ot-midterm-v0 / README.md

Add new SentenceTransformer model

f54f71c verified 20 days ago

34.1 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:812
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: Snowflake/snowflake-arctic-embed-l
	widget:
	- source_sentence: 1. What ergonomic factors contributed to the incidence of lateral
	and medial epicondylitis among workers?
	sentences:
	- "ble, science-based suggestions for alterations during work-related, \nmusical\
	\ performance, or sporting activities. \nUse of the NIH framework of behavioral\
	\ study designs provided \nopportunity to examine the progression of development\
	\ and bene- \nﬁts and measurement used in ergonomic studies. 25 \nDirect measurements\
	\ to conﬁrm the effectiveness and accuracy \nof implemented ergonomic adaptations\
	\ were found in stage 0 \nand stage 1 studies. In stage 0, the basic science and\
	\ proof of \nprinciple stage, intervention development consistently included \n\
	measurements of motion and/or muscle activation measurements. \nVisualizations\
	\ of ergonomic design implementations or visual- \nizations of exercises applied\
	\ in the work setting were shown in"
	- "47. Ljung BO , Forsgren S , Fridén J . Substance P and calcitonin gene-related\
	\ peptide \nexpression at the extensor carpi radialis brevis muscle origin: implications\
	\ for \nthe etiology of tennis elbow. J Orthop Res . 1999;17:554–559 . \n48. Alfredson\
	\ H , Ljung BO , Thorsen K , Lorentzon R . In vivo investigation of ECRB \ntendons\
	\ with microdialysis technique–no signs of inﬂammation but high \namounts of glutamate\
	\ in tennis elbow. Acta Orthop Scand . 20 0 0;71:475–479 . \n49. Nirschl RP .\
	\ Lateral epicondylitis/tendinosis. In: Morrey BF, Sanchez-Sotelo J, \nMorrey\
	\ ME, eds. The Elbow and Its Disorders Elsevier; 2018:574–581 . \n50. Cha YK ,\
	\ Kim SJ , Park NH , Kim JY , Kim JH , Park JY . Magnetic resonance"
	- "shoulder, 21 of 88 elbow (19 lateral \nepicondylitis, 2 medial epicondylitis),\
	\ 19 of \n88 wrist, 13 of 88 ﬁngers 51.1% could not \nreturn to the same job \n\
	Tool: intolerance to vibration still present \nafter 4 Y, especially for women\
	\ \nErgonomic factors: poor seating, reaching, \npostures, and bad tool design\
	\ \nInterventions: decreased incidence rate of \nLE from 2.1 to 0.1 \nSmith 2001\
	\ Demonstrate the impact of a \nconcern by one worker to \nraise awareness of\
	\ \ndepartmental problems, \nleading to general packaging \nimprovements made\
	\ by the \nsupplier of seals of vacutainer \nneedles \nCase report \nA phlebotomist\
	\ with LE due to \nforceful gripping and \nrepetitive twisting of seal on \nvacutainer\
	\ needles \nRest: temporary rest from job"
	- source_sentence: 1. What were the effects of exercise compared to ergonomics on
	pain levels in the shoulder, elbows, and hand/wrists?
	sentences:
	- "palpation, maximal muscle \nstrength of arm and hand, \nfunction of arm and hand,\
	\ \nDisability of the Arm, Shoulder, \nand Hand questionnaire \nPain decreased\
	\ more in the exercise versus \nthe ergonomics group in the shoulder, \nelbows\
	\ and hand/wrists. The DASH scores \nincreased in the ergonomics group, but \n\
	decreased in the strengthening group. \nNumber of subjects with improved results\
	\ \nwere signiﬁcantly higher in the exercise \ngroup as compared to the ergonomics\
	\ \ngroup. \nResults can be generalized to adults with \nupper limb chronic pain\
	\ exposed to highly \nrepetitive and forceful manual work \nSoler-Font 2019 Compare\
	\ effects of 3 types of \nprevention interventions on \npain and work functioning\
	\ \nbetween nursing and control \ngroup"
	- "pose, study design and methods, study sample, intervention or ex- \nposures (when\
	\ appropriate), type of measurement and results or \noutcomes. The detailed information\
	\ in Tables 1 through 4 are listed \nin the appendix. A synthesis table, Table\
	\ 5, was constructed to in- \ntegrate the biomechanical and population exposure\
	\ results into a \nset of deﬁned harm reducing recommendations, and is presented\
	\ \nin the results section. \nResults \nTissue involvement and tissue-level interventions\
	\ \nAn anatomical landscape of the lateral elbow \nAn in-depth description of\
	\ elbow anatomy is provided by Mor- \nrey, 29 who describes the architecture of\
	\ the osteology, elbow joint \nstructures including their joint capsules and ligaments,\
	\ bursae, ves-"
	- "strength, and functional scores at 6 months when compared to use \nof a wrist\
	\ orthosis and corticosteroid injections. 84 \nVarious types of interventions\
	\ are used to alter or lessen the \nload on impacted tissues. The beneﬁt of unloading\
	\ or alteration of \nthe area of loading is based on the hypothesis that loading\
	\ of a \npainful tendon perpetuates nociceptive stimuli, and that the sec- \n\
	ondary hyperalgesia in tendinopathy is a response to ongoing no- \nciception.\
	\ 38 Counterforce bracing has been shown to alter force"
	- source_sentence: 1. What are the differences in strength and efficiency between
	the ECRB and ECRL muscles during isometric and dynamic conditions?
	sentences:
	- "about twice as great for the ECRB compared to \nthe ECRL muscle. \nECRB and ECRL\
	\ are eﬃcient synergists; the ECRB \nis stronger isometrically, the ECRL becomes\
	\ the \nstronger muscle as angular velocity increases. The \nsynergy of the ECRB\
	\ and ECRL takes 30% less \nmass in comparison to if one muscle would \ngenerate\
	\ the force. \nLjung 1999 To measure sarcomere length change in \nthe ECRB muscle\
	\ during ulnar deviation \nwith the wrist in both the neutral and \npronated position.\
	\ \nRepeated measures of sarcomere length \nchanges in 4 conditions: (1) wrist\
	\ in \nneutral (in radial-ulnar and forearm in \nneutral rotation), (2) forearm\
	\ neutral \nrotation + wrist in ulnar deviation, (3) \nwrist in neutral + forearm\
	\ in pronation,"
	- "oping LE. A high exposure (RSI > 5), older age, and self-perceived \npoor general\
	\ health were associated with incidence of LE. 133"
	- "lessen excursion of pronation and supination. For \ninstance, change tennis technique\
	\ to balance foot \nsupport, trunk rotation, and arm contribution to the \nforce\
	\ application to the racket, instead of getting the \nforce and speed mostly out\
	\ of the arm. \nHigher force production takes place during \neccentric contractions\
	\ for ECRB and ECRL. \nRepetitive movement with eccentric wrist \nextensor contractions\
	\ \n• Avoid or modify activities that require full-length \nweighted stretch of\
	\ ECRL and ECRB. \n• Seek design solutions so that objects do not need to \nbe\
	\ lowered manually at a fast rate. \nThe synergy of the ECRL and ERCB is a useful\
	\ \nmechanism for optimal function at higher \nangular velocities. \nHigh rapid\
	\ rate of force development,"
	- source_sentence: 1. What type of trial was conducted to compare corticoid and saline
	treatments in the study published in the Am J Sports Med in 2013?
	sentences:
	- "lessen excursion of pronation and supination. For \ninstance, change tennis technique\
	\ to balance foot \nsupport, trunk rotation, and arm contribution to the \nforce\
	\ application to the racket, instead of getting the \nforce and speed mostly out\
	\ of the arm. \nHigher force production takes place during \neccentric contractions\
	\ for ECRB and ECRL. \nRepetitive movement with eccentric wrist \nextensor contractions\
	\ \n• Avoid or modify activities that require full-length \nweighted stretch of\
	\ ECRL and ECRB. \n• Seek design solutions so that objects do not need to \nbe\
	\ lowered manually at a fast rate. \nThe synergy of the ECRL and ERCB is a useful\
	\ \nmechanism for optimal function at higher \nangular velocities. \nHigh rapid\
	\ rate of force development,"
	- "histology \nControls: tendon with tendinous plate (aponeurosis) \nExtensor carpi\
	\ radialis brevis (ECRB) degeneration \noccurs with age > 50 (not the peak years\
	\ for LE), \ntherefore age > 50 not a factor for LE \nLE: edema in aponeurotic\
	\ tissues, underneath \naponeurosis granulation tissue, with ﬁbrosis, and \nfree\
	\ nerve endings (pain), hypervascularization of \nthe aponeurosis \nLjung Forsgren,\
	\ Friden \n1990 \nTo describe substance P and calcitonin \ngene-related peptide\
	\ (CGRP) in \npatients with LE and healthy \nsubjects \nCross-sectional cohort\
	\ comparison of 6 \npatients intra-operatively, and biopsies \nof 6 healthy volunteers\
	\ \nSpecimens from patients included area \nclose to the bone, but area close\
	\ to the \nbone was not included in healthy \nsubjects"
	- "corticoid, or saline: a randomized, double-blind, placebo-controlled trial. Am\
	\ J \nSports Med . 2013;41:625–635 . \n193. Houck DA , Kraeutler MJ , Thornton\
	\ LB , McCarty EC , Bravman JT .T r e a t m e n t of \nlateral epicondylitis with\
	\ autologous blood, platelet-rich plasma, or corticos- \nteroid injections: a\
	\ systematic review of overlapping meta-analyses. Orthop J \nSports Med . 2019;7\
	\ . \n194. Li A , Wang H , Yu Z , et al. Platelet-rich plasma vs corticosteroids\
	\ for elbow \nepicondylitis: A systematic review and meta-analysis. Medicine (Baltimore)\
	\ . \n2019;98:e18358 . \n195. Arirachakaran A , Sukthuayat A , Sisayanarane T\
	\ , Laoratanavoraphong S , Kan- \nchanatawan W , Kongtharvonskul J . Platelet-rich\
	\ plasma versus autologous"
	- source_sentence: 1. What was the focus of the pilot study mentioned regarding tendinosis
	of the Achilles tendon?
	sentences:
	- "C.W. Stegink-Jansen, J.G. Bynum, A.L. Lambropoulos et al. / Journal of Hand Therapy\
	\ 34 (2021) 263–297 285 \nTable 3 ( continued ) \nAUTHOR/YEAR PURPOSE DESIGN SUBJECTS\
	\ EXPOSURES EXPOSURE MEASUREMENTS RESULTS \nHerquelot et al. 2013b \nScand J Work\
	\ Environ \nHealth 39(6):578-588 \nEstimate association between \noccupational\
	\ risk factors and \nincidence of LE \nCohort study (cross-sectional and \nincidence)\
	\ \n3710 workers; 1046 completed \nfollow-up \nRepetition, physical exertion,\
	\ arm \nmovements \nWorker: health assessment \nHazards in work: self-reported\
	\ \nRepetitive tasks and high physical \nexertion with elbow movements \ncontributed\
	\ to incidence of LE \nNordander et al. 2013 Explore relationships between \n\
	occupational risk factors and"
	- "179. Dick FD , Graveling RA , Munro W , Walker-Bone K . Workplace management\
	\ of \nupper limb disorders: a systematic review. Occup Med (Lond) . 2011;61:19–25\
	\ . \n180. Buchanan H , Van Niekerk L , Grimmer K . Work transition after hand\
	\ injury: a \nscoping review. J Hand Ther . 2020 . \n181. Rost KA , Alvero AM\
	\ . Participatory approaches to workplace safety man- \nagement: bridging the\
	\ gap between behavioral safety and participatory er- \ngonomics. Int J Occup\
	\ Saf Ergon . 2020;26:194–203 . \n182. Bernardes JM , Ruiz-Frutos C , Moro ARP\
	\ , Dias A . A low-cost and eﬃcient par- \nticipatory ergonomic intervention to\
	\ reduce the burden of work-related mus- \nculoskeletal disorders in an industrially\
	\ developing country: an experience re-"
	- "tendinosis of the Achilles tendon: a pilot study. AJR Am J Roentgenol . \n2007;189:W215–W220\
	\ . \n74. van Leeuwen WF , Janssen SJ , Ring D , Chen N . Incidental magnetic\
	\ resonance \nimaging signal changes in the extensor carpi radialis brevis origin\
	\ are more \ncommon with age. J Shoulder Elbow Surg . 2016;25:1175–1181 . \n75.\
	\ Rabago D , Lee KS , Ryan M , et al. Hypertonic dextrose and morrhuate sodium\
	\ \ninjections (prolotherapy) for lateral epicondylosis (tennis elbow): results\
	\ of a \nsingle-blind, pilot-level, randomized controlled trial. Am J Phys Med\
	\ Rehabil . \n2013;92:587–596 . \n76. Scarpone M , Rabago DP , Zgierska A , Arbogast\
	\ G , Snell E . The eﬃcacy \nof prolotherapy for lateral epicondylosis: a pilot\
	\ study. Clin J Sport Med ."
	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: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: cosine_accuracy@1
	value: 0.9545454545454546
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 1.0
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 1.0
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 1.0
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.9545454545454546
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.33333333333333326
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.20000000000000007
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.10000000000000003
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.9545454545454546
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 1.0
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 1.0
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 1.0
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.9832240797077936
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.9772727272727273
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.9772727272727273
	name: Cosine Map@100
	---

	# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). 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: [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 1024 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

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

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Normalize()
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

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

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

	# Download from the 🤗 Hub
	model = SentenceTransformer("shivXy/ot-midterm-v0")
	# Run inference
	sentences = [
	'1. What was the focus of the pilot study mentioned regarding tendinosis of the Achilles tendon?',
	'tendinosis of the Achilles tendon: a pilot study. AJR Am J Roentgenol . \n2007;189:W215–W220 . \n74. van Leeuwen WF , Janssen SJ , Ring D , Chen N . Incidental magnetic resonance \nimaging signal changes in the extensor carpi radialis brevis origin are more \ncommon with age. J Shoulder Elbow Surg . 2016;25:1175–1181 . \n75. Rabago D , Lee KS , Ryan M , et al. Hypertonic dextrose and morrhuate sodium \ninjections (prolotherapy) for lateral epicondylosis (tennis elbow): results of a \nsingle-blind, pilot-level, randomized controlled trial. Am J Phys Med Rehabil . \n2013;92:587–596 . \n76. Scarpone M , Rabago DP , Zgierska A , Arbogast G , Snell E . The eﬃcacy \nof prolotherapy for lateral epicondylosis: a pilot study. Clin J Sport Med .',
	'179. Dick FD , Graveling RA , Munro W , Walker-Bone K . Workplace management of \nupper limb disorders: a systematic review. Occup Med (Lond) . 2011;61:19–25 . \n180. Buchanan H , Van Niekerk L , Grimmer K . Work transition after hand injury: a \nscoping review. J Hand Ther . 2020 . \n181. Rost KA , Alvero AM . Participatory approaches to workplace safety man- \nagement: bridging the gap between behavioral safety and participatory er- \ngonomics. Int J Occup Saf Ergon . 2020;26:194–203 . \n182. Bernardes JM , Ruiz-Frutos C , Moro ARP , Dias A . A low-cost and eﬃcient par- \nticipatory ergonomic intervention to reduce the burden of work-related mus- \nculoskeletal disorders in an industrially developing country: an experience re-',
	]
	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

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.9545 \|
	\| cosine_accuracy@3 \| 1.0 \|
	\| cosine_accuracy@5 \| 1.0 \|
	\| cosine_accuracy@10 \| 1.0 \|
	\| cosine_precision@1 \| 0.9545 \|
	\| cosine_precision@3 \| 0.3333 \|
	\| cosine_precision@5 \| 0.2 \|
	\| cosine_precision@10 \| 0.1 \|
	\| cosine_recall@1 \| 0.9545 \|
	\| cosine_recall@3 \| 1.0 \|
	\| cosine_recall@5 \| 1.0 \|
	\| cosine_recall@10 \| 1.0 \|
	\| cosine_ndcg@10 \| 0.9832 \|
	\| cosine_mrr@10 \| 0.9773 \|
	\| cosine_map@100 \| 0.9773 \|

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Dataset

	#### Unnamed Dataset

	* Size: 812 training samples
	* Columns: <code>sentence_0</code> and <code>sentence_1</code>
	* Approximate statistics based on the first 812 samples:
	\| \| sentence_0 \| sentence_1 \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 12 tokens</li><li>mean: 24.85 tokens</li><li>max: 60 tokens</li></ul> \| <ul><li>min: 8 tokens</li><li>mean: 158.41 tokens</li><li>max: 320 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>2. What type of intervention is being compared to strength training in the study protocol by Sundstrup E and colleagues?</code> \| <code>ment for work-related lateral epicondylitis. Work . 2010;37:81–86 . <br>161. Parimalam P , Premalatha MR , Padmini DS , Ganguli AK . Participatory er- <br>gonomics in redesigning a dyeing tub for fabric dyers. Work . 2012;43:453–458 . <br>162. Harari D , Casarotto RA . Effectiveness of a multifaceted intervention to manage <br>musculoskeletal disorders in workers of a medium-sized company. Int J Occup <br>Saf Ergon . 2021;27:247–257 . <br>163. Sundstrup E , Jakobsen MD , Andersen CH , et al. Participatory ergonomic inter- <br>vention versus strength training on chronic pain and work disability in slaugh- <br>terhouse workers: study protocol for a single-blind, randomized controlled <br>trial. BMC Musculoskelet Disord . 2013;14:67 .</code> \|
	\| <code>2. What does the increased signal intensity in the proximal portion of the lateral collateral ligament suggest about the patient's condition?</code> \| <code>266 C.W. Stegink-Jansen, J.G. Bynum, A.L. Lambropoulos et al. / Journal of Hand Therapy 34 (2021) 263–297 <br>Fig. 3. Pathology. A 60-year-old female with right elbow pain for 5 weeks. (A) Coronal fat-suppressed FSE T2-weighted image showing mild thickening of the proximal <br>portion of the common extensor tendon with increased signal intensity (arrow), suggesting mild injury. Irregular thickening with increased signal intensity in the proximal <br>portion of the lateral collateral ligament (arrowhead) is also noted, suggesting mild injury. (B) and (C) Coronal PD FSE image and oblique radiograph showing cortical</code> \|
	\| <code>2. What factors are assessed in relation to lower extremity (LE) issues according to the systematic review?</code> \| <code>Manufacturing: <br>• Electronics <br>• Auto parts <br>• Windows <br>• Cabinets <br>• Medical equipment <br>• Fitness equipment <br>Healthcare (excluding direct <br>patient care): <br>• Hospitals <br>• Health research <br>Worker: structured interviews, <br>physical examinations <br>Environment: workplace walk <br>through <br>Hazards in work: individual <br>assessments of biomechanical and <br>psychosocial factors <br>LE related to frequency of forceful <br>exertions or forearm supination and <br>forceful lifting; increased odds of LE <br>related to being age 36-50, female, <br>or a smoker; high social support <br>appeared protective against LE <br>van Rijn et al. 2009 Assess relationship between <br>work-related physical factors, <br>psychosocial factors, and LE <br>Systematic review <br>13 studies: <br>• 9 cross-sectional</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 10
	- `per_device_eval_batch_size`: 10
	- `num_train_epochs`: 10
	- `multi_dataset_batch_sampler`: round_robin

	#### 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`: 10
	- `per_device_eval_batch_size`: 10
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 5e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1
	- `num_train_epochs`: 10
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `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`: False
	- `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`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| cosine_ndcg@10 \|
	\|:------:\|:----:\|:-------------:\|:--------------:\|
	\| 0.6098 \| 50 \| - \| 1.0 \|
	\| 1.0 \| 82 \| - \| 0.9773 \|
	\| 1.2195 \| 100 \| - \| 0.9664 \|
	\| 1.8293 \| 150 \| - \| 1.0 \|
	\| 2.0 \| 164 \| - \| 0.9832 \|
	\| 2.4390 \| 200 \| - \| 0.9832 \|
	\| 3.0 \| 246 \| - \| 0.9832 \|
	\| 3.0488 \| 250 \| - \| 0.9832 \|
	\| 3.6585 \| 300 \| - \| 0.9832 \|
	\| 4.0 \| 328 \| - \| 0.9832 \|
	\| 4.2683 \| 350 \| - \| 0.9832 \|
	\| 4.8780 \| 400 \| - \| 0.9832 \|
	\| 5.0 \| 410 \| - \| 0.9832 \|
	\| 5.4878 \| 450 \| - \| 0.9832 \|
	\| 6.0 \| 492 \| - \| 0.9832 \|
	\| 6.0976 \| 500 \| 0.6578 \| 0.9832 \|
	\| 6.7073 \| 550 \| - \| 0.9664 \|
	\| 7.0 \| 574 \| - \| 0.9664 \|
	\| 7.3171 \| 600 \| - \| 0.9664 \|
	\| 7.9268 \| 650 \| - \| 0.9664 \|
	\| 8.0 \| 656 \| - \| 0.9664 \|
	\| 8.5366 \| 700 \| - \| 0.9832 \|
	\| 9.0 \| 738 \| - \| 0.9832 \|
	\| 9.1463 \| 750 \| - \| 0.9832 \|
	\| 9.7561 \| 800 \| - \| 0.9832 \|
	\| 10.0 \| 820 \| - \| 0.9832 \|


	### Framework Versions
	- Python: 3.13.2
	- Sentence Transformers: 3.4.1
	- Transformers: 4.49.0
	- PyTorch: 2.6.0+cpu
	- Accelerate: 1.4.0
	- Datasets: 3.3.2
	- 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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