Dataset state after applying our data preparation algorithm.

<p>Dataset state after applying our data preparation algorithm.</p>

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Bibliographic Details
Main Author: Tanjim Taharat Aurpa (11250554) (author)
Other Authors: Md. Shoaib Ahmed (19832197) (author), Md. Mahbubur Rahman (4068862) (author), Md. Golam Moazzam (19832200) (author)
Published: 2024
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author Tanjim Taharat Aurpa (11250554)
author2 Md. Shoaib Ahmed (19832197)
Md. Mahbubur Rahman (4068862)
Md. Golam Moazzam (19832200)
author2_role author
author
author
author_facet Tanjim Taharat Aurpa (11250554)
Md. Shoaib Ahmed (19832197)
Md. Mahbubur Rahman (4068862)
Md. Golam Moazzam (19832200)
author_role author
dc.creator.none.fl_str_mv Tanjim Taharat Aurpa (11250554)
Md. Shoaib Ahmed (19832197)
Md. Mahbubur Rahman (4068862)
Md. Golam Moazzam (19832200)
dc.date.none.fl_str_mv 2024-10-10T17:31:27Z
dc.identifier.none.fl_str_mv 10.1371/journal.pone.0311161.t003
dc.relation.none.fl_str_mv https://figshare.com/articles/dataset/Dataset_state_after_applying_our_data_preparation_algorithm_/27206566
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Biotechnology
Cancer
Science Policy
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
providing detailed guidelines
generalized autoregressive pretraining
dataset comprising 11
categorizing instructional text
bert ), etc
accomplish specific tasks
label instruction category
label category meticulously
search allows people
people &# 8217
macro average scores
demonstrated unprecedented performance
creating knowledge bases
label instruction classification
various search styles
strategy &# 8217
proposed &# 8216
find sequential instructions
macro average score
instructnet &# 8217
label classification
search engines
macro f1
&# 8221
&# 8220
various topics
proposed architectures
performance metrics
level strategy
comprehensive knowledge
xlnet architecture
widely used
study uses
specialized objects
preferred resource
particular problems
oriented learning
noteworthy accomplishment
multiple categories
language understanding
identified areas
high level
forthcoming improvement
find solutions
evaluation process
daily essentials
become familiar
also essential
93 %,
121 observations
dc.title.none.fl_str_mv Dataset state after applying our data preparation algorithm.
dc.type.none.fl_str_mv Dataset
info:eu-repo/semantics/publishedVersion
dataset
description <p>Dataset state after applying our data preparation algorithm.</p>
eu_rights_str_mv openAccess
id Manara_0696ba88ee7fb7cc8b2f516dfbc5a62d
identifier_str_mv 10.1371/journal.pone.0311161.t003
network_acronym_str Manara
network_name_str ManaraRepo
oai_identifier_str oai:figshare.com:article/27206566
publishDate 2024
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv CC BY 4.0
spelling Dataset state after applying our data preparation algorithm.Tanjim Taharat Aurpa (11250554)Md. Shoaib Ahmed (19832197)Md. Mahbubur Rahman (4068862)Md. Golam Moazzam (19832200)BiotechnologyCancerScience PolicyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedproviding detailed guidelinesgeneralized autoregressive pretrainingdataset comprising 11categorizing instructional textbert ), etcaccomplish specific taskslabel instruction categorylabel category meticulouslysearch allows peoplepeople &# 8217macro average scoresdemonstrated unprecedented performancecreating knowledge baseslabel instruction classificationvarious search stylesstrategy &# 8217proposed &# 8216find sequential instructionsmacro average scoreinstructnet &# 8217label classificationsearch enginesmacro f1&# 8221&# 8220various topicsproposed architecturesperformance metricslevel strategycomprehensive knowledgexlnet architecturewidely usedstudy usesspecialized objectspreferred resourceparticular problemsoriented learningnoteworthy accomplishmentmultiple categorieslanguage understandingidentified areashigh levelforthcoming improvementfind solutionsevaluation processdaily essentialsbecome familiaralso essential93 %,121 observations<p>Dataset state after applying our data preparation algorithm.</p>2024-10-10T17:31:27ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0311161.t003https://figshare.com/articles/dataset/Dataset_state_after_applying_our_data_preparation_algorithm_/27206566CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/272065662024-10-10T17:31:27Z
spellingShingle Dataset state after applying our data preparation algorithm.
Tanjim Taharat Aurpa (11250554)
Biotechnology
Cancer
Science Policy
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
providing detailed guidelines
generalized autoregressive pretraining
dataset comprising 11
categorizing instructional text
bert ), etc
accomplish specific tasks
label instruction category
label category meticulously
search allows people
people &# 8217
macro average scores
demonstrated unprecedented performance
creating knowledge bases
label instruction classification
various search styles
strategy &# 8217
proposed &# 8216
find sequential instructions
macro average score
instructnet &# 8217
label classification
search engines
macro f1
&# 8221
&# 8220
various topics
proposed architectures
performance metrics
level strategy
comprehensive knowledge
xlnet architecture
widely used
study uses
specialized objects
preferred resource
particular problems
oriented learning
noteworthy accomplishment
multiple categories
language understanding
identified areas
high level
forthcoming improvement
find solutions
evaluation process
daily essentials
become familiar
also essential
93 %,
121 observations
status_str publishedVersion
title Dataset state after applying our data preparation algorithm.
title_full Dataset state after applying our data preparation algorithm.
title_fullStr Dataset state after applying our data preparation algorithm.
title_full_unstemmed Dataset state after applying our data preparation algorithm.
title_short Dataset state after applying our data preparation algorithm.
title_sort Dataset state after applying our data preparation algorithm.
topic Biotechnology
Cancer
Science Policy
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
providing detailed guidelines
generalized autoregressive pretraining
dataset comprising 11
categorizing instructional text
bert ), etc
accomplish specific tasks
label instruction category
label category meticulously
search allows people
people &# 8217
macro average scores
demonstrated unprecedented performance
creating knowledge bases
label instruction classification
various search styles
strategy &# 8217
proposed &# 8216
find sequential instructions
macro average score
instructnet &# 8217
label classification
search engines
macro f1
&# 8221
&# 8220
various topics
proposed architectures
performance metrics
level strategy
comprehensive knowledge
xlnet architecture
widely used
study uses
specialized objects
preferred resource
particular problems
oriented learning
noteworthy accomplishment
multiple categories
language understanding
identified areas
high level
forthcoming improvement
find solutions
evaluation process
daily essentials
become familiar
also essential
93 %,
121 observations