3
value: 3.382
- type: map_at_5
value: 3.805
- type: map_at_10
value: 4.314
- type: map_at_20
value: 4.662
- type: map_at_100
value: 5.133
- type: map_at_1000
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- type: recall_at_1
value: 2.083
- type: recall_at_3
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_1
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- type: precision_at_3
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- type: precision_at_5
value: 2.02
- type: precision_at_10
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- type: precision_at_20
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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
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- type: mrr_at_5
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- 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:
config: ara-ara
name: MTEB MLQARetrieval (ara-ara)
revision: 397ed406c1a7902140303e7faf60fff35b58d285
split: validation
type: facebook/mlqa
metrics:
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value: 34.439
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- type: map_at_20
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- type: map_at_1000
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- type: recall_at_1
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- type: recall_at_3
value: 37.524
- type: recall_at_5
value: 44.101
- type: recall_at_10
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- 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_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_20
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revision: 397ed406c1a7902140303e7faf60fff35b58d285
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revision: 397ed406c1a7902140303e7faf60fff35b58d285
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type: facebook/mlqa
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revision: 397ed406c1a7902140303e7faf60fff35b58d285
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revision: 397ed406c1a7902140303e7faf60fff35b58d285
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revision: 397ed406c1a7902140303e7faf60fff35b58d285
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name: MTEB MLQARetrieval (eng-ara)
revision: 397ed406c1a7902140303e7faf60fff35b58d285
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task:
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name: MTEB MLQARetrieval (spa-ara)
revision: 397ed406c1a7902140303e7faf60fff35b58d285
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revision: 397ed406c1a7902140303e7faf60fff35b58d285
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revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
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task:
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name: MTEB MrTidyRetrieval (arabic)
revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
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revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
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task:
type: Retrieval
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name: MTEB XPQARetrieval (ara-eng)
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
split: test
type: jinaai/xpqa
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task:
type: Retrieval
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config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
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task:
type: STS
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config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
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- 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 model finetuned from 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
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- Omartificial-Intelligence-Space/arabic-n_li-triplet
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
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:
pip install -U sentence-transformers
Then you can load this model and run inference.
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]
Evaluation
Metrics
Semantic Similarity
- Dataset:
sts-test-768 - Evaluated with
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
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
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
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
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 |
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:
anchor,positive, andnegative - Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 12 tokens
- mean: 23.93 tokens
- max: 155 tokens
- min: 9 tokens
- mean: 29.62 tokens
- max: 117 tokens
- min: 13 tokens
- mean: 33.95 tokens
- max: 149 tokens
- Samples:
anchor positive negative شخص على حصان يقفز فوق طائرة معطلةشخص في الهواء الطلق، على حصان.شخص في مطعم، يطلب عجة.أطفال يبتسمون و يلوحون للكاميراهناك أطفال حاضرونالاطفال يتجهمونصبي يقفز على لوح التزلج في منتصف الجسر الأحمر.الفتى يقوم بخدعة التزلجالصبي يتزلج على الرصيف - Loss:
MatryoshkaLosswith these parameters:{ "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:
anchor,positive, andnegative - Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 12 tokens
- mean: 49.5 tokens
- max: 246 tokens
- min: 8 tokens
- mean: 23.66 tokens
- max: 103 tokens
- min: 9 tokens
- mean: 25.33 tokens
- max: 82 tokens
- Samples:
anchor positive negative امرأتان يتعانقان بينما يحملان حزمةإمرأتان يحملان حزمةالرجال يتشاجرون خارج مطعمطفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة.طفلين يرتديان قميصاً مرقماً يغسلون أيديهمطفلين يرتديان سترة يذهبان إلى المدرسةرجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليسرجل يبيع الدونات لعميلامرأة تشرب قهوتها في مقهى صغير - Loss:
MatryoshkaLosswith these parameters:{ "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: 64per_device_eval_batch_size: 64warmup_ratio: 0.1fp16: Truebatch_sampler: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir: Falsedo_predict: Falseprediction_loss_only: Trueper_device_train_batch_size: 64per_device_eval_batch_size: 64per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonelearning_rate: 5e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1.0num_train_epochs: 3max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.1warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Falsefp16: Truefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Falsehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseeval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Nonedispatch_batches: Nonesplit_batches: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_sampler: no_duplicatesmulti_dataset_batch_sampler: proportional
Training Logs
Click to expand
| 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 |
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
@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
@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
@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}
}
Acknowledgments
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.
## 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},
}