Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection   Sentiment analysis for code-mixed social media text continues to be an
under-explored area. This work adds two common approaches: fine-tuning large
transformer models and sample efficient methods like ULMFiT. Prior work
demonstrates the efficacy of classical ML methods for polarity detection.
Fine-tuned general-purpose language representation models, such as those of the
BERT family are benchmarked along with classical machine learning and ensemble
methods. We show that NB-SVM beats RoBERTa by 6.2% (relative) F1. The best
performing model is a majority-vote ensemble which achieves an F1 of 0.707. The
leaderboard submission was made under the codalab username nirantk, with F1 of
0.689.

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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
3
The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: stat
10
The categories of this paper are: q-bio