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	---
	inference: false
	language:
	- ar
	library_name: sentence-transformers
	tags:
	- mteb
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:557850
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: sentence-transformers/LaBSE
	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-labse-Matryoshka
	results:
	- dataset:
	config: default
	name: MTEB BIOSSES (default)
	revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
	split: test
	type: mteb/biosses-sts
	metrics:
	- type: cosine_pearson
	value: 76.46793440999714
	- type: cosine_spearman
	value: 76.66439745271298
	- type: euclidean_pearson
	value: 76.52075972347127
	- type: euclidean_spearman
	value: 76.66439745271298
	- type: main_score
	value: 76.66439745271298
	- type: manhattan_pearson
	value: 76.68001857069733
	- type: manhattan_spearman
	value: 76.73066402288269
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB SICK-R (default)
	revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
	split: test
	type: mteb/sickr-sts
	metrics:
	- type: cosine_pearson
	value: 79.67657890693198
	- type: cosine_spearman
	value: 77.03286420274621
	- type: euclidean_pearson
	value: 78.1960735272073
	- type: euclidean_spearman
	value: 77.032855497919
	- type: main_score
	value: 77.03286420274621
	- type: manhattan_pearson
	value: 78.25627275994229
	- type: manhattan_spearman
	value: 77.00430810589081
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB STS12 (default)
	revision: a0d554a64d88156834ff5ae9920b964011b16384
	split: test
	type: mteb/sts12-sts
	metrics:
	- type: cosine_pearson
	value: 83.94288954523996
	- type: cosine_spearman
	value: 79.21432176112556
	- type: euclidean_pearson
	value: 81.21333251943913
	- type: euclidean_spearman
	value: 79.2152067330468
	- type: main_score
	value: 79.21432176112556
	- type: manhattan_pearson
	value: 81.16910737482634
	- type: manhattan_spearman
	value: 79.08756466301445
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB STS13 (default)
	revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
	split: test
	type: mteb/sts13-sts
	metrics:
	- type: cosine_pearson
	value: 77.48393909963059
	- type: cosine_spearman
	value: 79.54963868861196
	- type: euclidean_pearson
	value: 79.28416002197451
	- type: euclidean_spearman
	value: 79.54963861790114
	- type: main_score
	value: 79.54963868861196
	- type: manhattan_pearson
	value: 79.18653917582513
	- type: manhattan_spearman
	value: 79.46713533414295
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB STS14 (default)
	revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
	split: test
	type: mteb/sts14-sts
	metrics:
	- type: cosine_pearson
	value: 78.51596313692846
	- type: cosine_spearman
	value: 78.84601702652395
	- type: euclidean_pearson
	value: 78.55199809961427
	- type: euclidean_spearman
	value: 78.84603362286225
	- type: main_score
	value: 78.84601702652395
	- type: manhattan_pearson
	value: 78.52780170677605
	- type: manhattan_spearman
	value: 78.77744294039178
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB STS15 (default)
	revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
	split: test
	type: mteb/sts15-sts
	metrics:
	- type: cosine_pearson
	value: 84.53393478889929
	- type: cosine_spearman
	value: 85.60821849381648
	- type: euclidean_pearson
	value: 85.32813923250558
	- type: euclidean_spearman
	value: 85.6081835456016
	- type: main_score
	value: 85.60821849381648
	- type: manhattan_pearson
	value: 85.32782097916476
	- type: manhattan_spearman
	value: 85.58098670898562
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB STS16 (default)
	revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
	split: test
	type: mteb/sts16-sts
	metrics:
	- type: cosine_pearson
	value: 77.00196998325856
	- type: cosine_spearman
	value: 79.930951699069
	- type: euclidean_pearson
	value: 79.43196738390897
	- type: euclidean_spearman
	value: 79.93095112410258
	- type: main_score
	value: 79.930951699069
	- type: manhattan_pearson
	value: 79.33744358111427
	- type: manhattan_spearman
	value: 79.82939266539601
	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: 81.60289529424327
	- type: cosine_spearman
	value: 82.46806381979653
	- type: euclidean_pearson
	value: 81.32235058296072
	- type: euclidean_spearman
	value: 82.46676890643914
	- type: main_score
	value: 82.46806381979653
	- type: manhattan_pearson
	value: 81.43885277175312
	- type: manhattan_spearman
	value: 82.38955952718666
	task:
	type: STS
	- dataset:
	config: ar
	name: MTEB STS22 (ar)
	revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
	split: test
	type: mteb/sts22-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 49.58293768761314
	- type: cosine_spearman
	value: 57.261888789832874
	- type: euclidean_pearson
	value: 53.36549109538782
	- type: euclidean_spearman
	value: 57.261888789832874
	- type: main_score
	value: 57.261888789832874
	- type: manhattan_pearson
	value: 53.06640323833928
	- type: manhattan_spearman
	value: 57.05837935512948
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB STSBenchmark (default)
	revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
	split: test
	type: mteb/stsbenchmark-sts
	metrics:
	- type: cosine_pearson
	value: 81.43997935928729
	- type: cosine_spearman
	value: 82.04996129795596
	- type: euclidean_pearson
	value: 82.01917866996972
	- type: euclidean_spearman
	value: 82.04996129795596
	- type: main_score
	value: 82.04996129795596
	- type: manhattan_pearson
	value: 82.03487112040936
	- type: manhattan_spearman
	value: 82.03774605775651
	task:
	type: STS
	- dataset:
	config: default
	name: MTEB SummEval (default)
	revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
	split: test
	type: mteb/summeval
	metrics:
	- type: cosine_pearson
	value: 32.113475997147674
	- type: cosine_spearman
	value: 32.17194233764879
	- type: dot_pearson
	value: 32.113469728827255
	- type: dot_spearman
	value: 32.174771315355386
	- type: main_score
	value: 32.17194233764879
	- type: pearson
	value: 32.113475997147674
	- type: spearman
	value: 32.17194233764879
	task:
	type: Summarization
	- name: SentenceTransformer based on sentence-transformers/LaBSE
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 768
	type: sts-test-768
	metrics:
	- type: pearson_cosine
	value: 0.7269177710249681
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7225258779395222
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7259261785622463
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7210463582530393
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7259567884235211
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.722525823788783
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7269177712136122
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7225258771129475
	name: Spearman Dot
	- type: pearson_max
	value: 0.7269177712136122
	name: Pearson Max
	- type: spearman_max
	value: 0.7225258779395222
	name: Spearman Max
	- type: pearson_cosine
	value: 0.8143867576376295
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8205044914629483
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8203365887013151
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8203816698535976
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8201809453496319
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8205044914629483
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.8143867541070537
	name: Pearson Dot
	- type: spearman_dot
	value: 0.8205044914629483
	name: Spearman Dot
	- type: pearson_max
	value: 0.8203365887013151
	name: Pearson Max
	- type: spearman_max
	value: 0.8205044914629483
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 512
	type: sts-test-512
	metrics:
	- type: pearson_cosine
	value: 0.7268389724271859
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7224359411000278
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7241418669615103
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7195408311833029
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7248184919191593
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7212936866178097
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7252522928016701
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7205040482865328
	name: Spearman Dot
	- type: pearson_max
	value: 0.7268389724271859
	name: Pearson Max
	- type: spearman_max
	value: 0.7224359411000278
	name: Spearman Max
	- type: pearson_cosine
	value: 0.8143448965624136
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8211700903453509
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8217448619823571
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8216016599665544
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8216413349390971
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.82188122418776
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.8097020064483653
	name: Pearson Dot
	- type: spearman_dot
	value: 0.8147306090545295
	name: Spearman Dot
	- type: pearson_max
	value: 0.8217448619823571
	name: Pearson Max
	- type: spearman_max
	value: 0.82188122418776
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.7283468617741852
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7264294106954872
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7227711798003426
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.718067982079232
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7251492361775083
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7215068115809131
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7243396991648858
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7221390873398206
	name: Spearman Dot
	- type: pearson_max
	value: 0.7283468617741852
	name: Pearson Max
	- type: spearman_max
	value: 0.7264294106954872
	name: Spearman Max
	- type: pearson_cosine
	value: 0.8075613785257986
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8159258089804861
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8208711370091426
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8196747601014518
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8210210137439432
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8203004500356083
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7870611647231145
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7874848213991118
	name: Spearman Dot
	- type: pearson_max
	value: 0.8210210137439432
	name: Pearson Max
	- type: spearman_max
	value: 0.8203004500356083
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.7102082520621849
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7103917869311991
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7134729607181519
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.708895102058259
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7171545288118942
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7130380237150746
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6777774738547628
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6746474823963989
	name: Spearman Dot
	- type: pearson_max
	value: 0.7171545288118942
	name: Pearson Max
	- type: spearman_max
	value: 0.7130380237150746
	name: Spearman Max
	- type: pearson_cosine
	value: 0.8024378358145556
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8117561815472325
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.818920309459774
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8180515365910205
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8198346073356603
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8185162896024369
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7513270537478935
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7427542871546953
	name: Spearman Dot
	- type: pearson_max
	value: 0.8198346073356603
	name: Pearson Max
	- type: spearman_max
	value: 0.8185162896024369
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.6930745722517785
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6982194042238953
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6971382079778946
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6942362764367931
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7012627015062325
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6986972295835788
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6376735798940838
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6344835722310429
	name: Spearman Dot
	- type: pearson_max
	value: 0.7012627015062325
	name: Pearson Max
	- type: spearman_max
	value: 0.6986972295835788
	name: Spearman Max
	- type: pearson_cosine
	value: 0.7855080652087961
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7948979371698327
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8060407473462375
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8041199691999044
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8088262858195556
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8060483394849104
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.677754045289596
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6616232873061395
	name: Spearman Dot
	- type: pearson_max
	value: 0.8088262858195556
	name: Pearson Max
	- type: spearman_max
	value: 0.8060483394849104
	name: Spearman Max
	license: apache-2.0
	---

	# SentenceTransformer based on sentence-transformers/LaBSE

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) 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/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision e34fab64a3011d2176c99545a93d5cbddc9a91b7 -->
	- Maximum Sequence Length: 256 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': 256, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
	(3): Normalize()
	)
	```

	## 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-labse")
	# 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>
	-->

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	### Out-of-Scope Use

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	## 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 \| 0.7269 \|
	\| spearman_cosine \| 0.7225 \|
	\| pearson_manhattan \| 0.7259 \|
	\| spearman_manhattan \| 0.721 \|
	\| pearson_euclidean \| 0.726 \|
	\| spearman_euclidean \| 0.7225 \|
	\| pearson_dot \| 0.7269 \|
	\| spearman_dot \| 0.7225 \|
	\| pearson_max \| 0.7269 \|
	\| spearman_max \| 0.7225 \|

	#### 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 \| 0.7268 \|
	\| spearman_cosine \| 0.7224 \|
	\| pearson_manhattan \| 0.7241 \|
	\| spearman_manhattan \| 0.7195 \|
	\| pearson_euclidean \| 0.7248 \|
	\| spearman_euclidean \| 0.7213 \|
	\| pearson_dot \| 0.7253 \|
	\| spearman_dot \| 0.7205 \|
	\| pearson_max \| 0.7268 \|
	\| spearman_max \| 0.7224 \|

	#### 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 \| 0.7283 \|
	\| spearman_cosine \| 0.7264 \|
	\| pearson_manhattan \| 0.7228 \|
	\| spearman_manhattan \| 0.7181 \|
	\| pearson_euclidean \| 0.7251 \|
	\| spearman_euclidean \| 0.7215 \|
	\| pearson_dot \| 0.7243 \|
	\| spearman_dot \| 0.7221 \|
	\| pearson_max \| 0.7283 \|
	\| spearman_max \| 0.7264 \|

	#### 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 \| 0.7102 \|
	\| spearman_cosine \| 0.7104 \|
	\| pearson_manhattan \| 0.7135 \|
	\| spearman_manhattan \| 0.7089 \|
	\| pearson_euclidean \| 0.7172 \|
	\| spearman_euclidean \| 0.713 \|
	\| pearson_dot \| 0.6778 \|
	\| spearman_dot \| 0.6746 \|
	\| pearson_max \| 0.7172 \|
	\| spearman_max \| 0.713 \|

	#### 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 \| 0.6931 \|
	\| spearman_cosine \| 0.6982 \|
	\| pearson_manhattan \| 0.6971 \|
	\| spearman_manhattan \| 0.6942 \|
	\| pearson_euclidean \| 0.7013 \|
	\| spearman_euclidean \| 0.6987 \|
	\| pearson_dot \| 0.6377 \|
	\| spearman_dot \| 0.6345 \|
	\| pearson_max \| 0.7013 \|
	\| spearman_max \| 0.6987 \|

	#### 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 \| 0.8144 \|
	\| spearman_cosine \| 0.8205 \|
	\| pearson_manhattan \| 0.8203 \|
	\| spearman_manhattan \| 0.8204 \|
	\| pearson_euclidean \| 0.8202 \|
	\| spearman_euclidean \| 0.8205 \|
	\| pearson_dot \| 0.8144 \|
	\| spearman_dot \| 0.8205 \|
	\| pearson_max \| 0.8203 \|
	\| spearman_max \| 0.8205 \|

	#### 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 \| 0.8143 \|
	\| spearman_cosine \| 0.8212 \|
	\| pearson_manhattan \| 0.8217 \|
	\| spearman_manhattan \| 0.8216 \|
	\| pearson_euclidean \| 0.8216 \|
	\| spearman_euclidean \| 0.8219 \|
	\| pearson_dot \| 0.8097 \|
	\| spearman_dot \| 0.8147 \|
	\| pearson_max \| 0.8217 \|
	\| spearman_max \| 0.8219 \|

	#### 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 \| 0.8076 \|
	\| spearman_cosine \| 0.8159 \|
	\| pearson_manhattan \| 0.8209 \|
	\| spearman_manhattan \| 0.8197 \|
	\| pearson_euclidean \| 0.821 \|
	\| spearman_euclidean \| 0.8203 \|
	\| pearson_dot \| 0.7871 \|
	\| spearman_dot \| 0.7875 \|
	\| pearson_max \| 0.821 \|
	\| spearman_max \| 0.8203 \|

	#### 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 \| 0.8024 \|
	\| spearman_cosine \| 0.8118 \|
	\| pearson_manhattan \| 0.8189 \|
	\| spearman_manhattan \| 0.8181 \|
	\| pearson_euclidean \| 0.8198 \|
	\| spearman_euclidean \| 0.8185 \|
	\| pearson_dot \| 0.7513 \|
	\| spearman_dot \| 0.7428 \|
	\| pearson_max \| 0.8198 \|
	\| spearman_max \| 0.8185 \|

	#### 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 \| 0.7855 \|
	\| spearman_cosine \| 0.7949 \|
	\| pearson_manhattan \| 0.806 \|
	\| spearman_manhattan \| 0.8041 \|
	\| pearson_euclidean \| 0.8088 \|
	\| spearman_euclidean \| 0.806 \|
	\| pearson_dot \| 0.6778 \|
	\| spearman_dot \| 0.6616 \|
	\| pearson_max \| 0.8088 \|
	\| spearman_max \| 0.806 \|

	<!--
	## 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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	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
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	## 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: 4 tokens</li><li>mean: 9.99 tokens</li><li>max: 51 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 12.44 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 13.82 tokens</li><li>max: 49 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: 4 tokens</li><li>mean: 19.71 tokens</li><li>max: 100 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 9.37 tokens</li><li>max: 38 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 10.49 tokens</li><li>max: 34 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
	- `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`: 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
	- `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 \|
	\|:------:\|:----:\|:-------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|:----------------------------:\|
	\| None \| 0 \| - \| 0.7104 \| 0.7264 \| 0.7224 \| 0.6982 \| 0.7225 \|
	\| 0.0229 \| 200 \| 13.1738 \| - \| - \| - \| - \| - \|
	\| 0.0459 \| 400 \| 8.8127 \| - \| - \| - \| - \| - \|
	\| 0.0688 \| 600 \| 8.0984 \| - \| - \| - \| - \| - \|
	\| 0.0918 \| 800 \| 7.2984 \| - \| - \| - \| - \| - \|
	\| 0.1147 \| 1000 \| 7.5749 \| - \| - \| - \| - \| - \|
	\| 0.1377 \| 1200 \| 7.1292 \| - \| - \| - \| - \| - \|
	\| 0.1606 \| 1400 \| 6.6146 \| - \| - \| - \| - \| - \|
	\| 0.1835 \| 1600 \| 6.6523 \| - \| - \| - \| - \| - \|
	\| 0.2065 \| 1800 \| 6.1095 \| - \| - \| - \| - \| - \|
	\| 0.2294 \| 2000 \| 6.0841 \| - \| - \| - \| - \| - \|
	\| 0.2524 \| 2200 \| 6.3024 \| - \| - \| - \| - \| - \|
	\| 0.2753 \| 2400 \| 6.1941 \| - \| - \| - \| - \| - \|
	\| 0.2983 \| 2600 \| 6.1686 \| - \| - \| - \| - \| - \|
	\| 0.3212 \| 2800 \| 5.8317 \| - \| - \| - \| - \| - \|
	\| 0.3442 \| 3000 \| 6.0597 \| - \| - \| - \| - \| - \|
	\| 0.3671 \| 3200 \| 5.7832 \| - \| - \| - \| - \| - \|
	\| 0.3900 \| 3400 \| 5.7088 \| - \| - \| - \| - \| - \|
	\| 0.4130 \| 3600 \| 5.6988 \| - \| - \| - \| - \| - \|
	\| 0.4359 \| 3800 \| 5.5268 \| - \| - \| - \| - \| - \|
	\| 0.4589 \| 4000 \| 5.5543 \| - \| - \| - \| - \| - \|
	\| 0.4818 \| 4200 \| 5.3152 \| - \| - \| - \| - \| - \|
	\| 0.5048 \| 4400 \| 5.2894 \| - \| - \| - \| - \| - \|
	\| 0.5277 \| 4600 \| 5.1805 \| - \| - \| - \| - \| - \|
	\| 0.5506 \| 4800 \| 5.4559 \| - \| - \| - \| - \| - \|
	\| 0.5736 \| 5000 \| 5.3836 \| - \| - \| - \| - \| - \|
	\| 0.5965 \| 5200 \| 5.2626 \| - \| - \| - \| - \| - \|
	\| 0.6195 \| 5400 \| 5.2511 \| - \| - \| - \| - \| - \|
	\| 0.6424 \| 5600 \| 5.3308 \| - \| - \| - \| - \| - \|
	\| 0.6654 \| 5800 \| 5.2264 \| - \| - \| - \| - \| - \|
	\| 0.6883 \| 6000 \| 5.2881 \| - \| - \| - \| - \| - \|
	\| 0.7113 \| 6200 \| 5.1349 \| - \| - \| - \| - \| - \|
	\| 0.7342 \| 6400 \| 5.0872 \| - \| - \| - \| - \| - \|
	\| 0.7571 \| 6600 \| 4.5515 \| - \| - \| - \| - \| - \|
	\| 0.7801 \| 6800 \| 3.4312 \| - \| - \| - \| - \| - \|
	\| 0.8030 \| 7000 \| 3.1008 \| - \| - \| - \| - \| - \|
	\| 0.8260 \| 7200 \| 2.9582 \| - \| - \| - \| - \| - \|
	\| 0.8489 \| 7400 \| 2.8153 \| - \| - \| - \| - \| - \|
	\| 0.8719 \| 7600 \| 2.7214 \| - \| - \| - \| - \| - \|
	\| 0.8948 \| 7800 \| 2.5392 \| - \| - \| - \| - \| - \|
	\| 0.9177 \| 8000 \| 2.584 \| - \| - \| - \| - \| - \|
	\| 0.9407 \| 8200 \| 2.5384 \| - \| - \| - \| - \| - \|
	\| 0.9636 \| 8400 \| 2.4937 \| - \| - \| - \| - \| - \|
	\| 0.9866 \| 8600 \| 2.4155 \| - \| - \| - \| - \| - \|
	\| 1.0 \| 8717 \| - \| 0.8118 \| 0.8159 \| 0.8212 \| 0.7949 \| 0.8205 \|


	### 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}
	}
	```


	```markdown
	## Citation

	If you use the Arabic Matryoshka Embeddings Model, please cite it as follows:

	```bibtex
	@software{nacar2024,
	author = {Omer Nacar},
	title = {Arabic Matryoshka Embeddings Model - Arabic Labse Matryoshka},
	year = 2024,
	url = {https://huggingface.co/Omartificial-Intelligence-Space/Arabic-labse-Matryoshka},
	version = {1.0.0},
	}