---
base_model: Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
datasets:
- Omartificial-Intelligence-Space/Arabic-stsb
- Omartificial-Intelligence-Space/Arabic-NLi-Pair-Class
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
- mteb
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:947818
- loss:SoftmaxLoss
- loss:CosineSimilarityLoss
- transformers
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: >-
    SentenceTransformer based on
    Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.8390853221830158
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8410008255002589
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8276538954353795
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8360889200075982
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8274021671008013
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8357887501417183
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8154259766643255
      name: Pearson Dot
    - type: spearman_dot
      value: 0.81802827956939
      name: Spearman Dot
    - type: pearson_max
      value: 0.8390853221830158
      name: Pearson Max
    - type: spearman_max
      value: 0.8410008255002589
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.8130046542366043
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8172511596569861
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8113865863454744
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8164081961542164
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.810311097439534
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8157654465052717
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7907732563794702
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7886749863194292
      name: Spearman Dot
    - type: pearson_max
      value: 0.8130046542366043
      name: Pearson Max
    - type: spearman_max
      value: 0.8172511596569861
      name: Spearman Max
license: apache-2.0
---

# GATE-AraBert-v1

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-v1")
# 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      | 0.8391    |
| **spearman_cosine** | **0.841** |
| pearson_manhattan   | 0.8277    |
| spearman_manhattan  | 0.8361    |
| pearson_euclidean   | 0.8274    |
| spearman_euclidean  | 0.8358    |
| pearson_dot         | 0.8154    |
| spearman_dot        | 0.818     |
| pearson_max         | 0.8391    |
| spearman_max        | 0.841     |

#### 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      | 0.813      |
| **spearman_cosine** | **0.8173** |
| pearson_manhattan   | 0.8114     |
| spearman_manhattan  | 0.8164     |
| pearson_euclidean   | 0.8103     |
| spearman_euclidean  | 0.8158     |
| pearson_dot         | 0.7908     |
| spearman_dot        | 0.7887     |
| pearson_max         | 0.813      |
| spearman_max        | 0.8173     |


## Citation

### BibTeX

#### Sentence Transformers and SoftmaxLoss
```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",
}
```