Arabic-Triplet-Matryoshka-V2 / README.md

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Add new SentenceTransformer model.

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	---
	base_model: aubmindlab/bert-base-arabertv02
	datasets: []
	language: []
	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:1000000
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: فتى يرتدي اللون الأحمر ينزلق على متن عربة نفخة
	sentences:
	- اثنان من الشباب الآسيويين يتسكعون
	- فتى يلعب على عربة نفخة
	- فتى يثقب سكيناً في عربة نفخة
	- source_sentence: عامل بناء يقف على رافعة يضع ذراعًا كبيرًا على قمة قمة قيد الإنشاء.
	sentences:
	- الاطفال يركبون عربة متعة
	- شخص يقف
	- لا أحد يقف
	- source_sentence: رجل مع حفرة طاقة كبيرة يقف بجانب ابنته مع خرطوم المكنسة الكهربائية.
	sentences:
	- جنديان يحملان أسلحة
	- رجل يحمل مثقاب يقف بجانب فتاة تحمل خرطوم كهربائي
	- الرجل والفتاة يرسمون الجدران
	- source_sentence: رجل يرتدي قميص أسود يعزف على الجيتار.
	sentences:
	- الرجل يرتدي الأسود.
	- هناك رجل يفرغ
	- الرجل يرتدي قميصاً أزرق.
	- source_sentence: رجل يرتدي قميص (فيجاس) الأحمر يجلس على طاولة ويلعب بالكاميرا
	sentences:
	- رجل يلعب بالكاميرا
	- فتى يقفز في الهواء
	- الرجل يقف ويأخذ الصور
	model-index:
	- name: SentenceTransformer based on aubmindlab/bert-base-arabertv02
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 768
	type: sts-test-768
	metrics:
	- type: pearson_cosine
	value: 0.8137491067613172
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8139804248887779
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.805239691712325
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8071457719582591
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8053105962459932
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8078084689219578
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.8019135317246738
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7961388104098682
	name: Spearman Dot
	- type: pearson_max
	value: 0.8137491067613172
	name: Pearson Max
	- type: spearman_max
	value: 0.8139804248887779
	name: Spearman Max
	- type: pearson_cosine
	value: 0.8137491067613172
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8139804248887779
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.805239691712325
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8071457719582591
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8053105962459932
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8078084689219578
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.8019135317246738
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7961388104098682
	name: Spearman Dot
	- type: pearson_max
	value: 0.8137491067613172
	name: Pearson Max
	- type: spearman_max
	value: 0.8139804248887779
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 512
	type: sts-test-512
	metrics:
	- type: pearson_cosine
	value: 0.8127890716639393
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.813769735512917
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8045619532064516
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.806084784718251
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8047817340341926
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8067787363048019
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7985706834990611
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7926669266198092
	name: Spearman Dot
	- type: pearson_max
	value: 0.8127890716639393
	name: Pearson Max
	- type: spearman_max
	value: 0.813769735512917
	name: Spearman Max
	- type: pearson_cosine
	value: 0.8127890716639393
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.813769735512917
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8045619532064516
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.806084784718251
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8047817340341926
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8067787363048019
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7985706834990611
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7926669266198092
	name: Spearman Dot
	- type: pearson_max
	value: 0.8127890716639393
	name: Pearson Max
	- type: spearman_max
	value: 0.813769735512917
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.810388221021721
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8138356923403065
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8015100804443567
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8026219149891689
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8016089017435591
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8030480833628191
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.792265476718613
	name: Pearson Dot
	- type: spearman_dot
	value: 0.787067391010805
	name: Spearman Dot
	- type: pearson_max
	value: 0.810388221021721
	name: Pearson Max
	- type: spearman_max
	value: 0.8138356923403065
	name: Spearman Max
	- type: pearson_cosine
	value: 0.810388221021721
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8138356923403065
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8015100804443567
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8026219149891689
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8016089017435591
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8030480833628191
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.792265476718613
	name: Pearson Dot
	- type: spearman_dot
	value: 0.787067391010805
	name: Spearman Dot
	- type: pearson_max
	value: 0.810388221021721
	name: Pearson Max
	- type: spearman_max
	value: 0.8138356923403065
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.8071777671061434
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8128987608664245
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7969339482985063
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7972524285093451
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7971979787664204
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.797866628579141
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7752745908442699
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7685950685903284
	name: Spearman Dot
	- type: pearson_max
	value: 0.8071777671061434
	name: Pearson Max
	- type: spearman_max
	value: 0.8128987608664245
	name: Spearman Max
	- type: pearson_cosine
	value: 0.8071777671061434
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8128987608664245
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7969339482985063
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7972524285093451
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7971979787664204
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.797866628579141
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7752745908442699
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7685950685903284
	name: Spearman Dot
	- type: pearson_max
	value: 0.8071777671061434
	name: Pearson Max
	- type: spearman_max
	value: 0.8128987608664245
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.7992861493805723
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.809205854296297
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7841737408240652
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7848704254075567
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7865782078684138
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7874610680426495
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7341564461014968
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7244607540987561
	name: Spearman Dot
	- type: pearson_max
	value: 0.7992861493805723
	name: Pearson Max
	- type: spearman_max
	value: 0.809205854296297
	name: Spearman Max
	- type: pearson_cosine
	value: 0.7992861493805723
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.809205854296297
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7841737408240652
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7848704254075567
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7865782078684138
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7874610680426495
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7341564461014968
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7244607540987561
	name: Spearman Dot
	- type: pearson_max
	value: 0.7992861493805723
	name: Pearson Max
	- type: spearman_max
	value: 0.809205854296297
	name: Spearman Max
	---

	# SentenceTransformer based on aubmindlab/bert-base-arabertv02

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02). 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: [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) <!-- at revision 016fb9d6768f522a59c6e0d2d5d5d43a4e1bff60 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
	- Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(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:

	```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/Arabert-matro-v4")
	# Run inference
	sentences = [
	'رجل يرتدي قميص (فيجاس) الأحمر يجلس على طاولة ويلعب بالكاميرا',
	'رجل يلعب بالكاميرا',
	'الرجل يقف ويأخذ الصور',
	]
	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]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### Metrics

	#### Semantic Similarity
	* Dataset: `sts-test-768`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.8137 \|
	\| spearman_cosine \| 0.814 \|
	\| pearson_manhattan \| 0.8052 \|
	\| spearman_manhattan \| 0.8071 \|
	\| pearson_euclidean \| 0.8053 \|
	\| spearman_euclidean \| 0.8078 \|
	\| pearson_dot \| 0.8019 \|
	\| spearman_dot \| 0.7961 \|
	\| pearson_max \| 0.8137 \|
	\| spearman_max \| 0.814 \|

	#### Semantic Similarity
	* Dataset: `sts-test-512`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8128 \|
	\| spearman_cosine \| 0.8138 \|
	\| pearson_manhattan \| 0.8046 \|
	\| spearman_manhattan \| 0.8061 \|
	\| pearson_euclidean \| 0.8048 \|
	\| spearman_euclidean \| 0.8068 \|
	\| pearson_dot \| 0.7986 \|
	\| spearman_dot \| 0.7927 \|
	\| pearson_max \| 0.8128 \|
	\| spearman_max \| 0.8138 \|

	#### Semantic Similarity
	* Dataset: `sts-test-256`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8104 \|
	\| spearman_cosine \| 0.8138 \|
	\| pearson_manhattan \| 0.8015 \|
	\| spearman_manhattan \| 0.8026 \|
	\| pearson_euclidean \| 0.8016 \|
	\| spearman_euclidean \| 0.803 \|
	\| pearson_dot \| 0.7923 \|
	\| spearman_dot \| 0.7871 \|
	\| pearson_max \| 0.8104 \|
	\| spearman_max \| 0.8138 \|

	#### Semantic Similarity
	* Dataset: `sts-test-128`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8072 \|
	\| spearman_cosine \| 0.8129 \|
	\| pearson_manhattan \| 0.7969 \|
	\| spearman_manhattan \| 0.7973 \|
	\| pearson_euclidean \| 0.7972 \|
	\| spearman_euclidean \| 0.7979 \|
	\| pearson_dot \| 0.7753 \|
	\| spearman_dot \| 0.7686 \|
	\| pearson_max \| 0.8072 \|
	\| spearman_max \| 0.8129 \|

	#### Semantic Similarity
	* Dataset: `sts-test-64`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7993 \|
	\| spearman_cosine \| 0.8092 \|
	\| pearson_manhattan \| 0.7842 \|
	\| spearman_manhattan \| 0.7849 \|
	\| pearson_euclidean \| 0.7866 \|
	\| spearman_euclidean \| 0.7875 \|
	\| pearson_dot \| 0.7342 \|
	\| spearman_dot \| 0.7245 \|
	\| pearson_max \| 0.7993 \|
	\| spearman_max \| 0.8092 \|

	#### Semantic Similarity
	* Dataset: `sts-test-768`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.8137 \|
	\| spearman_cosine \| 0.814 \|
	\| pearson_manhattan \| 0.8052 \|
	\| spearman_manhattan \| 0.8071 \|
	\| pearson_euclidean \| 0.8053 \|
	\| spearman_euclidean \| 0.8078 \|
	\| pearson_dot \| 0.8019 \|
	\| spearman_dot \| 0.7961 \|
	\| pearson_max \| 0.8137 \|
	\| spearman_max \| 0.814 \|

	#### Semantic Similarity
	* Dataset: `sts-test-512`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8128 \|
	\| spearman_cosine \| 0.8138 \|
	\| pearson_manhattan \| 0.8046 \|
	\| spearman_manhattan \| 0.8061 \|
	\| pearson_euclidean \| 0.8048 \|
	\| spearman_euclidean \| 0.8068 \|
	\| pearson_dot \| 0.7986 \|
	\| spearman_dot \| 0.7927 \|
	\| pearson_max \| 0.8128 \|
	\| spearman_max \| 0.8138 \|

	#### Semantic Similarity
	* Dataset: `sts-test-256`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8104 \|
	\| spearman_cosine \| 0.8138 \|
	\| pearson_manhattan \| 0.8015 \|
	\| spearman_manhattan \| 0.8026 \|
	\| pearson_euclidean \| 0.8016 \|
	\| spearman_euclidean \| 0.803 \|
	\| pearson_dot \| 0.7923 \|
	\| spearman_dot \| 0.7871 \|
	\| pearson_max \| 0.8104 \|
	\| spearman_max \| 0.8138 \|

	#### Semantic Similarity
	* Dataset: `sts-test-128`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8072 \|
	\| spearman_cosine \| 0.8129 \|
	\| pearson_manhattan \| 0.7969 \|
	\| spearman_manhattan \| 0.7973 \|
	\| pearson_euclidean \| 0.7972 \|
	\| spearman_euclidean \| 0.7979 \|
	\| pearson_dot \| 0.7753 \|
	\| spearman_dot \| 0.7686 \|
	\| pearson_max \| 0.8072 \|
	\| spearman_max \| 0.8129 \|

	#### Semantic Similarity
	* Dataset: `sts-test-64`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7993 \|
	\| spearman_cosine \| 0.8092 \|
	\| pearson_manhattan \| 0.7842 \|
	\| spearman_manhattan \| 0.7849 \|
	\| pearson_euclidean \| 0.7866 \|
	\| spearman_euclidean \| 0.7875 \|
	\| pearson_dot \| 0.7342 \|
	\| spearman_dot \| 0.7245 \|
	\| pearson_max \| 0.7993 \|
	\| spearman_max \| 0.8092 \|

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 1,000,000 training samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:---------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 12.0 tokens</li><li>max: 69 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 31.78 tokens</li><li>max: 174 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 30.79 tokens</li><li>max: 216 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:-----------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| <code>ما الذي تتجنبه؟</code> \| <code>ما الذي تحاولين تجنبه دائماً؟</code> \| <code>أنا في حالة اكتئاب ماذا يجب أن أفعل؟</code> \|
	\| <code>رجل يقف عند لافتة صفراء</code> \| <code>رجل يقترب من علامة</code> \| <code>رجل بجانب لافتة زرقاء</code> \|
	\| <code>لماذا قام (مودي) بحظر أوراق نقدية بقيمة 500 و 1000 روبية؟</code> \| <code>لماذا قام مودي بإلغاء عملة الـ 500 و 1000 روبية؟ وما سبب إدخال عملة الـ 2000 روبية فجأة؟</code> \| <code>ما هو أفضل خيار بعد الانتهاء من البكالوريوس في الهندسة الميكانيكية؟</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"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: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 14.87 tokens</li><li>max: 70 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 7.54 tokens</li><li>max: 26 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 8.14 tokens</li><li>max: 23 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:-----------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------\|:---------------------------------------------------\|
	\| <code>امرأتان يتعانقان بينما يحملان حزمة</code> \| <code>إمرأتان يحملان حزمة</code> \| <code>الرجال يتشاجرون خارج مطعم</code> \|
	\| <code>طفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة.</code> \| <code>طفلين يرتديان قميصاً مرقماً يغسلون أيديهم</code> \| <code>طفلين يرتديان سترة يذهبان إلى المدرسة</code> \|
	\| <code>رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس</code> \| <code>رجل يبيع الدونات لعميل</code> \| <code>امرأة تشرب قهوتها في مقهى صغير</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"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
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `batch_sampler`: no_duplicates

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: no
	- `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
	- `torch_empty_cache_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`: 3
	- `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
	- `restore_callback_states_from_checkpoint`: 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, 'non_blocking': False, '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_eval_metrics`: False
	- `eval_on_start`: False
	- `eval_use_gather_object`: False
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### 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 \|
	\|:------:\|:-----:\|:-------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|:----------------------------:\|
	\| 0.0384 \| 200 \| 9.7813 \| - \| - \| - \| - \| - \|
	\| 0.0768 \| 400 \| 4.4771 \| - \| - \| - \| - \| - \|
	\| 0.1152 \| 600 \| 3.754 \| - \| - \| - \| - \| - \|
	\| 0.1536 \| 800 \| 3.4086 \| - \| - \| - \| - \| - \|
	\| 0.1920 \| 1000 \| 3.1323 \| - \| - \| - \| - \| - \|
	\| 0.2304 \| 1200 \| 2.9257 \| - \| - \| - \| - \| - \|
	\| 0.2688 \| 1400 \| 2.8363 \| - \| - \| - \| - \| - \|
	\| 0.3072 \| 1600 \| 2.6156 \| - \| - \| - \| - \| - \|
	\| 0.3456 \| 1800 \| 2.5428 \| - \| - \| - \| - \| - \|
	\| 0.3840 \| 2000 \| 2.4927 \| - \| - \| - \| - \| - \|
	\| 0.4223 \| 2200 \| 2.4 \| - \| - \| - \| - \| - \|
	\| 0.4607 \| 2400 \| 2.3193 \| - \| - \| - \| - \| - \|
	\| 0.4991 \| 2600 \| 2.2363 \| - \| - \| - \| - \| - \|
	\| 0.5375 \| 2800 \| 2.1929 \| - \| - \| - \| - \| - \|
	\| 0.5759 \| 3000 \| 2.1396 \| - \| - \| - \| - \| - \|
	\| 0.6143 \| 3200 \| 2.0481 \| - \| - \| - \| - \| - \|
	\| 0.6527 \| 3400 \| 2.0299 \| - \| - \| - \| - \| - \|
	\| 0.6911 \| 3600 \| 1.9895 \| - \| - \| - \| - \| - \|
	\| 0.7295 \| 3800 \| 1.9889 \| - \| - \| - \| - \| - \|
	\| 0.7679 \| 4000 \| 1.9319 \| - \| - \| - \| - \| - \|
	\| 0.8063 \| 4200 \| 1.8865 \| - \| - \| - \| - \| - \|
	\| 0.8447 \| 4400 \| 1.8349 \| - \| - \| - \| - \| - \|
	\| 0.8831 \| 4600 \| 1.8047 \| - \| - \| - \| - \| - \|
	\| 0.9215 \| 4800 \| 1.8009 \| - \| - \| - \| - \| - \|
	\| 0.9599 \| 5000 \| 1.7962 \| - \| - \| - \| - \| - \|
	\| 0.9983 \| 5200 \| 1.7231 \| - \| - \| - \| - \| - \|
	\| 1.0367 \| 5400 \| 0.0288 \| - \| - \| - \| - \| - \|
	\| 1.0751 \| 5600 \| 0.0 \| - \| - \| - \| - \| - \|
	\| 1.1135 \| 5800 \| 0.0 \| - \| - \| - \| - \| - \|
	\| 1.1519 \| 6000 \| 0.0 \| - \| - \| - \| - \| - \|
	\| 1.1902 \| 6200 \| 0.0 \| - \| - \| - \| - \| - \|
	\| 1.0056 \| 6400 \| 0.2935 \| - \| - \| - \| - \| - \|
	\| 1.0440 \| 6600 \| 1.7571 \| - \| - \| - \| - \| - \|
	\| 1.0824 \| 6800 \| 1.6487 \| - \| - \| - \| - \| - \|
	\| 1.1208 \| 7000 \| 1.6513 \| - \| - \| - \| - \| - \|
	\| 1.1591 \| 7200 \| 1.5466 \| - \| - \| - \| - \| - \|
	\| 1.1975 \| 7400 \| 1.4583 \| - \| - \| - \| - \| - \|
	\| 1.2359 \| 7600 \| 1.3805 \| - \| - \| - \| - \| - \|
	\| 1.2743 \| 7800 \| 1.3264 \| - \| - \| - \| - \| - \|
	\| 1.3127 \| 8000 \| 1.1898 \| - \| - \| - \| - \| - \|
	\| 1.3511 \| 8200 \| 1.1961 \| - \| - \| - \| - \| - \|
	\| 1.3895 \| 8400 \| 1.1749 \| - \| - \| - \| - \| - \|
	\| 1.4279 \| 8600 \| 1.1438 \| - \| - \| - \| - \| - \|
	\| 1.4663 \| 8800 \| 1.1481 \| - \| - \| - \| - \| - \|
	\| 1.5047 \| 9000 \| 1.089 \| - \| - \| - \| - \| - \|
	\| 1.5431 \| 9200 \| 1.1063 \| - \| - \| - \| - \| - \|
	\| 1.5815 \| 9400 \| 1.0759 \| - \| - \| - \| - \| - \|
	\| 1.6199 \| 9600 \| 1.0215 \| - \| - \| - \| - \| - \|
	\| 1.6583 \| 9800 \| 1.0244 \| - \| - \| - \| - \| - \|
	\| 1.6967 \| 10000 \| 1.0546 \| - \| - \| - \| - \| - \|
	\| 1.7351 \| 10200 \| 1.0355 \| - \| - \| - \| - \| - \|
	\| 1.7735 \| 10400 \| 1.0078 \| - \| - \| - \| - \| - \|
	\| 1.8119 \| 10600 \| 1.0102 \| - \| - \| - \| - \| - \|
	\| 1.8503 \| 10800 \| 0.9899 \| - \| - \| - \| - \| - \|
	\| 1.8887 \| 11000 \| 0.971 \| - \| - \| - \| - \| - \|
	\| 1.9270 \| 11200 \| 0.9676 \| - \| - \| - \| - \| - \|
	\| 1.9654 \| 11400 \| 0.9707 \| - \| - \| - \| - \| - \|
	\| 2.0038 \| 11600 \| 0.8222 \| - \| - \| - \| - \| - \|
	\| 2.0422 \| 11800 \| 0.0 \| - \| - \| - \| - \| - \|
	\| 2.0806 \| 12000 \| 0.0 \| - \| - \| - \| - \| - \|
	\| 2.1190 \| 12200 \| 0.0 \| - \| - \| - \| - \| - \|
	\| 2.1574 \| 12400 \| 0.0 \| - \| - \| - \| - \| - \|
	\| 2.1958 \| 12600 \| 0.0 \| - \| - \| - \| - \| - \|
	\| 2.0111 \| 12800 \| 0.2783 \| - \| - \| - \| - \| - \|
	\| 2.0495 \| 13000 \| 0.8261 \| - \| - \| - \| - \| - \|
	\| 2.0879 \| 13200 \| 0.868 \| - \| - \| - \| - \| - \|
	\| 2.1263 \| 13400 \| 0.8653 \| - \| - \| - \| - \| - \|
	\| 2.1647 \| 13600 \| 0.8647 \| - \| - \| - \| - \| - \|
	\| 2.2031 \| 13800 \| 0.8085 \| - \| - \| - \| - \| - \|
	\| 2.2415 \| 14000 \| 0.8122 \| - \| - \| - \| - \| - \|
	\| 2.2799 \| 14200 \| 0.7647 \| - \| - \| - \| - \| - \|
	\| 2.3183 \| 14400 \| 0.6959 \| - \| - \| - \| - \| - \|
	\| 2.3567 \| 14600 \| 0.7228 \| - \| - \| - \| - \| - \|
	\| 2.3951 \| 14800 \| 0.7303 \| - \| - \| - \| - \| - \|
	\| 2.4335 \| 15000 \| 0.7056 \| - \| - \| - \| - \| - \|
	\| 2.4719 \| 15200 \| 0.737 \| - \| - \| - \| - \| - \|
	\| 2.5103 \| 15400 \| 0.7016 \| - \| - \| - \| - \| - \|
	\| 2.5487 \| 15600 \| 0.7183 \| - \| - \| - \| - \| - \|
	\| 2.5538 \| 15627 \| - \| 0.8129 \| 0.8138 \| 0.8138 \| 0.8092 \| 0.8140 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.43.1
	- PyTorch: 2.2.2
	- Accelerate: 0.33.0
	- Datasets: 2.19.0
	- Tokenizers: 0.19.1

	## Citation

	### BibTeX

	#### Sentence Transformers
	```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",
	}
	```

	#### MatryoshkaLoss
	```bibtex
	@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
	```bibtex
	@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}
	}
	```

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