I have developed, trained and tested an NLP model. It is persisted in a pickle file. The model contains the data preprocessing function that includes text cleaning and new features engineered with word2vec.
With the trained model, I want to make predictions on a new text. The new text data, after preprocessing, won't contain the same engineered features of the training dataset.
Therefore my question is, how can the trained model make predictions on the new dataset as it has different engineered features (different numbers of columns and different columns)?
Should I preprocess the new text data and the training dataset as one dataset?