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Fine-tuned model weights for Stance Detection Benchmark System

dc.contributor.authorSchiller, Benjamin
dc.contributor.authorDaxenberger, Johannes
dc.contributor.authorGurevych, Iryna
dc.date.accessioned2021-07-03T22:09:32Z
dc.date.available2021-07-03T22:09:32Z
dc.date.issued2019
dc.identifier.urihttps://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2848
dc.descriptionThis collection includes model weights (BERT-based), fine-tuned in a multi-task setting on 10 heterogeneous stance detection datasets. For more information, please refer to the paper and the GitHub repository linked in the paper. DISCLAIMER: The user acknowledges and agrees that the data is provided on an “as­-is” basis and that the licensor makes no representations or warranties of any kind.en_US
dc.language.isoenen_US
dc.relationIsCitedBy;arXiv;2001.01565
dc.relationIsCitedBy;DOI;10.1007/s13218-021-00714-w
dc.rightsin Copyright
dc.rights.urihttps://rightsstatements.org/vocab/InC/1.0/
dc.subjectStance Detectionen_US
dc.subjectBenchmarken_US
dc.subjectModel weightsen_US
dc.subject.classification409-05 Interaktive und intelligente Systeme, Bild- und Sprachverarbeitung, Computergraphik und Visualisierungen_US
dc.subject.ddc004
dc.titleFine-tuned model weights for Stance Detection Benchmark Systemen_US
dc.typeModelen_US
tud.projectPTJ | 03VP02540 | ArgumenTexten_US
tud.unitTUDa


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