Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NIT COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for
  Identify Informative COVID-19 English Tweets   This paper presents the model submitted by the NIT_COVID-19 team for
identified informative COVID-19 English tweets at WNUT-2020 Task2. This shared
task addresses the problem of automatically identifying whether an English
tweet related to informative (novel coronavirus) or not. These informative
tweets provide information about recovered, confirmed, suspected, and death
cases as well as the location or travel history of the cases. The proposed
approach includes pre-processing techniques and pre-trained RoBERTa with
suitable hyperparameters for English coronavirus tweet classification. The
performance achieved by the proposed model for shared task WNUT 2020 Task2 is
89.14% in the F1-score metric.

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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: hep-ex
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The categories of this paper are: eess
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: q-bio
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The categories of this paper are: gr-qc
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The categories of this paper are: ao-sci
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The categories of this paper are: chem-ph
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The categories of this paper are: math