Add new SentenceTransformer model.

29efd03 verified 8 months ago

39.4 kB

	---
	language:
	- en
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dataset_size:100K<n<1M
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: distilbert/distilroberta-base
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: Test Rocks
	sentences:
	- Number of testimonies
	- People are at a pool.
	- I've never been to Asia
	- source_sentence: No animals.
	sentences:
	- We don't have a dog.
	- These boys are on bikes
	- A person is climbing.
	- source_sentence: Shrinking.
	sentences:
	- That doesn't seem fair.
	- A man reads the paper.
	- I've never been to Asia
	- source_sentence: Loire Valley
	sentences:
	- A Lake in Loire.
	- people stand near pole
	- A cat is licking itself.
	- source_sentence: It is well.
	sentences:
	- That's convenient.
	- away from the children
	- She hated the restaurant!
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on distilbert/distilroberta-base
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 768
	type: sts-dev-768
	metrics:
	- type: pearson_cosine
	value: 0.8413274730706258
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8478057476815382
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8414182910991368
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8394684211369814
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8423380151813549
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8401129676358965
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7854982058734802
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7814388303641997
	name: Spearman Dot
	- type: pearson_max
	value: 0.8423380151813549
	name: Pearson Max
	- type: spearman_max
	value: 0.8478057476815382
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 512
	type: sts-dev-512
	metrics:
	- type: pearson_cosine
	value: 0.8394744649386727
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8469596264857904
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8398552366754626
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8377241640608183
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8406514989809173
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8380050330376462
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7811135781647157
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7776714775017128
	name: Spearman Dot
	- type: pearson_max
	value: 0.8406514989809173
	name: Pearson Max
	- type: spearman_max
	value: 0.8469596264857904
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 256
	type: sts-dev-256
	metrics:
	- type: pearson_cosine
	value: 0.8326846589795867
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8435757360139872
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.835121668379584
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.833167770567356
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8359785864160201
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8337674519096212
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7499541215721716
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7452815230357489
	name: Spearman Dot
	- type: pearson_max
	value: 0.8359785864160201
	name: Pearson Max
	- type: spearman_max
	value: 0.8435757360139872
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 128
	type: sts-dev-128
	metrics:
	- type: pearson_cosine
	value: 0.8243384464323462
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8399706247679909
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8281897604718583
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8270317815639731
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8281918243965822
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8267242273030063
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7110017325551932
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7049602384186016
	name: Spearman Dot
	- type: pearson_max
	value: 0.8281918243965822
	name: Pearson Max
	- type: spearman_max
	value: 0.8399706247679909
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 64
	type: sts-dev-64
	metrics:
	- type: pearson_cosine
	value: 0.811599959622093
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8316629408285197
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8113103800424869
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8104916438729426
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8113924334973999
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8110877753624469
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.641225674602723
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6346995881423587
	name: Spearman Dot
	- type: pearson_max
	value: 0.811599959622093
	name: Pearson Max
	- type: spearman_max
	value: 0.8316629408285197
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 32
	type: sts-dev-32
	metrics:
	- type: pearson_cosine
	value: 0.7834130163353433
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.814057381112976
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7831854350286095
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7859760066096324
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7868628503474937
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7893614397994021
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.5533705216922039
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5449230360083127
	name: Spearman Dot
	- type: pearson_max
	value: 0.7868628503474937
	name: Pearson Max
	- type: spearman_max
	value: 0.814057381112976
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev 16
	type: sts-dev-16
	metrics:
	- type: pearson_cosine
	value: 0.7259201534121641
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7751337117844075
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7420762055565752
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7552849049126117
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7483211915991654
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.759888035465032
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.4387404126202509
	name: Pearson Dot
	- type: spearman_dot
	value: 0.42591442860202633
	name: Spearman Dot
	- type: pearson_max
	value: 0.7483211915991654
	name: Pearson Max
	- type: spearman_max
	value: 0.7751337117844075
	name: Spearman Max
	---

	# SentenceTransformer based on distilbert/distilroberta-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) 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: [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
	- 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: RobertaModel
	(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("mrm8488/distilroberta-base-ft-allnli-matryoshka-768-16-1e-128bs")
	# Run inference
	sentences = [
	'It is well.',
	"That's convenient.",
	'away from the children',
	]
	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 \| 0.8413 \|
	\| spearman_cosine \| 0.8478 \|
	\| pearson_manhattan \| 0.8414 \|
	\| spearman_manhattan \| 0.8395 \|
	\| pearson_euclidean \| 0.8423 \|
	\| spearman_euclidean \| 0.8401 \|
	\| pearson_dot \| 0.7855 \|
	\| spearman_dot \| 0.7814 \|
	\| pearson_max \| 0.8423 \|
	\| spearman_max \| 0.8478 \|

	#### 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 \| 0.8395 \|
	\| spearman_cosine \| 0.847 \|
	\| pearson_manhattan \| 0.8399 \|
	\| spearman_manhattan \| 0.8377 \|
	\| pearson_euclidean \| 0.8407 \|
	\| spearman_euclidean \| 0.838 \|
	\| pearson_dot \| 0.7811 \|
	\| spearman_dot \| 0.7777 \|
	\| pearson_max \| 0.8407 \|
	\| spearman_max \| 0.847 \|

	#### 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 \| 0.8327 \|
	\| spearman_cosine \| 0.8436 \|
	\| pearson_manhattan \| 0.8351 \|
	\| spearman_manhattan \| 0.8332 \|
	\| pearson_euclidean \| 0.836 \|
	\| spearman_euclidean \| 0.8338 \|
	\| pearson_dot \| 0.75 \|
	\| spearman_dot \| 0.7453 \|
	\| pearson_max \| 0.836 \|
	\| spearman_max \| 0.8436 \|

	#### 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 \| 0.8243 \|
	\| spearman_cosine \| 0.84 \|
	\| pearson_manhattan \| 0.8282 \|
	\| spearman_manhattan \| 0.827 \|
	\| pearson_euclidean \| 0.8282 \|
	\| spearman_euclidean \| 0.8267 \|
	\| pearson_dot \| 0.711 \|
	\| spearman_dot \| 0.705 \|
	\| pearson_max \| 0.8282 \|
	\| spearman_max \| 0.84 \|

	#### 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 \| 0.8116 \|
	\| spearman_cosine \| 0.8317 \|
	\| pearson_manhattan \| 0.8113 \|
	\| spearman_manhattan \| 0.8105 \|
	\| pearson_euclidean \| 0.8114 \|
	\| spearman_euclidean \| 0.8111 \|
	\| pearson_dot \| 0.6412 \|
	\| spearman_dot \| 0.6347 \|
	\| pearson_max \| 0.8116 \|
	\| spearman_max \| 0.8317 \|

	#### Semantic Similarity
	* Dataset: `sts-dev-32`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7834 \|
	\| spearman_cosine \| 0.8141 \|
	\| pearson_manhattan \| 0.7832 \|
	\| spearman_manhattan \| 0.786 \|
	\| pearson_euclidean \| 0.7869 \|
	\| spearman_euclidean \| 0.7894 \|
	\| pearson_dot \| 0.5534 \|
	\| spearman_dot \| 0.5449 \|
	\| pearson_max \| 0.7869 \|
	\| spearman_max \| 0.8141 \|

	#### Semantic Similarity
	* Dataset: `sts-dev-16`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7259 \|
	\| spearman_cosine \| 0.7751 \|
	\| pearson_manhattan \| 0.7421 \|
	\| spearman_manhattan \| 0.7553 \|
	\| pearson_euclidean \| 0.7483 \|
	\| spearman_euclidean \| 0.7599 \|
	\| pearson_dot \| 0.4387 \|
	\| spearman_dot \| 0.4259 \|
	\| pearson_max \| 0.7483 \|
	\| spearman_max \| 0.7751 \|

	<!--
	## Bias, Risks and Limitations

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

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	### 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/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: 557,850 training samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 7 tokens</li><li>mean: 10.38 tokens</li><li>max: 45 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:---------------------------------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------------------------\|
	\| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>A person is outdoors, on a horse.</code> \| <code>A person is at a diner, ordering an omelette.</code> \|
	\| <code>Children smiling and waving at camera</code> \| <code>There are children present</code> \| <code>The kids are frowning</code> \|
	\| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> \| <code>The boy does a skateboarding trick.</code> \| <code>The boy skates down the sidewalk.</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,
	32,
	16
	],
	"matryoshka_weights": [
	1,
	1,
	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: 18.02 tokens</li><li>max: 66 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 9.81 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 10.37 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,
	32,
	16
	],
	"matryoshka_weights": [
	1,
	1,
	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-16_spearman_cosine \| sts-dev-256_spearman_cosine \| sts-dev-32_spearman_cosine \| sts-dev-512_spearman_cosine \| sts-dev-64_spearman_cosine \| sts-dev-768_spearman_cosine \|
	\|:------:\|:----:\|:-------------:\|:-------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|
	\| 0.0229 \| 100 \| 29.0917 \| 14.1514 \| 0.7659 \| 0.7440 \| 0.7915 \| 0.7749 \| 0.7999 \| 0.7909 \| 0.7918 \|
	\| 0.0459 \| 200 \| 15.6915 \| 11.7031 \| 0.7718 \| 0.7487 \| 0.7940 \| 0.7776 \| 0.8005 \| 0.7931 \| 0.7871 \|
	\| 0.0688 \| 300 \| 14.3136 \| 11.1970 \| 0.7744 \| 0.7389 \| 0.7952 \| 0.7728 \| 0.8036 \| 0.7925 \| 0.7938 \|
	\| 0.0918 \| 400 \| 12.8122 \| 10.4416 \| 0.7899 \| 0.7536 \| 0.8040 \| 0.7764 \| 0.8065 \| 0.7953 \| 0.8018 \|
	\| 0.1147 \| 500 \| 12.1747 \| 10.5491 \| 0.7871 \| 0.7513 \| 0.8035 \| 0.7785 \| 0.8094 \| 0.7978 \| 0.8008 \|
	\| 0.1376 \| 600 \| 11.6784 \| 9.6618 \| 0.7785 \| 0.7465 \| 0.7956 \| 0.7762 \| 0.8027 \| 0.7953 \| 0.7935 \|
	\| 0.1606 \| 700 \| 11.9351 \| 9.3279 \| 0.7907 \| 0.7403 \| 0.7995 \| 0.7706 \| 0.8036 \| 0.7894 \| 0.7982 \|
	\| 0.1835 \| 800 \| 10.4998 \| 9.1538 \| 0.7911 \| 0.7516 \| 0.8043 \| 0.7820 \| 0.8078 \| 0.8025 \| 0.8010 \|
	\| 0.2065 \| 900 \| 10.6069 \| 9.0531 \| 0.7874 \| 0.7371 \| 0.7974 \| 0.7704 \| 0.8042 \| 0.7910 \| 0.8010 \|
	\| 0.2294 \| 1000 \| 10.0316 \| 8.9759 \| 0.7842 \| 0.7356 \| 0.7981 \| 0.7721 \| 0.8024 \| 0.7905 \| 0.7955 \|
	\| 0.2524 \| 1100 \| 10.199 \| 8.5398 \| 0.7863 \| 0.7322 \| 0.7961 \| 0.7691 \| 0.8002 \| 0.7910 \| 0.7936 \|
	\| 0.2753 \| 1200 \| 9.9393 \| 8.1356 \| 0.7860 \| 0.7304 \| 0.7990 \| 0.7682 \| 0.8025 \| 0.7908 \| 0.7954 \|
	\| 0.2982 \| 1300 \| 9.8711 \| 7.9177 \| 0.7932 \| 0.7319 \| 0.8028 \| 0.7708 \| 0.8067 \| 0.7924 \| 0.8013 \|
	\| 0.3212 \| 1400 \| 9.3594 \| 7.8870 \| 0.7892 \| 0.7296 \| 0.8032 \| 0.7710 \| 0.8070 \| 0.7961 \| 0.8030 \|
	\| 0.3441 \| 1500 \| 9.4534 \| 7.5756 \| 0.8003 \| 0.7518 \| 0.8078 \| 0.7857 \| 0.8112 \| 0.8063 \| 0.8068 \|
	\| 0.3671 \| 1600 \| 8.9061 \| 7.8164 \| 0.7781 \| 0.7390 \| 0.7942 \| 0.7761 \| 0.8002 \| 0.7968 \| 0.7941 \|
	\| 0.3900 \| 1700 \| 8.5164 \| 7.4869 \| 0.7934 \| 0.7530 \| 0.8063 \| 0.7864 \| 0.8120 \| 0.8055 \| 0.8080 \|
	\| 0.4129 \| 1800 \| 8.9262 \| 7.7155 \| 0.7846 \| 0.7301 \| 0.7991 \| 0.7728 \| 0.8065 \| 0.7945 \| 0.8003 \|
	\| 0.4359 \| 1900 \| 8.3242 \| 7.3068 \| 0.7850 \| 0.7273 \| 0.7976 \| 0.7710 \| 0.8020 \| 0.7904 \| 0.7976 \|
	\| 0.4588 \| 2000 \| 8.5374 \| 7.1026 \| 0.7845 \| 0.7272 \| 0.7993 \| 0.7717 \| 0.8042 \| 0.7925 \| 0.7963 \|
	\| 0.4818 \| 2100 \| 8.2304 \| 7.1601 \| 0.7879 \| 0.7354 \| 0.8015 \| 0.7719 \| 0.8059 \| 0.7944 \| 0.8029 \|
	\| 0.5047 \| 2200 \| 8.1347 \| 7.8267 \| 0.7715 \| 0.7230 \| 0.7889 \| 0.7626 \| 0.7956 \| 0.7849 \| 0.7930 \|
	\| 0.5276 \| 2300 \| 8.3057 \| 8.0057 \| 0.7622 \| 0.7148 \| 0.7814 \| 0.7572 \| 0.7881 \| 0.7769 \| 0.7836 \|
	\| 0.5506 \| 2400 \| 8.215 \| 7.6922 \| 0.7772 \| 0.7210 \| 0.7929 \| 0.7637 \| 0.7995 \| 0.7858 \| 0.7956 \|
	\| 0.5735 \| 2500 \| 8.4343 \| 7.2104 \| 0.7869 \| 0.7307 \| 0.8017 \| 0.7707 \| 0.8071 \| 0.7929 \| 0.8048 \|
	\| 0.5965 \| 2600 \| 8.159 \| 6.9977 \| 0.7893 \| 0.7297 \| 0.8031 \| 0.7733 \| 0.8071 \| 0.7928 \| 0.8045 \|
	\| 0.6194 \| 2700 \| 8.2048 \| 6.9465 \| 0.7859 \| 0.7280 \| 0.8006 \| 0.7725 \| 0.8052 \| 0.7926 \| 0.8004 \|
	\| 0.6423 \| 2800 \| 8.187 \| 7.3185 \| 0.7790 \| 0.7266 \| 0.7960 \| 0.7690 \| 0.8018 \| 0.7911 \| 0.7964 \|
	\| 0.6653 \| 2900 \| 8.4768 \| 7.5535 \| 0.7756 \| 0.7192 \| 0.7913 \| 0.7618 \| 0.7958 \| 0.7827 \| 0.7907 \|
	\| 0.6882 \| 3000 \| 8.4153 \| 7.3732 \| 0.7825 \| 0.7276 \| 0.7988 \| 0.7692 \| 0.8029 \| 0.7899 \| 0.7988 \|
	\| 0.7112 \| 3100 \| 7.9226 \| 6.8469 \| 0.7912 \| 0.7311 \| 0.8055 \| 0.7765 \| 0.8101 \| 0.7977 \| 0.8058 \|
	\| 0.7341 \| 3200 \| 8.1155 \| 6.7604 \| 0.7880 \| 0.7298 \| 0.8024 \| 0.7747 \| 0.8071 \| 0.7959 \| 0.8025 \|
	\| 0.7571 \| 3300 \| 6.8463 \| 5.4863 \| 0.8357 \| 0.7638 \| 0.8407 \| 0.8085 \| 0.8431 \| 0.8283 \| 0.8440 \|
	\| 0.7800 \| 3400 \| 5.2008 \| 5.2472 \| 0.8362 \| 0.7655 \| 0.8401 \| 0.8105 \| 0.8429 \| 0.8279 \| 0.8445 \|
	\| 0.8029 \| 3500 \| 4.5415 \| 5.1649 \| 0.8385 \| 0.7700 \| 0.8421 \| 0.8138 \| 0.8454 \| 0.8304 \| 0.8465 \|
	\| 0.8259 \| 3600 \| 4.4474 \| 5.0933 \| 0.8371 \| 0.7693 \| 0.8410 \| 0.8112 \| 0.8443 \| 0.8288 \| 0.8451 \|
	\| 0.8488 \| 3700 \| 4.12 \| 5.0555 \| 0.8396 \| 0.7718 \| 0.8439 \| 0.8140 \| 0.8463 \| 0.8311 \| 0.8471 \|
	\| 0.8718 \| 3800 \| 3.9104 \| 5.0147 \| 0.8386 \| 0.7749 \| 0.8432 \| 0.8129 \| 0.8459 \| 0.8304 \| 0.8471 \|
	\| 0.8947 \| 3900 \| 3.9054 \| 4.9966 \| 0.8379 \| 0.7733 \| 0.8424 \| 0.8125 \| 0.8456 \| 0.8296 \| 0.8464 \|
	\| 0.9176 \| 4000 \| 3.757 \| 4.9892 \| 0.8407 \| 0.7763 \| 0.8447 \| 0.8156 \| 0.8478 \| 0.8326 \| 0.8488 \|
	\| 0.9406 \| 4100 \| 3.7729 \| 4.9859 \| 0.8400 \| 0.7751 \| 0.8436 \| 0.8141 \| 0.8470 \| 0.8317 \| 0.8478 \|


	### 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}
	}
	```

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