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	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:378558
	- loss:MultipleNegativesRankingLoss
	base_model: intfloat/e5-base-v2
	widget:
	- source_sentence: Is intraoperative ketorolac an effective substitute for fentanyl
	in children undergoing outpatient adenotonsillectomy?
	sentences:
	- Ketorolac showed no advantage over fentanyl in reducing the incidence of PONV
	in children undergoing ADLAT.
	- The patients with IgAN and their first relatives showed significant higher Gal
	deficient IgA1 level than healthy controls, whereas patients spouses were the
	same as healthy controls. It can be suggested that the Gal deficient IgA1 might
	be inherited in Chinese patients with IgAN.
	- Our results indicated that triptolide enhanced and enriched the stemness in the
	PDAC cell lines at a low dose of 12.5 nM, but also resulted in the regression
	of tumors derived from these cells.
	- source_sentence: Is task specific fall prevention training effective for warfighters
	with transtibial amputations?
	sentences:
	- These results indicate that task specific fall prevention training is an effective
	rehabilitation method to reduce falls in persons with lower extremity transtibial
	amputations.
	- Don t press on the eye. For pain, give acetaminophen Tylenol . Don t give aspirin
	or ibuprofen Advil, Motrin , because they can increase bleeding.
	- Dermatophytes Trichophyton skin ,hair, ,nail Tri all Three Microsporum skin, hair
	My head on head we have skin and hair Epidermophyton skin, nails
	- source_sentence: Left horn of sinus venosus forms
	sentences:
	- Ki 67 expression is predictive of prognosis, and our prognostic model may become
	a useful tool for predicting prognosis in patients with stage I II extranodal
	NK T cell lymphoma, nasal type.
	- Evidence described here suggests that IFN λ is a good candidate inhibitor of viral
	replication in dengue infection. Mechanisms for the cellular and organismal interplay
	between DENV and IFN λ need to be further studied as they could provide insights
	into strategies to treat this disease. Furthermore, we report a novel epithelial
	model to study dengue infection in vitro.
	- Ans. A Coronary sinusRef Netter s Atlas of Human Embryology 2012 ed. pg. 96Heart
	tube embryonic derivativesembryonic structureGives rise to Proximal 1 3rd of bulbus
	cordisPrimitive trabeculated left ventricle Middle 1 3rd of bulbus cordisRight
	and left ventricular outflow tract Distal 1 3rd of bulbus cordis truncus arteriosus
	Ascending aorta and pulmonary trunk Left horn of sinus venosusCoronary sinus Right
	horn of sinus venosusSmooth part of right atrium Right common cardinal nerve and
	right anterior cardinal nerveSVC superior vena cava
	- source_sentence: Is implementation of national diabetes retinal screening programme
	associated with a lower proportion of patients referred to ophthalmology?
	sentences:
	- Introduction of a systematic retinal screening programme can reduce the proportion
	of patients referred to the ophthalmology clinic, and use ophthalmology services
	more efficiently.
	- A Obesity Medications for the treatment of obesity can be classified as catecholaminergic
	or serotonergic. Catecholaminergic medications include Amphetamines with high
	abuse potential The Non Amphetamine schedule IV appetite suppressants Phentermine,
	Diethyl propion Mazindol. The September 1997 withdrawal from the market of Flenfluramine
	Defenfluramine has made true serotonergic appetite medications unavailable. The
	SSRI antidepressants, E.g.., Fluoxetine Setraline, also have serotonergic activity
	but are not approved by the FDA for weight loss.
	- A i.e. Protein linked with glycosidic bond
	- source_sentence: Does amyloid peptide regulate calcium homoeostasis and arrhythmogenesis
	in pulmonary vein cardiomyocytes?
	sentences:
	- Hydroxy ethyl methaacrylate is a soft, flexible, water absorbing, plastic used
	to make soft contact lenses. It is a polymer of 2 hydroxyethyl methacrylate HEMA
	, a clear liquid component. Hard contact lenses are made from polymethyl methacrylate
	PMMA and Silicon.
	- Beta carotene has become popular in part because it s an antioxidant a substance
	that may protect cells from damage. A number of studies show that people who eat
	lots of fruits and vegetables that are rich in beta carotene and other vitamins
	and minerals have a lower risk of some cancers and heart disease. However, so
	far studies have not found that beta carotene supplements have the same health
	benefits as foods.
	- Aβ 25 35 has direct electrophysiological effects on PV cardiomyocytes.
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy
	model-index:
	- name: MPNet base trained on AllNLI triplets
	results:
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: eval dataset
	type: eval-dataset
	metrics:
	- type: cosine_accuracy
	value: 0.9937447168216399
	name: Cosine Accuracy
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: test dataset
	type: test-dataset
	metrics:
	- type: cosine_accuracy
	value: 0.9964285714285714
	name: Cosine Accuracy
	---

	# MPNet base trained on AllNLI triplets

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2). 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: [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) <!-- at revision 1c644c92ad3ba1efdad3f1451a637716616a20e8 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	- Language: en
	- License: apache-2.0

	### 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})
	(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("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'Does amyloid peptide regulate calcium homoeostasis and arrhythmogenesis in pulmonary vein cardiomyocytes?',
	'Aβ 25 35 has direct electrophysiological effects on PV cardiomyocytes.',
	'Beta carotene has become popular in part because it s an antioxidant a substance that may protect cells from damage. A number of studies show that people who eat lots of fruits and vegetables that are rich in beta carotene and other vitamins and minerals have a lower risk of some cancers and heart disease. However, so far studies have not found that beta carotene supplements have the same health benefits as foods.',
	]
	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

	#### Triplet

	* Datasets: `eval-dataset` and `test-dataset`
	* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

	\| Metric \| eval-dataset \| test-dataset \|
	\|:--------------------\|:-------------\|:-------------\|
	\| cosine_accuracy \| 0.9937 \| 0.9964 \|

	<!--
	## 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

	#### Unnamed Dataset


	* Size: 378,558 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| label \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:--------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 24.72 tokens</li><li>max: 147 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 88.11 tokens</li><li>max: 512 tokens</li></ul> \| <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| label \|
	\|:--------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------\|
	\| <code>Does tolbutamide alter glucose transport and metabolism in the embryonic mouse heart?</code> \| <code>Tolbutamide stimulates glucose uptake and metabolism in the embryonic heart, as occurs in adult extra pancreatic tissues. Glut 1 and HKI, but not GRP78, are likely involved in tolbutamide induced cardiac dysmorphogenesis.</code> \| <code>1.0</code> \|
	\| <code>Do flk1 cells derived from mouse embryonic stem cells reconstitute hematopoiesis in vivo in SCID mice?</code> \| <code>The Flk1 hematopoietic cells derived from ES cells reconstitute hematopoiesis in vivo and may become an alternative donor source for bone marrow transplantation.</code> \| <code>1.0</code> \|
	\| <code>Does systematic aging of degradable nanosuspension ameliorate vibrating mesh nebulizer performance?</code> \| <code>Nebulization of purified nanosuspensions resulted in droplet diameters of 7.0 µm. However, electrolyte supplementation and storage, which led to an increase in sample conductivity 10 20 µS cm , were capable of providing smaller droplet diameters during vibrating mesh nebulization 5.0 µm . No relevant change of NP properties i.e. size, morphology, remaining mass and molecular weight of the employed polymer was observed when incubated at 22 C for two weeks.</code> \| <code>1.0</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Evaluation Dataset

	#### Unnamed Dataset


	* Size: 47,320 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| label \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:--------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 24.45 tokens</li><li>max: 253 tokens</li></ul> \| <ul><li>min: 7 tokens</li><li>mean: 87.68 tokens</li><li>max: 512 tokens</li></ul> \| <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| label \|
	\|:-------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------\|
	\| <code>Does thrombospondin 2 gene silencing in human aortic smooth muscle cells improve cell attachment?</code> \| <code>siRNA mediated TSP 2 silencing of human aortic HAoSMCs improved cell attachment but had no effect on cell migration or proliferation. The effect on cell attachment was unrelated to changes in MMP activity.</code> \| <code>1.0</code> \|
	\| <code>What can you do to manage polycythemia vera?</code> \| <code>Most people with polycythemia vera take low dose aspirin. There are a lot of ways you can keep yourself comfortable and as healthy as possible Don t smoke or chew tobacco. Tobacco makes blood vessels narrow, which can make blood clots more likely. Get some light exercise, such as walking, to help your circulation and keep your heart healthy. Do leg and ankle exercises to stop clots from forming in the veins of your legs. Your doctor or a physical therapist can show you how. Bathe or shower in cool water if warm water makes you itch. Keep your skin moist with lotion, and try not to scratch.</code> \| <code>1.0</code> \|
	\| <code>Is weekly nab paclitaxel safe and effective in 65 years old patients with metastatic breast cancer a post hoc analysis?</code> \| <code>Weekly nab paclitaxel was safe and more efficacious compared with the q3w schedule and with solvent based taxanes in older patients with MBC.</code> \| <code>1.0</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

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

	- `do_predict`: True
	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `load_best_model_at_end`: True
	- `batch_sampler`: no_duplicates

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: True
	- `eval_strategy`: steps
	- `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
	- `torch_empty_cache_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`: True
	- `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
	- `include_for_metrics`: []
	- `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
	- `eval_on_start`: False
	- `use_liger_kernel`: False
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

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

	\| Epoch \| Step \| Training Loss \| Validation Loss \| eval-dataset_cosine_accuracy \| test-dataset_cosine_accuracy \|
	\|:----------:\|:--------:\|:-------------:\|:---------------:\|:----------------------------:\|:----------------------------:\|
	\| 0 \| 0 \| - \| - \| 0.9813 \| - \|
	\| 0.0085 \| 50 \| 1.8471 \| - \| - \| - \|
	\| 0.0169 \| 100 \| 0.5244 \| - \| - \| - \|
	\| 0.0254 \| 150 \| 0.2175 \| - \| - \| - \|
	\| 0.0338 \| 200 \| 0.1392 \| - \| - \| - \|
	\| 0.0423 \| 250 \| 0.1437 \| - \| - \| - \|
	\| 0.0507 \| 300 \| 0.142 \| - \| - \| - \|
	\| 0.0592 \| 350 \| 0.1295 \| - \| - \| - \|
	\| 0.0676 \| 400 \| 0.1238 \| - \| - \| - \|
	\| 0.0761 \| 450 \| 0.14 \| - \| - \| - \|
	\| 0.0845 \| 500 \| 0.1173 \| 0.1006 \| 0.9931 \| - \|
	\| 0.0930 \| 550 \| 0.1236 \| - \| - \| - \|
	\| 0.1014 \| 600 \| 0.1127 \| - \| - \| - \|
	\| 0.1099 \| 650 \| 0.1338 \| - \| - \| - \|
	\| 0.1183 \| 700 \| 0.1071 \| - \| - \| - \|
	\| 0.1268 \| 750 \| 0.1149 \| - \| - \| - \|
	\| 0.1352 \| 800 \| 0.1072 \| - \| - \| - \|
	\| 0.1437 \| 850 \| 0.1117 \| - \| - \| - \|
	\| 0.1522 \| 900 \| 0.1087 \| - \| - \| - \|
	\| 0.1606 \| 950 \| 0.1242 \| - \| - \| - \|
	\| 0.1691 \| 1000 \| 0.1039 \| 0.091 \| 0.9965 \| - \|
	\| 0.1775 \| 1050 \| 0.1043 \| - \| - \| - \|
	\| 0.1860 \| 1100 \| 0.1193 \| - \| - \| - \|
	\| 0.1944 \| 1150 \| 0.1028 \| - \| - \| - \|
	\| 0.2029 \| 1200 \| 0.1027 \| - \| - \| - \|
	\| 0.2113 \| 1250 \| 0.1075 \| - \| - \| - \|
	\| 0.2198 \| 1300 \| 0.1177 \| - \| - \| - \|
	\| 0.2282 \| 1350 \| 0.0937 \| - \| - \| - \|
	\| 0.2367 \| 1400 \| 0.1095 \| - \| - \| - \|
	\| 0.2451 \| 1450 \| 0.1054 \| - \| - \| - \|
	\| 0.2536 \| 1500 \| 0.1003 \| 0.0798 \| 0.9958 \| - \|
	\| 0.2620 \| 1550 \| 0.0952 \| - \| - \| - \|
	\| 0.2705 \| 1600 \| 0.1028 \| - \| - \| - \|
	\| 0.2790 \| 1650 \| 0.0988 \| - \| - \| - \|
	\| 0.2874 \| 1700 \| 0.0887 \| - \| - \| - \|
	\| 0.2959 \| 1750 \| 0.1027 \| - \| - \| - \|
	\| 0.3043 \| 1800 \| 0.0937 \| - \| - \| - \|
	\| 0.3128 \| 1850 \| 0.1031 \| - \| - \| - \|
	\| 0.3212 \| 1900 \| 0.0857 \| - \| - \| - \|
	\| 0.3297 \| 1950 \| 0.094 \| - \| - \| - \|
	\| 0.3381 \| 2000 \| 0.1044 \| 0.0721 \| 0.9954 \| - \|
	\| 0.3466 \| 2050 \| 0.0829 \| - \| - \| - \|
	\| 0.3550 \| 2100 \| 0.0934 \| - \| - \| - \|
	\| 0.3635 \| 2150 \| 0.0785 \| - \| - \| - \|
	\| 0.3719 \| 2200 \| 0.0938 \| - \| - \| - \|
	\| 0.3804 \| 2250 \| 0.0885 \| - \| - \| - \|
	\| 0.3888 \| 2300 \| 0.0907 \| - \| - \| - \|
	\| 0.3973 \| 2350 \| 0.0911 \| - \| - \| - \|
	\| 0.4057 \| 2400 \| 0.0891 \| - \| - \| - \|
	\| 0.4142 \| 2450 \| 0.0798 \| - \| - \| - \|
	\| 0.4227 \| 2500 \| 0.0856 \| 0.0655 \| 0.9935 \| - \|
	\| 0.4311 \| 2550 \| 0.0925 \| - \| - \| - \|
	\| 0.4396 \| 2600 \| 0.0778 \| - \| - \| - \|
	\| 0.4480 \| 2650 \| 0.0871 \| - \| - \| - \|
	\| 0.4565 \| 2700 \| 0.0769 \| - \| - \| - \|
	\| 0.4649 \| 2750 \| 0.0815 \| - \| - \| - \|
	\| 0.4734 \| 2800 \| 0.0697 \| - \| - \| - \|
	\| 0.4818 \| 2850 \| 0.0714 \| - \| - \| - \|
	\| 0.4903 \| 2900 \| 0.0788 \| - \| - \| - \|
	\| 0.4987 \| 2950 \| 0.0772 \| - \| - \| - \|
	\| 0.5072 \| 3000 \| 0.0825 \| 0.0618 \| 0.9917 \| - \|
	\| 0.5156 \| 3050 \| 0.0742 \| - \| - \| - \|
	\| 0.5241 \| 3100 \| 0.0784 \| - \| - \| - \|
	\| 0.5325 \| 3150 \| 0.0697 \| - \| - \| - \|
	\| 0.5410 \| 3200 \| 0.0791 \| - \| - \| - \|
	\| 0.5495 \| 3250 \| 0.0657 \| - \| - \| - \|
	\| 0.5579 \| 3300 \| 0.0779 \| - \| - \| - \|
	\| 0.5664 \| 3350 \| 0.0719 \| - \| - \| - \|
	\| 0.5748 \| 3400 \| 0.0656 \| - \| - \| - \|
	\| 0.5833 \| 3450 \| 0.0698 \| - \| - \| - \|
	\| 0.5917 \| 3500 \| 0.0678 \| 0.0578 \| 0.9903 \| - \|
	\| 0.6002 \| 3550 \| 0.0771 \| - \| - \| - \|
	\| 0.6086 \| 3600 \| 0.0645 \| - \| - \| - \|
	\| 0.6171 \| 3650 \| 0.078 \| - \| - \| - \|
	\| 0.6255 \| 3700 \| 0.064 \| - \| - \| - \|
	\| 0.6340 \| 3750 \| 0.0691 \| - \| - \| - \|
	\| 0.6424 \| 3800 \| 0.0634 \| - \| - \| - \|
	\| 0.6509 \| 3850 \| 0.0732 \| - \| - \| - \|
	\| 0.6593 \| 3900 \| 0.059 \| - \| - \| - \|
	\| 0.6678 \| 3950 \| 0.0671 \| - \| - \| - \|
	\| 0.6762 \| 4000 \| 0.0633 \| 0.0552 \| 0.9936 \| - \|
	\| 0.6847 \| 4050 \| 0.0732 \| - \| - \| - \|
	\| 0.6932 \| 4100 \| 0.0593 \| - \| - \| - \|
	\| 0.7016 \| 4150 \| 0.0639 \| - \| - \| - \|
	\| 0.7101 \| 4200 \| 0.0672 \| - \| - \| - \|
	\| 0.7185 \| 4250 \| 0.0604 \| - \| - \| - \|
	\| 0.7270 \| 4300 \| 0.0666 \| - \| - \| - \|
	\| 0.7354 \| 4350 \| 0.0594 \| - \| - \| - \|
	\| 0.7439 \| 4400 \| 0.0783 \| - \| - \| - \|
	\| 0.7523 \| 4450 \| 0.0654 \| - \| - \| - \|
	\| 0.7608 \| 4500 \| 0.0596 \| 0.0520 \| 0.9937 \| - \|
	\| 0.7692 \| 4550 \| 0.0654 \| - \| - \| - \|
	\| 0.7777 \| 4600 \| 0.0511 \| - \| - \| - \|
	\| 0.7861 \| 4650 \| 0.0641 \| - \| - \| - \|
	\| 0.7946 \| 4700 \| 0.0609 \| - \| - \| - \|
	\| 0.8030 \| 4750 \| 0.0591 \| - \| - \| - \|
	\| 0.8115 \| 4800 \| 0.0496 \| - \| - \| - \|
	\| 0.8199 \| 4850 \| 0.0624 \| - \| - \| - \|
	\| 0.8284 \| 4900 \| 0.0639 \| - \| - \| - \|
	\| 0.8369 \| 4950 \| 0.056 \| - \| - \| - \|
	\| 0.8453 \| 5000 \| 0.0641 \| 0.0487 \| 0.9947 \| - \|
	\| 0.8538 \| 5050 \| 0.0608 \| - \| - \| - \|
	\| 0.8622 \| 5100 \| 0.0725 \| - \| - \| - \|
	\| 0.8707 \| 5150 \| 0.055 \| - \| - \| - \|
	\| 0.8791 \| 5200 \| 0.0556 \| - \| - \| - \|
	\| 0.8876 \| 5250 \| 0.0489 \| - \| - \| - \|
	\| 0.8960 \| 5300 \| 0.0513 \| - \| - \| - \|
	\| 0.9045 \| 5350 \| 0.0493 \| - \| - \| - \|
	\| 0.9129 \| 5400 \| 0.0574 \| - \| - \| - \|
	\| 0.9214 \| 5450 \| 0.0665 \| - \| - \| - \|
	\| 0.9298 \| 5500 \| 0.0588 \| 0.0475 \| 0.9937 \| - \|
	\| 0.9383 \| 5550 \| 0.0557 \| - \| - \| - \|
	\| 0.9467 \| 5600 \| 0.0497 \| - \| - \| - \|
	\| 0.9552 \| 5650 \| 0.0592 \| - \| - \| - \|
	\| 0.9637 \| 5700 \| 0.0526 \| - \| - \| - \|
	\| 0.9721 \| 5750 \| 0.0683 \| - \| - \| - \|
	\| 0.9806 \| 5800 \| 0.0588 \| - \| - \| - \|
	\| 0.9890 \| 5850 \| 0.0541 \| - \| - \| - \|
	\| 0.9975 \| 5900 \| 0.0636 \| - \| - \| - \|
	\| 1.0 \| 5915 \| - \| - \| - \| 0.9964 \|

	* The bold row denotes the saved checkpoint.
	</details>

	### Framework Versions
	- Python: 3.11.10
	- Sentence Transformers: 3.3.0
	- Transformers: 4.46.2
	- PyTorch: 2.5.1+cu124
	- Accelerate: 1.1.1
	- Datasets: 3.1.0
	- Tokenizers: 0.20.3

	## 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",
	}
	```

	#### 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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