<jupyter_start><jupyter_text>训练英文<jupyter_code>#!export SQUAD_DIR=./BERT-for-Chinese-Question-Answering-master/data/squad_cn

!python3 run_squad.py  --bert_model bert-base-uncased --do_train   --do_predict  --train_file ./BERT-for-Chinese-Question-Answering-master/data/sample/sample_dev.json --predict_file ./BERT-for-Chinese-Question-Answering-master/data/sample/sample_train.json  --train_batch_size 12  --learning_rate 3e-5   --num_train_epochs 2.0   --max_seq_length 384   --doc_stride 128   --output_dir ../data/squad<jupyter_output>04/24/2019 09:03:28 - INFO - __main__ -   device: cpu n_gpu: 0, distributed training: False, 16-bits training: False
04/24/2019 09:03:28 - WARNING - pytorch_pretrained_bert.tokenization -   The pre-trained model you are loading is an uncased model but you have set `do_lower_case` to False. We are setting `do_lower_case=True` for you but you may want to check this behavior.
04/24/2019 09:03:30 - INFO - pytorch_pretrained_bert.tokenization -   loading vocabulary file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt from cache at /root/.pytorch_pretrained_bert/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084
04/24/2019 09:03:31 - INFO - pytorch_pretrained_bert.modeling -   loading archive file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz from cache at /root/.pytorch_pretrained_bert/distributed_-1/9c41111e2de84547a463fd39217199738d1e3deb72d4fec4399[...]<jupyter_text>训练中文<jupyter_code>!rm -rf ../data/squad2
!mkdir ../data/squad2

#!export SQUAD_DIR=./BERT-for-Chinese-Question-Answering-master/data/squad_cn

!python3 run_squad.py  --bert_model BERT-for-Chinese-Question-Answering-master/data/model_cn/ --do_train   --do_predict  --train_file ./BERT-for-Chinese-Question-Answering-master/data/squad_cn/squad_dev.json --predict_file ./BERT-for-Chinese-Question-Answering-master/data/squad_cn/squad_train.json  --train_batch_size 12  --learning_rate 3e-5   --num_train_epochs 2.0   --max_seq_length 384   --doc_stride 128   --output_dir ../data/squad2
!rm -rf ../data/squad1
!mkdir ../data/squad1

#!export SQUAD_DIR=./BERT-for-Chinese-Question-Answering-master/data/squad_cn

!python3 run_squad.py  --bert_model bert-base-chinese --do_train   --do_predict  --train_file ./BERT-for-Chinese-Question-Answering-master/data/squad_cn/squad_dev.json --predict_file ./BERT-for-Chinese-Question-Answering-master/data/squad_cn/squad_train.json  --train_batch_size 12  --learning_rate 3e-5   --num_train_epochs 2.0   --max_seq_length 384   --doc_stride 128   --output_dir ../data/squad1<jupyter_output>04/24/2019 10:11:08 - INFO - __main__ -   device: cpu n_gpu: 0, distributed training: False, 16-bits training: False
04/24/2019 10:11:08 - WARNING - pytorch_pretrained_bert.tokenization -   The pre-trained model you are loading is an uncased model but you have set `do_lower_case` to False. We are setting `do_lower_case=True` for you but you may want to check this behavior.
04/24/2019 10:11:09 - INFO - pytorch_pretrained_bert.tokenization -   loading vocabulary file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt from cache at /root/.pytorch_pretrained_bert/8a0c070123c1f794c42a29c6904beb7c1b8715741e235bee04aca2c7636fc83f.9b42061518a39ca00b8b52059fd2bede8daa613f8a8671500e518a8c29de8c00
04/24/2019 10:11:09 - WARNING - __main__ -   Could not find answer: '\u72d7\u559c\u6b22\u5403\u9aa8\u5934' vs. '\u5f53\u7136\u559c\u6b22\u4e86'
04/24/2019 10:11:10 - INFO - pytorch_pretrained_bert.file_utils -   https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-c[...]
{'fraction_non_alphanumeric': 0.11822125813449023, 'fraction_numerical': 0.113882863340564, 'mean_word_length': 4.473293768545994, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, 'https://': 4, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '351106', 'license': 'MIT', 'n_tokens_mistral': 1705, 'n_tokens_neox': 1469, 'n_words': 229, 'path': 'examples/python run_squad.py.ipynb', 'repo_name': 'napoler/bert-cn', 'size': '3688'}