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Thank you for releasing the code associated with your paper. I greatly appreciate your contribution to the field and the effort put into sharing your work with the community. I am currently studying your paper and trying to reproduce the experiments presented in the appendix, such as:
Detectability under perturbation,
Automatic quality evaluations,
Diversity effects, and others.
However, it seems the current repository only includes code for generating watermarked language and detecting scores. Would it be possible to provide additional code or instructions to help reproduce these experiments?
The text was updated successfully, but these errors were encountered:
We have the details for paraphrasing in the paper.
For random word deletion, here is an example code.
import random
def word_deletion(s, ratio=0.2):
keep = [random.random() for _ in range(len(s))]
res = [si for i, si in enumerate(s) if keep[i] > ratio]
if not res:
return [s[random.randint(0, len(s) - 1)]]
return res
text = "A B C D E."
delete_text = ' '.join(word_deletion(text.split(' '), ratio=0.2))
print(text)
print(delete_text)
Thank you for releasing the code associated with your paper. I greatly appreciate your contribution to the field and the effort put into sharing your work with the community. I am currently studying your paper and trying to reproduce the experiments presented in the appendix, such as:
Detectability under perturbation,
Automatic quality evaluations,
Diversity effects, and others.
However, it seems the current repository only includes code for generating watermarked language and detecting scores. Would it be possible to provide additional code or instructions to help reproduce these experiments?
The text was updated successfully, but these errors were encountered: