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	---
	base_model: aubmindlab/bert-base-arabertv02
	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
	- loss:CosineSimilarityLoss
	- mteb
	model-index:
	- name: silma-ai/silma-embeddding-matryoshka-v0.1
	results:
	- dataset:
	config: ar
	name: MTEB MassiveIntentClassification (ar)
	revision: 4672e20407010da34463acc759c162ca9734bca6
	split: test
	type: mteb/amazon_massive_intent
	metrics:
	- type: accuracy
	value: 56.445864156018835
	- type: f1
	value: 53.58282538318122
	- type: f1_weighted
	value: 56.821808211639315
	- type: main_score
	value: 56.445864156018835
	task:
	type: Classification
	- dataset:
	config: en
	name: MTEB MassiveIntentClassification (en)
	revision: 4672e20407010da34463acc759c162ca9734bca6
	split: test
	type: mteb/amazon_massive_intent
	metrics:
	- type: accuracy
	value: 47.40080699394754
	- type: f1
	value: 44.729286773524755
	- type: f1_weighted
	value: 47.83506683571795
	- type: main_score
	value: 47.40080699394754
	task:
	type: Classification
	- dataset:
	config: ar
	name: MTEB MassiveIntentClassification (ar)
	revision: 4672e20407010da34463acc759c162ca9734bca6
	split: validation
	type: mteb/amazon_massive_intent
	metrics:
	- type: accuracy
	value: 56.97983275946876
	- type: f1
	value: 53.809263807080086
	- type: f1_weighted
	value: 57.14993215193604
	- type: main_score
	value: 56.97983275946876
	task:
	type: Classification
	- dataset:
	config: en
	name: MTEB MassiveIntentClassification (en)
	revision: 4672e20407010da34463acc759c162ca9734bca6
	split: validation
	type: mteb/amazon_massive_intent
	metrics:
	- type: accuracy
	value: 47.683226758485006
	- type: f1
	value: 44.905317333393775
	- type: f1_weighted
	value: 48.051379514830195
	- type: main_score
	value: 47.683226758485006
	task:
	type: Classification
	- dataset:
	config: ar
	name: MTEB MassiveScenarioClassification (ar)
	revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
	split: test
	type: mteb/amazon_massive_scenario
	metrics:
	- type: accuracy
	value: 63.31876260928042
	- type: f1
	value: 63.197056314678754
	- type: f1_weighted
	value: 62.7166315473092
	- type: main_score
	value: 63.31876260928042
	task:
	type: Classification
	- dataset:
	config: en
	name: MTEB MassiveScenarioClassification (en)
	revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
	split: test
	type: mteb/amazon_massive_scenario
	metrics:
	- type: accuracy
	value: 53.35574983187627
	- type: f1
	value: 50.35837223252574
	- type: f1_weighted
	value: 54.11644042208904
	- type: main_score
	value: 53.35574983187627
	task:
	type: Classification
	- dataset:
	config: ar
	name: MTEB MassiveScenarioClassification (ar)
	revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
	split: validation
	type: mteb/amazon_massive_scenario
	metrics:
	- type: accuracy
	value: 62.26758484997541
	- type: f1
	value: 62.477928166560325
	- type: f1_weighted
	value: 61.92238394647396
	- type: main_score
	value: 62.26758484997541
	task:
	type: Classification
	- dataset:
	config: en
	name: MTEB MassiveScenarioClassification (en)
	revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
	split: validation
	type: mteb/amazon_massive_scenario
	metrics:
	- type: accuracy
	value: 52.62174126906049
	- type: f1
	value: 50.470501485026716
	- type: f1_weighted
	value: 53.16459392827557
	- type: main_score
	value: 52.62174126906049
	task:
	type: Classification
	- dataset:
	config: en-en
	name: MTEB STS17 (en-en)
	revision: faeb762787bd10488a50c8b5be4a3b82e411949c
	split: test
	type: mteb/sts17-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 74.33941506827517
	- type: cosine_spearman
	value: 74.42197838273297
	- type: euclidean_pearson
	value: 75.33836191339782
	- type: euclidean_spearman
	value: 74.37385193453852
	- type: main_score
	value: 74.42197838273297
	- type: manhattan_pearson
	value: 75.41881517194568
	- type: manhattan_spearman
	value: 74.47237277057877
	- type: pearson
	value: 74.33941645999855
	- type: spearman
	value: 74.42197838273297
	task:
	type: STS
	- dataset:
	config: nl-en
	name: MTEB STS17 (nl-en)
	revision: faeb762787bd10488a50c8b5be4a3b82e411949c
	split: test
	type: mteb/sts17-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 31.84872826199112
	- type: cosine_spearman
	value: 32.22496230755917
	- type: euclidean_pearson
	value: 21.830860533929688
	- type: euclidean_spearman
	value: 21.38205815348658
	- type: main_score
	value: 32.22496230755917
	- type: manhattan_pearson
	value: 21.852430479395576
	- type: manhattan_spearman
	value: 21.37848326556159
	- type: pearson
	value: 31.84872485436001
	- type: spearman
	value: 32.22496230755917
	task:
	type: STS
	- dataset:
	config: en-ar
	name: MTEB STS17 (en-ar)
	revision: faeb762787bd10488a50c8b5be4a3b82e411949c
	split: test
	type: mteb/sts17-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 43.37529327788584
	- type: cosine_spearman
	value: 42.763149514327225
	- type: euclidean_pearson
	value: 39.625411905897394
	- type: euclidean_spearman
	value: 39.26727199746294
	- type: main_score
	value: 42.763149514327225
	- type: manhattan_pearson
	value: 40.49857681486655
	- type: manhattan_spearman
	value: 40.63669314166475
	- type: pearson
	value: 43.37529078998193
	- type: spearman
	value: 42.763149514327225
	task:
	type: STS
	- dataset:
	config: en-tr
	name: MTEB STS17 (en-tr)
	revision: faeb762787bd10488a50c8b5be4a3b82e411949c
	split: test
	type: mteb/sts17-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 17.16722415938186
	- type: cosine_spearman
	value: 15.590330355526344
	- type: euclidean_pearson
	value: 4.430499555984906
	- type: euclidean_spearman
	value: 2.729050802084264
	- type: main_score
	value: 15.590330355526344
	- type: manhattan_pearson
	value: 2.805408490135879
	- type: manhattan_spearman
	value: 1.5237347692119627
	- type: pearson
	value: 17.167228709176676
	- type: spearman
	value: 15.590330355526344
	task:
	type: STS
	- dataset:
	config: fr-en
	name: MTEB STS17 (fr-en)
	revision: faeb762787bd10488a50c8b5be4a3b82e411949c
	split: test
	type: mteb/sts17-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 36.093945717347395
	- type: cosine_spearman
	value: 37.33997345407934
	- type: euclidean_pearson
	value: 23.156103022485055
	- type: euclidean_spearman
	value: 20.62925594786342
	- type: main_score
	value: 37.33997345407934
	- type: manhattan_pearson
	value: 22.035024322719813
	- type: manhattan_spearman
	value: 19.147522562438795
	- type: pearson
	value: 36.09395175426761
	- type: spearman
	value: 37.33997345407934
	task:
	type: STS
	- dataset:
	config: en-de
	name: MTEB STS17 (en-de)
	revision: faeb762787bd10488a50c8b5be4a3b82e411949c
	split: test
	type: mteb/sts17-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 29.064411455563
	- type: cosine_spearman
	value: 29.232781114344697
	- type: euclidean_pearson
	value: 16.90458086330736
	- type: euclidean_spearman
	value: 17.462020565289887
	- type: main_score
	value: 29.232781114344697
	- type: manhattan_pearson
	value: 16.882446230243286
	- type: manhattan_spearman
	value: 17.06144091941576
	- type: pearson
	value: 29.06441922605839
	- type: spearman
	value: 29.232781114344697
	task:
	type: STS
	- dataset:
	config: es-en
	name: MTEB STS17 (es-en)
	revision: faeb762787bd10488a50c8b5be4a3b82e411949c
	split: test
	type: mteb/sts17-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 27.686316587339473
	- type: cosine_spearman
	value: 28.650995973102205
	- type: euclidean_pearson
	value: 12.954885279630565
	- type: euclidean_spearman
	value: 11.970815927480198
	- type: main_score
	value: 28.650995973102205
	- type: manhattan_pearson
	value: 12.079730127474948
	- type: manhattan_spearman
	value: 10.606967901984147
	- type: pearson
	value: 27.68631836666537
	- type: spearman
	value: 28.650995973102205
	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: 84.12612492708037
	- type: cosine_spearman
	value: 84.24703763883515
	- type: euclidean_pearson
	value: 81.38085140113648
	- type: euclidean_spearman
	value: 83.17403450502965
	- type: main_score
	value: 84.24703763883515
	- type: manhattan_pearson
	value: 81.18466522597414
	- type: manhattan_spearman
	value: 82.61184409962614
	- type: pearson
	value: 84.12612546419625
	- type: spearman
	value: 84.25077492152536
	task:
	type: STS
	- dataset:
	config: it-en
	name: MTEB STS17 (it-en)
	revision: faeb762787bd10488a50c8b5be4a3b82e411949c
	split: test
	type: mteb/sts17-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 27.697680546701868
	- type: cosine_spearman
	value: 25.19277336255784
	- type: euclidean_pearson
	value: 13.964798090314115
	- type: euclidean_spearman
	value: 10.512169361528596
	- type: main_score
	value: 25.19277336255784
	- type: manhattan_pearson
	value: 13.537525485694433
	- type: manhattan_spearman
	value: 10.334001560105834
	- type: pearson
	value: 27.697681880242325
	- type: spearman
	value: 25.19277336255784
	task:
	type: STS
	- dataset:
	config: de-en
	name: MTEB STS22.v2 (de-en)
	revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
	split: test
	type: mteb/sts22-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 32.87548760760924
	- type: cosine_spearman
	value: 30.69782036694315
	- type: euclidean_pearson
	value: 29.925045225262142
	- type: euclidean_spearman
	value: 34.076021250318334
	- type: main_score
	value: 30.69782036694315
	- type: manhattan_pearson
	value: 30.815090565180945
	- type: manhattan_spearman
	value: 34.91615861045259
	- type: pearson
	value: 32.8754813614174
	- type: spearman
	value: 30.69782036694315
	task:
	type: STS
	- dataset:
	config: zh-en
	name: MTEB STS22.v2 (zh-en)
	revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
	split: test
	type: mteb/sts22-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 23.93269292232737
	- type: cosine_spearman
	value: 16.781461291066496
	- type: euclidean_pearson
	value: 20.87679825681155
	- type: euclidean_spearman
	value: 13.764510796592536
	- type: main_score
	value: 16.781461291066496
	- type: manhattan_pearson
	value: 23.416430850444588
	- type: manhattan_spearman
	value: 17.10405713909058
	- type: pearson
	value: 23.932682034899777
	- type: spearman
	value: 16.781461291066496
	task:
	type: STS
	- dataset:
	config: ar
	name: MTEB STS22.v2 (ar)
	revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
	split: test
	type: mteb/sts22-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 51.73784691362425
	- type: cosine_spearman
	value: 60.01035490847343
	- type: euclidean_pearson
	value: 52.717195602630305
	- type: euclidean_spearman
	value: 60.22164097529916
	- type: main_score
	value: 60.01035490847343
	- type: manhattan_pearson
	value: 53.04979941729716
	- type: manhattan_spearman
	value: 60.393100473647706
	- type: pearson
	value: 51.73784381247053
	- type: spearman
	value: 60.020906672817276
	task:
	type: STS
	- dataset:
	config: es-en
	name: MTEB STS22.v2 (es-en)
	revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
	split: test
	type: mteb/sts22-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 47.917244237624864
	- type: cosine_spearman
	value: 53.23173373821509
	- type: euclidean_pearson
	value: 48.172861539004636
	- type: euclidean_spearman
	value: 53.32970069145014
	- type: main_score
	value: 53.23173373821509
	- type: manhattan_pearson
	value: 48.163716825216646
	- type: manhattan_spearman
	value: 53.77963871495307
	- type: pearson
	value: 47.91724405724847
	- type: spearman
	value: 53.23173373821509
	task:
	type: STS
	- dataset:
	config: pl-en
	name: MTEB STS22.v2 (pl-en)
	revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
	split: test
	type: mteb/sts22-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 43.66748993183993
	- type: cosine_spearman
	value: 38.518248671828594
	- type: euclidean_pearson
	value: 50.475058499541134
	- type: euclidean_spearman
	value: 44.76070858743843
	- type: main_score
	value: 38.518248671828594
	- type: manhattan_pearson
	value: 50.576185727010014
	- type: manhattan_spearman
	value: 45.5306304403841
	- type: pearson
	value: 43.66750472144702
	- type: spearman
	value: 38.518248671828594
	task:
	type: STS
	- dataset:
	config: en
	name: MTEB STS22.v2 (en)
	revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
	split: test
	type: mteb/sts22-crosslingual-sts
	metrics:
	- type: cosine_pearson
	value: 56.41373213565263
	- type: cosine_spearman
	value: 59.03774516602592
	- type: euclidean_pearson
	value: 54.173092638047294
	- type: euclidean_spearman
	value: 59.130444355085885
	- type: main_score
	value: 59.03774516602592
	- type: manhattan_pearson
	value: 54.18950361517434
	- type: manhattan_spearman
	value: 58.78927227383971
	- type: pearson
	value: 56.413733329868045
	- type: spearman
	value: 59.03774516602592
	task:
	type: STS
	license: apache-2.0
	language:
	- ar
	- en
	---

	# SILMA Arabic Matryoshka Embedding Model 0.1

	The SILMA Arabic Matryoshka Embedding Model 0.1 is an advanced Arabic text embedding model designed to produce powerful, contextually rich representations of text,
	facilitating a wide range of applications, from semantic search to document classification.

	This model leverages the innovative Matryoshka Embedding technique which can be used in different dimensions to optimize the speed, storage, and accuracy trade-offs.

	## Usage

	### Direct Usage (Sentence Transformers)

	First, install the Sentence Transformers library:

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

	Then load the model

	```python
	from sentence_transformers import SentenceTransformer
	from sentence_transformers.util import cos_sim
	import pandas as pd

	model_name = "silma-ai/silma-embeddding-matryoshka-0.1"
	model = SentenceTransformer(model_name)
	```

	### Samples

	Using Matryoshka, you can specify the first `(n)` dimensions to represent each text.

	In the following samples, you can check how each dimension affects the `cosine similarity` between a query and the two inputs.

	You can notice the in most cases, even too low dimension (i.e. 8) can produce acceptable semantic similarity scores.

	#### [+] Short Sentence Similarity

	```python
	query = "الطقس اليوم مشمس"
	sentence_1 = "الجو اليوم كان مشمسًا ورائعًا"
	sentence_2 = "الطقس اليوم غائم"

	scores = []
	for dim in [768, 256, 48, 16, 8]:

	query_embedding = model.encode(query)[:dim]

	sent1_score = cos_sim(query_embedding, model.encode(sentence_1)[:dim])[0][0].tolist()
	sent2_score = cos_sim(query_embedding, model.encode(sentence_2)[:dim])[0][0].tolist()

	scores.append({
	"dim": dim,
	"valid_top": sent1_score > sent2_score,
	"sent1_score": sent1_score,
	"sent2_score": sent2_score,
	})

	scores_df = pd.DataFrame(scores)
	print(scores_df.to_markdown(index=False))

	# \| dim \| valid_top \| sent1_score \| sent2_score \|
	# \|------:\|:------------\|--------------:\|--------------:\|
	# \| 768 \| True \| 0.479942 \| 0.233572 \|
	# \| 256 \| True \| 0.509289 \| 0.208452 \|
	# \| 48 \| True \| 0.598825 \| 0.191677 \|
	# \| 16 \| True \| 0.917707 \| 0.458854 \|
	# \| 8 \| True \| 0.948563 \| 0.675662 \|

	```

	#### [+] Long Sentence Similarity

	```python
	query = "الكتاب يتحدث عن أهمية الذكاء الاصطناعي في تطوير المجتمعات الحديثة"
	sentence_1 = "في هذا الكتاب، يناقش الكاتب كيف يمكن للتكنولوجيا أن تغير العالم"
	sentence_2 = "الكاتب يتحدث عن أساليب الطبخ التقليدية في دول البحر الأبيض المتوسط"

	scores = []
	for dim in [768, 256, 48, 16, 8]:

	query_embedding = model.encode(query)[:dim]

	sent1_score = cos_sim(query_embedding, model.encode(sentence_1)[:dim])[0][0].tolist()
	sent2_score = cos_sim(query_embedding, model.encode(sentence_2)[:dim])[0][0].tolist()

	scores.append({
	"dim": dim,
	"valid_top": sent1_score > sent2_score,
	"sent1_score": sent1_score,
	"sent2_score": sent2_score,
	})

	scores_df = pd.DataFrame(scores)
	print(scores_df.to_markdown(index=False))

	# \| dim \| valid_top \| sent1_score \| sent2_score \|
	# \|------:\|:------------\|--------------:\|--------------:\|
	# \| 768 \| True \| 0.637418 \| 0.262693 \|
	# \| 256 \| True \| 0.614761 \| 0.268267 \|
	# \| 48 \| True \| 0.758887 \| 0.384649 \|
	# \| 16 \| True \| 0.885737 \| 0.204213 \|
	# \| 8 \| True \| 0.918684 \| 0.146478 \|
	```

	#### [+] Question to Paragraph Matching

	```python
	query = "ما هي فوائد ممارسة الرياضة؟"
	sentence_1 = "ممارسة الرياضة بشكل منتظم تساعد على تحسين الصحة العامة واللياقة البدنية"
	sentence_2 = "تعليم الأطفال في سن مبكرة يساعدهم على تطوير المهارات العقلية بسرعة"

	scores = []
	for dim in [768, 256, 48, 16, 8]:

	query_embedding = model.encode(query)[:dim]

	sent1_score = cos_sim(query_embedding, model.encode(sentence_1)[:dim])[0][0].tolist()
	sent2_score = cos_sim(query_embedding, model.encode(sentence_2)[:dim])[0][0].tolist()

	scores.append({
	"dim": dim,
	"valid_top": sent1_score > sent2_score,
	"sent1_score": sent1_score,
	"sent2_score": sent2_score,
	})

	scores_df = pd.DataFrame(scores)
	print(scores_df.to_markdown(index=False))

	# \| dim \| valid_top \| sent1_score \| sent2_score \|
	# \|------:\|:------------\|--------------:\|--------------:\|
	# \| 768 \| True \| 0.520329 \| 0.00295128 \|
	# \| 256 \| True \| 0.556088 \| -0.017764 \|
	# \| 48 \| True \| 0.586194 \| -0.110691 \|
	# \| 16 \| True \| 0.606462 \| -0.331682 \|
	# \| 8 \| True \| 0.689649 \| -0.359202 \|
	```

	#### [+] Message to Intent-Name Mapping

	```python
	query = "أرغب في حجز تذكرة طيران من دبي الى القاهرة يوم الثلاثاء القادم"
	sentence_1 = "حجز رحلة"
	sentence_2 = "إلغاء حجز"

	scores = []
	for dim in [768, 256, 48, 16, 8]:

	query_embedding = model.encode(query)[:dim]

	sent1_score = cos_sim(query_embedding, model.encode(sentence_1)[:dim])[0][0].tolist()
	sent2_score = cos_sim(query_embedding, model.encode(sentence_2)[:dim])[0][0].tolist()

	scores.append({
	"dim": dim,
	"valid_top": sent1_score > sent2_score,
	"sent1_score": sent1_score,
	"sent2_score": sent2_score,
	})

	scores_df = pd.DataFrame(scores)
	print(scores_df.to_markdown(index=False))

	# \| dim \| valid_top \| sent1_score \| sent2_score \|
	# \|------:\|:------------\|--------------:\|--------------:\|
	# \| 768 \| True \| 0.476535 \| 0.221451 \|
	# \| 256 \| True \| 0.392701 \| 0.224967 \|
	# \| 48 \| True \| 0.316223 \| 0.0210683 \|
	# \| 16 \| False \| -0.0242871 \| 0.0250766 \|
	# \| 8 \| True \| -0.215241 \| -0.258904 \|
	```

	## Training Details

	We curated a dataset [silma-ai/silma-arabic-triplets-dataset-v1.0](https://huggingface.co/datasets/silma-ai/silma-arabic-triplets-dataset-v1.0) which
	contains more than `2.25M` records of (anchor, positive and negative) Arabic/English samples.
	Only the first `600` samples were taken to be the `eval` dataset, while the rest were used for fine-tuning.

	This produced a finetuned `Matryoshka` model based on [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) with the following hyperparameters:

	- `per_device_train_batch_size`: 250
	- `per_device_eval_batch_size`: 10
	- `learning_rate`: 1e-05
	- `num_train_epochs`: 3
	- `bf16`: True
	- `dataloader_drop_last`: True
	- `optim`: adamw_torch_fused
	- `batch_sampler`: no_duplicates

	[training script](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/matryoshka/matryoshka_sts.py)

	### Framework Versions
	- Python: 3.10.14
	- Sentence Transformers: 3.2.0
	- Transformers: 4.45.2
	- PyTorch: 2.3.1
	- Accelerate: 1.0.1
	- Datasets: 3.0.1
	- Tokenizers: 0.20.1

	### 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})
	)
	```

	### Citation:

	#### BibTeX:

	```bibtex
	@misc{silma2024embedding,
	author = {Abu Bakr Soliman, Karim Ouda, SILMA AI},
	title = {SILMA Embedding Matryoshka 0.1},
	year = {2024},
	publisher = {Hugging Face},
	howpublished = {\url{https://huggingface.co/silma-ai/silma-embeddding-matryoshka-0.1}},
	}
	```

	#### APA:

	```apa
	Abu Bakr Soliman, Karim Ouda, SILMA AI. (2024). SILMA Embedding Matryoshka STS 0.1 [Model]. Hugging Face. https://huggingface.co/silma-ai/silma-embeddding-matryoshka-0.1
	```

	#### 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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