Lol living up to the name "hellisotherpeople" I guess. https://huggingface.co/spaces/Hellisotherpeople/Gadsby/blob/main/Text-Generation.py https://huggingface.

Lol living up to the name "hellisotherpeople" I guess. https://huggingface.co/spaces/Hellisotherpeople/Gadsby/blob/main/Text-Generation.py https://huggingface.co/spaces/Hellisotherpeople/Gadsby/blob/... next_token_candidates = torch.multinomial(probs, num_samples=int(number_of_tokens_to_sample)) ## 10000 random samples word_list = [] for candidate_string in next_token_candidates: for candidate in candidate_string: resulting_string = tokenizer.decode(candidate) #skip_special_tokens=True, clean_up_tokenization_spaces=True) ###Constrained text generation starts HERE ##Lipogram - No naughty strings used if mode == "lipogram" or mode == "e-prime": if all(nauty_string not in resulting_string for nauty_string in naughty_strings): ## This returns at the first naughty strings return resulting_string That, to me, reads as "grab tokens as long as they don't contain the bad string(s)", and the code that uses it just repeats that. It doesn't seem, to me, to eliminate tokens at the model level, but I could be wrong, I'm no expert. I'd expect the model itself not to generate candidates that contained the "bad" string. Merry Christmas.