Keras Text Preprocessing - Saving Tokenizer object to file for scoring

Question

I've trained a sentiment classifier model using Keras library by following the below steps(broadly).

1. Convert Text corpus into sequences using Tokenizer object/class
2. Build a model using the model.fit() method
3. Evaluate this model

Now for scoring using this model, I was able to save the model to a file and load from a file. However I've not found a way to save the Tokenizer object to file. Without this I'll have to process the corpus every time I need to score even a single sentence. Is there a way around this?

Answer 1

The most common way is to use either pickle or joblib. Here you have an example on how to use pickle in order to save Tokenizer:

import pickle  # saving with open('tokenizer.pickle', 'wb') as handle:     pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)  # loading with open('tokenizer.pickle', 'rb') as handle:     tokenizer = pickle.load(handle) 

Answer 2

Tokenizer class has a function to save date into JSON format:

tokenizer_json = tokenizer.to_json() with io.open('tokenizer.json', 'w', encoding='utf-8') as f:     f.write(json.dumps(tokenizer_json, ensure_ascii=False)) 

The data can be loaded using tokenizer_from_json function from keras_preprocessing.text:

with open('tokenizer.json') as f:     data = json.load(f)     tokenizer = tokenizer_from_json(data)