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	---
	inference: false
	language:
	- ar
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:557850
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	- mteb
	base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
	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-all-nli-triplet-Matryoshka
	results:
	- dataset:
	config: default
	name: MTEB BIOSSES (default)
	revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
	split: test
	type: mteb/biosses-sts
	metrics:
	- type: cosine_pearson
	value: 81.20578037912223
	- type: cosine_spearman
	value: 77.43670420687278
	- type: euclidean_pearson
	value: 74.60444698819703
	- type: euclidean_spearman
	value: 72.25767053642666
	- type: main_score
	value: 77.43670420687278
	- type: manhattan_pearson
	value: 73.86951335383257
	- type: manhattan_spearman
	value: 71.41608509527123
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB SICK-R (default)
	revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
	split: test
	type: mteb/sickr-sts
	metrics:
	- type: cosine_pearson
	value: 83.11155556919923
	- type: cosine_spearman
	value: 79.39435627520159
	- type: euclidean_pearson
	value: 81.05225024180342
	- type: euclidean_spearman
	value: 79.09926890001618
	- type: main_score
	value: 79.39435627520159
	- type: manhattan_pearson
	value: 80.74351302609706
	- type: manhattan_spearman
	value: 78.826254748334
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB STS12 (default)
	revision: a0d554a64d88156834ff5ae9920b964011b16384
	split: test
	type: mteb/sts12-sts
	metrics:
	- type: cosine_pearson
	value: 85.10074960888633
	- type: cosine_spearman
	value: 78.93043293576132
	- type: euclidean_pearson
	value: 84.1168219787408
	- type: euclidean_spearman
	value: 78.44739559202252
	- type: main_score
	value: 78.93043293576132
	- type: manhattan_pearson
	value: 83.79447841594396
	- type: manhattan_spearman
	value: 77.94028171700384
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB STS13 (default)
	revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
	split: test
	type: mteb/sts13-sts
	metrics:
	- type: cosine_pearson
	value: 81.34459901517775
	- type: cosine_spearman
	value: 82.73032633919925
	- type: euclidean_pearson
	value: 82.83546499367434
	- type: euclidean_spearman
	value: 83.29701673615389
	- type: main_score
	value: 82.73032633919925
	- type: manhattan_pearson
	value: 82.63480502797324
	- type: manhattan_spearman
	value: 83.05016589615636
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB STS14 (default)
	revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
	split: test
	type: mteb/sts14-sts
	metrics:
	- type: cosine_pearson
	value: 82.53179983763488
	- type: cosine_spearman
	value: 81.64974497557361
	- type: euclidean_pearson
	value: 83.03981070806898
	- type: euclidean_spearman
	value: 82.65556168300631
	- type: main_score
	value: 81.64974497557361
	- type: manhattan_pearson
	value: 82.83722360191446
	- type: manhattan_spearman
	value: 82.4164264119
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB STS15 (default)
	revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
	split: test
	type: mteb/sts15-sts
	metrics:
	- type: cosine_pearson
	value: 86.5684162475647
	- type: cosine_spearman
	value: 87.62163215009723
	- type: euclidean_pearson
	value: 87.3068288651339
	- type: euclidean_spearman
	value: 88.03508640722863
	- type: main_score
	value: 87.62163215009723
	- type: manhattan_pearson
	value: 87.21818681800193
	- type: manhattan_spearman
	value: 87.94690511382603
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB STS16 (default)
	revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
	split: test
	type: mteb/sts16-sts
	metrics:
	- type: cosine_pearson
	value: 81.70518105237446
	- type: cosine_spearman
	value: 83.66083698795428
	- type: euclidean_pearson
	value: 82.80400684544435
	- type: euclidean_spearman
	value: 83.39926895275799
	- type: main_score
	value: 83.66083698795428
	- type: manhattan_pearson
	value: 82.44430538731845
	- type: manhattan_spearman
	value: 82.99600783826028
	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: 82.23229967696153
	- type: cosine_spearman
	value: 82.40039006538706
	- type: euclidean_pearson
	value: 79.21322872573518
	- type: euclidean_spearman
	value: 79.14230529579783
	- type: main_score
	value: 82.40039006538706
	- type: manhattan_pearson
	value: 79.1476348987964
	- type: manhattan_spearman
	value: 78.82381660638143
	task:
	type: STS
	- dataset:
	config: ar
	name: MTEB STS22 (ar)
	revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
	split: test
	type: mteb/sts22-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 45.95767124518871
	- type: cosine_spearman
	value: 51.37922888872568
	- type: euclidean_pearson
	value: 45.519471121310126
	- type: euclidean_spearman
	value: 51.45605803385654
	- type: main_score
	value: 51.37922888872568
	- type: manhattan_pearson
	value: 45.98761117909666
	- type: manhattan_spearman
	value: 51.48451973989366
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB STSBenchmark (default)
	revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
	split: test
	type: mteb/stsbenchmark-sts
	metrics:
	- type: cosine_pearson
	value: 85.38916827757183
	- type: cosine_spearman
	value: 86.16303183485594
	- type: euclidean_pearson
	value: 85.16406897245115
	- type: euclidean_spearman
	value: 85.40364087457081
	- type: main_score
	value: 86.16303183485594
	- type: manhattan_pearson
	value: 84.96853193915084
	- type: manhattan_spearman
	value: 85.13238442843544
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB SummEval (default)
	revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
	split: test
	type: mteb/summeval
	metrics:
	- type: cosine_pearson
	value: 30.077426987171158
	- type: cosine_spearman
	value: 30.163682020271608
	- type: dot_pearson
	value: 27.31125295906803
	- type: dot_spearman
	value: 29.138235153208193
	- type: main_score
	value: 30.163682020271608
	- type: pearson
	value: 30.077426987171158
	- type: spearman
	value: 30.163682020271608
	task:
	type: Summarization
	- name: >-
	SentenceTransformer based on
	sentence-transformers/paraphrase-multilingual-mpnet-base-v2
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 768
	type: sts-test-768
	metrics:
	- type: pearson_cosine
	value: 0.8538831619509135
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.861625750018802
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8496745674597512
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8513333417508545
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8516261261374778
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8540549341060195
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7281308266536204
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7230282720855726
	name: Spearman Dot
	- type: pearson_max
	value: 0.8538831619509135
	name: Pearson Max
	- type: spearman_max
	value: 0.861625750018802
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 512
	type: sts-test-512
	metrics:
	- type: pearson_cosine
	value: 0.8542379189261009
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8609329396560859
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8486657899695456
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8512120732504748
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8505249483849495
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8538738365440234
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7075618032859148
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7028728329509918
	name: Spearman Dot
	- type: pearson_max
	value: 0.8542379189261009
	name: Pearson Max
	- type: spearman_max
	value: 0.8609329396560859
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.8486308733045101
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8578681811996274
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8404506123980291
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.845565163232125
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8414758099131773
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8471566121478254
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6668664182302968
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6651222481800894
	name: Spearman Dot
	- type: pearson_max
	value: 0.8486308733045101
	name: Pearson Max
	- type: spearman_max
	value: 0.8578681811996274
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.8389761445410956
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8499312736457453
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8287388421834582
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8353046807483782
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8297699263897746
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8371843253238523
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.5855876200722326
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5834920267418124
	name: Spearman Dot
	- type: pearson_max
	value: 0.8389761445410956
	name: Pearson Max
	- type: spearman_max
	value: 0.8499312736457453
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.8290685425698586
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8429054799136109
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8100968316314205
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8221121550434057
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8129044863346081
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8255133471709527
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.5067257944655903
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5109761436588146
	name: Spearman Dot
	- type: pearson_max
	value: 0.8290685425698586
	name: Pearson Max
	- type: spearman_max
	value: 0.8429054799136109
	name: Spearman Max
	license: apache-2.0
	---

	# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) 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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 79f2382ceacceacdf38563d7c5d16b9ff8d725d6 -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- Omartificial-Intelligence-Space/arabic-n_li-triplet
	<!-- - 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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
	(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/Arabic-Nli-Matryoshka")
	# 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]
	```

	<!--
	### 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 \| 0.8539 \|
	\| spearman_cosine \| 0.8616 \|
	\| pearson_manhattan \| 0.8497 \|
	\| spearman_manhattan \| 0.8513 \|
	\| pearson_euclidean \| 0.8516 \|
	\| spearman_euclidean \| 0.8541 \|
	\| pearson_dot \| 0.7281 \|
	\| spearman_dot \| 0.723 \|
	\| pearson_max \| 0.8539 \|
	\| spearman_max \| 0.8616 \|

	#### 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 \| 0.8542 \|
	\| spearman_cosine \| 0.8609 \|
	\| pearson_manhattan \| 0.8487 \|
	\| spearman_manhattan \| 0.8512 \|
	\| pearson_euclidean \| 0.8505 \|
	\| spearman_euclidean \| 0.8539 \|
	\| pearson_dot \| 0.7076 \|
	\| spearman_dot \| 0.7029 \|
	\| pearson_max \| 0.8542 \|
	\| spearman_max \| 0.8609 \|

	#### 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 \| 0.8486 \|
	\| spearman_cosine \| 0.8579 \|
	\| pearson_manhattan \| 0.8405 \|
	\| spearman_manhattan \| 0.8456 \|
	\| pearson_euclidean \| 0.8415 \|
	\| spearman_euclidean \| 0.8472 \|
	\| pearson_dot \| 0.6669 \|
	\| spearman_dot \| 0.6651 \|
	\| pearson_max \| 0.8486 \|
	\| spearman_max \| 0.8579 \|

	#### 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 \| 0.839 \|
	\| spearman_cosine \| 0.8499 \|
	\| pearson_manhattan \| 0.8287 \|
	\| spearman_manhattan \| 0.8353 \|
	\| pearson_euclidean \| 0.8298 \|
	\| spearman_euclidean \| 0.8372 \|
	\| pearson_dot \| 0.5856 \|
	\| spearman_dot \| 0.5835 \|
	\| pearson_max \| 0.839 \|
	\| spearman_max \| 0.8499 \|

	#### 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 \| 0.8291 \|
	\| spearman_cosine \| 0.8429 \|
	\| pearson_manhattan \| 0.8101 \|
	\| spearman_manhattan \| 0.8221 \|
	\| pearson_euclidean \| 0.8129 \|
	\| spearman_euclidean \| 0.8255 \|
	\| pearson_dot \| 0.5067 \|
	\| spearman_dot \| 0.511 \|
	\| pearson_max \| 0.8291 \|
	\| spearman_max \| 0.8429 \|

	<!--
	## 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.
	-->

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	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## 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: <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: 5 tokens</li><li>mean: 10.33 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 13.21 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 15.32 tokens</li><li>max: 53 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:------------------------------------------------------------\|:--------------------------------------------\|:------------------------------------\|
	\| <code>شخص على حصان يقفز فوق طائرة معطلة</code> \| <code>شخص في الهواء الطلق، على حصان.</code> \| <code>شخص في مطعم، يطلب عجة.</code> \|
	\| <code>أطفال يبتسمون و يلوحون للكاميرا</code> \| <code>هناك أطفال حاضرون</code> \| <code>الاطفال يتجهمون</code> \|
	\| <code>صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.</code> \| <code>الفتى يقوم بخدعة التزلج</code> \| <code>الصبي يتزلج على الرصيف</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: 5 tokens</li><li>mean: 21.86 tokens</li><li>max: 105 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 10.22 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 11.2 tokens</li><li>max: 33 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`: 256
	- `per_device_eval_batch_size`: 256
	- `num_train_epochs`: 1
	- `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
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 256
	- `per_device_eval_batch_size`: 256
	- `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

	</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 \|
	\|:------:\|:----:\|:-------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|:----------------------------:\|
	\| 0.2294 \| 500 \| 10.1279 \| - \| - \| - \| - \| - \|
	\| 0.4587 \| 1000 \| 8.0384 \| - \| - \| - \| - \| - \|
	\| 0.6881 \| 1500 \| 7.3484 \| - \| - \| - \| - \| - \|
	\| 0.9174 \| 2000 \| 4.2216 \| - \| - \| - \| - \| - \|
	\| 1.0 \| 2180 \| - \| 0.8499 \| 0.8579 \| 0.8609 \| 0.8429 \| 0.8616 \|


	### 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
	```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}
	}
	```

	## <span style="color:blue">Acknowledgments</span>

	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.


	```markdown
	## 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},
	}