---
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:
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      name: MTEB MIRACLRetrieval (ar)
      revision: main
      split: dev
      type: miracl/mmteb-miracl
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      value: 7.546
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      value: 8.949
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      value: 11.355
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    - type: ndcg_at_1000
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    - type: map_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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      revision: 397ed406c1a7902140303e7faf60fff35b58d285
      split: validation
      type: facebook/mlqa
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    task:
      type: Retrieval
  - dataset:
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      name: MTEB MLQARetrieval (hin-ara)
      revision: 397ed406c1a7902140303e7faf60fff35b58d285
      split: validation
      type: facebook/mlqa
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    task:
      type: Retrieval
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      name: MTEB MLQARetrieval (vie-ara)
      revision: 397ed406c1a7902140303e7faf60fff35b58d285
      split: validation
      type: facebook/mlqa
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  - dataset:
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      revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
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      type: jinaai/mintakaqa
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  - dataset:
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      revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
      split: test
      type: mteb/mrtidy
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      value: 41.018100000000004
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      value: 11.049000000000001
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      value: 16.040399999999998
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      value: 36.2297
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      value: 11.9153
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      value: 11.0016
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      value: 33.2628
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      value: 12.5879
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      value: 12.9185
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      value: 27.142500000000002
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      value: 7.4169
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      value: 10.584
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      value: 22.1808
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      value: 11.3579
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      value: 12.942300000000001
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      value: 50.8088
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      value: 11.1589
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      value: 26.7249
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      value: 47.8686
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      value: 7.991099999999999
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      value: 17.9774
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      value: 51.970499999999994
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      value: 9.6156
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      value: 15.770600000000002
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      value: 48.1304
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      value: 10.6987
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      value: 12.1846
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      value: 43.4295
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      value: 10.9555
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      value: 13.037199999999999
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      value: 9.7255
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      value: 9.9304
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      value: 11.1782
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      value: 9.1991
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      value: 3.4078999999999997
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      value: 50.8088
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      value: 11.1589
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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
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      value: 46.7327
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      value: 12.203899999999999
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      value: 20.2543
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      value: 45.3585
    - type: nauc_mrr_at_10_std
      value: 11.9694
    - type: nauc_mrr_at_10_diff1
      value: 19.6598
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      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
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      value: 12.0156
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      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](https://huggingface.co./Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2) <!-- at revision 5ce4f80f3ede26de623d6ac10681399dba5c684a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
    - [all-nli](https://huggingface.co./datasets/Omartificial-Intelligence-Space/Arabic-NLi-Pair-Class)
    - [sts](https://huggingface.co./datasets/Omartificial-Intelligence-Space/arabic-stsb)
- **Language:** ar


## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Omartificial-Intelligence-Space/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 [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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 [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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     |