README.md

metadata

base_model: Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka
datasets:
  - Omartificial-Intelligence-Space/Arabic-stsb
language:
  - ar
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:947818
  - loss:SoftmaxLoss
  - loss:CosineSimilarityLoss
widget:
  - source_sentence: امرأة تكتب شيئاً
    sentences:
      - مراهق يتحدث إلى فتاة عبر كاميرا الإنترنت
      - امرأة تقطع البصل الأخضر.
      - مجموعة من كبار السن يتظاهرون حول طاولة الطعام.
  - source_sentence: تتشكل النجوم في مناطق تكوين النجوم، والتي تنشأ نفسها من السحب الجزيئية.
    sentences:
      - لاعب كرة السلة على وشك تسجيل نقاط لفريقه.
      - المقال التالي مأخوذ من نسختي من "أطلس البطريق الجديد للتاريخ الوسطى"
      - قد يكون من الممكن أن يوجد نظام شمسي مثل نظامنا خارج المجرة
  - source_sentence: >-
      تحت السماء الزرقاء مع الغيوم البيضاء، يصل طفل لمس مروحة طائرة واقفة على
      حقل من العشب.
    sentences:
      - امرأة تحمل كأساً
      - طفل يحاول لمس مروحة طائرة
      - اثنان من عازبين عن الشرب يستعدون للعشاء
  - source_sentence: رجل في منتصف العمر يحلق لحيته في غرفة ذات جدران بيضاء والتي لا تبدو كحمام
    sentences:
      - فتى يخطط اسمه على مكتبه
      - رجل ينام
      - المرأة وحدها وهي نائمة في غرفة نومها
  - source_sentence: الكلب البني مستلقي على جانبه على سجادة بيج، مع جسم أخضر في المقدمة.
    sentences:
      - شخص طويل القامة
      - المرأة تنظر من النافذة.
      - لقد مات الكلب
model-index:
  - name: Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka
    results:
      - dataset:
          config: ar
          name: MTEB MIRACLRetrieval (ar)
          revision: main
          split: dev
          type: miracl/mmteb-miracl
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            value: 6.181
          - type: ndcg_at_3
            value: 7.546
          - type: ndcg_at_5
            value: 8.949
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            value: 11.355
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            value: 13.562
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            value: 17.749000000000002
          - type: ndcg_at_1000
            value: 21.715999999999998
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          - type: map_at_1000
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            value: 11.758000000000001
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            value: 18.275
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          - type: recall_at_1000
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        task:
          type: Retrieval
      - dataset:
          config: ar
          name: MTEB MIRACLRetrievalHardNegatives (ar)
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
          split: dev
          type: mteb/miracl-hard-negatives
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          - type: nauc_recall_at_10_std
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          - type: nauc_recall_at_10_diff1
            value: 11.0016
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            value: 33.2628
          - type: nauc_recall_at_20_std
            value: 12.5879
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            value: 7.4169
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            value: 50.8088
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            value: 11.1589
          - type: nauc_precision_at_1_diff1
            value: 26.7249
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            value: 47.8686
          - type: nauc_precision_at_3_std
            value: 7.991099999999999
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            value: 17.9774
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          - type: nauc_precision_at_5_std
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            value: 48.1304
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          - type: nauc_precision_at_100_std
            value: 9.7255
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            value: 9.9304
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            value: 26.7249
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            value: 45.7771
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            value: 11.3476
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            value: 21.182599999999997
          - type: nauc_mrr_at_5_max
            value: 46.7327
          - type: nauc_mrr_at_5_std
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          - type: nauc_mrr_at_5_diff1
            value: 20.2543
          - type: nauc_mrr_at_10_max
            value: 45.3585
          - type: nauc_mrr_at_10_std
            value: 11.9694
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            value: 19.6598
          - type: nauc_mrr_at_20_max
            value: 44.5577
          - type: nauc_mrr_at_20_std
            value: 12.004900000000001
          - type: nauc_mrr_at_20_diff1
            value: 19.7057
          - type: nauc_mrr_at_100_max
            value: 44.1008
          - type: nauc_mrr_at_100_std
            value: 11.9877
          - type: nauc_mrr_at_100_diff1
            value: 19.683899999999998
          - type: nauc_mrr_at_1000_max
            value: 44.088
          - type: nauc_mrr_at_1000_std
            value: 12.0156
          - type: nauc_mrr_at_1000_diff1
            value: 19.6552
          - type: main_score
            value: 9.058
        task:
          type: Retrieval

GATE-AraBert-v0

This is a General Arabic Text Embedding trained using SentenceTransformers in a multi-task setup. The system trains on the AllNLI and on the STS dataset.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity
• Training Datasets:
  • all-nli
  • sts
• Language: ar

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/GATE-AraBert-v0")
# Run inference
sentences = [
    'الكلب البني مستلقي على جانبه على سجادة بيج، مع جسم أخضر في المقدمة.',
    'لقد مات الكلب',
    'شخص طويل القامة',
]
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-dev
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8384
spearman_cosine	0.8389
pearson_manhattan	0.8248
spearman_manhattan	0.8329
pearson_euclidean	0.825
spearman_euclidean	0.8337
pearson_dot	0.8072
spearman_dot	0.8098
pearson_max	0.8384
spearman_max	0.8389

Semantic Similarity

• Dataset: sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7908
spearman_cosine	0.7893
pearson_manhattan	0.7923
spearman_manhattan	0.7947
pearson_euclidean	0.7904
spearman_euclidean	0.7934
pearson_dot	0.7404
spearman_dot	0.7354
pearson_max	0.7923
spearman_max	0.7947