Evaluation improvements #217
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rrutmann
requested changes
May 13, 2025
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| document_scores_df = pd.DataFrame(document_scores) | ||
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Collaborator
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Why is the filtering necessary? We handle the case of missing / ummatched document IDs later by filtering on documents that are common for the annotators we are currently comparing
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A few comments on the notebook:
- You define
jsonl_path = "annotations__educational_content__en__gt.jsonl". This fails for me because the file is missing. Should this begt_annotations_pathinstead? - Why do we plot the standard deviations as a histogram, but the spread as a cumulative distribution?
- In the section about spreads, why do we print Document ID 1 and 2? Shouldn't they be the same? Or is this just a sanity check?
- For the evaluation of our predictions, we aggregate the human annotations using majority voting. In the notebook, we're only looking at the mean of the human annotations. Should we add info about the majority voting as well? E.g. we could add a plot of the distribution of the human annotations aggregated with majority voting.
The computations look correct to me.
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