Learning Curves Prediction for a Transformers-based Model
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/159492 |
Resumo: | Cruz, F., & Castelli, M. (2023). Learning Curves Prediction for a Transformers-based Model. Emerging Science Journal, 7(5), 1491-1500. https://doi.org/10.28991/ESJ-2023-07-05-03 |
id |
RCAP_708bea98fe14104073029567e7ac8473 |
---|---|
oai_identifier_str |
oai:run.unl.pt:10362/159492 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Learning Curves Prediction for a Transformers-based ModelDataset SizeDocument Data ExtractionFine-TuningLearning CurvesTransformersGeneralCruz, F., & Castelli, M. (2023). Learning Curves Prediction for a Transformers-based Model. Emerging Science Journal, 7(5), 1491-1500. https://doi.org/10.28991/ESJ-2023-07-05-03One of the main challenges when training or fine-tuning a machine learning model concerns the number of observations necessary to achieve satisfactory performance. While, in general, more training observations result in a better-performing model, collecting more data can be time-consuming, expensive, or even impossible. For this reason, investigating the relationship between the dataset's size and the performance of a machine learning model is fundamental to deciding, with a certain likelihood, the minimum number of observations that are necessary to ensure a satisfactory-performing model is obtained as a result of the training process. The learning curve represents the relationship between the dataset’s size and the performance of the model and is especially useful when choosing a model for a specific task or planning the annotation work of a dataset. Thus, the purpose of this paper is to find the functions that best fit the learning curves of a Transformers-based model (LayoutLM) when fine-tuned to extract information from invoices. Two new datasets of invoices are made available for such a task. Combined with a third dataset already available online, 22 sub-datasets are defined, and their learning curves are plotted based on cross-validation results. The functions are fit using a non-linear least squares technique. The results show that both a biasymptotic and a Morgan-Mercer-Flodin function fit the learning curves extremely well. Also, an empirical relation is presented to predict the learning curve from a single parameter that may be easily obtained in the early stage of the annotation process.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNCruz, FranciscoCastelli, Mauro2023-11-02T22:08:59Z2023-10-012023-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10application/pdfhttp://hdl.handle.net/10362/159492eng2610-9182PURE: 63539763https://doi.org/10.28991/ESJ-2023-07-05-03info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:41:53Zoai:run.unl.pt:10362/159492Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:57:34.100523Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Learning Curves Prediction for a Transformers-based Model |
title |
Learning Curves Prediction for a Transformers-based Model |
spellingShingle |
Learning Curves Prediction for a Transformers-based Model Cruz, Francisco Dataset Size Document Data Extraction Fine-Tuning Learning Curves Transformers General |
title_short |
Learning Curves Prediction for a Transformers-based Model |
title_full |
Learning Curves Prediction for a Transformers-based Model |
title_fullStr |
Learning Curves Prediction for a Transformers-based Model |
title_full_unstemmed |
Learning Curves Prediction for a Transformers-based Model |
title_sort |
Learning Curves Prediction for a Transformers-based Model |
author |
Cruz, Francisco |
author_facet |
Cruz, Francisco Castelli, Mauro |
author_role |
author |
author2 |
Castelli, Mauro |
author2_role |
author |
dc.contributor.none.fl_str_mv |
NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School RUN |
dc.contributor.author.fl_str_mv |
Cruz, Francisco Castelli, Mauro |
dc.subject.por.fl_str_mv |
Dataset Size Document Data Extraction Fine-Tuning Learning Curves Transformers General |
topic |
Dataset Size Document Data Extraction Fine-Tuning Learning Curves Transformers General |
description |
Cruz, F., & Castelli, M. (2023). Learning Curves Prediction for a Transformers-based Model. Emerging Science Journal, 7(5), 1491-1500. https://doi.org/10.28991/ESJ-2023-07-05-03 |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-11-02T22:08:59Z 2023-10-01 2023-10-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/159492 |
url |
http://hdl.handle.net/10362/159492 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2610-9182 PURE: 63539763 https://doi.org/10.28991/ESJ-2023-07-05-03 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
10 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799138158074396672 |