- Write predict_one for CLI use
- Test different postprocessing methods
- Data augmentation
- Try non-templated commands
- Beam search with custom eval?
- Different metrics: https://huggingface.co/docs/datasets/metrics
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notebooks:data_preprocess.ipynb: adds additional data sourcesgraphs.ipynb: visualize results from experimentsold: directory of other ipynb that we're ignoring...
src:config.py: configuration used bydata_utils.py: reading/saving/context/encode/decode from competitiondataset.py: subclassing torch datasets; LBL vs Blocked dataset?diverse_beam_search.py: expanding hugging face beam searchgenerate.py: prediction and scoring utilitiesmodified_beam_search.py: another modified hugging face beam searchonnx.py: conversion to/from onnx ML formatpreprocess.py: preprocesses datarun.py: main loop for trainingtrainer.py: overrides hugging face trainer to override some optionstune.py: contains a list of experiments by modifying config
webappdemo_app.py: starts a flask servereval.py: used for predictions using the model on hugging facerequirements.txt: python requirementstrain.py: runsexperiments()fromsrc/tune.py