

def fit(self, texts):
    if self.lowercase:
        texts = [text.lower() for text in texts]
    self.encoder = token_encoder(texts, character=self.character, max_features=(self.max_features - 3), min_df=self.min_df)
    self.encoder['PAD'] = 0
    self.encoder['END'] = 1
    self.encoder['UNK'] = 2
    self.decoder = dict(zip(self.encoder.values(), self.encoder.keys()))
    self.n_features = len(self.encoder)
    return self

dataset/ETHPy150Open IndicoDataSolutions/Foxhound/foxhound/preprocessing.py Tokenizer.fit/original