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CCL2022-track1

数据连接

data_augmentation 该目录为数据增强

本项目使用的训练数据的链接: https://pan.baidu.com/s/1LQ2MI9G789RWUTVnk3CetA 提取码: dsbg

文本纠错

CRA-Spell

训练 
python train.py \
--model_dir ./chinese_roberta_wwm_ext_pytorch/ \
--local_rank -1 \
--per_gpu_train_batch_size 32 \
--num_train_epochs 20 \
--use_cuda 

预测 
python predict.py \
--model_dir ./finetune_model/checkpoint-20/ \
--env_pos 0 \
--local_rank -1 \
--use_cuda
预训练模型链接:https://pan.baidu.com/s/1XKUtbcwax9C05oovkERPBg 提取码:6666 

macbert4csc

cd macbert4csc
训练 python train.py
预测 python infer.py --text_file ../yaclc-csc_test.src --save_path ../predict/roberta_01.txt
预训练模型链接: https://pan.baidu.com/s/1bit6Htu2QKUzzR8X1D9sJA 提取码: btgw

ReaLiSe

cd ReaLiSe
python change_data_format.py 调整数据格式
训练 sh train.sh
预测 sh test.sh
预训练模型链接:https://pan.baidu.com/s/1N8kro7dAGgmumi9YS_nN7A?pwd=6666 提取码:6666

RoBERTa_01

cd RoBERTa_01
python change_data_format.py 调整数据格式
训练 sh pipe.sh
预测 sh predict.sh
预训练模型链接: https://pan.baidu.com/s/1zX8s1RB3CTlRXGRcWmwKZg 提取码: 1v2p

模型结果合并

合并各个模型的检错结果,然后利用候选集进行纠错。

python ensemble.py

各个模型使用的都是论文原始环境。

ccl2022-track1's People

Contributors

huac-z-ai avatar huac-z avatar

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