Nihal D'Souza
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Updating README with yaml metadata
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README.md
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Since the start of the COVID-19 pandemic, there has been a widespread increase in the amount of hate-speech being propagated online against the Asian community. This project builds upon and explores the work of He et al. Their COVID-HATE dataset contains 206 million tweets focused around anti-Asian hate speech. Using tweet data from before the COVID-19 pandemic, as well as the COVID-HATE dataset from He et al, we performed transfer learning. We tested several different models, including BERT, RoBERTa, LSTM, and BERT-CNN.
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Some of these models hindered the performance of He et al’s model, while others improved it.
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language: en
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tags:
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- transfer-learning
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- bert
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- hatespeech
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- covid19
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license: "MIT License"
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datasets:
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- COVID-HATE
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metrics:
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- f1-score
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---
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Since the start of the COVID-19 pandemic, there has been a widespread increase in the amount of hate-speech being propagated online against the Asian community. This project builds upon and explores the work of He et al. Their COVID-HATE dataset contains 206 million tweets focused around anti-Asian hate speech. Using tweet data from before the COVID-19 pandemic, as well as the COVID-HATE dataset from He et al, we performed transfer learning. We tested several different models, including BERT, RoBERTa, LSTM, and BERT-CNN.
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Some of these models hindered the performance of He et al’s model, while others improved it.
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