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Pre-training large neural language models, such as BERT, has led toimpressive gains on many natural language processing (NLP) tasks. Although thismethod has proven to be effective for many domains, it might not always providedesirable benefits. In this paper, we study the effects of hateful pre-trainingon low-resource hate speech classification tasks. While previous studies on theEnglish language have emphasized its importance, we aim to augment theirobservations with some non-obvious insights. We evaluate different variationsof tweet-based BERT models pre-trained on hateful, non-hateful, and mixedsubsets of a 40M tweet dataset. This evaluation is carried out for the Indianlanguages Hindi and Marathi. This paper is empirical evidence that hatefulpre-training is not the best pre-training option for hate speech detection. Weshow that pre-training on non-hateful text from the target domain providessimilar or better results. Further, we introduce HindTweetBERT andMahaTweetBERT, the first publicly available BERT models pre-trained on Hindiand Marathi tweets, respectively. We show that they provide state-of-the-artperformance on hate speech classification tasks. We also release hateful BERTfor the two languages and a gold hate speech evaluation benchmark HateEval-Hiand HateEval-Mr consisting of manually labeled 2000 tweets each. The models anddata are available at https://github.com/l3cube-pune/MarathiNLP .

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