Dataset has been developed to address this critical social problem. The problem can be divided into two tasks:
- The first task, Sexism Identification, is a binary classification problem to classify the text as sexist or non-sexist.
- The second task, Sexism Categorization, is a multi-class classification problem where texts labelled as sexist are classified into one of five labels: ideological-inequality, stereotypingdominance, objectification, sexual-violence and misogyny-non-sexual-violence
Our Project is divided into 8 files. Following are the files with the description of what it contains:
- XLM_R.ipynb: Contains XLM-R model code and results
- ROBERTA.ipynb: Contains Roberta model code and results
- Multilinguial_BERT.ipynb: Contains Multilinguial BERT model code and results
- BERT_and_BETO.ipynb : Contains BERT and BETO models codes and results
- Bertweet.ipynb: Contains Bertweet model code and results
- Exist_Data_Augmentation.ipynb: Contains code for data augmentation and stored results on drive
- Augmented_BERT_&_BETO.ipynb: Contains BERT and BETO models trained on augmented data codes and results
- Class_Balanced_BERT_&_BETO.ipynb: Contains BERT and BETO models trained on class balanced data codes and results
- Create a folder named
SEXISM DATA
on your google drive - Upload the 2 train and test files in this folder
- Now run each of the above 8 code files independently from colab.
- Run all cells of each file in sequence.
- This will fetch the results.
All results are in the report as well as the individual results for each model are in their respective python notebooks file also.