Add new SentenceTransformer model.

cca29bf verified 5 months ago

35.6 kB

	---
	language:
	- en
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dataset_size:10K<n<100K
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: l3cube-pune/indic-sentence-similarity-sbert
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: Excuse me.
	sentences:
	- um pardon me
	- A man is opening mail.
	- The girls are indoors.
	- source_sentence: Double pig.
	sentences:
	- Ah, triple pig!
	- a girl poses for camera
	- Girls dance together.
	- source_sentence: People pose.
	sentences:
	- People are smiling.
	- I know a few old ones.
	- The boy fell off his bike.
	- source_sentence: A man sings.
	sentences:
	- People singing
	- A man is playing golf.
	- The women are eating bread.
	- source_sentence: Then he ran.
	sentences:
	- He then started to run.
	- A man plays the flute.
	- A couple sit on the couch
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on l3cube-pune/indic-sentence-similarity-sbert
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 768
	type: sts-dev-768
	metrics:
	- type: pearson_cosine
	value: 0.8608857207512975
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8662860178080238
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.858692209351004
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8612472945208892
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.858472048314985
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8611276457994067
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.8258747949887901
	name: Pearson Dot
	- type: spearman_dot
	value: 0.8259736371824636
	name: Spearman Dot
	- type: pearson_max
	value: 0.8608857207512975
	name: Pearson Max
	- type: spearman_max
	value: 0.8662860178080238
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 512
	type: sts-dev-512
	metrics:
	- type: pearson_cosine
	value: 0.8594405198312016
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8648571300070264
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8574291650964095
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8598780673781499
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8574540367546871
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8600722932569861
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.822340474813523
	name: Pearson Dot
	- type: spearman_dot
	value: 0.8226609928783558
	name: Spearman Dot
	- type: pearson_max
	value: 0.8594405198312016
	name: Pearson Max
	- type: spearman_max
	value: 0.8648571300070264
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 256
	type: sts-dev-256
	metrics:
	- type: pearson_cosine
	value: 0.8506120561071212
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8575982860981437
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.852829777566948
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8552667517015687
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8526934293405145
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8551077930316164
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7943956137623474
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7963976287579885
	name: Spearman Dot
	- type: pearson_max
	value: 0.852829777566948
	name: Pearson Max
	- type: spearman_max
	value: 0.8575982860981437
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 128
	type: sts-dev-128
	metrics:
	- type: pearson_cosine
	value: 0.8410977354989039
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.850480817077266
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8461619224798919
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8490393633313068
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8458138708136093
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.848719989437845
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7755878071062363
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7755629190322909
	name: Spearman Dot
	- type: pearson_max
	value: 0.8461619224798919
	name: Pearson Max
	- type: spearman_max
	value: 0.850480817077266
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 64
	type: sts-dev-64
	metrics:
	- type: pearson_cosine
	value: 0.8176550213032908
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8307913870285043
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8291830276998975
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8320477651805375
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8311109004860973
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8333955109708812
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7153413665605783
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7181274999679498
	name: Spearman Dot
	- type: pearson_max
	value: 0.8311109004860973
	name: Pearson Max
	- type: spearman_max
	value: 0.8333955109708812
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 768
	type: sts-test-768
	metrics:
	- type: pearson_cosine
	value: 0.8491592809545866
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8568871215102605
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8572052385387118
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.856617432589014
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8568623186549655
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8567096295439565
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7968828934121807
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7879173370882538
	name: Spearman Dot
	- type: pearson_max
	value: 0.8572052385387118
	name: Pearson Max
	- type: spearman_max
	value: 0.8568871215102605
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 512
	type: sts-test-512
	metrics:
	- type: pearson_cosine
	value: 0.8507070298067174
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8575370129160172
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8564033014649287
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8560352984315738
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8561906595447021
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8560701630452845
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7973312469719326
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7873345752731498
	name: Spearman Dot
	- type: pearson_max
	value: 0.8564033014649287
	name: Pearson Max
	- type: spearman_max
	value: 0.8575370129160172
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.8467375811334358
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8523459221020806
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8515524299355154
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8516309696270962
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8505975029491393
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8504082169041302
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7756647219222156
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7687165011432322
	name: Spearman Dot
	- type: pearson_max
	value: 0.8515524299355154
	name: Pearson Max
	- type: spearman_max
	value: 0.8523459221020806
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.8377317518267889
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.84715184876888
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.846568244977152
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8487991796570058
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8456229087328332
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.847227591472
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7502527212449147
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7415962106597614
	name: Spearman Dot
	- type: pearson_max
	value: 0.846568244977152
	name: Pearson Max
	- type: spearman_max
	value: 0.8487991796570058
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.8173604263806156
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8315612974155435
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8319781289166863
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8347311175148256
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8334921243463637
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8350960592133633
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6935445265890855
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6843746062699552
	name: Spearman Dot
	- type: pearson_max
	value: 0.8334921243463637
	name: Pearson Max
	- type: spearman_max
	value: 0.8350960592133633
	name: Spearman Max
	---

	# SentenceTransformer based on l3cube-pune/indic-sentence-similarity-sbert

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [l3cube-pune/indic-sentence-similarity-sbert](https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) 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: [l3cube-pune/indic-sentence-similarity-sbert](https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert) <!-- at revision b07ef91a96390f3e35ce94ddb42340861519bf07 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
	- 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: 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})
	)
	```

	## 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("ammumadhu/indic-bert-nli-matryoshka")
	# Run inference
	sentences = [
	'Then he ran.',
	'He then started to run.',
	'A man plays the flute.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### Metrics

	#### Semantic Similarity
	* Dataset: `sts-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 \| 0.8609 \|
	\| spearman_cosine \| 0.8663 \|
	\| pearson_manhattan \| 0.8587 \|
	\| spearman_manhattan \| 0.8612 \|
	\| pearson_euclidean \| 0.8585 \|
	\| spearman_euclidean \| 0.8611 \|
	\| pearson_dot \| 0.8259 \|
	\| spearman_dot \| 0.826 \|
	\| pearson_max \| 0.8609 \|
	\| spearman_max \| 0.8663 \|

	#### 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 \| 0.8594 \|
	\| spearman_cosine \| 0.8649 \|
	\| pearson_manhattan \| 0.8574 \|
	\| spearman_manhattan \| 0.8599 \|
	\| pearson_euclidean \| 0.8575 \|
	\| spearman_euclidean \| 0.8601 \|
	\| pearson_dot \| 0.8223 \|
	\| spearman_dot \| 0.8227 \|
	\| pearson_max \| 0.8594 \|
	\| spearman_max \| 0.8649 \|

	#### 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 \| 0.8506 \|
	\| spearman_cosine \| 0.8576 \|
	\| pearson_manhattan \| 0.8528 \|
	\| spearman_manhattan \| 0.8553 \|
	\| pearson_euclidean \| 0.8527 \|
	\| spearman_euclidean \| 0.8551 \|
	\| pearson_dot \| 0.7944 \|
	\| spearman_dot \| 0.7964 \|
	\| pearson_max \| 0.8528 \|
	\| spearman_max \| 0.8576 \|

	#### 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 \| 0.8411 \|
	\| spearman_cosine \| 0.8505 \|
	\| pearson_manhattan \| 0.8462 \|
	\| spearman_manhattan \| 0.849 \|
	\| pearson_euclidean \| 0.8458 \|
	\| spearman_euclidean \| 0.8487 \|
	\| pearson_dot \| 0.7756 \|
	\| spearman_dot \| 0.7756 \|
	\| pearson_max \| 0.8462 \|
	\| spearman_max \| 0.8505 \|

	#### 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 \| 0.8177 \|
	\| spearman_cosine \| 0.8308 \|
	\| pearson_manhattan \| 0.8292 \|
	\| spearman_manhattan \| 0.832 \|
	\| pearson_euclidean \| 0.8311 \|
	\| spearman_euclidean \| 0.8334 \|
	\| pearson_dot \| 0.7153 \|
	\| spearman_dot \| 0.7181 \|
	\| pearson_max \| 0.8311 \|
	\| spearman_max \| 0.8334 \|

	#### 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 \| 0.8492 \|
	\| spearman_cosine \| 0.8569 \|
	\| pearson_manhattan \| 0.8572 \|
	\| spearman_manhattan \| 0.8566 \|
	\| pearson_euclidean \| 0.8569 \|
	\| spearman_euclidean \| 0.8567 \|
	\| pearson_dot \| 0.7969 \|
	\| spearman_dot \| 0.7879 \|
	\| pearson_max \| 0.8572 \|
	\| spearman_max \| 0.8569 \|

	#### 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 \| 0.8507 \|
	\| spearman_cosine \| 0.8575 \|
	\| pearson_manhattan \| 0.8564 \|
	\| spearman_manhattan \| 0.856 \|
	\| pearson_euclidean \| 0.8562 \|
	\| spearman_euclidean \| 0.8561 \|
	\| pearson_dot \| 0.7973 \|
	\| spearman_dot \| 0.7873 \|
	\| pearson_max \| 0.8564 \|
	\| spearman_max \| 0.8575 \|

	#### 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 \| 0.8467 \|
	\| spearman_cosine \| 0.8523 \|
	\| pearson_manhattan \| 0.8516 \|
	\| spearman_manhattan \| 0.8516 \|
	\| pearson_euclidean \| 0.8506 \|
	\| spearman_euclidean \| 0.8504 \|
	\| pearson_dot \| 0.7757 \|
	\| spearman_dot \| 0.7687 \|
	\| pearson_max \| 0.8516 \|
	\| spearman_max \| 0.8523 \|

	#### 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 \| 0.8377 \|
	\| spearman_cosine \| 0.8472 \|
	\| pearson_manhattan \| 0.8466 \|
	\| spearman_manhattan \| 0.8488 \|
	\| pearson_euclidean \| 0.8456 \|
	\| spearman_euclidean \| 0.8472 \|
	\| pearson_dot \| 0.7503 \|
	\| spearman_dot \| 0.7416 \|
	\| pearson_max \| 0.8466 \|
	\| spearman_max \| 0.8488 \|

	#### 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 \| 0.8174 \|
	\| spearman_cosine \| 0.8316 \|
	\| pearson_manhattan \| 0.832 \|
	\| spearman_manhattan \| 0.8347 \|
	\| pearson_euclidean \| 0.8335 \|
	\| spearman_euclidean \| 0.8351 \|
	\| pearson_dot \| 0.6935 \|
	\| spearman_dot \| 0.6844 \|
	\| pearson_max \| 0.8335 \|
	\| spearman_max \| 0.8351 \|

	<!--
	## 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.
	-->

	## Training Details

	### Training Dataset

	#### sentence-transformers/all-nli

	* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
	* Size: 10,000 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: 18.8 tokens</li><li>max: 89 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 11.84 tokens</li><li>max: 36 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 12.39 tokens</li><li>max: 38 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:----------------------------------------------------------------------------------------------------------\|:--------------------------------------------------\|:-------------------------------------------------------\|
	\| <code>Side view of a female triathlete during the run.</code> \| <code>A woman runs</code> \| <code>A man sits</code> \|
	\| <code>Confused person standing in the middle of the trolley tracks trying to figure out the signs.</code> \| <code>A person is on the tracks.</code> \| <code>A man sits in an airplane.</code> \|
	\| <code>A woman in a black shirt, jean shorts and white tennis shoes is bowling.</code> \| <code>A woman is bowling in casual clothes</code> \| <code>A woman bowling wins an outfit of clothes</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

	#### sentence-transformers/all-nli

	* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
	* 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: 6 tokens</li><li>mean: 18.54 tokens</li><li>max: 74 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 9.97 tokens</li><li>max: 30 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 10.59 tokens</li><li>max: 29 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------\|:--------------------------------------------------------\|
	\| <code>Two women are embracing while holding to go packages.</code> \| <code>Two woman are holding packages.</code> \| <code>The men are fighting outside a deli.</code> \|
	\| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> \| <code>Two kids in numbered jerseys wash their hands.</code> \| <code>Two kids in jackets walk to school.</code> \|
	\| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> \| <code>A man selling donuts to a customer.</code> \| <code>A woman drinks her coffee in a small cafe.</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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 128
	- `per_device_eval_batch_size`: 128
	- `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
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 128
	- `per_device_eval_batch_size`: 128
	- `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
	- `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`: no_duplicates
	- `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 \|
	\|:------:\|:----:\|:-------------:\|:------:\|:---------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|:----------------------------:\|
	\| 0.3797 \| 30 \| 7.9432 \| 4.2806 \| 0.8509 \| 0.8570 \| 0.8633 \| 0.8311 \| 0.8644 \| - \| - \| - \| - \| - \|
	\| 0.7595 \| 60 \| 6.1701 \| 3.9498 \| 0.8505 \| 0.8576 \| 0.8649 \| 0.8308 \| 0.8663 \| - \| - \| - \| - \| - \|
	\| 1.0 \| 79 \| - \| - \| - \| - \| - \| - \| - \| 0.8472 \| 0.8523 \| 0.8575 \| 0.8316 \| 0.8569 \|


	### 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: 2.19.2
	- 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}
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->