metadata

inference: false
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: sentence-transformers/LaBSE
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-labse-Matryoshka
    results:
      - dataset:
          config: default
          name: MTEB BIOSSES (default)
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
          split: test
          type: mteb/biosses-sts
        metrics:
          - type: cosine_pearson
            value: 76.46793440999714
          - type: cosine_spearman
            value: 76.66439745271298
          - type: euclidean_pearson
            value: 76.52075972347127
          - type: euclidean_spearman
            value: 76.66439745271298
          - type: main_score
            value: 76.66439745271298
          - type: manhattan_pearson
            value: 76.68001857069733
          - type: manhattan_spearman
            value: 76.73066402288269
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB SICK-R (default)
          revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
          split: test
          type: mteb/sickr-sts
        metrics:
          - type: cosine_pearson
            value: 79.67657890693198
          - type: cosine_spearman
            value: 77.03286420274621
          - type: euclidean_pearson
            value: 78.1960735272073
          - type: euclidean_spearman
            value: 77.032855497919
          - type: main_score
            value: 77.03286420274621
          - type: manhattan_pearson
            value: 78.25627275994229
          - type: manhattan_spearman
            value: 77.00430810589081
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS12 (default)
          revision: a0d554a64d88156834ff5ae9920b964011b16384
          split: test
          type: mteb/sts12-sts
        metrics:
          - type: cosine_pearson
            value: 83.94288954523996
          - type: cosine_spearman
            value: 79.21432176112556
          - type: euclidean_pearson
            value: 81.21333251943913
          - type: euclidean_spearman
            value: 79.2152067330468
          - type: main_score
            value: 79.21432176112556
          - type: manhattan_pearson
            value: 81.16910737482634
          - type: manhattan_spearman
            value: 79.08756466301445
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS13 (default)
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
          split: test
          type: mteb/sts13-sts
        metrics:
          - type: cosine_pearson
            value: 77.48393909963059
          - type: cosine_spearman
            value: 79.54963868861196
          - type: euclidean_pearson
            value: 79.28416002197451
          - type: euclidean_spearman
            value: 79.54963861790114
          - type: main_score
            value: 79.54963868861196
          - type: manhattan_pearson
            value: 79.18653917582513
          - type: manhattan_spearman
            value: 79.46713533414295
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS14 (default)
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
          split: test
          type: mteb/sts14-sts
        metrics:
          - type: cosine_pearson
            value: 78.51596313692846
          - type: cosine_spearman
            value: 78.84601702652395
          - type: euclidean_pearson
            value: 78.55199809961427
          - type: euclidean_spearman
            value: 78.84603362286225
          - type: main_score
            value: 78.84601702652395
          - type: manhattan_pearson
            value: 78.52780170677605
          - type: manhattan_spearman
            value: 78.77744294039178
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS15 (default)
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
          split: test
          type: mteb/sts15-sts
        metrics:
          - type: cosine_pearson
            value: 84.53393478889929
          - type: cosine_spearman
            value: 85.60821849381648
          - type: euclidean_pearson
            value: 85.32813923250558
          - type: euclidean_spearman
            value: 85.6081835456016
          - type: main_score
            value: 85.60821849381648
          - type: manhattan_pearson
            value: 85.32782097916476
          - type: manhattan_spearman
            value: 85.58098670898562
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS16 (default)
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
          split: test
          type: mteb/sts16-sts
        metrics:
          - type: cosine_pearson
            value: 77.00196998325856
          - type: cosine_spearman
            value: 79.930951699069
          - type: euclidean_pearson
            value: 79.43196738390897
          - type: euclidean_spearman
            value: 79.93095112410258
          - type: main_score
            value: 79.930951699069
          - type: manhattan_pearson
            value: 79.33744358111427
          - type: manhattan_spearman
            value: 79.82939266539601
        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: 81.60289529424327
          - type: cosine_spearman
            value: 82.46806381979653
          - type: euclidean_pearson
            value: 81.32235058296072
          - type: euclidean_spearman
            value: 82.46676890643914
          - type: main_score
            value: 82.46806381979653
          - type: manhattan_pearson
            value: 81.43885277175312
          - type: manhattan_spearman
            value: 82.38955952718666
        task:
          type: STS
      - dataset:
          config: ar
          name: MTEB STS22 (ar)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cosine_pearson
            value: 49.58293768761314
          - type: cosine_spearman
            value: 57.261888789832874
          - type: euclidean_pearson
            value: 53.36549109538782
          - type: euclidean_spearman
            value: 57.261888789832874
          - type: main_score
            value: 57.261888789832874
          - type: manhattan_pearson
            value: 53.06640323833928
          - type: manhattan_spearman
            value: 57.05837935512948
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STSBenchmark (default)
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
          split: test
          type: mteb/stsbenchmark-sts
        metrics:
          - type: cosine_pearson
            value: 81.43997935928729
          - type: cosine_spearman
            value: 82.04996129795596
          - type: euclidean_pearson
            value: 82.01917866996972
          - type: euclidean_spearman
            value: 82.04996129795596
          - type: main_score
            value: 82.04996129795596
          - type: manhattan_pearson
            value: 82.03487112040936
          - type: manhattan_spearman
            value: 82.03774605775651
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB SummEval (default)
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
          split: test
          type: mteb/summeval
        metrics:
          - type: cosine_pearson
            value: 32.113475997147674
          - type: cosine_spearman
            value: 32.17194233764879
          - type: dot_pearson
            value: 32.113469728827255
          - type: dot_spearman
            value: 32.174771315355386
          - type: main_score
            value: 32.17194233764879
          - type: pearson
            value: 32.113475997147674
          - type: spearman
            value: 32.17194233764879
        task:
          type: Summarization
  - name: SentenceTransformer based on sentence-transformers/LaBSE
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 768
          type: sts-test-768
        metrics:
          - type: pearson_cosine
            value: 0.7269177710249681
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7225258779395222
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7259261785622463
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7210463582530393
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7259567884235211
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.722525823788783
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7269177712136122
            name: Pearson Dot
          - type: spearman_dot
            value: 0.7225258771129475
            name: Spearman Dot
          - type: pearson_max
            value: 0.7269177712136122
            name: Pearson Max
          - type: spearman_max
            value: 0.7225258779395222
            name: Spearman Max
          - type: pearson_cosine
            value: 0.8143867576376295
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8205044914629483
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8203365887013151
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8203816698535976
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8201809453496319
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8205044914629483
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.8143867541070537
            name: Pearson Dot
          - type: spearman_dot
            value: 0.8205044914629483
            name: Spearman Dot
          - type: pearson_max
            value: 0.8203365887013151
            name: Pearson Max
          - type: spearman_max
            value: 0.8205044914629483
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 512
          type: sts-test-512
        metrics:
          - type: pearson_cosine
            value: 0.7268389724271859
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7224359411000278
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7241418669615103
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7195408311833029
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7248184919191593
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7212936866178097
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7252522928016701
            name: Pearson Dot
          - type: spearman_dot
            value: 0.7205040482865328
            name: Spearman Dot
          - type: pearson_max
            value: 0.7268389724271859
            name: Pearson Max
          - type: spearman_max
            value: 0.7224359411000278
            name: Spearman Max
          - type: pearson_cosine
            value: 0.8143448965624136
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8211700903453509
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8217448619823571
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8216016599665544
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8216413349390971
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.82188122418776
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.8097020064483653
            name: Pearson Dot
          - type: spearman_dot
            value: 0.8147306090545295
            name: Spearman Dot
          - type: pearson_max
            value: 0.8217448619823571
            name: Pearson Max
          - type: spearman_max
            value: 0.82188122418776
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 256
          type: sts-test-256
        metrics:
          - type: pearson_cosine
            value: 0.7283468617741852
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7264294106954872
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7227711798003426
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.718067982079232
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7251492361775083
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7215068115809131
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7243396991648858
            name: Pearson Dot
          - type: spearman_dot
            value: 0.7221390873398206
            name: Spearman Dot
          - type: pearson_max
            value: 0.7283468617741852
            name: Pearson Max
          - type: spearman_max
            value: 0.7264294106954872
            name: Spearman Max
          - type: pearson_cosine
            value: 0.8075613785257986
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8159258089804861
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8208711370091426
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8196747601014518
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8210210137439432
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8203004500356083
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7870611647231145
            name: Pearson Dot
          - type: spearman_dot
            value: 0.7874848213991118
            name: Spearman Dot
          - type: pearson_max
            value: 0.8210210137439432
            name: Pearson Max
          - type: spearman_max
            value: 0.8203004500356083
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 128
          type: sts-test-128
        metrics:
          - type: pearson_cosine
            value: 0.7102082520621849
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7103917869311991
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7134729607181519
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.708895102058259
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7171545288118942
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7130380237150746
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.6777774738547628
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6746474823963989
            name: Spearman Dot
          - type: pearson_max
            value: 0.7171545288118942
            name: Pearson Max
          - type: spearman_max
            value: 0.7130380237150746
            name: Spearman Max
          - type: pearson_cosine
            value: 0.8024378358145556
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8117561815472325
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.818920309459774
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8180515365910205
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8198346073356603
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8185162896024369
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7513270537478935
            name: Pearson Dot
          - type: spearman_dot
            value: 0.7427542871546953
            name: Spearman Dot
          - type: pearson_max
            value: 0.8198346073356603
            name: Pearson Max
          - type: spearman_max
            value: 0.8185162896024369
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test 64
          type: sts-test-64
        metrics:
          - type: pearson_cosine
            value: 0.6930745722517785
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6982194042238953
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.6971382079778946
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.6942362764367931
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7012627015062325
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.6986972295835788
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.6376735798940838
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6344835722310429
            name: Spearman Dot
          - type: pearson_max
            value: 0.7012627015062325
            name: Pearson Max
          - type: spearman_max
            value: 0.6986972295835788
            name: Spearman Max
          - type: pearson_cosine
            value: 0.7855080652087961
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7948979371698327
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8060407473462375
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8041199691999044
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8088262858195556
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8060483394849104
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.677754045289596
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6616232873061395
            name: Spearman Dot
          - type: pearson_max
            value: 0.8088262858195556
            name: Pearson Max
          - type: spearman_max
            value: 0.8060483394849104
            name: Spearman Max
license: apache-2.0

SentenceTransformer based on sentence-transformers/LaBSE

This is a sentence-transformers model finetuned from sentence-transformers/LaBSE 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/LaBSE
Maximum Sequence Length: 256 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  (3): Normalize()
)

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/Arabic-labse")
# 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	0.7269
spearman_cosine	0.7225
pearson_manhattan	0.7259
spearman_manhattan	0.721
pearson_euclidean	0.726
spearman_euclidean	0.7225
pearson_dot	0.7269
spearman_dot	0.7225
pearson_max	0.7269
spearman_max	0.7225

Semantic Similarity

Dataset: sts-test-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7268
spearman_cosine	0.7224
pearson_manhattan	0.7241
spearman_manhattan	0.7195
pearson_euclidean	0.7248
spearman_euclidean	0.7213
pearson_dot	0.7253
spearman_dot	0.7205
pearson_max	0.7268
spearman_max	0.7224

Semantic Similarity

Dataset: sts-test-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7283
spearman_cosine	0.7264
pearson_manhattan	0.7228
spearman_manhattan	0.7181
pearson_euclidean	0.7251
spearman_euclidean	0.7215
pearson_dot	0.7243
spearman_dot	0.7221
pearson_max	0.7283
spearman_max	0.7264

Semantic Similarity

Dataset: sts-test-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7102
spearman_cosine	0.7104
pearson_manhattan	0.7135
spearman_manhattan	0.7089
pearson_euclidean	0.7172
spearman_euclidean	0.713
pearson_dot	0.6778
spearman_dot	0.6746
pearson_max	0.7172
spearman_max	0.713

Semantic Similarity

Dataset: sts-test-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6931
spearman_cosine	0.6982
pearson_manhattan	0.6971
spearman_manhattan	0.6942
pearson_euclidean	0.7013
spearman_euclidean	0.6987
pearson_dot	0.6377
spearman_dot	0.6345
pearson_max	0.7013
spearman_max	0.6987

Semantic Similarity

Dataset: sts-test-768
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8144
spearman_cosine	0.8205
pearson_manhattan	0.8203
spearman_manhattan	0.8204
pearson_euclidean	0.8202
spearman_euclidean	0.8205
pearson_dot	0.8144
spearman_dot	0.8205
pearson_max	0.8203
spearman_max	0.8205

Semantic Similarity

Dataset: sts-test-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8143
spearman_cosine	0.8212
pearson_manhattan	0.8217
spearman_manhattan	0.8216
pearson_euclidean	0.8216
spearman_euclidean	0.8219
pearson_dot	0.8097
spearman_dot	0.8147
pearson_max	0.8217
spearman_max	0.8219

Semantic Similarity

Dataset: sts-test-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8076
spearman_cosine	0.8159
pearson_manhattan	0.8209
spearman_manhattan	0.8197
pearson_euclidean	0.821
spearman_euclidean	0.8203
pearson_dot	0.7871
spearman_dot	0.7875
pearson_max	0.821
spearman_max	0.8203

Semantic Similarity

Dataset: sts-test-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8024
spearman_cosine	0.8118
pearson_manhattan	0.8189
spearman_manhattan	0.8181
pearson_euclidean	0.8198
spearman_euclidean	0.8185
pearson_dot	0.7513
spearman_dot	0.7428
pearson_max	0.8198
spearman_max	0.8185

Semantic Similarity

Dataset: sts-test-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7855
spearman_cosine	0.7949
pearson_manhattan	0.806
spearman_manhattan	0.8041
pearson_euclidean	0.8088
spearman_euclidean	0.806
pearson_dot	0.6778
spearman_dot	0.6616
pearson_max	0.8088
spearman_max	0.806

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, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 4 tokens mean: 9.99 tokens max: 51 tokens	min: 4 tokens mean: 12.44 tokens max: 49 tokens	min: 5 tokens mean: 13.82 tokens max: 49 tokens

Samples:

anchor	positive	negative
`شخص على حصان يقفز فوق طائرة معطلة`	`شخص في الهواء الطلق، على حصان.`	`شخص في مطعم، يطلب عجة.`
`أطفال يبتسمون و يلوحون للكاميرا`	`هناك أطفال حاضرون`	`الاطفال يتجهمون`
`صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.`	`الفتى يقوم بخدعة التزلج`	`الصبي يتزلج على الرصيف`

Loss: MatryoshkaLoss with 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, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 4 tokens mean: 19.71 tokens max: 100 tokens	min: 4 tokens mean: 9.37 tokens max: 38 tokens	min: 4 tokens mean: 10.49 tokens max: 34 tokens

Samples:

anchor	positive	negative
`امرأتان يتعانقان بينما يحملان حزمة`	`إمرأتان يحملان حزمة`	`الرجال يتشاجرون خارج مطعم`
`طفلين 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 أيديهم`	`طفلين يرتديان سترة يذهبان إلى المدرسة`
`رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس`	`رجل يبيع الدونات لعميل`	`امرأة تشرب قهوتها في مقهى صغير`

Loss: MatryoshkaLoss with 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: 64
per_device_eval_batch_size: 64
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

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
None	0	-	0.7104	0.7264	0.7224	0.6982	0.7225
0.0229	200	13.1738	-	-	-	-	-
0.0459	400	8.8127	-	-	-	-	-
0.0688	600	8.0984	-	-	-	-	-
0.0918	800	7.2984	-	-	-	-	-
0.1147	1000	7.5749	-	-	-	-	-
0.1377	1200	7.1292	-	-	-	-	-
0.1606	1400	6.6146	-	-	-	-	-
0.1835	1600	6.6523	-	-	-	-	-
0.2065	1800	6.1095	-	-	-	-	-
0.2294	2000	6.0841	-	-	-	-	-
0.2524	2200	6.3024	-	-	-	-	-
0.2753	2400	6.1941	-	-	-	-	-
0.2983	2600	6.1686	-	-	-	-	-
0.3212	2800	5.8317	-	-	-	-	-
0.3442	3000	6.0597	-	-	-	-	-
0.3671	3200	5.7832	-	-	-	-	-
0.3900	3400	5.7088	-	-	-	-	-
0.4130	3600	5.6988	-	-	-	-	-
0.4359	3800	5.5268	-	-	-	-	-
0.4589	4000	5.5543	-	-	-	-	-
0.4818	4200	5.3152	-	-	-	-	-
0.5048	4400	5.2894	-	-	-	-	-
0.5277	4600	5.1805	-	-	-	-	-
0.5506	4800	5.4559	-	-	-	-	-
0.5736	5000	5.3836	-	-	-	-	-
0.5965	5200	5.2626	-	-	-	-	-
0.6195	5400	5.2511	-	-	-	-	-
0.6424	5600	5.3308	-	-	-	-	-
0.6654	5800	5.2264	-	-	-	-	-
0.6883	6000	5.2881	-	-	-	-	-
0.7113	6200	5.1349	-	-	-	-	-
0.7342	6400	5.0872	-	-	-	-	-
0.7571	6600	4.5515	-	-	-	-	-
0.7801	6800	3.4312	-	-	-	-	-
0.8030	7000	3.1008	-	-	-	-	-
0.8260	7200	2.9582	-	-	-	-	-
0.8489	7400	2.8153	-	-	-	-	-
0.8719	7600	2.7214	-	-	-	-	-
0.8948	7800	2.5392	-	-	-	-	-
0.9177	8000	2.584	-	-	-	-	-
0.9407	8200	2.5384	-	-	-	-	-
0.9636	8400	2.4937	-	-	-	-	-
0.9866	8600	2.4155	-	-	-	-	-
1.0	8717	-	0.8118	0.8159	0.8212	0.7949	0.8205

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}, 
}