Hello everyone,

I have started working on building a classifier to detect spoilers. I wanted to train my model first on a public general dataset, and then fine-tune the model by training it further on a movie/book-specific dataset.

A lot of text classification models make use of one-hot encoding the bag of words as the input layer, and then creating an FFNN, and I was considering doing the same. However, the number of features will differ for both the datasets, as all words will not be common for both datasets. I’m kinda stuck at this point, any help would be appreciated. Apologies if the doubt is too basic. Thanks.