Evaluator column loading #200
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The documentation is not available anymore as the PR was closed or merged. |
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Great I'll test tomorrow morning! |
fxmarty
reviewed
Jul 25, 2022
| @@ -119,7 +121,8 @@ def prepare_data(self, data: Union[str, Dataset], input_column: str, label_colum | |||
| references = data[label_column] | |||
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| metric_inputs = {"references": references} | |||
| pipeline_inputs = [join_by.join(element) for element in data[input_column]] | |||
| data = data.map(lambda x: {input_column: join_by.join(x[input_column])}) | |||
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Out of curiosity: is this faster than a list comprehension?
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I don't think so, but it will not load the data into memory.
mathemakitten
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Aug 3, 2022
* add `DatasetColumn` class * add __iter__ * make style * adapt QA * adapt NER
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Instead of loading the pipeline inputs into memory this PR just wraps the original dataset in a
DatasetColumnclass. This allows to evaluate on huge datasets (e.g. ImageNet) without OOM errors.cc @fxmarty