#
# dataset-name, MI, cutoff, clusters, singletons,
# precision, recall, f1, --- for child-directed
# precision, recall, f1, -- for silver
# precision, recall, f1, -- for basic
#
# mini-fuzz is hard to compare and incomplete.
# mini_fuzz 4.35 0.65 366 3326
#
# These two are for the "missing words" data.
# micro_fuzz_missing 4.03 0.65 135 540
# micro_discrim_missing 3.62 0.5 124 292
#
micro_discrim 3.62 0.5 124 292 0.651 0.580 0.614 0.507 0.491 0.499 0.571 0.482 0.523
micro_fuzzier 3.95 0.65 136 544 0.657 0.538 0.592 0.490 0.443 0.465 0.557 0.441 0.493
micro_diss 4.00 0.65 135 537 0.654 0.537 0.590 0.493 0.443 0.467 0.560 0.440 0.493
micro_fuzz 4.03 0.65 135 540 0.660 0.541 0.595 0.492 0.445 0.467 0.555 0.440 0.491
micro_fizz 4.29 0.75 115 833 0.665 0.486 0.562 0.483 0.402 0.439 0.555 0.403 0.467
micro_dissier 4.30 0.75 115 833 0.660 0.481 0.556 0.487 0.405 0.442 0.464 0.421 0.442
micro_dissiest 4.46 0.85 85 1175 0.666 0.441 0.531 0.462 0.339 0.391 0.536 0.343 0.418