dynamic scoring for LM #188
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vince62s
reviewed
Jan 22, 2025
vince62s
reviewed
Jan 22, 2025
| @@ -92,6 +92,9 @@ def tokenize_string(self, string, side="src", is_train=False): | |||
| kwargs = {"max_length": self.max_length, "truncation": True} | |||
| else: | |||
| kwargs = {} | |||
| string = string.replace(DefaultTokens.SEP, "\n").replace( | |||
| DefaultTokens.MASK_BEFORE, self.tokenizers[side].pad_token | |||
| ) | |||
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shouldn't we handle it the same way for other tokenizers ?
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I have only tested for that one (with eurollm) at this point; an error will be raised with the others.
vince62s
reviewed
Jan 22, 2025
vince62s
reviewed
Jan 22, 2025
vince62s
reviewed
Jan 22, 2025
vince62s
reviewed
Jan 22, 2025
vince62s
reviewed
Jan 22, 2025
Comment on lines
+80
to
+103
| if is_seq2seq: | ||
| predictor = Translator.from_config( # we need to review opt/config stuff in translator | ||
| model, | ||
| self.vocabs, | ||
| predict_config, | ||
| model_config, | ||
| device_id=gpu_rank, | ||
| global_scorer=scorer, | ||
| report_align=predict_config.report_align, | ||
| report_score=False, | ||
| logger=None, | ||
| ) | ||
| else: | ||
| predictor = GeneratorLM.from_config( | ||
| model, | ||
| self.vocabs, | ||
| predict_config, | ||
| model_config, | ||
| device_id=gpu_rank, | ||
| global_scorer=scorer, | ||
| report_align=predict_config.report_align, | ||
| report_score=False, | ||
| logger=None, | ||
| ) |
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Maybe cleaner to just define a predictor_class in the condition, and call predictor_class(*) once, since they should have the same signature.
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This PRs adapts the ScoringPreparator for LM architecture and fixes the ignore_prompt method of the loss for LM validation with left padding.
We noticed that the
filtertoolongtransform should not be used along with thehuggingface_tokenizetransform. See#191