product_matching/experiments/euclidean_product_matching.py [5:22]:
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print(mz.__version__)
print(mz.__path__)

model_name = "intersection"
BOX_DIM = 2*50
MAX_LEN_LEFT = 28
MAX_LEN_RIGHT = 100
EMB_DIM = 100
BATCH_SIZE = 10
NEG_SIZE = 4
DROPOUT = 0.4
task = mz.tasks.Ranking(loss=mz.losses.RankCrossEntropyLoss(num_neg=NEG_SIZE))
task.metrics = [
    mz.metrics.NormalizedDiscountedCumulativeGain(k=3),
    mz.metrics.NormalizedDiscountedCumulativeGain(k=5),
    mz.metrics.MeanAveragePrecision()
]
print(task)
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product_matching/experiments/hyperboloid_product_matching.py [5:22]:
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print(mz.__version__)
print(mz.__path__)

model_name = "intersection"
BOX_DIM = 2*50
MAX_LEN_LEFT = 28
MAX_LEN_RIGHT = 100
EMB_DIM = 100
BATCH_SIZE = 10
NEG_SIZE = 4
DROPOUT = 0.4
task = mz.tasks.Ranking(loss=mz.losses.RankCrossEntropyLoss(num_neg=NEG_SIZE))
task.metrics = [
    mz.metrics.NormalizedDiscountedCumulativeGain(k=3),
    mz.metrics.NormalizedDiscountedCumulativeGain(k=5),
    mz.metrics.MeanAveragePrecision()
]
print(task)
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