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3,411 to 3,420 of 3,451 Results

hesml_v2r1_githubRelease.zip Feb 17, 2022 - Reproducible experiments on word and sentence similarity measures for the biomedical domain Archivo ZIP - 65,8 MB - MD5: 903d58ac1442816cfe8ea134741dbfe2 This file contains the Java and Python code for executing the experiments detailed in this dataset and develop in HESML V2R1. IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/hesml_v2r1_githubRelease.zip
readme.pdf Feb 17, 2022 - Reproducible experiments on word and sentence similarity measures for the biomedical domain Adobe PDF - 107,3 KB - MD5: afc8cb21bcf6dce92789e79d0c3584bb This file details the information sources and dependencies.
BERTExperiments.tar.gz Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain Archivo Gzip - 20,2 GB - MD5: 25f14da7080c2ee1b84713300d286c46 This file contains all the BERT models and dependencies evaluated in HESML V2R1 as detailed in [1]. IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/BERTExperiments.tar.gz
BioCManuscriptCorpus.zip Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain Archivo ZIP - 42,8 GB - MD5: 6c8d651f74559f16cac879722b46bff8 Datos This file contains the BioC XML files in unicode format from PMC Corpus The dataset has been downloaded from 1 on 5 June, 2019. IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/BioCManuscriptCorpus.zip
CharacterAndSentenceEmbeddings.tar.gz Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain Archivo Gzip - 19,4 GB - MD5: a27819702fb003f346d7ec541533a480 This file contains all the character and sentence pretrained models evaluated in HESML V2R1 as detailed in [1]. IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/CharacterAndSentenceEmbeddings.tar.gz
Dependencies.tar.gz.cpt Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain application/mac-compactpro - 10,4 GB - MD5: 3c265c455fe16950e93ce03387bc92d0 This file contains all the dependencies and external sources for executing the experiments in [1]. IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/Dependencies.tar.gz.cpt. In order to obtain a decrypt password for this, you should sign and obtain a license for the National Library of Medicine (NLM) of th...
hesml_v2r1_dockerRelease.tar.gz.cpt Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain application/mac-compactpro - 6,3 GB - MD5: 05370eef043129f0783b5901440c6cef This file contains the Docker-based image with all the pre-installed software and tools for executing the experiments detailed in this dataset and develop in HESML V2R1. IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/hesml_v2r1_dockerRelease.tar.gz.cpt. In order to obtain a decrypt password for this, yo...
PreprocessedBioCCorpus.zip Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain Archivo ZIP - 100,9 GB - MD5: 079326772685a95c86e0ed003137bd42 IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/PreprocessedBioCCorpus.zip
WordEmbeddings.tar.gz Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain Archivo Gzip - 39,0 GB - MD5: 6ccf1d8b29b824cafd36ac1d3a238d1b This file contains the Word Embedding pretrained models detailed in HESML V2R1 [1] and our pretrained model based on Fastext [3] in the BioC PMC Corpus [4]. Our pretrained model has been trained on Fastext skipgram model using the parameters from [1] in the BioC PMC Corpus [4]. IMPORTANT NOTE!. If main link fails you can download this file from ht...
Datebase_autogenic_meditation_training.xlsx Jun 8, 2021 - Autogenic meditation training in a randomized controlled trial: a framework for promoting mental health and attention regulation in children Hoja de cálculo MS Excel - 19,2 KB - MD5: 1c47892186d9cb9377f42b7d31c23b13

hesml_v2r1_githubRelease.zip

Feb 17, 2022 - Reproducible experiments on word and sentence similarity measures for the biomedical domain

Archivo ZIP - 65,8 MB -

This file contains the Java and Python code for executing the experiments detailed in this dataset and develop in HESML V2R1. IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/hesml_v2r1_githubRelease.zip

readme.pdf

Feb 17, 2022 - Reproducible experiments on word and sentence similarity measures for the biomedical domain

Adobe PDF - 107,3 KB -

This file details the information sources and dependencies.

BERTExperiments.tar.gz

Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain

Archivo Gzip - 20,2 GB -

This file contains all the BERT models and dependencies evaluated in HESML V2R1 as detailed in [1]. IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/BERTExperiments.tar.gz

BioCManuscriptCorpus.zip

Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain

Archivo ZIP - 42,8 GB -

Datos

This file contains the BioC XML files in unicode format from PMC Corpus The dataset has been downloaded from 1 on 5 June, 2019. IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/BioCManuscriptCorpus.zip

CharacterAndSentenceEmbeddings.tar.gz

Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain

Archivo Gzip - 19,4 GB -

This file contains all the character and sentence pretrained models evaluated in HESML V2R1 as detailed in [1]. IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/CharacterAndSentenceEmbeddings.tar.gz

Dependencies.tar.gz.cpt

Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain

application/mac-compactpro - 10,4 GB -

This file contains all the dependencies and external sources for executing the experiments in [1]. IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/Dependencies.tar.gz.cpt. In order to obtain a decrypt password for this, you should sign and obtain a license for the National Library of Medicine (NLM) of th...

hesml_v2r1_dockerRelease.tar.gz.cpt

Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain

application/mac-compactpro - 6,3 GB -

This file contains the Docker-based image with all the pre-installed software and tools for executing the experiments detailed in this dataset and develop in HESML V2R1. IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/hesml_v2r1_dockerRelease.tar.gz.cpt. In order to obtain a decrypt password for this, yo...

PreprocessedBioCCorpus.zip

Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain

Archivo ZIP - 100,9 GB -

IMPORTANT NOTE!. If main link fails you can download this file from https://doi.org/10.21950/PreprocessedBioCCorpus.zip

WordEmbeddings.tar.gz

Nov 8, 2021 - Reproducible experiments on word and sentence similarity measures for the biomedical domain

Archivo Gzip - 39,0 GB -

This file contains the Word Embedding pretrained models detailed in HESML V2R1 [1] and our pretrained model based on Fastext [3] in the BioC PMC Corpus [4]. Our pretrained model has been trained on Fastext skipgram model using the parameters from [1] in the BioC PMC Corpus [4]. IMPORTANT NOTE!. If main link fails you can download this file from ht...

Datebase_autogenic_meditation_training.xlsx

Jun 8, 2021 - Autogenic meditation training in a randomized controlled trial: a framework for promoting mental health and attention regulation in children

Hoja de cálculo MS Excel - 19,2 KB -

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