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Add new SentenceTransformer model.

0785fcb verified 6 months ago

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	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:557850
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: tomaarsen/mpnet-base-all-nli-triplet
	datasets: []
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: ذكر متوازن بعناية يقف على قدم واحدة بالقرب من منطقة شاطئ المحيط
	النظيفة
	sentences:
	- رجل يقدم عرضاً
	- هناك رجل بالخارج قرب الشاطئ
	- رجل يجلس على أريكه
	- source_sentence: رجل يقفز إلى سريره القذر
	sentences:
	- السرير قذر.
	- رجل يضحك أثناء غسيل الملابس
	- الرجل على القمر
	- source_sentence: الفتيات بالخارج
	sentences:
	- امرأة تلف الخيط إلى كرات بجانب كومة من الكرات
	- فتيان يركبان في جولة متعة
	- ثلاث فتيات يقفون سوية في غرفة واحدة تستمع وواحدة تكتب على الحائط والثالثة تتحدث
	إليهن
	- source_sentence: الرجل يرتدي قميصاً أزرق.
	sentences:
	- رجل يرتدي قميصاً أزرق يميل إلى الجدار بجانب الطريق مع شاحنة زرقاء وسيارة حمراء
	مع الماء في الخلفية.
	- كتاب القصص مفتوح
	- رجل يرتدي قميص أسود يعزف على الجيتار.
	- source_sentence: يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة
	شابة.
	sentences:
	- ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه
	- رجل يستلقي على وجهه على مقعد في الحديقة.
	- الشاب نائم بينما الأم تقود ابنتها إلى الحديقة
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 768
	type: sts-test-768
	metrics:
	- type: pearson_cosine
	value: 0.66986244175229
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.675651628513557
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6943200977280434
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6839707658313092
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6973190148612566
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6872926092972673
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.5534197296097646
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5421965591416092
	name: Spearman Dot
	- type: pearson_max
	value: 0.6973190148612566
	name: Pearson Max
	- type: spearman_max
	value: 0.6872926092972673
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 512
	type: sts-test-512
	metrics:
	- type: pearson_cosine
	value: 0.6628171358537143
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.670314701212355
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6916567677127377
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6815748132707206
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6948756461188812
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.685329042213794
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.5229142840207227
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5113740757424073
	name: Spearman Dot
	- type: pearson_max
	value: 0.6948756461188812
	name: Pearson Max
	- type: spearman_max
	value: 0.685329042213794
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.6368313837029833
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6512526280069127
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6832129716443456
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.674638334774044
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6843664039671002
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6760040651639672
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.4266095536126992
	name: Pearson Dot
	- type: spearman_dot
	value: 0.4179376458107888
	name: Spearman Dot
	- type: pearson_max
	value: 0.6843664039671002
	name: Pearson Max
	- type: spearman_max
	value: 0.6760040651639672
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.6147896744901056
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6354730852658397
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6730782159165468
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6652649799789521
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.676407799774529
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6691409653459247
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.35130869784942953
	name: Pearson Dot
	- type: spearman_dot
	value: 0.3445374275232203
	name: Spearman Dot
	- type: pearson_max
	value: 0.676407799774529
	name: Pearson Max
	- type: spearman_max
	value: 0.6691409653459247
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.5789158725954748
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6081197115891086
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6578631744829946
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6518503436513217
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6629734628760299
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6570510967281272
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.24034366392620327
	name: Pearson Dot
	- type: spearman_dot
	value: 0.2331392769925126
	name: Spearman Dot
	- type: pearson_max
	value: 0.6629734628760299
	name: Pearson Max
	- type: spearman_max
	value: 0.6570510967281272
	name: Spearman Max
	---

	# SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [tomaarsen/mpnet-base-all-nli-triplet](https://huggingface.co/tomaarsen/mpnet-base-all-nli-triplet) on the Omartificial-Intelligence-Space/arabic-n_li-triplet 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: [tomaarsen/mpnet-base-all-nli-triplet](https://huggingface.co/tomaarsen/mpnet-base-all-nli-triplet) <!-- at revision e88732e5620f3592bf6566604be9a6a5cad814ec -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- Omartificial-Intelligence-Space/arabic-n_li-triplet
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
	- Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
	(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("Omartificial-Intelligence-Space/mpnet-base-all-nli-triplet-Arabic-mpnet_base")
	# Run inference
	sentences = [
	'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.',
	'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه',
	'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة',
	]
	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-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 \| 0.6699 \|
	\| spearman_cosine \| 0.6757 \|
	\| pearson_manhattan \| 0.6943 \|
	\| spearman_manhattan \| 0.684 \|
	\| pearson_euclidean \| 0.6973 \|
	\| spearman_euclidean \| 0.6873 \|
	\| pearson_dot \| 0.5534 \|
	\| spearman_dot \| 0.5422 \|
	\| pearson_max \| 0.6973 \|
	\| spearman_max \| 0.6873 \|

	#### 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 \| 0.6628 \|
	\| spearman_cosine \| 0.6703 \|
	\| pearson_manhattan \| 0.6917 \|
	\| spearman_manhattan \| 0.6816 \|
	\| pearson_euclidean \| 0.6949 \|
	\| spearman_euclidean \| 0.6853 \|
	\| pearson_dot \| 0.5229 \|
	\| spearman_dot \| 0.5114 \|
	\| pearson_max \| 0.6949 \|
	\| spearman_max \| 0.6853 \|

	#### 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 \| 0.6368 \|
	\| spearman_cosine \| 0.6513 \|
	\| pearson_manhattan \| 0.6832 \|
	\| spearman_manhattan \| 0.6746 \|
	\| pearson_euclidean \| 0.6844 \|
	\| spearman_euclidean \| 0.676 \|
	\| pearson_dot \| 0.4266 \|
	\| spearman_dot \| 0.4179 \|
	\| pearson_max \| 0.6844 \|
	\| spearman_max \| 0.676 \|

	#### 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 \| 0.6148 \|
	\| spearman_cosine \| 0.6355 \|
	\| pearson_manhattan \| 0.6731 \|
	\| spearman_manhattan \| 0.6653 \|
	\| pearson_euclidean \| 0.6764 \|
	\| spearman_euclidean \| 0.6691 \|
	\| pearson_dot \| 0.3513 \|
	\| spearman_dot \| 0.3445 \|
	\| pearson_max \| 0.6764 \|
	\| spearman_max \| 0.6691 \|

	#### 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 \| 0.5789 \|
	\| spearman_cosine \| 0.6081 \|
	\| pearson_manhattan \| 0.6579 \|
	\| spearman_manhattan \| 0.6519 \|
	\| pearson_euclidean \| 0.663 \|
	\| spearman_euclidean \| 0.6571 \|
	\| pearson_dot \| 0.2403 \|
	\| spearman_dot \| 0.2331 \|
	\| pearson_max \| 0.663 \|
	\| spearman_max \| 0.6571 \|

	<!--
	## 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.
	-->

	## Training Details

	### Training Dataset

	#### Omartificial-Intelligence-Space/arabic-n_li-triplet

	* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
	* 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: 12 tokens</li><li>mean: 23.93 tokens</li><li>max: 155 tokens</li></ul> \| <ul><li>min: 9 tokens</li><li>mean: 29.62 tokens</li><li>max: 117 tokens</li></ul> \| <ul><li>min: 13 tokens</li><li>mean: 33.95 tokens</li><li>max: 149 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:------------------------------------------------------------\|:--------------------------------------------\|:------------------------------------\|
	\| <code>شخص على حصان يقفز فوق طائرة معطلة</code> \| <code>شخص في الهواء الطلق، على حصان.</code> \| <code>شخص في مطعم، يطلب عجة.</code> \|
	\| <code>أطفال يبتسمون و يلوحون للكاميرا</code> \| <code>هناك أطفال حاضرون</code> \| <code>الاطفال يتجهمون</code> \|
	\| <code>صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.</code> \| <code>الفتى يقوم بخدعة التزلج</code> \| <code>الصبي يتزلج على الرصيف</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

	#### Omartificial-Intelligence-Space/arabic-n_li-triplet

	* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
	* 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: 12 tokens</li><li>mean: 49.5 tokens</li><li>max: 246 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 23.66 tokens</li><li>max: 103 tokens</li></ul> \| <ul><li>min: 9 tokens</li><li>mean: 25.33 tokens</li><li>max: 82 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:-----------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------\|:---------------------------------------------------\|
	\| <code>امرأتان يتعانقان بينما يحملان حزمة</code> \| <code>إمرأتان يحملان حزمة</code> \| <code>الرجال يتشاجرون خارج مطعم</code> \|
	\| <code>طفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة.</code> \| <code>طفلين يرتديان قميصاً مرقماً يغسلون أيديهم</code> \| <code>طفلين يرتديان سترة يذهبان إلى المدرسة</code> \|
	\| <code>رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس</code> \| <code>رجل يبيع الدونات لعميل</code> \| <code>امرأة تشرب قهوتها في مقهى صغير</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

	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `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
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `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`: 3
	- `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, '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_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \| 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 \|
	\|:------:\|:-----:\|:-------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|:----------------------------:\|
	\| 0.0229 \| 200 \| 21.5318 \| - \| - \| - \| - \| - \|
	\| 0.0459 \| 400 \| 17.2344 \| - \| - \| - \| - \| - \|
	\| 0.0688 \| 600 \| 15.393 \| - \| - \| - \| - \| - \|
	\| 0.0918 \| 800 \| 13.7897 \| - \| - \| - \| - \| - \|
	\| 0.1147 \| 1000 \| 13.534 \| - \| - \| - \| - \| - \|
	\| 0.1377 \| 1200 \| 12.2683 \| - \| - \| - \| - \| - \|
	\| 0.1606 \| 1400 \| 10.9271 \| - \| - \| - \| - \| - \|
	\| 0.1835 \| 1600 \| 11.071 \| - \| - \| - \| - \| - \|
	\| 0.2065 \| 1800 \| 10.0153 \| - \| - \| - \| - \| - \|
	\| 0.2294 \| 2000 \| 9.8463 \| - \| - \| - \| - \| - \|
	\| 0.2524 \| 2200 \| 10.0194 \| - \| - \| - \| - \| - \|
	\| 0.2753 \| 2400 \| 9.8371 \| - \| - \| - \| - \| - \|
	\| 0.2983 \| 2600 \| 9.6315 \| - \| - \| - \| - \| - \|
	\| 0.3212 \| 2800 \| 8.9858 \| - \| - \| - \| - \| - \|
	\| 0.3442 \| 3000 \| 9.1876 \| - \| - \| - \| - \| - \|
	\| 0.3671 \| 3200 \| 8.8028 \| - \| - \| - \| - \| - \|
	\| 0.3900 \| 3400 \| 8.6075 \| - \| - \| - \| - \| - \|
	\| 0.4130 \| 3600 \| 8.4285 \| - \| - \| - \| - \| - \|
	\| 0.4359 \| 3800 \| 8.1258 \| - \| - \| - \| - \| - \|
	\| 0.4589 \| 4000 \| 8.2508 \| - \| - \| - \| - \| - \|
	\| 0.4818 \| 4200 \| 7.8037 \| - \| - \| - \| - \| - \|
	\| 0.5048 \| 4400 \| 7.7133 \| - \| - \| - \| - \| - \|
	\| 0.5277 \| 4600 \| 7.5006 \| - \| - \| - \| - \| - \|
	\| 0.5506 \| 4800 \| 7.7025 \| - \| - \| - \| - \| - \|
	\| 0.5736 \| 5000 \| 7.7593 \| - \| - \| - \| - \| - \|
	\| 0.5965 \| 5200 \| 7.6305 \| - \| - \| - \| - \| - \|
	\| 0.6195 \| 5400 \| 7.7502 \| - \| - \| - \| - \| - \|
	\| 0.6424 \| 5600 \| 7.5624 \| - \| - \| - \| - \| - \|
	\| 0.6654 \| 5800 \| 7.5287 \| - \| - \| - \| - \| - \|
	\| 0.6883 \| 6000 \| 7.4261 \| - \| - \| - \| - \| - \|
	\| 0.7113 \| 6200 \| 7.239 \| - \| - \| - \| - \| - \|
	\| 0.7342 \| 6400 \| 7.1631 \| - \| - \| - \| - \| - \|
	\| 0.7571 \| 6600 \| 7.6865 \| - \| - \| - \| - \| - \|
	\| 0.7801 \| 6800 \| 7.6124 \| - \| - \| - \| - \| - \|
	\| 0.8030 \| 7000 \| 6.9936 \| - \| - \| - \| - \| - \|
	\| 0.8260 \| 7200 \| 6.7331 \| - \| - \| - \| - \| - \|
	\| 0.8489 \| 7400 \| 6.4542 \| - \| - \| - \| - \| - \|
	\| 0.8719 \| 7600 \| 6.1994 \| - \| - \| - \| - \| - \|
	\| 0.8948 \| 7800 \| 5.9798 \| - \| - \| - \| - \| - \|
	\| 0.9177 \| 8000 \| 5.7808 \| - \| - \| - \| - \| - \|
	\| 0.9407 \| 8200 \| 5.6952 \| - \| - \| - \| - \| - \|
	\| 0.9636 \| 8400 \| 5.5082 \| - \| - \| - \| - \| - \|
	\| 0.9866 \| 8600 \| 5.4421 \| - \| - \| - \| - \| - \|
	\| 1.0095 \| 8800 \| 3.0309 \| - \| - \| - \| - \| - \|
	\| 1.0026 \| 9000 \| 1.1835 \| - \| - \| - \| - \| - \|
	\| 1.0256 \| 9200 \| 8.1196 \| - \| - \| - \| - \| - \|
	\| 1.0485 \| 9400 \| 8.0326 \| - \| - \| - \| - \| - \|
	\| 1.0715 \| 9600 \| 8.5028 \| - \| - \| - \| - \| - \|
	\| 1.0944 \| 9800 \| 7.6923 \| - \| - \| - \| - \| - \|
	\| 1.1174 \| 10000 \| 8.029 \| - \| - \| - \| - \| - \|
	\| 1.1403 \| 10200 \| 7.5052 \| - \| - \| - \| - \| - \|
	\| 1.1632 \| 10400 \| 7.1177 \| - \| - \| - \| - \| - \|
	\| 1.1862 \| 10600 \| 6.9594 \| - \| - \| - \| - \| - \|
	\| 1.2091 \| 10800 \| 6.6662 \| - \| - \| - \| - \| - \|
	\| 1.2321 \| 11000 \| 6.6903 \| - \| - \| - \| - \| - \|
	\| 1.2550 \| 11200 \| 6.9523 \| - \| - \| - \| - \| - \|
	\| 1.2780 \| 11400 \| 6.676 \| - \| - \| - \| - \| - \|
	\| 1.3009 \| 11600 \| 6.7141 \| - \| - \| - \| - \| - \|
	\| 1.3238 \| 11800 \| 6.568 \| - \| - \| - \| - \| - \|
	\| 1.3468 \| 12000 \| 6.8938 \| - \| - \| - \| - \| - \|
	\| 1.3697 \| 12200 \| 6.3745 \| - \| - \| - \| - \| - \|
	\| 1.3927 \| 12400 \| 6.2513 \| - \| - \| - \| - \| - \|
	\| 1.4156 \| 12600 \| 6.2589 \| - \| - \| - \| - \| - \|
	\| 1.4386 \| 12800 \| 6.1388 \| - \| - \| - \| - \| - \|
	\| 1.4615 \| 13000 \| 6.1835 \| - \| - \| - \| - \| - \|
	\| 1.4845 \| 13200 \| 5.9004 \| - \| - \| - \| - \| - \|
	\| 1.5074 \| 13400 \| 5.7891 \| - \| - \| - \| - \| - \|
	\| 1.5303 \| 13600 \| 5.6184 \| - \| - \| - \| - \| - \|
	\| 1.5533 \| 13800 \| 5.9762 \| - \| - \| - \| - \| - \|
	\| 1.5762 \| 14000 \| 5.9737 \| - \| - \| - \| - \| - \|
	\| 1.5992 \| 14200 \| 5.8563 \| - \| - \| - \| - \| - \|
	\| 1.6221 \| 14400 \| 5.8904 \| - \| - \| - \| - \| - \|
	\| 1.6451 \| 14600 \| 5.8484 \| - \| - \| - \| - \| - \|
	\| 1.6680 \| 14800 \| 5.8906 \| - \| - \| - \| - \| - \|
	\| 1.6909 \| 15000 \| 5.7613 \| - \| - \| - \| - \| - \|
	\| 1.7139 \| 15200 \| 5.5744 \| - \| - \| - \| - \| - \|
	\| 1.7368 \| 15400 \| 5.6569 \| - \| - \| - \| - \| - \|
	\| 1.7598 \| 15600 \| 5.7439 \| - \| - \| - \| - \| - \|
	\| 1.7827 \| 15800 \| 5.5593 \| - \| - \| - \| - \| - \|
	\| 1.8057 \| 16000 \| 5.2935 \| - \| - \| - \| - \| - \|
	\| 1.8286 \| 16200 \| 5.088 \| - \| - \| - \| - \| - \|
	\| 1.8516 \| 16400 \| 5.0167 \| - \| - \| - \| - \| - \|
	\| 1.8745 \| 16600 \| 4.84 \| - \| - \| - \| - \| - \|
	\| 1.8974 \| 16800 \| 4.6731 \| - \| - \| - \| - \| - \|
	\| 1.9204 \| 17000 \| 4.6404 \| - \| - \| - \| - \| - \|
	\| 1.9433 \| 17200 \| 4.6413 \| - \| - \| - \| - \| - \|
	\| 1.9663 \| 17400 \| 4.4495 \| - \| - \| - \| - \| - \|
	\| 1.9892 \| 17600 \| 4.4262 \| - \| - \| - \| - \| - \|
	\| 2.0122 \| 17800 \| 2.01 \| - \| - \| - \| - \| - \|
	\| 2.0053 \| 18000 \| 1.8418 \| - \| - \| - \| - \| - \|
	\| 2.0282 \| 18200 \| 6.2714 \| - \| - \| - \| - \| - \|
	\| 2.0512 \| 18400 \| 6.1742 \| - \| - \| - \| - \| - \|
	\| 2.0741 \| 18600 \| 6.5996 \| - \| - \| - \| - \| - \|
	\| 2.0971 \| 18800 \| 6.0907 \| - \| - \| - \| - \| - \|
	\| 2.1200 \| 19000 \| 6.2418 \| - \| - \| - \| - \| - \|
	\| 2.1429 \| 19200 \| 5.7817 \| - \| - \| - \| - \| - \|
	\| 2.1659 \| 19400 \| 5.7073 \| - \| - \| - \| - \| - \|
	\| 2.1888 \| 19600 \| 5.2645 \| - \| - \| - \| - \| - \|
	\| 2.2118 \| 19800 \| 5.3451 \| - \| - \| - \| - \| - \|
	\| 2.2347 \| 20000 \| 5.2453 \| - \| - \| - \| - \| - \|
	\| 2.2577 \| 20200 \| 5.6161 \| - \| - \| - \| - \| - \|
	\| 2.2806 \| 20400 \| 5.2289 \| - \| - \| - \| - \| - \|
	\| 2.3035 \| 20600 \| 5.3888 \| - \| - \| - \| - \| - \|
	\| 2.3265 \| 20800 \| 5.2483 \| - \| - \| - \| - \| - \|
	\| 2.3494 \| 21000 \| 5.5791 \| - \| - \| - \| - \| - \|
	\| 2.3724 \| 21200 \| 5.1643 \| - \| - \| - \| - \| - \|
	\| 2.3953 \| 21400 \| 5.1231 \| - \| - \| - \| - \| - \|
	\| 2.4183 \| 21600 \| 5.1055 \| - \| - \| - \| - \| - \|
	\| 2.4412 \| 21800 \| 5.1778 \| - \| - \| - \| - \| - \|
	\| 2.4642 \| 22000 \| 5.0466 \| - \| - \| - \| - \| - \|
	\| 2.4871 \| 22200 \| 4.8321 \| - \| - \| - \| - \| - \|
	\| 2.5100 \| 22400 \| 4.7056 \| - \| - \| - \| - \| - \|
	\| 2.5330 \| 22600 \| 4.6858 \| - \| - \| - \| - \| - \|
	\| 2.5559 \| 22800 \| 4.9189 \| - \| - \| - \| - \| - \|
	\| 2.5789 \| 23000 \| 4.912 \| - \| - \| - \| - \| - \|
	\| 2.6018 \| 23200 \| 4.8289 \| - \| - \| - \| - \| - \|
	\| 2.6248 \| 23400 \| 4.8959 \| - \| - \| - \| - \| - \|
	\| 2.6477 \| 23600 \| 4.9441 \| - \| - \| - \| - \| - \|
	\| 2.6706 \| 23800 \| 4.9334 \| - \| - \| - \| - \| - \|
	\| 2.6936 \| 24000 \| 4.8328 \| - \| - \| - \| - \| - \|
	\| 2.7165 \| 24200 \| 4.601 \| - \| - \| - \| - \| - \|
	\| 2.7395 \| 24400 \| 4.834 \| - \| - \| - \| - \| - \|
	\| 2.7624 \| 24600 \| 5.152 \| - \| - \| - \| - \| - \|
	\| 2.7854 \| 24800 \| 4.9232 \| - \| - \| - \| - \| - \|
	\| 2.8083 \| 25000 \| 4.6556 \| - \| - \| - \| - \| - \|
	\| 2.8312 \| 25200 \| 4.6229 \| - \| - \| - \| - \| - \|
	\| 2.8542 \| 25400 \| 4.5768 \| - \| - \| - \| - \| - \|
	\| 2.8771 \| 25600 \| 4.3619 \| - \| - \| - \| - \| - \|
	\| 2.9001 \| 25800 \| 4.3608 \| - \| - \| - \| - \| - \|
	\| 2.9230 \| 26000 \| 4.2834 \| - \| - \| - \| - \| - \|
	\| 2.9403 \| 26151 \| - \| 0.6355 \| 0.6513 \| 0.6703 \| 0.6081 \| 0.6757 \|

	</details>

	### Framework Versions
	- Python: 3.9.18
	- Sentence Transformers: 3.0.1
	- Transformers: 4.40.0
	- PyTorch: 2.2.2+cu121
	- Accelerate: 0.26.1
	- Datasets: 2.19.0
	- 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}
	}
	```

	#### 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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