Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FH-SWF SG at GermEval 2021: Using Transformer-Based Language Models to
  Identify Toxic, Engaging, & Fact-Claiming Comments   In this paper we describe the methods we used for our submissions to the
GermEval 2021 shared task on the identification of toxic, engaging, and
fact-claiming comments. For all three subtasks we fine-tuned freely available
transformer-based models from the Huggingface model hub. We evaluated the
performance of various pre-trained models after fine-tuning on 80% of the
training data with different hyperparameters and submitted predictions of the
two best performing resulting models. We found that this approach worked best
for subtask 3, for which we achieved an F1-score of 0.736.

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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
3
The categories of this paper are: physics