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	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dense
	- generated_from_trainer
	- dataset_size:3615666
	- loss:CachedMultipleNegativesSymmetricRankingLoss
	- loss:CachedMultipleNegativesRankingLoss
	widget:
	- source_sentence: what is the difference between body spray and eau de toilette?
	sentences:
	- Eau de Toilette (EDT) is ideal for those that may find the EDP or Perfume oil
	too strong, with 7%-12% fragrance concentration in alcohol. Gives four to five
	hours wear. Body Mist is a light refreshing fragrance perfect for layering with
	other products from the same family. 3-5% fragrance concentration in alcohol.
	- To join the Army as an enlisted member you must usually take the Armed Services
	Vocational Aptitude Battery (ASVAB) test and get a good score. The maximum ASVAB
	score is 99. For enlistment into the Army you must get a minimum ASVAB score of
	31.
	- 'Points needed to redeem rewards with Redbox Perks: 1,500 points = FREE 1-night
	DVD rental. 1,750 points = FREE Blu-ray rental. 2,500 points = FREE 1-night Game
	rental.'
	- source_sentence: who sings soa theme song
	sentences:
	- 'unpleasant. Not pleasant or agreeable: bad, disagreeable, displeasing, offensive,
	uncongenial, unsympathetic. Informal: icky.npleasant. Not pleasant or agreeable:
	bad, disagreeable, displeasing, offensive, uncongenial, unsympathetic. Informal:
	icky.'
	- "â\x80\x9C Song from MAS*H (Suicide Is Painless) â\x80\x9D is a song written\
	\ by Johnny Mandel (music) and Mike Altman (lyrics), which was the theme song\
	\ for both the movie and TV series MAS*H. Mike Altman is the son of the original\
	\ filmâ\x80\x99s director, Robert Altman, and was 14 years old when he wrote the\
	\ songâ\x80\x99s lyrics. On the compilation the song is titled Theme from M.A.S.H.\
	\ . 2 Killarmy sampled the music for their 1997 track 5 Stars from the Silent\
	\ Weapons for Quiet Wars album. 3 Edgar Cruz recorded an instrumental cover of\
	\ the song for his 1997 album Reminiscence titled MAS*H Theme."
	- (2) This Life is the theme song for the FX television series Sons of Anarchy,
	written by singer-songwriter Curtis Stigers, Velvet Revolver guitarist Dave Kushner,
	producer Bob Thiele Jr. and show creator Kurt Sutter while it was performed by
	Curtis Stigers & The Forest Rangers.
	- source_sentence: what is virility mean
	sentences:
	- That leads us to income taxes. Income tax rates. The top income tax rate in the
	United States is 39.6 percent. That ranked 33rd highest on a list of the top rates
	in 116 nations compiled this year by KPMG, an international tax advisory corporation.
	- 'Princeton''s WordNet(0.00 / 0 votes)Rate this definition: virility(noun) the
	masculine property of being capable of copulation and procreation. manfulness,
	manliness, virility(noun) the trait of being manly; having the characteristics
	of an adult male.'
	- 'Click on the thesaurus category heading under the button in an entry to see the
	synonyms and related words for that meaning. the strength and power that are considered
	typical qualities of a man, especially sexual energy virility symbol: His car
	is a red turbo-charged Porsche, the classic virility symbol. Synonyms and related
	words. Physical strength:strength, force, energy...'
	- source_sentence: what does the greek term demokratia translate to mean
	sentences:
	- 'From Wikipedia, the free encyclopedia. A democracy (Greek, demokratia) means
	rule by the people. The name is used for different forms of government, where
	the people can take part in the decisions that affect the way their community
	is run. In modern times, there are different ways this can be done:rom Wikipedia,
	the free encyclopedia. A democracy (Greek, demokratia) means rule by the people.
	The name is used for different forms of government, where the people can take
	part in the decisions that affect the way their community is run. In modern times,
	there are different ways this can be done:'
	- 'Freebase(0.00 / 0 votes)Rate this definition: Demokratia is a direct democracy,
	as opposed to the modern representative democracy. It was used in ancient Greece,
	most notably Athens, and began its use around 500 BCE. In a participant government,
	citizens who wish to have a say in government can participate in it. Demokratia
	excluded women, foreigners, and slaves.'
	- sal volatile. 1. an aromatic alcoholic solution of ammonium carbonate, the chief
	ingredient in smelling salts. Some say water was thrown into the flame, others
	that it was Spirits of sal volatile. He disengaged one of them presently, and
	felt in his pocket for the sal volatile.
	- source_sentence: how did triangular trade benefit european colonies in the americas
	sentences:
	- Road to Perdition (soundtrack) Road to Perdition is the soundtrack, on the Decca
	Records label, of the 2002 Academy Award-winning and Golden Globe-nominated film
	Road to Perdition starring Tyler Hoechlin, Tom Hanks, Jennifer Jason Leigh, Jude
	Law, Daniel Craig and Paul Newman. The original score was composed by Thomas Newman.[4]
	- Ohm's law Ohm's law states that the current through a conductor between two points
	is directly proportional to the voltage across the two points. Introducing the
	constant of proportionality, the resistance,[1] one arrives at the usual mathematical
	equation that describes this relationship:[2]
	- Triangular trade New England also benefited from the trade, as many merchants
	from New England, especially the state of Rhode Island, replaced the role of Europe
	in the triangle. New England also made rum from the Caribbean sugar and molasses,
	which it shipped to Africa as well as within the New World.[7] Yet, the "triangle
	trade" as considered in relation to New England was a piecemeal operation. No
	New England traders are known to have completed a sequential circuit of the full
	triangle, which took a calendar year on average, according to historian Clifford
	Shipton.[8] The concept of the New England Triangular trade was first suggested,
	inconclusively, in an 1866 book by George H. Moore, was picked up in 1872 by historian
	George C. Mason, and reached full consideration from a lecture in 1887 by American
	businessman and historian William B. Weeden.[9] The song "Molasses to Rum" from
	the musical 1776 vividly describes this form of the triangular trade.
	datasets:
	- sentence-transformers/msmarco-msmarco-distilbert-base-v3
	- sentence-transformers/gooaq
	- sentence-transformers/natural-questions
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy@1
	- cosine_accuracy@3
	- cosine_accuracy@5
	- cosine_accuracy@10
	- cosine_precision@1
	- cosine_precision@3
	- cosine_precision@5
	- cosine_precision@10
	- cosine_recall@1
	- cosine_recall@3
	- cosine_recall@5
	- cosine_recall@10
	- cosine_ndcg@10
	- cosine_mrr@10
	- cosine_map@100
	model-index:
	- name: ModernBERT-small-Retrieval-BEIR-Tuned
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoMSMARCO
	type: NanoMSMARCO
	metrics:
	- type: cosine_accuracy@1
	value: 0.12
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.26
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.34
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.54
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.12
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.08666666666666666
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.068
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.054000000000000006
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.12
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.26
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.34
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.54
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.3042672965887713
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.2326269841269841
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.25001278764546914
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoNQ
	type: NanoNQ
	metrics:
	- type: cosine_accuracy@1
	value: 0.22
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.46
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.56
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.58
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.22
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.15333333333333332
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.11200000000000002
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.05800000000000001
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.2
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.42
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.52
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.54
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.38133852434929755
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.3473333333333333
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.33718328116218177
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoHotpotQA
	type: NanoHotpotQA
	metrics:
	- type: cosine_accuracy@1
	value: 0.64
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.7
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.76
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.86
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.64
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.29333333333333333
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.20799999999999996
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.126
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.32
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.44
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.52
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.63
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.5626299119428719
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.6950555555555556
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.4762967674536366
	name: Cosine Map@100
	- task:
	type: nano-beir
	name: Nano BEIR
	dataset:
	name: NanoBEIR mean
	type: NanoBEIR_mean
	metrics:
	- type: cosine_accuracy@1
	value: 0.32666666666666666
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.47333333333333333
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.5533333333333333
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.66
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.32666666666666666
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.17777777777777778
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.12933333333333333
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.07933333333333334
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.21333333333333335
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.3733333333333333
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.46
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.57
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.41607857762698025
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.42500529100529105
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.3544976120870958
	name: Cosine Map@100
	---

	# ModernBERT-small-Retrieval-BEIR-Tuned

	This is a [sentence-transformers](https://www.SBERT.net) model trained on the [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) and [natural_questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) datasets. 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.


	This model is based on the wide architecture of [johnnyboycurtis/ModernBERT-small](https://huggingface.co/johnnyboycurtis/ModernBERT-small)

	```
	small_modernbert_config = ModernBertConfig(
	hidden_size=384, # A common dimension for small embedding models
	num_hidden_layers=12, # Significantly fewer layers than the base's 22
	num_attention_heads=6, # Must be a divisor of hidden_size
	intermediate_size=1536, # 4 * hidden_size -- VERY WIDE!!
	max_position_embeddings=1024, # Max sequence length for the model; originally 8192
	)

	model = ModernBertModel(modernbert_small_config)
	```

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	<!-- - Base model: [Unknown](https://huggingface.co/unknown) -->
	- Maximum Sequence Length: 1024 tokens
	- Output Dimensionality: 384 dimensions
	- Similarity Function: Cosine Similarity
	- Training Datasets:
	- [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3)
	- [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
	- [natural_questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
	- 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': 1024, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
	(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})
	)
	```

	## 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 = [
	'how did triangular trade benefit european colonies in the americas',
	'Triangular trade New England also benefited from the trade, as many merchants from New England, especially the state of Rhode Island, replaced the role of Europe in the triangle. New England also made rum from the Caribbean sugar and molasses, which it shipped to Africa as well as within the New World.[7] Yet, the "triangle trade" as considered in relation to New England was a piecemeal operation. No New England traders are known to have completed a sequential circuit of the full triangle, which took a calendar year on average, according to historian Clifford Shipton.[8] The concept of the New England Triangular trade was first suggested, inconclusively, in an 1866 book by George H. Moore, was picked up in 1872 by historian George C. Mason, and reached full consideration from a lecture in 1887 by American businessman and historian William B. Weeden.[9] The song "Molasses to Rum" from the musical 1776 vividly describes this form of the triangular trade.',
	"Ohm's law Ohm's law states that the current through a conductor between two points is directly proportional to the voltage across the two points. Introducing the constant of proportionality, the resistance,[1] one arrives at the usual mathematical equation that describes this relationship:[2]",
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 384]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities)
	# tensor([[ 1.0000, 0.4886, -0.0460],
	# [ 0.4886, 1.0000, -0.0096],
	# [-0.0460, -0.0096, 1.0000]])
	```

	<!--
	### 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.
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	## Evaluation

	### Metrics

	#### Information Retrieval

	* Datasets: `NanoMSMARCO`, `NanoNQ` and `NanoHotpotQA`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| NanoMSMARCO \| NanoNQ \| NanoHotpotQA \|
	\|:--------------------\|:------------\|:-----------\|:-------------\|
	\| cosine_accuracy@1 \| 0.12 \| 0.22 \| 0.64 \|
	\| cosine_accuracy@3 \| 0.26 \| 0.46 \| 0.7 \|
	\| cosine_accuracy@5 \| 0.34 \| 0.56 \| 0.76 \|
	\| cosine_accuracy@10 \| 0.54 \| 0.58 \| 0.86 \|
	\| cosine_precision@1 \| 0.12 \| 0.22 \| 0.64 \|
	\| cosine_precision@3 \| 0.0867 \| 0.1533 \| 0.2933 \|
	\| cosine_precision@5 \| 0.068 \| 0.112 \| 0.208 \|
	\| cosine_precision@10 \| 0.054 \| 0.058 \| 0.126 \|
	\| cosine_recall@1 \| 0.12 \| 0.2 \| 0.32 \|
	\| cosine_recall@3 \| 0.26 \| 0.42 \| 0.44 \|
	\| cosine_recall@5 \| 0.34 \| 0.52 \| 0.52 \|
	\| cosine_recall@10 \| 0.54 \| 0.54 \| 0.63 \|
	\| cosine_ndcg@10 \| 0.3043 \| 0.3813 \| 0.5626 \|
	\| cosine_mrr@10 \| 0.2326 \| 0.3473 \| 0.6951 \|
	\| cosine_map@100 \| 0.25 \| 0.3372 \| 0.4763 \|

	#### Nano BEIR

	* Dataset: `NanoBEIR_mean`
	* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) with these parameters:
	```json
	{
	"dataset_names": [
	"MSMARCO",
	"NQ",
	"HotpotQA"
	]
	}
	```

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.3267 \|
	\| cosine_accuracy@3 \| 0.4733 \|
	\| cosine_accuracy@5 \| 0.5533 \|
	\| cosine_accuracy@10 \| 0.66 \|
	\| cosine_precision@1 \| 0.3267 \|
	\| cosine_precision@3 \| 0.1778 \|
	\| cosine_precision@5 \| 0.1293 \|
	\| cosine_precision@10 \| 0.0793 \|
	\| cosine_recall@1 \| 0.2133 \|
	\| cosine_recall@3 \| 0.3733 \|
	\| cosine_recall@5 \| 0.46 \|
	\| cosine_recall@10 \| 0.57 \|
	\| cosine_ndcg@10 \| 0.4161 \|
	\| cosine_mrr@10 \| 0.425 \|
	\| cosine_map@100 \| 0.3545 \|

	<!--
	## 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 Datasets

	#### msmarco

	* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9)
	* Size: 502,939 training samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:---------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 9.17 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 18 tokens</li><li>mean: 79.48 tokens</li><li>max: 211 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 81.21 tokens</li><li>max: 230 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>what are the liberal arts?</code> \| <code>liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.</code> \| <code>liberal arts definition. The areas of learning that cultivate general intellectual ability rather than technical or professional skills. The term liberal arts is often used as a synonym for humanities, although the liberal arts also include the sciences. The word liberal comes from the Latin liberalis, meaning suitable for a free man, as opposed to a slave.</code> \|
	\| <code>what is the mechanism of action of fibrinolytic or thrombolytic drugs?</code> \| <code>BailliÃ¨re's Clinical Haematology. 6 Mechanism of action of the thrombolytic agents. 6 Mechanism of action of the thrombolytic agents JEFFREY I. WEITZ Fibrin formed during the haemostatic, inflammatory or tissue repair process serves a temporary role, and must be degraded to restore normal tissue function and structure.</code> \| <code>Fibrinolytic drug. Fibrinolytic drug, also called thrombolytic drug, any agent that is capable of stimulating the dissolution of a blood clot (thrombus). Fibrinolytic drugs work by activating the so-called fibrinolytic pathway.</code> \|
	\| <code>what is normal plat count</code> \| <code>78 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).The average platelet count is 237,000 per mcL in men and 266,000 per mcL in women.8 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).</code> \| <code>Platelet Count. A platelet count is part of a complete blood count test. It is ordered when a patient experiences unexplainable bruises or when small cuts and wounds take longer time to heal. A normal platelet count of an average person is 150,000 to 450,000 platelets per microliter of blood.Others may have abnormal platelet count but it doesnât indicate any abnormality.latelets, red cells, and plasma are the major components that form the human blood. Platelets are irregular shaped molecules with a colorless body and sticky surface that forms into clots to help stop the bleeding. When a personâs normal platelet count is compromised that personâs life might be put in danger.</code> \|
	* Loss: [<code>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim",
	"mini_batch_size": 64
	}
	```

	#### gooaq

	* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
	* Size: 3,012,496 training samples
	* Columns: <code>anchor</code> and <code>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \|
	\|:--------\|:----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 8 tokens</li><li>mean: 12.19 tokens</li><li>max: 22 tokens</li></ul> \| <ul><li>min: 13 tokens</li><li>mean: 58.34 tokens</li><li>max: 124 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \|
	\|:---------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>is toprol xl the same as metoprolol?</code> \| <code>Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure.</code> \|
	\| <code>are you experienced cd steve hoffman?</code> \| <code>The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design.</code> \|
	\| <code>how are babushka dolls made?</code> \| <code>Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting.</code> \|
	* Loss: [<code>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim",
	"mini_batch_size": 64
	}
	```

	#### natural_questions

	* Dataset: [natural_questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
	* Size: 100,231 training samples
	* Columns: <code>anchor</code> and <code>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 10 tokens</li><li>mean: 12.47 tokens</li><li>max: 23 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 138.32 tokens</li><li>max: 556 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \|
	\|:----------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>when did richmond last play in a preliminary final</code> \| <code>Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tig...</code> \|
	\| <code>who sang what in the world's come over you</code> \| <code>Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel.</code> \|
	\| <code>who produces the most wool in the world</code> \| <code>Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.</code> \|
	* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim",
	"mini_batch_size": 64
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 256
	- `weight_decay`: 0.01
	- `lr_scheduler_type`: cosine
	- `warmup_ratio`: 0.1
	- `bf16`: True
	- `bf16_full_eval`: True
	- `load_best_model_at_end`: True

	#### 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`: 256
	- `per_device_eval_batch_size`: 8
	- `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`: 0.01
	- `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`: cosine
	- `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`: True
	- `fp16`: False
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: True
	- `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`: None
	- `hub_always_push`: False
	- `hub_revision`: None
	- `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
	- `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
	- `liger_kernel_config`: None
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional
	- `router_mapping`: {}
	- `learning_rate_mapping`: {}

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| NanoMSMARCO_cosine_ndcg@10 \| NanoNQ_cosine_ndcg@10 \| NanoHotpotQA_cosine_ndcg@10 \| NanoBEIR_mean_cosine_ndcg@10 \|
	\|:----------:\|:---------:\|:-------------:\|:--------------------------:\|:---------------------:\|:---------------------------:\|:----------------------------:\|
	\| -1 \| -1 \| - \| 0.2890 \| 0.3652 \| 0.5065 \| 0.3869 \|
	\| 0.0708 \| 1000 \| 1.0794 \| - \| - \| - \| - \|
	\| 0.1416 \| 2000 \| 1.007 \| 0.2944 \| 0.3596 \| 0.5221 \| 0.3920 \|
	\| 0.2124 \| 3000 \| 0.9012 \| - \| - \| - \| - \|
	\| 0.2832 \| 4000 \| 0.8323 \| 0.3070 \| 0.3797 \| 0.5284 \| 0.4050 \|
	\| 0.3540 \| 5000 \| 0.7548 \| - \| - \| - \| - \|
	\| 0.4248 \| 6000 \| 0.6963 \| 0.2909 \| 0.3768 \| 0.5560 \| 0.4079 \|
	\| 0.4956 \| 7000 \| 0.6655 \| - \| - \| - \| - \|
	\| 0.5664 \| 8000 \| 0.6235 \| 0.3049 \| 0.3778 \| 0.5497 \| 0.4108 \|
	\| 0.6372 \| 9000 \| 0.6202 \| - \| - \| - \| - \|
	\| 0.7080 \| 10000 \| 0.6276 \| 0.3072 \| 0.3778 \| 0.5613 \| 0.4154 \|
	\| 0.7788 \| 11000 \| 0.6101 \| - \| - \| - \| - \|
	\| 0.8496 \| 12000 \| 0.6016 \| 0.3049 \| 0.3756 \| 0.5635 \| 0.4147 \|
	\| 0.9204 \| 13000 \| 0.6063 \| - \| - \| - \| - \|
	\| 0.9912 \| 14000 \| 0.5905 \| 0.3043 \| 0.3813 \| 0.5626 \| 0.4161 \|
	\| 1.0619 \| 15000 \| 0.5734 \| - \| - \| - \| - \|
	\| 1.1327 \| 16000 \| 0.581 \| 0.3119 \| 0.3764 \| 0.5555 \| 0.4146 \|
	\| 1.2035 \| 17000 \| 0.5744 \| - \| - \| - \| - \|
	\| 1.2743 \| 18000 \| 0.5769 \| 0.3121 \| 0.3682 \| 0.5566 \| 0.4123 \|
	\| 1.3451 \| 19000 \| 0.5773 \| - \| - \| - \| - \|
	\| 1.4159 \| 20000 \| 0.5767 \| 0.3132 \| 0.3656 \| 0.5602 \| 0.4130 \|
	\| 1.4867 \| 21000 \| 0.5662 \| - \| - \| - \| - \|
	\| 1.5575 \| 22000 \| 0.5662 \| 0.3204 \| 0.3656 \| 0.5557 \| 0.4139 \|
	\| 1.6283 \| 23000 \| 0.5586 \| - \| - \| - \| - \|
	\| 1.6991 \| 24000 \| 0.5659 \| 0.3209 \| 0.3664 \| 0.5599 \| 0.4157 \|
	\| 1.7699 \| 25000 \| 0.578 \| - \| - \| - \| - \|
	\| 1.8407 \| 26000 \| 0.5749 \| 0.3132 \| 0.3656 \| 0.5599 \| 0.4129 \|
	\| 1.9115 \| 27000 \| 0.5845 \| - \| - \| - \| - \|
	\| 1.9823 \| 28000 \| 0.5769 \| 0.3132 \| 0.3664 \| 0.5611 \| 0.4136 \|
	\| 2.0531 \| 29000 \| 0.5714 \| - \| - \| - \| - \|
	\| 2.1239 \| 30000 \| 0.5696 \| 0.3132 \| 0.3673 \| 0.5606 \| 0.4137 \|
	\| 2.1947 \| 31000 \| 0.568 \| - \| - \| - \| - \|
	\| 2.2655 \| 32000 \| 0.5767 \| 0.3209 \| 0.3664 \| 0.5602 \| 0.4158 \|
	\| 2.3363 \| 33000 \| 0.5785 \| - \| - \| - \| - \|
	\| 2.4071 \| 34000 \| 0.5666 \| 0.3206 \| 0.3664 \| 0.5604 \| 0.4158 \|
	\| 2.4779 \| 35000 \| 0.5608 \| - \| - \| - \| - \|
	\| 2.5487 \| 36000 \| 0.5563 \| 0.3206 \| 0.3656 \| 0.5602 \| 0.4155 \|
	\| 2.6195 \| 37000 \| 0.5768 \| - \| - \| - \| - \|
	\| 2.6903 \| 38000 \| 0.569 \| 0.3206 \| 0.3664 \| 0.5602 \| 0.4158 \|
	\| 2.7611 \| 39000 \| 0.5723 \| - \| - \| - \| - \|
	\| 2.8319 \| 40000 \| 0.5714 \| 0.3206 \| 0.3664 \| 0.5606 \| 0.4159 \|
	\| 2.9027 \| 41000 \| 0.5621 \| - \| - \| - \| - \|
	\| 2.9735 \| 42000 \| 0.5724 \| 0.3206 \| 0.3664 \| 0.5602 \| 0.4158 \|
	\| -1 \| -1 \| - \| 0.3043 \| 0.3813 \| 0.5626 \| 0.4161 \|

	* The bold row denotes the saved checkpoint.

	### Framework Versions
	- Python: 3.11.13
	- Sentence Transformers: 5.0.0
	- Transformers: 4.53.1
	- PyTorch: 2.7.1+cu128
	- Accelerate: 1.8.1
	- Datasets: 4.0.0
	- Tokenizers: 0.21.2

	## 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",
	}
	```

	#### CachedMultipleNegativesRankingLoss
	```bibtex
	@misc{gao2021scaling,
	title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
	author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
	year={2021},
	eprint={2101.06983},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
	}
	```

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