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	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dataset_size:1K<n<10K
	- loss:MatryoshkaLoss
	- loss:CoSENTLoss
	base_model: intfloat/multilingual-e5-large
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: El hombre captura una pelota
	sentences:
	- Un hombre lanza una pelota en el aire.
	- Un hombre se encuentra tocando una flauta de madera.
	- La mujer está maquillándose usando sombra de ojos.
	- source_sentence: Un hombre está buscando algo.
	sentences:
	- En un mercado de granjeros, se encuentra un hombre.
	- Se acerca a la pista un avión suizo de color blanco.
	- dos chicas jóvenes se abrazan en la hierba.
	- source_sentence: El avión está tocando tierra.
	sentences:
	- El avión animado se encuentra en proceso de aterrizaje.
	- La capital de Siria fue golpeada por dos explosiones
	- Violentos incidentes afectan a estudiantes chinos en Francia
	- source_sentence: Un hombre saltando la cuerda.
	sentences:
	- Un hombre está saltando la cuerda.
	- Una mujer entrena a su perro para saltar en el aire.
	- Los gatitos están comiendo de los platos.
	- source_sentence: tres perros gruñendo entre sí
	sentences:
	- Dos perros se aproximan uno al otro en el pasto.
	- Una mujer sonriente brinda cariño a un pequeño bebé.
	- Una mujer está montando a caballo en el campo.
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on intfloat/multilingual-e5-large
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 768
	type: sts-dev-768
	metrics:
	- type: pearson_cosine
	value: 0.8279951103268512
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8342643795984531
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8228439538329566
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.834870903153992
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8231076969394738
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8349270059177344
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.8196281042113861
	name: Pearson Dot
	- type: spearman_dot
	value: 0.8248683461954115
	name: Spearman Dot
	- type: pearson_max
	value: 0.8279951103268512
	name: Pearson Max
	- type: spearman_max
	value: 0.8349270059177344
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 512
	type: sts-dev-512
	metrics:
	- type: pearson_cosine
	value: 0.8236357426336446
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8332692872015282
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8217552769156274
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8331746060276878
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8217859136681092
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8334069456110773
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.8101789790612713
	name: Pearson Dot
	- type: spearman_dot
	value: 0.8179205607773823
	name: Spearman Dot
	- type: pearson_max
	value: 0.8236357426336446
	name: Pearson Max
	- type: spearman_max
	value: 0.8334069456110773
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 256
	type: sts-dev-256
	metrics:
	- type: pearson_cosine
	value: 0.816222860848086
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8303708513421737
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8178715987143794
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8301047046554985
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8183826652089494
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8301804247624904
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7878741921967743
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7904844114269662
	name: Spearman Dot
	- type: pearson_max
	value: 0.8183826652089494
	name: Pearson Max
	- type: spearman_max
	value: 0.8303708513421737
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 128
	type: sts-dev-128
	metrics:
	- type: pearson_cosine
	value: 0.794202606017138
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8198385906414491
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8088714046889546
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8222921243120748
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8092312345267045
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8220266161646009
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7341586721030032
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7351749794310246
	name: Spearman Dot
	- type: pearson_max
	value: 0.8092312345267045
	name: Pearson Max
	- type: spearman_max
	value: 0.8222921243120748
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 64
	type: sts-dev-64
	metrics:
	- type: pearson_cosine
	value: 0.7727295051414095
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8076629783565549
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7976419723073269
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8147883308842346
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7979124462870892
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8123832197697319
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6725844492342726
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6673162832940408
	name: Spearman Dot
	- type: pearson_max
	value: 0.7979124462870892
	name: Pearson Max
	- type: spearman_max
	value: 0.8147883308842346
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 768
	type: sts-test-768
	metrics:
	- type: pearson_cosine
	value: 0.8630482725201897
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8813284718659181
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8770818288812614
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8810971983428288
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8770132070253477
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8812162173545179
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.8581811981775829
	name: Pearson Dot
	- type: spearman_dot
	value: 0.8707402246720045
	name: Spearman Dot
	- type: pearson_max
	value: 0.8770818288812614
	name: Pearson Max
	- type: spearman_max
	value: 0.8813284718659181
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 512
	type: sts-test-512
	metrics:
	- type: pearson_cosine
	value: 0.8589909139210625
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8799604919891442
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8744468387217347
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8791142262015441
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8747974723064821
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8795698184784307
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.8464185524060444
	name: Pearson Dot
	- type: spearman_dot
	value: 0.8549652098582826
	name: Spearman Dot
	- type: pearson_max
	value: 0.8747974723064821
	name: Pearson Max
	- type: spearman_max
	value: 0.8799604919891442
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.8528262537030415
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8762917275750132
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8715060008387856
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8780718380107112
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.87251419758469
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8788770265821976
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.801980870958869
	name: Pearson Dot
	- type: spearman_dot
	value: 0.8007112694661982
	name: Spearman Dot
	- type: pearson_max
	value: 0.87251419758469
	name: Pearson Max
	- type: spearman_max
	value: 0.8788770265821976
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.8392066286150661
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8692426944903685
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8631603748425567
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8715673768304316
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8643871758114816
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8724091426441261
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7461565194503229
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7403017354497338
	name: Spearman Dot
	- type: pearson_max
	value: 0.8643871758114816
	name: Pearson Max
	- type: spearman_max
	value: 0.8724091426441261
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.8213671607347727
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8621003145087452
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8530869243121955
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8631973638935834
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.854140567169475
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8632627342101252
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6853599968011839
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6726454086764928
	name: Spearman Dot
	- type: pearson_max
	value: 0.854140567169475
	name: Pearson Max
	- type: spearman_max
	value: 0.8632627342101252
	name: Spearman Max
	---

	# SentenceTransformer based on intfloat/multilingual-e5-large

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) on the clibrain/stsb_multi_es_aug_gpt3.5-turbo_2 dataset. It maps sentences & paragraphs to a 1024-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: [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 1024 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- stsb_multi_es_aug
	<!-- - 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: XLMRobertaModel
	(1): Pooling({'word_embedding_dimension': 1024, '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})
	(2): 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("mrm8488/multilingual-e5-large-ft-sts-spanish-matryoshka-768-64-5e")
	# Run inference
	sentences = [
	'tres perros gruñendo entre sí',
	'Dos perros se aproximan uno al otro en el pasto.',
	'Una mujer sonriente brinda cariño a un pequeño bebé.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 1024]

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.828 \|
	\| spearman_cosine \| 0.8343 \|
	\| pearson_manhattan \| 0.8228 \|
	\| spearman_manhattan \| 0.8349 \|
	\| pearson_euclidean \| 0.8231 \|
	\| spearman_euclidean \| 0.8349 \|
	\| pearson_dot \| 0.8196 \|
	\| spearman_dot \| 0.8249 \|
	\| pearson_max \| 0.828 \|
	\| spearman_max \| 0.8349 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8236 \|
	\| spearman_cosine \| 0.8333 \|
	\| pearson_manhattan \| 0.8218 \|
	\| spearman_manhattan \| 0.8332 \|
	\| pearson_euclidean \| 0.8218 \|
	\| spearman_euclidean \| 0.8334 \|
	\| pearson_dot \| 0.8102 \|
	\| spearman_dot \| 0.8179 \|
	\| pearson_max \| 0.8236 \|
	\| spearman_max \| 0.8334 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8162 \|
	\| spearman_cosine \| 0.8304 \|
	\| pearson_manhattan \| 0.8179 \|
	\| spearman_manhattan \| 0.8301 \|
	\| pearson_euclidean \| 0.8184 \|
	\| spearman_euclidean \| 0.8302 \|
	\| pearson_dot \| 0.7879 \|
	\| spearman_dot \| 0.7905 \|
	\| pearson_max \| 0.8184 \|
	\| spearman_max \| 0.8304 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7942 \|
	\| spearman_cosine \| 0.8198 \|
	\| pearson_manhattan \| 0.8089 \|
	\| spearman_manhattan \| 0.8223 \|
	\| pearson_euclidean \| 0.8092 \|
	\| spearman_euclidean \| 0.822 \|
	\| pearson_dot \| 0.7342 \|
	\| spearman_dot \| 0.7352 \|
	\| pearson_max \| 0.8092 \|
	\| spearman_max \| 0.8223 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7727 \|
	\| spearman_cosine \| 0.8077 \|
	\| pearson_manhattan \| 0.7976 \|
	\| spearman_manhattan \| 0.8148 \|
	\| pearson_euclidean \| 0.7979 \|
	\| spearman_euclidean \| 0.8124 \|
	\| pearson_dot \| 0.6726 \|
	\| spearman_dot \| 0.6673 \|
	\| pearson_max \| 0.7979 \|
	\| spearman_max \| 0.8148 \|

	#### 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 \| 0.863 \|
	\| spearman_cosine \| 0.8813 \|
	\| pearson_manhattan \| 0.8771 \|
	\| spearman_manhattan \| 0.8811 \|
	\| pearson_euclidean \| 0.877 \|
	\| spearman_euclidean \| 0.8812 \|
	\| pearson_dot \| 0.8582 \|
	\| spearman_dot \| 0.8707 \|
	\| pearson_max \| 0.8771 \|
	\| spearman_max \| 0.8813 \|

	#### 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 \| 0.859 \|
	\| spearman_cosine \| 0.88 \|
	\| pearson_manhattan \| 0.8744 \|
	\| spearman_manhattan \| 0.8791 \|
	\| pearson_euclidean \| 0.8748 \|
	\| spearman_euclidean \| 0.8796 \|
	\| pearson_dot \| 0.8464 \|
	\| spearman_dot \| 0.855 \|
	\| pearson_max \| 0.8748 \|
	\| spearman_max \| 0.88 \|

	#### 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 \| 0.8528 \|
	\| spearman_cosine \| 0.8763 \|
	\| pearson_manhattan \| 0.8715 \|
	\| spearman_manhattan \| 0.8781 \|
	\| pearson_euclidean \| 0.8725 \|
	\| spearman_euclidean \| 0.8789 \|
	\| pearson_dot \| 0.802 \|
	\| spearman_dot \| 0.8007 \|
	\| pearson_max \| 0.8725 \|
	\| spearman_max \| 0.8789 \|

	#### 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 \| 0.8392 \|
	\| spearman_cosine \| 0.8692 \|
	\| pearson_manhattan \| 0.8632 \|
	\| spearman_manhattan \| 0.8716 \|
	\| pearson_euclidean \| 0.8644 \|
	\| spearman_euclidean \| 0.8724 \|
	\| pearson_dot \| 0.7462 \|
	\| spearman_dot \| 0.7403 \|
	\| pearson_max \| 0.8644 \|
	\| spearman_max \| 0.8724 \|

	#### 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 \| 0.8214 \|
	\| spearman_cosine \| 0.8621 \|
	\| pearson_manhattan \| 0.8531 \|
	\| spearman_manhattan \| 0.8632 \|
	\| pearson_euclidean \| 0.8541 \|
	\| spearman_euclidean \| 0.8633 \|
	\| pearson_dot \| 0.6854 \|
	\| spearman_dot \| 0.6726 \|
	\| pearson_max \| 0.8541 \|
	\| spearman_max \| 0.8633 \|

	<!--
	## Bias, Risks and Limitations

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	### Recommendations

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	## Training Details

	### Training Dataset

	#### stsb_multi_es_aug

	* Dataset: stsb_multi_es_aug
	* Size: 2,697 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 8 tokens</li><li>mean: 22.25 tokens</li><li>max: 68 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 22.01 tokens</li><li>max: 79 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 2.67</li><li>max: 5.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------\|:-------------------------------\|
	\| <code>El pájaro de tamaño reducido se posó con delicadeza en una rama cubierta de escarcha.</code> \| <code>Un ave de color amarillo descansaba tranquilamente en una rama.</code> \| <code>3.200000047683716</code> \|
	\| <code>Una chica está tocando la flauta en un parque.</code> \| <code>Un grupo de músicos está tocando en un escenario al aire libre.</code> \| <code>1.286</code> \|
	\| <code>La aclamada escritora británica, Doris Lessing, galardonada con el premio Nobel, fallece</code> \| <code>La destacada autora británica, Doris Lessing, reconocida con el prestigioso Premio Nobel, muere</code> \| <code>4.199999809265137</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "CoSENTLoss",
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	### Evaluation Dataset

	#### stsb_multi_es_aug

	* Dataset: stsb_multi_es_aug
	* Size: 697 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:--------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 8 tokens</li><li>mean: 22.76 tokens</li><li>max: 67 tokens</li></ul> \| <ul><li>min: 7 tokens</li><li>mean: 22.26 tokens</li><li>max: 63 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 2.3</li><li>max: 5.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------\|
	\| <code>Un incendio ocurrido en un hospital psiquiátrico ruso resultó en la trágica muerte de 38 personas.</code> \| <code>Se teme que el incendio en un hospital psiquiátrico ruso cause la pérdida de la vida de 38 individuos.</code> \| <code>4.199999809265137</code> \|
	\| <code>"Street dijo que el otro individuo a veces se siente avergonzado de su fiesta, lo cual provoca risas en la multitud"</code> \| <code>"A veces, el otro tipo se encuentra avergonzado de su fiesta y no se le puede culpar."</code> \| <code>3.5</code> \|
	\| <code>El veterano diplomático de Malasia tuvo un encuentro con Suu Kyi el miércoles en la casa del lago en Yangon donde permanece bajo arresto domiciliario.</code> \| <code>Razali Ismail tuvo una reunión de 90 minutos con Suu Kyi, quien ganó el Premio Nobel de la Paz en 1991, en su casa del lago donde está recluida.</code> \| <code>3.691999912261963</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "CoSENTLoss",
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 5
	- `warmup_ratio`: 0.1
	- `fp16`: True

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `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`: 5
	- `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
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| loss \| sts-dev-128_spearman_cosine \| sts-dev-256_spearman_cosine \| sts-dev-512_spearman_cosine \| sts-dev-64_spearman_cosine \| sts-dev-768_spearman_cosine \| 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 \|
	\|:------:\|:----:\|:-------------:\|:-------:\|:---------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|:----------------------------:\|
	\| 0.5917 \| 100 \| 21.7032 \| 21.7030 \| 0.8030 \| 0.8124 \| 0.8205 \| 0.7839 \| 0.8215 \| - \| - \| - \| - \| - \|
	\| 1.1834 \| 200 \| 21.4019 \| 24.0898 \| 0.7839 \| 0.7972 \| 0.8038 \| 0.7680 \| 0.8062 \| - \| - \| - \| - \| - \|
	\| 1.7751 \| 300 \| 21.2168 \| 22.5421 \| 0.7909 \| 0.8027 \| 0.8058 \| 0.7786 \| 0.8068 \| - \| - \| - \| - \| - \|
	\| 2.3669 \| 400 \| 20.7049 \| 23.6522 \| 0.7938 \| 0.8049 \| 0.8108 \| 0.7873 \| 0.8123 \| - \| - \| - \| - \| - \|
	\| 2.9586 \| 500 \| 20.5077 \| 23.6100 \| 0.8017 \| 0.8116 \| 0.8155 \| 0.7893 \| 0.8185 \| - \| - \| - \| - \| - \|
	\| 3.5503 \| 600 \| 19.2725 \| 24.7539 \| 0.8133 \| 0.8254 \| 0.8291 \| 0.8032 \| 0.8314 \| - \| - \| - \| - \| - \|
	\| 4.1420 \| 700 \| 19.0841 \| 26.5286 \| 0.8210 \| 0.8298 \| 0.8333 \| 0.8102 \| 0.8333 \| - \| - \| - \| - \| - \|
	\| 4.7337 \| 800 \| 18.6847 \| 26.8158 \| 0.8198 \| 0.8304 \| 0.8333 \| 0.8077 \| 0.8343 \| - \| - \| - \| - \| - \|
	\| 5.0 \| 845 \| - \| - \| - \| - \| - \| - \| - \| 0.8692 \| 0.8763 \| 0.8800 \| 0.8621 \| 0.8813 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.0
	- Transformers: 4.41.1
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.30.1
	- Datasets: 2.19.1
	- 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}
	}
	```

	#### CoSENTLoss
	```bibtex
	@online{kexuefm-8847,
	title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
	author={Su Jianlin},
	year={2022},
	month={Jan},
	url={https://kexue.fm/archives/8847},
	}
	```

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