Instructions to use nihaldsouza1/covid-hatespeech-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nihaldsouza1/covid-hatespeech-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nihaldsouza1/covid-hatespeech-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nihaldsouza1/covid-hatespeech-detection") model = AutoModelForSequenceClassification.from_pretrained("nihaldsouza1/covid-hatespeech-detection") - Notebooks
- Google Colab
- Kaggle
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. Some of these models hindered the performance of He et al’s model, while others improved it.
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