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_base_ = [
'../../_base_/schedules/schedule_adadelta_18e.py',
'../../_base_/default_runtime.py'
]
categories = [
'address', 'book', 'company', 'game', 'government', 'movie', 'name',
'organization', 'position', 'scene'
]
test_ann_file = 'data/cluener2020/dev.json'
train_ann_file = 'data/cluener2020/train.json'
vocab_file = 'data/cluener2020/vocab.txt'
max_len = 128
loader = dict(
type='HardDiskLoader',
repeat=1,
parser=dict(type='LineJsonParser', keys=['text', 'label']))
ner_convertor = dict(
type='NerConvertor',
annotation_type='bio',
vocab_file=vocab_file,
categories=categories,
max_len=max_len)
test_pipeline = [
dict(type='NerTransform', label_convertor=ner_convertor, max_len=max_len),
dict(type='ToTensorNER')
]
train_pipeline = [
dict(type='NerTransform', label_convertor=ner_convertor, max_len=max_len),
dict(type='ToTensorNER')
]
dataset_type = 'NerDataset'
train = dict(
type=dataset_type,
ann_file=train_ann_file,
loader=loader,
pipeline=train_pipeline,
test_mode=False)
test = dict(
type=dataset_type,
ann_file=test_ann_file,
loader=loader,
pipeline=test_pipeline,
test_mode=True)
data = dict(
samples_per_gpu=8, workers_per_gpu=2, train=train, val=test, test=test)
evaluation = dict(interval=1, metric='f1-score')
model = dict(
type='NerClassifier',
encoder=dict(
type='BertEncoder',
max_position_embeddings=512,
init_cfg=dict(
type='Pretrained',
checkpoint='https://download.openmmlab.com/mmocr/ner/'
'bert_softmax/bert_pretrain.pth')),
decoder=dict(type='FCDecoder'),
loss=dict(type='MaskedCrossEntropyLoss'),
label_convertor=ner_convertor)
test_cfg = None
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