| _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 |
|
|
