2fb72f3f0c120b8c67d90926b692b27139a503a4614b4a5822c5185352a619e3  RESULTS.md
59ec57a860c9e1b6187e4d58462f89e9807c04d1c37cd12bc6304c8f5a2dc2da  RESULTS.json
5b1e3e14c820bbb840ce84df3c4cb84ea9143706a19ee59feeaf52d57239fdfd  crops.csv
f9e968cebe04894813654e414732adf7b0a9b9b2f1f7133fa82a4d4565d20e43  embeddings.npz
dd8e47127826abfc0f0f0bb344e1fb0b309ee566b8eb923e8164278eb949e83c  embeddings-index.csv
2287ed0c42d839f199845754cfc09b4c56790ef21b435b90561aba31fd7a4019  metric1-corpus-spread.csv
0fdb008cb8c77012c2f8f7d80dca69c063f269d9200ec79b6df2e0544403a34e  metric1-draws.csv
19558ea1840b7e697b67550968b7f61a149ab568af9d396caf2fa413e7801629  metric2-render-centroid.csv
97dbc1e4791660d0c784c0e229928207c7338433475cfbeffe8def7fc0870795  metric2-pairs.csv
a3c351fe4698f4a099b089be2c223b9ee449c2ca5ec36826a1a4511fc7b479d4  RUN.log
3aed3e5e964f876497e67ccb01b3b157faa2c6dcb8c6017320d964d16ca8a22a  run_bench.py
c4856e9a33ecb930f10019e6d3562d18ec1c549deb565821f6678e33ce78bae1  crop_windows.sh