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	---
	language:
	- en
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- loss:MatryoshkaLoss
	- loss:CoSENTLoss
	base_model: distilbert/distilbert-base-uncased
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: The gate is yellow.
	sentences:
	- The gate is blue.
	- The person is starting a fire.
	- A woman is bungee jumping.
	- source_sentence: A plane in the sky.
	sentences:
	- Two airplanes in the sky.
	- A man is standing in the rain.
	- There are two men near a wall.
	- source_sentence: A woman is reading.
	sentences:
	- A woman is writing something.
	- A woman is applying eye shadow.
	- A dog and a red ball in the air.
	- source_sentence: A baby is laughing.
	sentences:
	- The baby laughed in his car seat.
	- Suicide bomber strikes in Syria
	- Bangladesh Islamist execution upheld
	- source_sentence: A woman is dancing.
	sentences:
	- A woman is dancing in railway station.
	- The flag was moving in the air.
	- three dogs growling On one another
	pipeline_tag: sentence-similarity
	co2_eq_emissions:
	emissions: 7.871164130493101
	energy_consumed: 0.020249867843471606
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
	ram_total_size: 31.777088165283203
	hours_used: 0.112
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	model-index:
	- name: SentenceTransformer based on distilbert/distilbert-base-uncased
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 768
	type: sts-dev-768
	metrics:
	- type: pearson_cosine
	value: 0.8647737221000229
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8747521728687471
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8627734228763478
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8657556253211545
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.862712112144467
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8657615257280495
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7442745641899206
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7513830366520415
	name: Spearman Dot
	- type: pearson_max
	value: 0.8647737221000229
	name: Pearson Max
	- type: spearman_max
	value: 0.8747521728687471
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 512
	type: sts-dev-512
	metrics:
	- type: pearson_cosine
	value: 0.8628378541768764
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8741345340758229
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8619744745534216
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8651450292937584
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8622841683977804
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8653280682431165
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.746359236761633
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7540849763868891
	name: Spearman Dot
	- type: pearson_max
	value: 0.8628378541768764
	name: Pearson Max
	- type: spearman_max
	value: 0.8741345340758229
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 256
	type: sts-dev-256
	metrics:
	- type: pearson_cosine
	value: 0.8588975886507025
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8714341050301952
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8590790006287132
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8634123185807864
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8591861535833625
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8628587088112977
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7185871795192371
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7288595287151053
	name: Spearman Dot
	- type: pearson_max
	value: 0.8591861535833625
	name: Pearson Max
	- type: spearman_max
	value: 0.8714341050301952
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 128
	type: sts-dev-128
	metrics:
	- type: pearson_cosine
	value: 0.8528583626543365
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8687502864484896
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8509433708242649
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.857615159782176
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8531616082767298
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8580823134153918
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.697019210549756
	name: Pearson Dot
	- type: spearman_dot
	value: 0.705924438927243
	name: Spearman Dot
	- type: pearson_max
	value: 0.8531616082767298
	name: Pearson Max
	- type: spearman_max
	value: 0.8687502864484896
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 64
	type: sts-dev-64
	metrics:
	- type: pearson_cosine
	value: 0.8340115410608493
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.858682843519445
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8351566362279711
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8445869885309296
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.838674217877368
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8460894143343873
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6579249229659768
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6712615573330701
	name: Spearman Dot
	- type: pearson_max
	value: 0.838674217877368
	name: Pearson Max
	- type: spearman_max
	value: 0.858682843519445
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 768
	type: sts-test-768
	metrics:
	- type: pearson_cosine
	value: 0.833720870548252
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8469501140979906
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8484755252691695
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8470024066861298
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8492651445573072
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8475238481800537
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6701649984837568
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6526285131648061
	name: Spearman Dot
	- type: pearson_max
	value: 0.8492651445573072
	name: Pearson Max
	- type: spearman_max
	value: 0.8475238481800537
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 512
	type: sts-test-512
	metrics:
	- type: pearson_cosine
	value: 0.8325595554355977
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8467500241650668
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8474378528408064
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8462571021525837
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.848182316243596
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8466275072216626
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6736686039338646
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6572299516736647
	name: Spearman Dot
	- type: pearson_max
	value: 0.848182316243596
	name: Pearson Max
	- type: spearman_max
	value: 0.8467500241650668
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.8225923032714455
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8403145699624681
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8420998942805191
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8419520394692916
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8434867831513
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8428522494561291
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6230179114374444
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6061595939729718
	name: Spearman Dot
	- type: pearson_max
	value: 0.8434867831513
	name: Pearson Max
	- type: spearman_max
	value: 0.8428522494561291
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.8149976807930366
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8349547446101432
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8351661617446753
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8360899024374612
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8375785243041524
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8375574347771609
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.5958381414366161
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5793444545861678
	name: Spearman Dot
	- type: pearson_max
	value: 0.8375785243041524
	name: Pearson Max
	- type: spearman_max
	value: 0.8375574347771609
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.7981336004264228
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8269913105115189
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8238799955007295
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8289121477853545
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8278657744625194
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8314643517951371
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.5206433480609991
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5067194535547845
	name: Spearman Dot
	- type: pearson_max
	value: 0.8278657744625194
	name: Pearson Max
	- type: spearman_max
	value: 0.8314643517951371
	name: Spearman Max
	---

	# SentenceTransformer based on distilbert/distilbert-base-uncased

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) 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: [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 6cdc0aad91f5ae2e6712e91bc7b65d1cf5c05411 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb)
	- Language: en
	<!-- - 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: DistilBertModel
	(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("tomaarsen/distilbert-base-uncased-sts-matryoshka")
	# Run inference
	sentences = [
	'A woman is dancing.',
	'A woman is dancing in railway station.',
	'The flag was moving in the air.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(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/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8648 \|
	\| spearman_cosine \| 0.8748 \|
	\| pearson_manhattan \| 0.8628 \|
	\| spearman_manhattan \| 0.8658 \|
	\| pearson_euclidean \| 0.8627 \|
	\| spearman_euclidean \| 0.8658 \|
	\| pearson_dot \| 0.7443 \|
	\| spearman_dot \| 0.7514 \|
	\| pearson_max \| 0.8648 \|
	\| spearman_max \| 0.8748 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8628 \|
	\| spearman_cosine \| 0.8741 \|
	\| pearson_manhattan \| 0.862 \|
	\| spearman_manhattan \| 0.8651 \|
	\| pearson_euclidean \| 0.8623 \|
	\| spearman_euclidean \| 0.8653 \|
	\| pearson_dot \| 0.7464 \|
	\| spearman_dot \| 0.7541 \|
	\| pearson_max \| 0.8628 \|
	\| spearman_max \| 0.8741 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8589 \|
	\| spearman_cosine \| 0.8714 \|
	\| pearson_manhattan \| 0.8591 \|
	\| spearman_manhattan \| 0.8634 \|
	\| pearson_euclidean \| 0.8592 \|
	\| spearman_euclidean \| 0.8629 \|
	\| pearson_dot \| 0.7186 \|
	\| spearman_dot \| 0.7289 \|
	\| pearson_max \| 0.8592 \|
	\| spearman_max \| 0.8714 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8529 \|
	\| spearman_cosine \| 0.8688 \|
	\| pearson_manhattan \| 0.8509 \|
	\| spearman_manhattan \| 0.8576 \|
	\| pearson_euclidean \| 0.8532 \|
	\| spearman_euclidean \| 0.8581 \|
	\| pearson_dot \| 0.697 \|
	\| spearman_dot \| 0.7059 \|
	\| pearson_max \| 0.8532 \|
	\| spearman_max \| 0.8688 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.834 \|
	\| spearman_cosine \| 0.8587 \|
	\| pearson_manhattan \| 0.8352 \|
	\| spearman_manhattan \| 0.8446 \|
	\| pearson_euclidean \| 0.8387 \|
	\| spearman_euclidean \| 0.8461 \|
	\| pearson_dot \| 0.6579 \|
	\| spearman_dot \| 0.6713 \|
	\| pearson_max \| 0.8387 \|
	\| spearman_max \| 0.8587 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.8337 \|
	\| spearman_cosine \| 0.847 \|
	\| pearson_manhattan \| 0.8485 \|
	\| spearman_manhattan \| 0.847 \|
	\| pearson_euclidean \| 0.8493 \|
	\| spearman_euclidean \| 0.8475 \|
	\| pearson_dot \| 0.6702 \|
	\| spearman_dot \| 0.6526 \|
	\| pearson_max \| 0.8493 \|
	\| spearman_max \| 0.8475 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8326 \|
	\| spearman_cosine \| 0.8468 \|
	\| pearson_manhattan \| 0.8474 \|
	\| spearman_manhattan \| 0.8463 \|
	\| pearson_euclidean \| 0.8482 \|
	\| spearman_euclidean \| 0.8466 \|
	\| pearson_dot \| 0.6737 \|
	\| spearman_dot \| 0.6572 \|
	\| pearson_max \| 0.8482 \|
	\| spearman_max \| 0.8468 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8226 \|
	\| spearman_cosine \| 0.8403 \|
	\| pearson_manhattan \| 0.8421 \|
	\| spearman_manhattan \| 0.842 \|
	\| pearson_euclidean \| 0.8435 \|
	\| spearman_euclidean \| 0.8429 \|
	\| pearson_dot \| 0.623 \|
	\| spearman_dot \| 0.6062 \|
	\| pearson_max \| 0.8435 \|
	\| spearman_max \| 0.8429 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.815 \|
	\| spearman_cosine \| 0.835 \|
	\| pearson_manhattan \| 0.8352 \|
	\| spearman_manhattan \| 0.8361 \|
	\| pearson_euclidean \| 0.8376 \|
	\| spearman_euclidean \| 0.8376 \|
	\| pearson_dot \| 0.5958 \|
	\| spearman_dot \| 0.5793 \|
	\| pearson_max \| 0.8376 \|
	\| spearman_max \| 0.8376 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.7981 \|
	\| spearman_cosine \| 0.827 \|
	\| pearson_manhattan \| 0.8239 \|
	\| spearman_manhattan \| 0.8289 \|
	\| pearson_euclidean \| 0.8279 \|
	\| spearman_euclidean \| 0.8315 \|
	\| pearson_dot \| 0.5206 \|
	\| spearman_dot \| 0.5067 \|
	\| pearson_max \| 0.8279 \|
	\| spearman_max \| 0.8315 \|

	<!--
	## Bias, Risks and Limitations

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

	<!--
	### Recommendations

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

	### Training Dataset

	#### sentence-transformers/stsb

	* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
	* Size: 5,749 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: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 28 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:-----------------------------------------------------------\|:----------------------------------------------------------------------\|:------------------\|
	\| <code>A plane is taking off.</code> \| <code>An air plane is taking off.</code> \| <code>1.0</code> \|
	\| <code>A man is playing a large flute.</code> \| <code>A man is playing a flute.</code> \| <code>0.76</code> \|
	\| <code>A man is spreading shreded cheese on a pizza.</code> \| <code>A man is spreading shredded cheese on an uncooked pizza.</code> \| <code>0.76</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/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

	#### sentence-transformers/stsb

	* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
	* Size: 1,500 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: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 45 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:--------------------------------------------------\|:------------------------------------------------------\|:------------------\|
	\| <code>A man with a hard hat is dancing.</code> \| <code>A man wearing a hard hat is dancing.</code> \| <code>1.0</code> \|
	\| <code>A young child is riding a horse.</code> \| <code>A child is riding a horse.</code> \| <code>0.95</code> \|
	\| <code>A man is feeding a mouse to a snake.</code> \| <code>The man is feeding a mouse to the snake.</code> \| <code>1.0</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/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`: 4
	- `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`: False
	- `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`: 4
	- `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, '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`: None
	- `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`: 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 \|
	\|:------:\|:----:\|:-------------:\|:-------:\|:---------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|:----------------------------:\|
	\| 0.2778 \| 100 \| 23.266 \| 21.5517 \| 0.8305 \| 0.8355 \| 0.8361 \| 0.8157 \| 0.8366 \| - \| - \| - \| - \| - \|
	\| 0.5556 \| 200 \| 21.8736 \| 21.6172 \| 0.8327 \| 0.8388 \| 0.8446 \| 0.8206 \| 0.8453 \| - \| - \| - \| - \| - \|
	\| 0.8333 \| 300 \| 21.6241 \| 22.0565 \| 0.8475 \| 0.8538 \| 0.8556 \| 0.8345 \| 0.8565 \| - \| - \| - \| - \| - \|
	\| 1.1111 \| 400 \| 21.075 \| 23.6719 \| 0.8545 \| 0.8581 \| 0.8634 \| 0.8435 \| 0.8644 \| - \| - \| - \| - \| - \|
	\| 1.3889 \| 500 \| 20.4122 \| 22.5926 \| 0.8592 \| 0.8624 \| 0.8650 \| 0.8436 \| 0.8656 \| - \| - \| - \| - \| - \|
	\| 1.6667 \| 600 \| 20.6586 \| 22.5999 \| 0.8514 \| 0.8563 \| 0.8595 \| 0.8389 \| 0.8597 \| - \| - \| - \| - \| - \|
	\| 1.9444 \| 700 \| 20.3262 \| 22.2965 \| 0.8582 \| 0.8631 \| 0.8666 \| 0.8465 \| 0.8667 \| - \| - \| - \| - \| - \|
	\| 2.2222 \| 800 \| 19.7948 \| 23.1844 \| 0.8621 \| 0.8659 \| 0.8688 \| 0.8499 \| 0.8694 \| - \| - \| - \| - \| - \|
	\| 2.5 \| 900 \| 19.2826 \| 23.1351 \| 0.8653 \| 0.8687 \| 0.8703 \| 0.8547 \| 0.8710 \| - \| - \| - \| - \| - \|
	\| 2.7778 \| 1000 \| 19.1063 \| 23.7141 \| 0.8641 \| 0.8672 \| 0.8691 \| 0.8531 \| 0.8695 \| - \| - \| - \| - \| - \|
	\| 3.0556 \| 1100 \| 19.4575 \| 23.0055 \| 0.8673 \| 0.8702 \| 0.8726 \| 0.8574 \| 0.8728 \| - \| - \| - \| - \| - \|
	\| 3.3333 \| 1200 \| 18.0727 \| 24.9288 \| 0.8659 \| 0.8692 \| 0.8715 \| 0.8565 \| 0.8722 \| - \| - \| - \| - \| - \|
	\| 3.6111 \| 1300 \| 18.1698 \| 25.3114 \| 0.8675 \| 0.8701 \| 0.8728 \| 0.8576 \| 0.8734 \| - \| - \| - \| - \| - \|
	\| 3.8889 \| 1400 \| 18.2321 \| 25.3777 \| 0.8688 \| 0.8714 \| 0.8741 \| 0.8587 \| 0.8748 \| - \| - \| - \| - \| - \|
	\| 4.0 \| 1440 \| - \| - \| - \| - \| - \| - \| - \| 0.8350 \| 0.8403 \| 0.8468 \| 0.8270 \| 0.8470 \|


	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 0.020 kWh
	- Carbon Emitted: 0.008 kg of CO2
	- Hours Used: 0.112 hours

	### Training Hardware
	- On Cloud: No
	- GPU Model: 1 x NVIDIA GeForce RTX 3090
	- CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
	- RAM Size: 31.78 GB

	### Framework Versions
	- Python: 3.11.6
	- Sentence Transformers: 3.0.0.dev0
	- Transformers: 4.41.0.dev0
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.26.1
	- Datasets: 2.18.0
	- 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}
	}
	```

	#### 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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