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License: CC BY NC
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ZENODO
Software . 2026
License: CC BY NC
Data sources: Datacite
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Automatic Detection in Twitter of Non-Traumatic Grief due to Deaths by COVID-19 – Trained Transformer Model (NT-Grief_EN)

Authors: Mata Vazquez, Jacinto; Pachón Álvarez, Victoria; Gualda, Estrella; Araujo Hernádez, Miriam; García Navarro, Esperanza Begoña;

Automatic Detection in Twitter of Non-Traumatic Grief due to Deaths by COVID-19 – Trained Transformer Model (NT-Grief_EN)

Abstract

This record contains the trained transformer-based model described in Automatic Detection in Twitter of Non-Traumatic Grief due to Deaths by COVID-19 (IEEE Access) (https://doi.org/10.1109/ACCESS.2023.3343149). This release provides the final trained model weights and tokenizer to support reproducibility and reuse for research purposes.

Keywords

non-traumatic grief, grief detection, NLP, transformers, Twitter, mental health

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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