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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 | |