bert_config-->bert模型参数设置 init_checkpoint-->预训练好的模型 max_seq_length-->一个序列的最大长度 output_dir-->结果输出文件(包括日志文件) do_lower_case-->是否小写处理(针对英文) 其他的字面意思 跑不动? 有些时候发现跑demo的时候会出现各种问题,这里简单汇总一下 1. No such file or directory!
Pre-Training with Whole Word Masking for Chinese BERT(中文BERT-wwm系列模型) - Chinese-BERT-wwm/README_EN.md at master · jiaqi-bit/Chinese-BERT-wwm
dev-v2.0.json evaluate-v2.0.py Download these to some directory $SQUAD_DIR. On Cloud TPU you can run with BERT-Large as follows: python run_squad.py \ --vocab_file=$BERT_LARGE_DIR/vocab.txt \ --bert_config_file=$BERT_LARGE_DIR/bert_config.json \ --init_checkpoint=$BERT_LARGE_DIR...
>>> config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json') >>> model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config) """ config = kwargs.pop("config", None) state_dict = kwargs.pop("state_dict", None) ...
train-v2.0.json dev-v2.0.json evaluate-v2.0.py Download these to some directory$SQUAD_DIR. On Cloud TPU you can run with BERT-Large as follows: python run_squad.py \ --vocab_file=$BERT_LARGE_DIR/vocab.txt \ --bert_config_file=$BERT_LARGE_DIR/bert_config.json \ --init_checkpoint...
train-v2.0.json dev-v2.0.json evaluate-v2.0.py Download these to some directory$SQUAD_DIR. On Cloud TPU you can run with BERT-Large as follows: python run_squad.py \ --vocab_file=$BERT_LARGE_DIR/vocab.txt \ --bert_config_file=$BERT_LARGE_DIR/bert_config.json \ --init_checkpoint...
train-v2.0.json dev-v2.0.json evaluate-v2.0.py Download these to some directory $SQUAD_DIR. On Cloud TPU you can run with BERT-Large as follows: python run_squad.py \ --vocab_file=$BERT_LARGE_DIR/vocab.txt \ --bert_config_file=$BERT_LARGE_DIR/bert_config.json \ --init_checkpoint...
https://github.com/google-research/bert BERT *** New May 31st, 2019: Whole Word Masking Models *** This is a release of several new models which were the result of an improvement the pre-pro ...
4096 bert_config_path: data/demo_config/bert_config.json checkpoints: ./output data_dir: data/train do_test: False epoch: 100 generate_neg_sample: True in_tokens: True init_checkpoint: None is_distributed: False learning_rate: 0.0001 loss_scaling: 8.0 lr_scheduler: linear_warmup_decay max...
BERT, orBidirectionalEncoderRepresentations 来源于Transformers, 是一种新的预训练语言表达的方法,是解决自然语言处理(NLP)任务最先进的方法。 Our academic paper which describes BERT in detail and provides full results on a number of tasks can be found here: ...