def __init__(self):
is_pair = True
class_labels = ['0', '1']
metric = CompositeEvalMetric()
metric.add(F1(average='micro'))
super(MultiRCTask, self).__init__(class_labels,
metric, is_pair, output_format="jsonl")
def __init__(self):
is_pair = True
class_labels = ['0', '1']
metric = CompositeEvalMetric()
metric.add(F1())
metric.add(Accuracy())
super(QQPTask, self).__init__(class_labels, metric, is_pair)
def __init__(self):
is_pair = True
class_labels = ['0', '1']
metric = CompositeEvalMetric()
metric.add(F1())
metric.add(Accuracy())
super(ReCoRDTask, self).__init__(class_labels,
metric, is_pair, output_format="jsonl")
def evaluate(net, dataloader, context):
f1 = F1()
for i, data in enumerate(dataloader):
for idx in range(0, len(data)):
data[idx] = data[idx].astype(np.float32).reshape(
(-1, 1)).as_in_context(context)
output, decoded = net(*data[:-1])
f1.update(data[len(data) - 1], output)
return float(f1.get()[1])
def __init__(self, *args, **kwargs): # passthrough arguments to TSVDataset
# (filename, field_separator=nlp.data.Splitter(','), num_discard_samples=1, field_indices=[2,1])
self.args = args
self.kwargs = kwargs
is_pair = False
class_labels = ['0', '1']
metric = CompositeEvalMetric()
metric.add(F1())
metric.add(Accuracy())
super(TSVClassificationTask, self).__init__(class_labels, metric,
is_pair)
dataset = nlp.data.TSVDataset(*self.args, **self.kwargs)
# do the split
train_sampler, val_sampler = get_split_samplers(dataset,
split_ratio=0.8)
self.trainset = SampledDataset(dataset, train_sampler)
self.valset = SampledDataset(dataset, val_sampler)
def get_metric():
"""Get metrics Accuracy and F1"""
metric = CompositeEvalMetric()
for child_metric in [Accuracy(), F1()]:
metric.add(child_metric)
return metric
def get_metric(cls):
"""Get metrics Accuracy and F1"""
metric = CompositeEvalMetric()
for child_metric in [Accuracy(), F1(average='micro')]:
metric.add(child_metric)
return metric