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I have the following code:

from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("t5-small")
model = T5ForConditionalGeneration.from_pretrained("t5-small")

input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids

sequence_ids = model.generate(input_ids)
sequences = tokenizer.batch_decode(sequence_ids)
sequences

Currently it produces this:

['<pad><extra_id_0> park offers<extra_id_1> the<extra_id_2> park.</s>']

Is there a way to prevent the generator to produce certain words (e.g. stopwords = ["park", "offer"])?

littleworth
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1 Answers1

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after looking at the docs found out there is a bad_words_ids parameter that you can pass in the generate()

given a bad word list you can create the id list using

tokenizer(bad_words, add_special_tokens=False).input_ids

input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
bad_words = ["park", "offers"]
bad_words_ids = tokenizer(bad_words, add_special_tokens=False).input_ids 
#[[2447], [704]]

sequence_ids = model.generate(input_ids, bad_words_ids=bad_words_ids)
#tensor([[    0, 32099,  1061,    19,     3,     9,   710,  1482,   550,    45, 32098,     8, 32097,  1061,     5,     1]])

sequences = tokenizer.batch_decode(sequence_ids)
print(sequences) 
#['<pad><extra_id_0> Park is a short walk away from<extra_id_1> the<extra_id_2> Park.</s>']

Notice how the word "Park" is appearing now. This is because the tokenizer identifies park (id 2447) and Park (id 1061) as 2 different tokens. This may depend on the tokenizer you use (there are case-insensitive tokenizers). If you don't want this to happen you can add Park into the bad word list as well.

Colab demo

cmgchess
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