E5-all-nli-triplet-Matryoshka / README.md

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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: intfloat/multilingual-e5-small
	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 intfloat/multilingual-e5-small
	results:
	- dataset:
	config: ar
	name: MTEB MIRACLRetrievalHardNegatives (ar)
	revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
	split: dev
	type: mteb/miracl-hard-negatives
	metrics:
	- type: main_score
	value: 33.441
	task:
	type: Retrieval
	- dataset:
	config: ara-ara
	name: MTEB MLQARetrieval (ara-ara)
	revision: 397ed406c1a7902140303e7faf60fff35b58d285
	split: test
	type: facebook/mlqa
	metrics:
	- type: main_score
	value: 64.488
	task:
	type: Retrieval
	- dataset:
	config: ar
	name: MTEB MintakaRetrieval (ar)
	revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
	split: test
	type: jinaai/mintakaqa
	metrics:
	- type: main_score
	value: 16.162
	task:
	type: Retrieval
	- dataset:
	config: default
	name: MTEB SadeemQuestionRetrieval (default)
	revision: 3cb0752b182e5d5d740df547748b06663c8e0bd9
	split: test
	type: sadeem-ai/sadeem-ar-eval-retrieval-questions
	metrics:
	- type: main_score
	value: 63.235
	task:
	type: Retrieval
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 384
	type: sts-test-384
	metrics:
	- type: pearson_cosine
	value: 0.7883137447514015
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7971624317482785
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7845904338398069
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7939541836133244
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7882887522003604
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7971601260546269
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7883137483129774
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7971605875966696
	name: Spearman Dot
	- type: pearson_max
	value: 0.7883137483129774
	name: Pearson Max
	- type: spearman_max
	value: 0.7971624317482785
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.7851969391652749
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7968026743946358
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7852783784725356
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7935883492889713
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7882018230746569
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7963116553267949
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7786421988393841
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7867782644180616
	name: Spearman Dot
	- type: pearson_max
	value: 0.7882018230746569
	name: Pearson Max
	- type: spearman_max
	value: 0.7968026743946358
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.7754967709350954
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7933453885370457
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7832834632297865
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7907589269176767
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7867583047946054
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7935816990844704
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7317253736607925
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7335574962775742
	name: Spearman Dot
	- type: pearson_max
	value: 0.7867583047946054
	name: Pearson Max
	- type: spearman_max
	value: 0.7935816990844704
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.7625204599039478
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7837078735068292
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7752889433866854
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7790888579029828
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.777961287133872
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7815940757356076
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6685094830550401
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6621206899696827
	name: Spearman Dot
	- type: pearson_max
	value: 0.777961287133872
	name: Pearson Max
	- type: spearman_max
	value: 0.7837078735068292
	name: Spearman Max
	---

	# SentenceTransformer based on intfloat/multilingual-e5-small

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) on the Omartificial-Intelligence-Space/arabic-n_li-triplet dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision 0a68dcd3dad5b4962a78daa930087728292b241d -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 384 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: BertModel
	(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Normalize()
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import SentenceTransformer

	# Download from the 🤗 Hub
	model = SentenceTransformer("Omartificial-Intelligence-Space/E5-Matro")
	# 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, 384]

	# 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-384`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7883 \|
	\| spearman_cosine \| 0.7972 \|
	\| pearson_manhattan \| 0.7846 \|
	\| spearman_manhattan \| 0.794 \|
	\| pearson_euclidean \| 0.7883 \|
	\| spearman_euclidean \| 0.7972 \|
	\| pearson_dot \| 0.7883 \|
	\| spearman_dot \| 0.7972 \|
	\| pearson_max \| 0.7883 \|
	\| spearman_max \| 0.7972 \|

	#### 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 \| 0.7852 \|
	\| spearman_cosine \| 0.7968 \|
	\| pearson_manhattan \| 0.7853 \|
	\| spearman_manhattan \| 0.7936 \|
	\| pearson_euclidean \| 0.7882 \|
	\| spearman_euclidean \| 0.7963 \|
	\| pearson_dot \| 0.7786 \|
	\| spearman_dot \| 0.7868 \|
	\| pearson_max \| 0.7882 \|
	\| spearman_max \| 0.7968 \|

	#### 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 \| 0.7755 \|
	\| spearman_cosine \| 0.7933 \|
	\| pearson_manhattan \| 0.7833 \|
	\| spearman_manhattan \| 0.7908 \|
	\| pearson_euclidean \| 0.7868 \|
	\| spearman_euclidean \| 0.7936 \|
	\| pearson_dot \| 0.7317 \|
	\| spearman_dot \| 0.7336 \|
	\| pearson_max \| 0.7868 \|
	\| spearman_max \| 0.7936 \|

	#### 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 \| 0.7625 \|
	\| spearman_cosine \| 0.7837 \|
	\| pearson_manhattan \| 0.7753 \|
	\| spearman_manhattan \| 0.7791 \|
	\| pearson_euclidean \| 0.778 \|
	\| spearman_euclidean \| 0.7816 \|
	\| pearson_dot \| 0.6685 \|
	\| spearman_dot \| 0.6621 \|
	\| pearson_max \| 0.778 \|
	\| spearman_max \| 0.7837 \|

	<!--
	## 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

	#### 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: 5 tokens</li><li>mean: 10.33 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 13.21 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 15.32 tokens</li><li>max: 53 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": [
	384,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	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: 5 tokens</li><li>mean: 21.86 tokens</li><li>max: 105 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 10.22 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 11.2 tokens</li><li>max: 33 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": [
	384,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

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

	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `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`: 32
	- `per_device_eval_batch_size`: 32
	- `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
	\| Epoch \| Step \| Training Loss \| sts-test-128_spearman_cosine \| sts-test-256_spearman_cosine \| sts-test-384_spearman_cosine \| sts-test-64_spearman_cosine \|
	\|:------:\|:-----:\|:-------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|
	\| 0.0344 \| 200 \| 13.1208 \| - \| - \| - \| - \|
	\| 0.0688 \| 400 \| 9.1894 \| - \| - \| - \| - \|
	\| 0.1033 \| 600 \| 8.0222 \| - \| - \| - \| - \|
	\| 0.1377 \| 800 \| 7.2405 \| - \| - \| - \| - \|
	\| 0.1721 \| 1000 \| 7.1622 \| - \| - \| - \| - \|
	\| 0.2065 \| 1200 \| 6.4282 \| - \| - \| - \| - \|
	\| 0.2409 \| 1400 \| 6.0936 \| - \| - \| - \| - \|
	\| 0.2753 \| 1600 \| 5.99 \| - \| - \| - \| - \|
	\| 0.3098 \| 1800 \| 5.6939 \| - \| - \| - \| - \|
	\| 0.3442 \| 2000 \| 5.694 \| - \| - \| - \| - \|
	\| 0.3786 \| 2200 \| 5.2366 \| - \| - \| - \| - \|
	\| 0.4130 \| 2400 \| 5.2994 \| - \| - \| - \| - \|
	\| 0.4474 \| 2600 \| 5.2079 \| - \| - \| - \| - \|
	\| 0.4818 \| 2800 \| 5.0532 \| - \| - \| - \| - \|
	\| 0.5163 \| 3000 \| 4.9978 \| - \| - \| - \| - \|
	\| 0.5507 \| 3200 \| 5.1764 \| - \| - \| - \| - \|
	\| 0.5851 \| 3400 \| 5.1315 \| - \| - \| - \| - \|
	\| 0.6195 \| 3600 \| 5.0198 \| - \| - \| - \| - \|
	\| 0.6539 \| 3800 \| 5.0308 \| - \| - \| - \| - \|
	\| 0.6883 \| 4000 \| 5.1631 \| - \| - \| - \| - \|
	\| 0.7228 \| 4200 \| 4.7916 \| - \| - \| - \| - \|
	\| 0.7572 \| 4400 \| 4.363 \| - \| - \| - \| - \|
	\| 0.7916 \| 4600 \| 3.2357 \| - \| - \| - \| - \|
	\| 0.8260 \| 4800 \| 2.9915 \| - \| - \| - \| - \|
	\| 0.8604 \| 5000 \| 2.8143 \| - \| - \| - \| - \|
	\| 0.8949 \| 5200 \| 2.6125 \| - \| - \| - \| - \|
	\| 0.9293 \| 5400 \| 2.5493 \| - \| - \| - \| - \|
	\| 0.9637 \| 5600 \| 2.4991 \| - \| - \| - \| - \|
	\| 0.9981 \| 5800 \| 2.163 \| - \| - \| - \| - \|
	\| 1.0325 \| 6000 \| 0.0 \| - \| - \| - \| - \|
	\| 1.0669 \| 6200 \| 0.0 \| - \| - \| - \| - \|
	\| 1.1014 \| 6400 \| 0.0 \| - \| - \| - \| - \|
	\| 1.1358 \| 6600 \| 0.0 \| - \| - \| - \| - \|
	\| 1.1702 \| 6800 \| 0.0 \| - \| - \| - \| - \|
	\| 1.2046 \| 7000 \| 0.0 \| - \| - \| - \| - \|
	\| 1.2390 \| 7200 \| 0.0 \| - \| - \| - \| - \|
	\| 1.2734 \| 7400 \| 0.0 \| - \| - \| - \| - \|
	\| 1.3079 \| 7600 \| 0.0 \| - \| - \| - \| - \|
	\| 1.3423 \| 7800 \| 0.0 \| - \| - \| - \| - \|
	\| 1.3767 \| 8000 \| 0.0 \| - \| - \| - \| - \|
	\| 1.4111 \| 8200 \| 0.0037 \| - \| - \| - \| - \|
	\| 1.4455 \| 8400 \| 0.0372 \| - \| - \| - \| - \|
	\| 1.4800 \| 8600 \| 0.0221 \| - \| - \| - \| - \|
	\| 1.0229 \| 8800 \| 4.3738 \| - \| - \| - \| - \|
	\| 1.0573 \| 9000 \| 6.338 \| - \| - \| - \| - \|
	\| 1.0917 \| 9200 \| 6.2223 \| - \| - \| - \| - \|
	\| 1.1261 \| 9400 \| 5.8673 \| - \| - \| - \| - \|
	\| 1.1606 \| 9600 \| 5.5907 \| - \| - \| - \| - \|
	\| 1.1950 \| 9800 \| 5.0307 \| - \| - \| - \| - \|
	\| 1.2294 \| 10000 \| 4.9193 \| - \| - \| - \| - \|
	\| 1.2638 \| 10200 \| 4.8798 \| - \| - \| - \| - \|
	\| 1.2982 \| 10400 \| 4.401 \| - \| - \| - \| - \|
	\| 1.3326 \| 10600 \| 4.2705 \| - \| - \| - \| - \|
	\| 1.3671 \| 10800 \| 4.3023 \| - \| - \| - \| - \|
	\| 1.4015 \| 11000 \| 4.1344 \| - \| - \| - \| - \|
	\| 1.4359 \| 11200 \| 4.0464 \| - \| - \| - \| - \|
	\| 1.4703 \| 11400 \| 4.0115 \| - \| - \| - \| - \|
	\| 1.5047 \| 11600 \| 3.9206 \| - \| - \| - \| - \|
	\| 1.5391 \| 11800 \| 4.0106 \| - \| - \| - \| - \|
	\| 1.5736 \| 12000 \| 4.1365 \| - \| - \| - \| - \|
	\| 1.6080 \| 12200 \| 4.0401 \| - \| - \| - \| - \|
	\| 1.6424 \| 12400 \| 4.0602 \| - \| - \| - \| - \|
	\| 1.6768 \| 12600 \| 4.076 \| - \| - \| - \| - \|
	\| 1.7112 \| 12800 \| 3.97 \| - \| - \| - \| - \|
	\| 1.7457 \| 13000 \| 3.7905 \| - \| - \| - \| - \|
	\| 1.7801 \| 13200 \| 2.414 \| - \| - \| - \| - \|
	\| 1.8145 \| 13400 \| 2.1811 \| - \| - \| - \| - \|
	\| 1.8489 \| 13600 \| 2.1183 \| - \| - \| - \| - \|
	\| 1.8833 \| 13800 \| 2.0578 \| - \| - \| - \| - \|
	\| 1.9177 \| 14000 \| 2.0173 \| - \| - \| - \| - \|
	\| 1.9522 \| 14200 \| 2.0093 \| - \| - \| - \| - \|
	\| 1.9866 \| 14400 \| 1.9467 \| - \| - \| - \| - \|
	\| 2.0210 \| 14600 \| 0.4674 \| - \| - \| - \| - \|
	\| 2.0554 \| 14800 \| 0.0 \| - \| - \| - \| - \|
	\| 2.0898 \| 15000 \| 0.0 \| - \| - \| - \| - \|
	\| 2.1242 \| 15200 \| 0.0 \| - \| - \| - \| - \|
	\| 2.1587 \| 15400 \| 0.0 \| - \| - \| - \| - \|
	\| 2.1931 \| 15600 \| 0.0 \| - \| - \| - \| - \|
	\| 2.2275 \| 15800 \| 0.0 \| - \| - \| - \| - \|
	\| 2.2619 \| 16000 \| 0.0 \| - \| - \| - \| - \|
	\| 2.2963 \| 16200 \| 0.0 \| - \| - \| - \| - \|
	\| 2.3308 \| 16400 \| 0.0 \| - \| - \| - \| - \|
	\| 2.3652 \| 16600 \| 0.0 \| - \| - \| - \| - \|
	\| 2.3996 \| 16800 \| 0.0 \| - \| - \| - \| - \|
	\| 2.4340 \| 17000 \| 0.0 \| - \| - \| - \| - \|
	\| 2.4684 \| 17200 \| 0.0256 \| - \| - \| - \| - \|
	\| 2.0114 \| 17400 \| 2.4155 \| - \| - \| - \| - \|
	\| 2.0170 \| 17433 \| - \| 0.7933 \| 0.7968 \| 0.7972 \| 0.7837 \|


	### 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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