Content Combined texts from: - Jigsaw Unintended Bias in Toxicity Classification ([link](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification)) - Toxic Comment Classification Challenge ([link](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)) - Detecting Insults in Social Commentary ([link](https://www.kaggle.com/c/detecting-insults-in-social-commentary)) - Automated Hate Speech Detection and the Problem of Offensive Language ([link](https://github.com/t-davidson/hate-speech-and-offensive-language)) - Hate speech dataset from a white supremacist forum ([link](https://github.com/aitor-garcia-p/hate-speech-dataset)) Usage import pandas as pd import pickle with open('toxicity.pkl', 'rb') as f: data = pickle.load(f) test = data[data.Training\_evaluation\_split.eq('Evaluation')].drop(columns=['Source', 'Training\_evaluation\_split']) train = data[data.Training\_evaluation\_split.eq('Training')].drop(columns=['Source', 'Training\_evaluation\_split']) test\_x = test.text testy\_ = test.Toxicity train\_x = train.text train\_y = train.Toxicity