Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring Transformer Based Models to Identify Hate Speech and Offensive
  Content in English and Indo-Aryan Languages   Hate speech is considered to be one of the major issues currently plaguing
online social media. Repeated and repetitive exposure to hate speech has been
shown to create physiological effects on the target users. Thus, hate speech,
in all its forms, should be addressed on these platforms in order to maintain
good health. In this paper, we explored several Transformer based machine
learning models for the detection of hate speech and offensive content in
English and Indo-Aryan languages at FIRE 2021. We explore several models such
as mBERT, XLMR-large, XLMR-base by team name "Super Mario". Our models came 2nd
position in Code-Mixed Data set (Macro F1: 0.7107), 2nd position in Hindi
two-class classification(Macro F1: 0.7797), 4th in English four-class category
(Macro F1: 0.8006) and 12th in English two-class category (Macro F1: 0.6447).

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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: math
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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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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: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
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The categories of this paper are: astro-ph