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3,411 to 3,420 of 3,447 Results
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
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...
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...
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
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...
Archivo Gzip - 2,7 MB - MD5: c79dfaaeba4970321576a7ce1650b40f
Texto plano - 4,8 KB - MD5: a4c7c08bab5647aea051fd0737be1b0e
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