Support for nanotron #11
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thomwolf
changed the title
| dataset = Dataset.from_list( | ||
| [{k: str(v) for k, v in asdict(detail).items()} for detail in task_details] | ||
| ) | ||
| # try: |
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If you remove the high level try catch, please add other try catches to prevent the other possible failures
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are we sure we want to silently catch mistake or should we not rather let the run fail?
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No because we still want the results to be saved locally. That way we can upload them by hand instead of having to redo the whole eval.
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| class GenerativeTaskDatasetNanotron(DynamicBatchDataset): | ||
| def __getitem__(self, index) -> Request: |
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Why do you need your own class? (Is it only to return the index with the item?)
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base_model does not use the index for each sample, that means that we need to accommodate the dataset to nanotron.
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Yes but I'm unsure why we need to grab the index for brr
| config_cls: Type = Config, | ||
| model_config_cls: Optional[Type] = None, | ||
| model_cls: Optional[Type] = None, | ||
| ): |
Support for Nanotron models --- Co-authored-by: Nathan Habib <nathan.habib@huggingface.co> Co-authored-by: clementine@huggingface.co <clementine@huggingface.co> Co-authored-by: Clémentine Fourrier <22726840+clefourrier@users.noreply.github.com>
Support for Nanotron models --- Co-authored-by: Nathan Habib <nathan.habib@huggingface.co> Co-authored-by: clementine@huggingface.co <clementine@huggingface.co> Co-authored-by: Clémentine Fourrier <22726840+clefourrier@users.noreply.github.com>
Support for Nanotron models