Add SFT params random_offset_probability, label_masking #2005
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sanagno
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Mar 7, 2023
| @@ -54,13 +64,20 @@ def __call__(self, features): | |||
| list(map(lambda x: x[1], flatten_message["offset_mapping"])), | |||
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this should not be required now
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Could you make a suggested change (ctrl+g)?
sanagno
reviewed
Mar 7, 2023
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| # append eos token to each messages | ||
| assert self.tokenizer.eos_token | ||
| messages = [m + self.tokenizer.eos_token for m in messages] |
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Did we converge to this? Using the end of prompt-reply the same as eos? Not totally against it if eveyone agrees
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EOS at end of messages simplifies the decoding, TRLX also relies on eos tokens (i.e. would either have to be patched or eos could be set to <human>). The format is a compromise between framing format v3 and old <bot> <user> format v2.
sanagno
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Mar 7, 2023
alando46
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Mar 10, 2023
Added SFT training parameters: - `random_offset_probability` (float, default: 0.5): probability of random offset into conversations when conversation > max_length - `label_masking` (bool, default: true): if true only loss for tokens of assistant replies is calculated else for all tokens (including prompter)
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Added SFT training parameters:
random_offset_probability(float, default: 0.5): probability of random offset into conversations when conversation > max_lengthlabel_masking(bool, default: true): if true only loss for tokens of assistant replies is calculated else for all tokens (including prompter)