Add new SentenceTransformer model

7a6cb0a verified 19 days ago

99.9 kB

	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:68534726
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: how to sign legal documents as power of attorney?
	sentences:
	- 'After the principal''s name, write “by” and then sign your own name. Under or
	after the signature line, indicate your status as POA by including any of the
	following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.'
	- Most earthquakes occur along the edge of the oceanic and continental plates. The
	earth's crust (the outer layer of the planet) is made up of several pieces, called
	plates. The plates under the oceans are called oceanic plates and the rest are
	continental plates.
	- Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully
	removed from the configuration.
	- source_sentence: what is upwork
	sentences:
	- Upwork, formerly Elance-oDesk, is a global freelancing platform where businesses
	and independent professionals connect and collaborate remotely.In 2015, Elance-oDesk
	was rebranded as Upwork. It is based out of Mountain View and San Francisco, California.pwork
	has nine million registered freelancers and four million registered clients. Three
	million jobs are posted annually, worth a total of $1 billion USD, making it the
	world's largest freelancer marketplace.
	- Upwork, formerly Elance-oDesk, is a global freelancing platform where businesses
	and independent professionals connect and collaborate remotely.In 2015, Elance-oDesk
	was rebranded as Upwork. It is based out of Mountain View and San Francisco, California.pwork
	has nine million registered freelancers and four million registered clients. Three
	million jobs are posted annually, worth a total of $1 billion USD, making it the
	world's largest freelancer marketplace.
	- 'That is, while fructose consumption may increase uric acid levels, to actually
	precipitate a gout attack, you need to deviate from the narrow band of normal
	blood pH range: 7.35 to 7.45. Ideally you wanna be at 7.45 or slightly above.'
	- source_sentence: how many km is a mile
	sentences:
	- Periodontal disease is a bacterial infection of the gums and bone that if not
	treated, can cause you to lose your teeth. Medical research is now showing that
	these bacteria in your mouth can also travel through your bloodstream into other
	organs in the body.
	- Master the formula for converting kilometers to miles. 1 kilometer is equal to
	0.621371 miles (often shortened to .62).1 mile is equal to 1.609344 kilometers.
	Thus, to convert kilometers to miles, simply multiply the number of kilometers
	by 0.62137. For example, let's say you start with 5 kilometers. People are often
	interested in this conversion because they want to know how many miles are in
	a 5K run. The formula is 5 X 0.62137= 3.1 miles.
	- To find out how many kilometers in miles, multiply by this factor or simply use
	the converter below. 1 Mile = 1.609344 Kilometers. Mile is an imperial and US
	customary length unit and equals to 5280 feet. The abbreviation is mi. Kilometer
	is a metric length unit and equals to 1000 meters.
	- source_sentence: A group of children walking on a trail.
	sentences:
	- The man is performing.
	- Children are walking.
	- The people are adults.
	- source_sentence: A boy with a basketballs glowers at the camera.
	sentences:
	- The boy is smiling
	- The boy scowls
	- Surfer in red catches a wave.
	datasets:
	- sentence-transformers/gooaq
	- sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
	- sentence-transformers/s2orc
	- sentence-transformers/all-nli
	- sentence-transformers/paq
	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: '[REPRODUCE] Static Embeddings with BERT uncased tokenizer finetuned on various
	datasets'
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoClimateFEVER
	type: NanoClimateFEVER
	metrics:
	- type: cosine_accuracy@1
	value: 0.32
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.54
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.64
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.82
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.32
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.152
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.11199999999999999
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.15666666666666665
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.25
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.31633333333333336
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.44133333333333336
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.35027529831718174
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.4537698412698412
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.2754610667422747
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoDBPedia
	type: NanoDBPedia
	metrics:
	- type: cosine_accuracy@1
	value: 0.64
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.88
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.92
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.94
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.64
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.6066666666666667
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.5479999999999999
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.45399999999999996
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.05820050708225643
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.1660478879214754
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.2233296888728599
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.32642161484749216
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.5611886908023029
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7551904761904763
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.42159733554382045
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoFEVER
	type: NanoFEVER
	metrics:
	- type: cosine_accuracy@1
	value: 0.54
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.82
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.84
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.94
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.54
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2733333333333334
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.18
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09999999999999998
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.5066666666666666
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.7566666666666667
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.8033333333333332
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9033333333333333
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7223300246075101
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.6857460317460319
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.6591296848555135
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoFiQA2018
	type: NanoFiQA2018
	metrics:
	- type: cosine_accuracy@1
	value: 0.22
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.44
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.5
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.64
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.22
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.18666666666666668
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.132
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09799999999999999
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.12688888888888888
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.29007936507936505
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.3347460317460317
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.453015873015873
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.33206103177846985
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.34974603174603175
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.2723064374777477
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoHotpotQA
	type: NanoHotpotQA
	metrics:
	- type: cosine_accuracy@1
	value: 0.66
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.82
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.86
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.94
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.66
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.35999999999999993
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.264
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.14799999999999996
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.33
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.54
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.66
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.74
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.6507660730204244
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.746690476190476
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.5743825107321581
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoMSMARCO
	type: NanoMSMARCO
	metrics:
	- type: cosine_accuracy@1
	value: 0.16
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.44
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.54
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.66
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.16
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.14666666666666667
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.10800000000000001
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.066
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.16
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.44
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.54
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.66
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.4069260774532657
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.3269126984126984
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.34104660879940385
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoNFCorpus
	type: NanoNFCorpus
	metrics:
	- type: cosine_accuracy@1
	value: 0.4
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.54
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.6
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.7
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.4
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.34666666666666673
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.3
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.24400000000000002
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.06140064224956239
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.09381944627241434
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.11465220470723159
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.13758064454249494
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.3251344168353932
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.49083333333333345
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.15346080343511273
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoNQ
	type: NanoNQ
	metrics:
	- type: cosine_accuracy@1
	value: 0.2
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.46
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.58
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.68
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.2
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.15333333333333332
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.12000000000000002
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.07400000000000001
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.19
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.44
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.55
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.67
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.4284752232212853
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.3555714285714285
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.35954687250943856
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoQuoraRetrieval
	type: NanoQuoraRetrieval
	metrics:
	- type: cosine_accuracy@1
	value: 0.8
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.92
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.96
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.98
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.8
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.35999999999999993
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.23999999999999996
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.12799999999999997
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.7106666666666667
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8653333333333333
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.9226666666666667
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9593333333333334
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.874423773707081
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.8666666666666666
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.8354028527028526
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoSCIDOCS
	type: NanoSCIDOCS
	metrics:
	- type: cosine_accuracy@1
	value: 0.28
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.52
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.62
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.72
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.28
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.22666666666666666
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.184
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.14
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.059666666666666666
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.1416666666666667
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.18966666666666665
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.2886666666666667
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.2657817193581118
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.4188571428571429
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.20270708890067454
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoArguAna
	type: NanoArguAna
	metrics:
	- type: cosine_accuracy@1
	value: 0.12
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.48
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.6
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.68
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.12
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.15999999999999998
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.12
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.068
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.12
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.48
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.6
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.68
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.4064179360568565
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.31785714285714284
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.33454708384798976
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoSciFact
	type: NanoSciFact
	metrics:
	- type: cosine_accuracy@1
	value: 0.52
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.64
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.68
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.74
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.52
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.22
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.14400000000000002
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.08
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.485
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.61
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.655
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.72
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.6053823991819648
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.5862222222222221
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.5721097562068183
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoTouche2020
	type: NanoTouche2020
	metrics:
	- type: cosine_accuracy@1
	value: 0.5918367346938775
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.9183673469387755
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.9795918367346939
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 1.0
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.5918367346938775
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.5850340136054422
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.6000000000000001
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.5204081632653061
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.0405610423291237
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.12039267252775386
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.20296687044371778
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.3313283589291373
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.5594653746925154
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.749514091350826
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.4414984325557448
	name: Cosine Map@100
	- task:
	type: nano-beir
	name: Nano BEIR
	dataset:
	name: NanoBEIR mean
	type: NanoBEIR_mean
	metrics:
	- type: cosine_accuracy@1
	value: 0.41937205651491377
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.6475667189952904
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.7168916797488225
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.8030769230769231
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.41937205651491377
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2942333856619571
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.23784615384615387
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.17172370486656197
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.23120905747819215
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.399538926035975
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.4702072919822955
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.5623856275385894
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.4991252337717202
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.5464290448780245
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.41870742571611924
	name: Cosine Map@100
	---

	# [REPRODUCE] Static Embeddings with BERT uncased tokenizer finetuned on various datasets

	This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq), [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1), [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc), [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) and [paq](https://huggingface.co/datasets/sentence-transformers/paq) datasets. It maps sentences & paragraphs to a 1024-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: [Unknown](https://huggingface.co/unknown) -->
	- Maximum Sequence Length: inf tokens
	- Output Dimensionality: 1024 dimensions
	- Similarity Function: Cosine Similarity
	- Training Datasets:
	- [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
	- [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1)
	- [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc)
	- [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli)
	- [paq](https://huggingface.co/datasets/sentence-transformers/paq)
	- 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): StaticEmbedding(
	(embedding): EmbeddingBag(30522, 1024, mode='mean')
	)
	)
	```

	## 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("carlfeynman/reproduce-static-retrieval-mrl-en-v1")
	# Run inference
	sentences = [
	'A boy with a basketballs glowers at the camera.',
	'The boy scowls',
	'The boy is smiling',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 1024]

	# 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

	#### Information Retrieval

	* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| NanoClimateFEVER \| NanoDBPedia \| NanoFEVER \| NanoFiQA2018 \| NanoHotpotQA \| NanoMSMARCO \| NanoNFCorpus \| NanoNQ \| NanoQuoraRetrieval \| NanoSCIDOCS \| NanoArguAna \| NanoSciFact \| NanoTouche2020 \|
	\|:--------------------\|:-----------------\|:------------\|:-----------\|:-------------\|:-------------\|:------------\|:-------------\|:-----------\|:-------------------\|:------------\|:------------\|:------------\|:---------------\|
	\| cosine_accuracy@1 \| 0.32 \| 0.64 \| 0.54 \| 0.22 \| 0.66 \| 0.16 \| 0.4 \| 0.2 \| 0.8 \| 0.28 \| 0.12 \| 0.52 \| 0.5918 \|
	\| cosine_accuracy@3 \| 0.54 \| 0.88 \| 0.82 \| 0.44 \| 0.82 \| 0.44 \| 0.54 \| 0.46 \| 0.92 \| 0.52 \| 0.48 \| 0.64 \| 0.9184 \|
	\| cosine_accuracy@5 \| 0.64 \| 0.92 \| 0.84 \| 0.5 \| 0.86 \| 0.54 \| 0.6 \| 0.58 \| 0.96 \| 0.62 \| 0.6 \| 0.68 \| 0.9796 \|
	\| cosine_accuracy@10 \| 0.82 \| 0.94 \| 0.94 \| 0.64 \| 0.94 \| 0.66 \| 0.7 \| 0.68 \| 0.98 \| 0.72 \| 0.68 \| 0.74 \| 1.0 \|
	\| cosine_precision@1 \| 0.32 \| 0.64 \| 0.54 \| 0.22 \| 0.66 \| 0.16 \| 0.4 \| 0.2 \| 0.8 \| 0.28 \| 0.12 \| 0.52 \| 0.5918 \|
	\| cosine_precision@3 \| 0.2 \| 0.6067 \| 0.2733 \| 0.1867 \| 0.36 \| 0.1467 \| 0.3467 \| 0.1533 \| 0.36 \| 0.2267 \| 0.16 \| 0.22 \| 0.585 \|
	\| cosine_precision@5 \| 0.152 \| 0.548 \| 0.18 \| 0.132 \| 0.264 \| 0.108 \| 0.3 \| 0.12 \| 0.24 \| 0.184 \| 0.12 \| 0.144 \| 0.6 \|
	\| cosine_precision@10 \| 0.112 \| 0.454 \| 0.1 \| 0.098 \| 0.148 \| 0.066 \| 0.244 \| 0.074 \| 0.128 \| 0.14 \| 0.068 \| 0.08 \| 0.5204 \|
	\| cosine_recall@1 \| 0.1567 \| 0.0582 \| 0.5067 \| 0.1269 \| 0.33 \| 0.16 \| 0.0614 \| 0.19 \| 0.7107 \| 0.0597 \| 0.12 \| 0.485 \| 0.0406 \|
	\| cosine_recall@3 \| 0.25 \| 0.166 \| 0.7567 \| 0.2901 \| 0.54 \| 0.44 \| 0.0938 \| 0.44 \| 0.8653 \| 0.1417 \| 0.48 \| 0.61 \| 0.1204 \|
	\| cosine_recall@5 \| 0.3163 \| 0.2233 \| 0.8033 \| 0.3347 \| 0.66 \| 0.54 \| 0.1147 \| 0.55 \| 0.9227 \| 0.1897 \| 0.6 \| 0.655 \| 0.203 \|
	\| cosine_recall@10 \| 0.4413 \| 0.3264 \| 0.9033 \| 0.453 \| 0.74 \| 0.66 \| 0.1376 \| 0.67 \| 0.9593 \| 0.2887 \| 0.68 \| 0.72 \| 0.3313 \|
	\| cosine_ndcg@10 \| 0.3503 \| 0.5612 \| 0.7223 \| 0.3321 \| 0.6508 \| 0.4069 \| 0.3251 \| 0.4285 \| 0.8744 \| 0.2658 \| 0.4064 \| 0.6054 \| 0.5595 \|
	\| cosine_mrr@10 \| 0.4538 \| 0.7552 \| 0.6857 \| 0.3497 \| 0.7467 \| 0.3269 \| 0.4908 \| 0.3556 \| 0.8667 \| 0.4189 \| 0.3179 \| 0.5862 \| 0.7495 \|
	\| cosine_map@100 \| 0.2755 \| 0.4216 \| 0.6591 \| 0.2723 \| 0.5744 \| 0.341 \| 0.1535 \| 0.3595 \| 0.8354 \| 0.2027 \| 0.3345 \| 0.5721 \| 0.4415 \|

	#### Nano BEIR

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.4194 \|
	\| cosine_accuracy@3 \| 0.6476 \|
	\| cosine_accuracy@5 \| 0.7169 \|
	\| cosine_accuracy@10 \| 0.8031 \|
	\| cosine_precision@1 \| 0.4194 \|
	\| cosine_precision@3 \| 0.2942 \|
	\| cosine_precision@5 \| 0.2378 \|
	\| cosine_precision@10 \| 0.1717 \|
	\| cosine_recall@1 \| 0.2312 \|
	\| cosine_recall@3 \| 0.3995 \|
	\| cosine_recall@5 \| 0.4702 \|
	\| cosine_recall@10 \| 0.5624 \|
	\| cosine_ndcg@10 \| 0.4991 \|
	\| cosine_mrr@10 \| 0.5464 \|
	\| cosine_map@100 \| 0.4187 \|

	<!--
	## 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

	#### 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>question</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| question \| answer \|
	\|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> \| <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> \|
	* Samples:
	\| question \| answer \|
	\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>what is the difference between broilers and layers?</code> \| <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> \|
	\| <code>what is the difference between chronological order and spatial order?</code> \| <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> \|
	\| <code>is kamagra same as viagra?</code> \| <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	1024,
	512,
	256,
	128,
	64,
	32
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	#### msmarco

	* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
	* Size: 502,939 training samples
	* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| positive \| negative \|
	\|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 11 characters</li><li>mean: 33.26 characters</li><li>max: 197 characters</li></ul> \| <ul><li>min: 96 characters</li><li>mean: 356.24 characters</li><li>max: 1006 characters</li></ul> \| <ul><li>min: 68 characters</li><li>mean: 327.52 characters</li><li>max: 995 characters</li></ul> \|
	* Samples:
	\| query \| positive \| negative \|
	\|:---------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>when was the sullivan acts</code> \| <code>Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law.</code> \| <code>Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law.</code> \|
	\| <code>can lavender grow indoors</code> \| <code>Growing Lavender Indoors. People ALWAYS ask if you can grow lavender indoors. Well, you can, but most Lavender does best outside. Here is our winter experiment to show you what it would look like. This is one of our 4 Lavender Babies from Fall 2010. Our test specimen is L. x intermedia 'Grosso'.</code> \| <code>Lavender can be grown indoors with a bit of effort to keep it in the conditions it loves to thrive. First off begin with choosing a variety that is better able to tolerate the conditions inside a home. To successfully grow Lavender indoors you need to create optimal growing conditions which is hard to do inside a house.</code> \|
	\| <code>what kind of barley do you malt</code> \| <code>Barley is a wonderfully versatile cereal grain with a rich nutlike flavor and an appealing chewy, pasta-like consistency. Its appearance resembles wheat berries, although it is slightly lighter in color. Sprouted barley is naturally high in maltose, a sugar that serves as the basis for both malt syrup sweetener.</code> \| <code>Specialty grains that can be used in this way are usually barley, malted or unmalted, that has been treated differently at the malting company. Crystal malt is one of the specialty grains. It is available in a whole range of colors, from 20 to 120 Lovibond. Crystal malt is malted barley that is heated while wet.</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	1024,
	512,
	256,
	128,
	64,
	32
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	#### s2orc

	* Dataset: [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) at [8cfc394](https://huggingface.co/datasets/sentence-transformers/s2orc/tree/8cfc394e83b2ebfcf38f90b508aea383df742439)
	* Size: 90,000 training samples
	* Columns: <code>title</code> and <code>abstract</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| title \| abstract \|
	\|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 31 characters</li><li>mean: 80.02 characters</li><li>max: 185 characters</li></ul> \| <ul><li>min: 84 characters</li><li>mean: 635.31 characters</li><li>max: 1023 characters</li></ul> \|
	* Samples:
	\| title \| abstract \|
	\|:----------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>Modeling Method of Flow Diversion of the Three Outlets in Jingjiang Reach Under Unsteady Flow Conditions</code> \| <code>The Yangtze River Flood Protection Physical Model is built under the financial support of World Bank loan.Based on theoretical analysis and experimental study,a modeling method of flow diversion of the three outlets in Jingjiang Reach under unsteady flow conditions was established for the model.Validation tests under both steady and unsteady flow conditions manifested that with this modeling method,the experimental flow diversion proves to be consistent with that of the prototype and therefore meets the requirements for precision.Being validated,this modeling method has been applied to Yangtze River Flood Protection Physical Model to study the flood routing features in Jingjiang reach.</code> \|
	\| <code>Enlightening on medical administration by clinical governance in British</code> \| <code>Medical quality and safety were the responsibilities of medical system in view of British clinical governance. Medical regulation institutes were considered to be built and be authorized regulation rights. British medical administration was introduced and its enlightening in China was mentioned.</code> \|
	\| <code>APPLICATION OF A FUZZY MULTI-CRITERIA DECISION-MAKING MODEL FOR SHIPPING COMPANY PERFORMANCE EVALUATION</code> \| <code>Combining fuzzy set theory, Analytic Hierarchy Process (AHP) and concept of entropy, a fuzzy Multiple Criteria Decision-Making (MCDM) model for shipping company performance evaluation is proposed. First, the AHP is used to construct subjective weights for all criteria and sub-criteria. Then, linguistic values characterized by triangular fuzzy numbers and trapezoidal fuzzy numbers are used to denote the evaluation values of all alternatives with respect to various subjective and objective criteria. Finally, the aggregation fuzzy assessment of different shipping companies is ranked to determine the best selection. Utilizing this fuzzy MCDM model, the decision-maker's fuzzy assessment and the trade-off between various evaluations criteria can be taken into account in the aggregation process, thus ensuring more effective and accurate decision-making.</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	1024,
	512,
	256,
	128,
	64,
	32
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	#### allnli

	* Dataset: [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
	* Size: 557,850 training samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 18 characters</li><li>mean: 34.88 characters</li><li>max: 193 characters</li></ul> \| <ul><li>min: 15 characters</li><li>mean: 46.49 characters</li><li>max: 181 characters</li></ul> \| <ul><li>min: 16 characters</li><li>mean: 50.47 characters</li><li>max: 204 characters</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:---------------------------------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------------------------\|
	\| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>A person is outdoors, on a horse.</code> \| <code>A person is at a diner, ordering an omelette.</code> \|
	\| <code>Children smiling and waving at camera</code> \| <code>There are children present</code> \| <code>The kids are frowning</code> \|
	\| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> \| <code>The boy does a skateboarding trick.</code> \| <code>The boy skates down the sidewalk.</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	1024,
	512,
	256,
	128,
	64,
	32
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	#### paq

	* Dataset: [paq](https://huggingface.co/datasets/sentence-transformers/paq) at [74601d8](https://huggingface.co/datasets/sentence-transformers/paq/tree/74601d8d731019bc9c627ffc4271cdd640e1e748)
	* Size: 64,371,441 training samples
	* Columns: <code>query</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| answer \|
	\|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 25 characters</li><li>mean: 50.56 characters</li><li>max: 104 characters</li></ul> \| <ul><li>min: 509 characters</li><li>mean: 620.96 characters</li><li>max: 773 characters</li></ul> \|
	* Samples:
	\| query \| answer \|
	\|:----------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>in veetla visheshanga ganesh is the husband of</code> \| <code>Veetla Visheshanga a song which reminds Ganga's memory. She is actually not Ganga but Gowri and her lover is the groom named Ganesh. When both were about to marry they were stopped by some goons because of which Gowri fell from the mountain but survived with injuries. Gopal who found the truth brought Ganesh to unite them. Gopal insists Gowri to marry Ganesh as both of them are lovers to which Gowri unwillingly accepts. But while Ganesh tries to tie the Mangal Sutra, Gowri stops him and she goes to Gopal saying that he may not need her but she needs him</code> \|
	\| <code>when did simon property group became a publicly traded company</code> \| <code>of the S&P 100. Simon Property Group has been the subject of several lawsuits and investigations regarding civil rights and discrimination. Simon Property Group was formed in 1993 when the majority of the shopping center interests of Melvin Simon & Associates became a publicly traded company. Melvin Simon & Associates, owned by brothers Melvin Simon and Herbert Simon, was founded in 1960 in Indianapolis, Indiana, and had long been one of the top shopping center developers in the United States. In 1996, Simon DeBartolo Group was created when Simon Property merged with former rival DeBartolo Realty Corp. This was shortly</code> \|
	\| <code>what was the nationality of antoine faivre</code> \| <code>Theosophy (Boehmian) below. "Theosophy": The scholar of esotericism Wouter Hanegraaff described Christian theosophy as "one of the major currents in the history of Western esotericism". Christian theosophy is an under-researched area; a general history of it has never been written. The French scholar Antoine Faivre had a specific interest in the theosophers and illuminists of the eighteenth and nineteenth centuries. He wrote his doctoral thesis on Karl von Eckartshausen and Christian theosophy. Scholars of esotericism have argued that Faivre's definition of Western esotericism relies on his own specialist focus on Christian theosophy, Renaissance Hermeticism, and Romantic "Naturphilosophie" and therefore creates an "ideal"</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	1024,
	512,
	256,
	128,
	64,
	32
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	### Evaluation Datasets

	#### 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 evaluation samples
	* Columns: <code>question</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| question \| answer \|
	\|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> \| <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> \|
	* Samples:
	\| question \| answer \|
	\|:-----------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>how do i program my directv remote with my tv?</code> \| <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> \|
	\| <code>are rodrigues fruit bats nocturnal?</code> \| <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> \|
	\| <code>why does your heart rate increase during exercise bbc bitesize?</code> \| <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	1024,
	512,
	256,
	128,
	64,
	32
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	#### msmarco

	* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
	* Size: 502,939 evaluation samples
	* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| positive \| negative \|
	\|:--------\|:------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 10 characters</li><li>mean: 33.36 characters</li><li>max: 137 characters</li></ul> \| <ul><li>min: 67 characters</li><li>mean: 347.87 characters</li><li>max: 906 characters</li></ul> \| <ul><li>min: 57 characters</li><li>mean: 318.18 characters</li><li>max: 906 characters</li></ul> \|
	* Samples:
	\| query \| positive \| negative \|
	\|:-------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>is cabinet refacing worth the cost?</code> \| <code>Fans of refacing say this mini-makeover can give a kitchen a whole new look at a much lower cost than installing all-new cabinets. Cabinet refacing can save up to 50 percent compared to the cost of replacing, says Cheryl Catalano, owner of Kitchen Solvers, a cabinet refacing franchise in Napierville, Illinois. From.</code> \| <code>Most cabinet refacing projects cost about $4,000 to $10,000. The price varies based on the materials you select and the size and configuration of your kitchen. Wood veneer doors, for example, will cost less than solid wood doors.</code> \|
	\| <code>is the fovea ethmoidalis a bone</code> \| <code>Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a âcock's comb,â that projects intracranially and attaches to the falx cerebri.</code> \| <code>Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a âcock's comb,â that projects intracranially and attaches to the falx cerebri.</code> \|
	\| <code>average pitches per inning</code> \| <code>The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work.</code> \| <code>The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work.</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	1024,
	512,
	256,
	128,
	64,
	32
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	#### s2orc

	* Dataset: [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) at [8cfc394](https://huggingface.co/datasets/sentence-transformers/s2orc/tree/8cfc394e83b2ebfcf38f90b508aea383df742439)
	* Size: 10,000 evaluation samples
	* Columns: <code>title</code> and <code>abstract</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| title \| abstract \|
	\|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 31 characters</li><li>mean: 80.04 characters</li><li>max: 198 characters</li></ul> \| <ul><li>min: 96 characters</li><li>mean: 653.93 characters</li><li>max: 1023 characters</li></ul> \|
	* Samples:
	\| title \| abstract \|
	\|:-------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>Screen Printing Ink Film Thickness Analysis of the Passive RFID Tag Antenna</code> \| <code>The relationship between the screen mesh and the theoretical and practical ink film thickness was analyzed based on the main influencing factors of the ink film thickness by screen printing.A calculation model for the ink thickness was established based on the screen under static and compressive deformation.The relation curve between the screen mesh and the ink film thickness was fitted and the suitable printing craft parameter was chosen to print two kinds of RFID tag antennas.The fluctuation of the antenna resistance was analyzed to demonstrate the reliability of the passive RFID tag antenna manufactured by screen printing technology.</code> \|
	\| <code>Subclinical organ damage and cardiovascular risk prediction</code> \| <code>AbstractTraditional cardiovascular risk factors have poor prognostic value for individuals and screening for subclinical organ damage has been recommended in hypertension in recent guidelines. The aim of this review was to investigate the clinical impact of the additive prognostic information provided by measuring subclinical organ damage. We have (i) reviewed recent studies linking markers of subclinical organ damage in the heart, blood vessels and kidney to cardiovascular risk; (ii) discussed the evidence for improvement in cardiovascular risk prediction using markers of subclinical organ damage; (iii) investigated which and how many markers to measure and (iv) finally discussed whether measuring subclinical organ damage provided benefits beyond risk prediction. In conclusion, more studies and if possible randomized studies are needed to investigate (i) the importance of markers of subclinical organ damage for risk discrimination, calibration and reclassification; and (ii) the econom...</code> \|
	\| <code>A Novel Approach to Simulate Climate Change Impacts on Vascular Epiphytes: Case Study in Taiwan</code> \| <code>In the wet tropics, epiphytes form a conspicuous layer in the forest canopy, support abundant coexisting biota, and are known to have a critical influence on forest hydrology and nutrient cycling. Since canopy-dwelling plants have no vascular connection to the ground or their host plants, they are likely more sensitive to environmental changes than their soil-rooted counterparts, subsequently regarded as one of the groups most vulnerable to global climate change. Epiphytes have adapted to life in highly dynamic forest canopies by producing many, mostly wind-dispersed, seeds or spores. Consequently, epiphytes should colonize trees rapidly, which, in addition to atmospheric sensitivity and short life cycles, make epiphytes suitable climate change indicators. In this study, we assess the impact of climate change on Taiwanese epiphytes using a modeling approach.</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	1024,
	512,
	256,
	128,
	64,
	32
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	#### allnli

	* Dataset: [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
	* Size: 6,584 evaluation samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 15 characters</li><li>mean: 72.82 characters</li><li>max: 300 characters</li></ul> \| <ul><li>min: 12 characters</li><li>mean: 34.11 characters</li><li>max: 126 characters</li></ul> \| <ul><li>min: 11 characters</li><li>mean: 36.38 characters</li><li>max: 121 characters</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------\|:--------------------------------------------------------\|
	\| <code>Two women are embracing while holding to go packages.</code> \| <code>Two woman are holding packages.</code> \| <code>The men are fighting outside a deli.</code> \|
	\| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> \| <code>Two kids in numbered jerseys wash their hands.</code> \| <code>Two kids in jackets walk to school.</code> \|
	\| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> \| <code>A man selling donuts to a customer.</code> \| <code>A woman drinks her coffee in a small cafe.</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	1024,
	512,
	256,
	128,
	64,
	32
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	#### paq

	* Dataset: [paq](https://huggingface.co/datasets/sentence-transformers/paq) at [74601d8](https://huggingface.co/datasets/sentence-transformers/paq/tree/74601d8d731019bc9c627ffc4271cdd640e1e748)
	* Size: 64,371,441 evaluation samples
	* Columns: <code>query</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| answer \|
	\|:--------\|:-----------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 25 characters</li><li>mean: 51.3 characters</li><li>max: 108 characters</li></ul> \| <ul><li>min: 504 characters</li><li>mean: 623.09 characters</li><li>max: 835 characters</li></ul> \|
	* Samples:
	\| query \| answer \|
	\|:---------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>when did season 3 of the voice brasil start</code> \| <code>The Voice Brasil (season 3) The third season of "The Voice Brasil", premiered on Rede Globo on September 18, 2014 in the 10:30 p.m. (BRT/AMT) slot immediately following the primetime telenovela "Império". The 22- and 24-year-old sertanejo duo Danilo Reis e Rafael won the competition on December 25, 2014 with 43% of the votes cast. This marked Lulu Santos' first win as a coach, the first stolen artist to win a Brazilian season of "The Voice", and the first time in any "The Voice" franchise that a duo won the competition. Online applications for "The Voice Brasil" were open on</code> \|
	\| <code>when did the little ranger first come out</code> \| <code>Gang" theme song was an instrumental medley of "London Bridge", "Here We Go Round the Mulberry Bush" and "The Farmer in the Dell". It remained in use until the series ended in 1944. The Little Ranger The Little Ranger is a 1938 "Our Gang" short comedy film directed by Gordon Douglas. It was the 169th short in the "Our Gang" series, and the first produced by Metro-Goldwyn-Mayer, who purchased the rights to the series from creator Hal Roach. Snubbed by his girlfriend Darla, Alfalfa accepts the invitation of tomboyish Muggsy to attend the local picture show. While watching the adventures</code> \|
	\| <code>what is the name of rachel's sister in ninjaaiden</code> \| <code>her among ten female characters who have never been featured on their games' cover arts, Samir Torres of VentureBeat wrote that while "Team Ninja sexualy exploits all of their female characters, yet Rachel somehow got axed from every modern "Ninja Gaiden" box art." Rachel (Ninja Gaiden) In 2004's "Ninja Gaiden", Rachel is a fiend hunter whom the game's protagonist Ryu Hayabusa meets in the Holy Vigoor Empire, where she is on a mission to destroy the fiends, as well as find her missing sister, Alma, who has become a Greater Fiend. Soon after they first meet, she is captured but</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	1024,
	512,
	256,
	128,
	64,
	32
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 16384
	- `per_device_eval_batch_size`: 4096
	- `learning_rate`: 0.2
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 16384
	- `per_device_eval_batch_size`: 4096
	- `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`: 0.2
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 1
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.1
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: False
	- `fp16`: True
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: None
	- `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
	\| Epoch \| Step \| Training Loss \| gooaq loss \| msmarco loss \| s2orc loss \| allnli loss \| paq loss \| NanoClimateFEVER_cosine_ndcg@10 \| NanoDBPedia_cosine_ndcg@10 \| NanoFEVER_cosine_ndcg@10 \| NanoFiQA2018_cosine_ndcg@10 \| NanoHotpotQA_cosine_ndcg@10 \| NanoMSMARCO_cosine_ndcg@10 \| NanoNFCorpus_cosine_ndcg@10 \| NanoNQ_cosine_ndcg@10 \| NanoQuoraRetrieval_cosine_ndcg@10 \| NanoSCIDOCS_cosine_ndcg@10 \| NanoArguAna_cosine_ndcg@10 \| NanoSciFact_cosine_ndcg@10 \| NanoTouche2020_cosine_ndcg@10 \| NanoBEIR_mean_cosine_ndcg@10 \|
	\|:------:\|:----:\|:-------------:\|:----------:\|:------------:\|:----------:\|:-----------:\|:--------:\|:-------------------------------:\|:--------------------------:\|:------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:---------------------:\|:---------------------------------:\|:--------------------------:\|:--------------------------:\|:--------------------------:\|:-----------------------------:\|:----------------------------:\|
	\| 0.0002 \| 1 \| 43.5181 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0597 \| 250 \| 17.804 \| 2.1081 \| 12.8291 \| 10.8194 \| 14.2895 \| 5.3792 \| 0.3202 \| 0.5446 \| 0.6721 \| 0.3176 \| 0.6222 \| 0.3867 \| 0.3022 \| 0.3952 \| 0.8741 \| 0.2474 \| 0.3986 \| 0.5913 \| 0.5463 \| 0.4783 \|
	\| 0.1195 \| 500 \| 9.6842 \| 1.6991 \| 12.2374 \| 10.6084 \| 13.9790 \| 4.7183 \| 0.3148 \| 0.5759 \| 0.7063 \| 0.3640 \| 0.6250 \| 0.3846 \| 0.2832 \| 0.4168 \| 0.8659 \| 0.2537 \| 0.3744 \| 0.5732 \| 0.5509 \| 0.4837 \|
	\| 0.1792 \| 750 \| 8.7691 \| 1.6922 \| 12.0631 \| 10.3970 \| 12.4485 \| 4.4473 \| 0.3496 \| 0.5664 \| 0.7157 \| 0.3179 \| 0.6585 \| 0.3826 \| 0.2934 \| 0.4040 \| 0.8782 \| 0.2523 \| 0.3845 \| 0.5962 \| 0.5502 \| 0.4884 \|
	\| 0.2389 \| 1000 \| 8.606 \| 1.6685 \| 11.7765 \| 10.2828 \| 12.4139 \| 4.2823 \| 0.3509 \| 0.5636 \| 0.7026 \| 0.3249 \| 0.6562 \| 0.4049 \| 0.3123 \| 0.4174 \| 0.8673 \| 0.2657 \| 0.3969 \| 0.5582 \| 0.5514 \| 0.4902 \|
	\| 0.2987 \| 1250 \| 8.4178 \| 1.6072 \| 11.7581 \| 9.2590 \| 12.8865 \| 4.2231 \| 0.3341 \| 0.5587 \| 0.7103 \| 0.3354 \| 0.6534 \| 0.4033 \| 0.3116 \| 0.4294 \| 0.8663 \| 0.2718 \| 0.4048 \| 0.5891 \| 0.5466 \| 0.4934 \|
	\| 0.3584 \| 1500 \| 8.1084 \| 1.6751 \| 11.8237 \| 9.8291 \| 11.5805 \| 4.1559 \| 0.3345 \| 0.5668 \| 0.7094 \| 0.3287 \| 0.6535 \| 0.3948 \| 0.3311 \| 0.4098 \| 0.8632 \| 0.2649 \| 0.4171 \| 0.5913 \| 0.5514 \| 0.4936 \|
	\| 0.4182 \| 1750 \| 7.9489 \| 1.5858 \| 11.8367 \| 9.8385 \| 13.0328 \| 4.0980 \| 0.3543 \| 0.5464 \| 0.6984 \| 0.3158 \| 0.6582 \| 0.3862 \| 0.3233 \| 0.4201 \| 0.8665 \| 0.2743 \| 0.3924 \| 0.5909 \| 0.5577 \| 0.4911 \|
	\| 0.4779 \| 2000 \| 8.2594 \| 1.6123 \| 11.8052 \| 9.9075 \| 11.3651 \| 4.0788 \| 0.3491 \| 0.5551 \| 0.7208 \| 0.3235 \| 0.6570 \| 0.4058 \| 0.3220 \| 0.4215 \| 0.8801 \| 0.2629 \| 0.4143 \| 0.5998 \| 0.5514 \| 0.4972 \|
	\| 0.5376 \| 2250 \| 8.299 \| 1.6416 \| 11.7180 \| 9.9462 \| 10.7895 \| 4.0423 \| 0.3636 \| 0.5582 \| 0.7071 \| 0.3048 \| 0.6649 \| 0.3951 \| 0.3248 \| 0.4316 \| 0.8804 \| 0.2561 \| 0.4252 \| 0.6036 \| 0.5484 \| 0.4972 \|
	\| 0.5974 \| 2500 \| 7.7807 \| 1.6518 \| 11.7898 \| 9.9235 \| 11.1670 \| 4.0001 \| 0.3639 \| 0.5556 \| 0.7288 \| 0.3148 \| 0.6525 \| 0.3979 \| 0.3178 \| 0.4436 \| 0.8860 \| 0.2593 \| 0.4208 \| 0.5935 \| 0.5581 \| 0.4994 \|
	\| 0.6571 \| 2750 \| 7.8997 \| 1.5797 \| 11.6813 \| 9.5124 \| 11.4893 \| 3.9633 \| 0.3465 \| 0.5562 \| 0.7084 \| 0.3101 \| 0.6631 \| 0.4102 \| 0.3194 \| 0.4410 \| 0.8805 \| 0.2566 \| 0.4261 \| 0.5983 \| 0.5552 \| 0.4978 \|
	\| 0.7168 \| 3000 \| 8.0204 \| 1.5620 \| 11.6746 \| 9.6655 \| 10.8783 \| 3.9539 \| 0.3439 \| 0.5569 \| 0.7295 \| 0.3173 \| 0.6606 \| 0.4129 \| 0.3180 \| 0.4521 \| 0.8888 \| 0.2576 \| 0.4012 \| 0.6065 \| 0.5560 \| 0.5001 \|
	\| 0.7766 \| 3250 \| 8.0225 \| 1.4596 \| 11.5664 \| 9.6954 \| 10.9838 \| 3.9493 \| 0.3496 \| 0.5626 \| 0.7239 \| 0.3330 \| 0.6551 \| 0.4197 \| 0.3129 \| 0.4491 \| 0.8893 \| 0.2726 \| 0.4061 \| 0.6103 \| 0.5555 \| 0.5031 \|
	\| 0.8363 \| 3500 \| 7.6933 \| 1.5522 \| 11.6974 \| 9.1753 \| 11.2026 \| 3.9082 \| 0.3581 \| 0.5570 \| 0.7170 \| 0.3216 \| 0.6492 \| 0.4018 \| 0.3204 \| 0.4360 \| 0.8841 \| 0.2675 \| 0.4031 \| 0.6052 \| 0.5553 \| 0.4982 \|
	\| 0.8961 \| 3750 \| 7.711 \| 1.5267 \| 11.6615 \| 9.4673 \| 11.3195 \| 3.8847 \| 0.3563 \| 0.5613 \| 0.7162 \| 0.3265 \| 0.6497 \| 0.4109 \| 0.3253 \| 0.4384 \| 0.8713 \| 0.2657 \| 0.4195 \| 0.6058 \| 0.5566 \| 0.5003 \|
	\| 0.9558 \| 4000 \| 7.8549 \| 1.5300 \| 11.6244 \| 9.1383 \| 11.0781 \| 3.8785 \| 0.3533 \| 0.5609 \| 0.7153 \| 0.3285 \| 0.6528 \| 0.4069 \| 0.3250 \| 0.4382 \| 0.8744 \| 0.2642 \| 0.4068 \| 0.5961 \| 0.5595 \| 0.4986 \|
	\| 1.0 \| 4185 \| - \| - \| - \| - \| - \| - \| 0.3503 \| 0.5612 \| 0.7223 \| 0.3321 \| 0.6508 \| 0.4069 \| 0.3251 \| 0.4285 \| 0.8744 \| 0.2658 \| 0.4064 \| 0.6054 \| 0.5595 \| 0.4991 \|


	### Framework Versions
	- Python: 3.10.15
	- Sentence Transformers: 3.3.1
	- Transformers: 4.47.1
	- PyTorch: 2.4.1
	- Accelerate: 1.1.1
	- Datasets: 3.2.0
	- Tokenizers: 0.21.0

	## 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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