NER_for_Chinese_Address / ner_args.json
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{
"bert_dir": "./model_hub/chinese-bert-wwm-ext/",
"output_dir": "./checkpoint/cluener",
"data_dir": "./data/",
"data_path": "./data/cluener/ner_data",
"labels": [
"address",
"book",
"company",
"game",
"government",
"movie",
"name",
"organization",
"position",
"scene"
],
"bio_labels": [
"O",
"B-address",
"I-address",
"B-book",
"I-book",
"B-company",
"I-company",
"B-game",
"I-game",
"B-government",
"I-government",
"B-movie",
"I-movie",
"B-name",
"I-name",
"B-organization",
"I-organization",
"B-position",
"I-position",
"B-scene",
"I-scene"
],
"num_labels": 21,
"label2id": {
"O": 0,
"B-address": 1,
"I-address": 2,
"B-book": 3,
"I-book": 4,
"B-company": 5,
"I-company": 6,
"B-game": 7,
"I-game": 8,
"B-government": 9,
"I-government": 10,
"B-movie": 11,
"I-movie": 12,
"B-name": 13,
"I-name": 14,
"B-organization": 15,
"I-organization": 16,
"B-position": 17,
"I-position": 18,
"B-scene": 19,
"I-scene": 20
},
"id2label": {
"0": "O",
"1": "B-address",
"2": "I-address",
"3": "B-book",
"4": "I-book",
"5": "B-company",
"6": "I-company",
"7": "B-game",
"8": "I-game",
"9": "B-government",
"10": "I-government",
"11": "B-movie",
"12": "I-movie",
"13": "B-name",
"14": "I-name",
"15": "B-organization",
"16": "I-organization",
"17": "B-position",
"18": "I-position",
"19": "B-scene",
"20": "I-scene"
},
"max_seq_len": 512,
"epochs": 3,
"train_batch_size": 12,
"dev_batch_size": 12,
"bert_learning_rate": 3e-05,
"crf_learning_rate": 0.003,
"adam_epsilon": 1e-08,
"weight_decay": 0.01,
"warmup_proportion": 0.01,
"save_step": 500
}