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sr-eval/README.md

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To run this evaluation script, you need the following:
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* Set of predictions in MMIF format (either from the preds folder in this repo
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or generated from the [SWT app](https://github.com/clamsproject/app-swt-detection) )
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or generated from the [SWT app](https://github.com/clamsproject/app-swt-detection/tree/6b12498fc596327ec47933b7f785044da2f8cf2f)
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* Set of golds in csv format (either downloaded from the annotations repository
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using goldretriever.py, or your own set that exactly matches the format present in [aapb-annotations](https://github.com/clamsproject/aapb-annotations/tree/main/scene-recognition/golds))
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using goldretriever.py, or your own set that exactly matches the format present in [aapb-annotations](https://github.com/clamsproject/aapb-annotations/tree/9cbe41aa124da73a0158bfc0b4dbf8bafe6d460d/scene-recognition/golds)
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There are three arguments when running the script: `-mmif-dir`, `-gold-dir`, and `count-subtypes`.
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The first two are directories that contain the predictions and golds, respectively. The third is a boolean value that
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* `preds@app-swt-detection<VERSION-NUMBER>@<BATCH-NAME>`.
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Note that only the first one is required, as `-gold-dir` defaults to the set of golds downloaded (using `goldretriever`)
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from the [aapb-annotations](https://github.com/clamsproject/aapb-annotations/tree/main/scene-recognition/golds) repo,
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from the [aapb-annotations](https://github.com/clamsproject/aapb-annotations/tree/9cbe41aa124da73a0158bfc0b4dbf8bafe6d460d/scene-recognition/golds) repo,
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and `count-subtypes` defaults to `False`.
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# Usage

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