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Spico197 avatar Spico197 commented on July 3, 2024 1

支持的。可以参考这里的启动方法:

# DCFEE Baselines: DCFEE-O, DCFEE-M
MODEL_TYPE=DCFEE
MODEL_STR=DCFEE
echo "---> ${MODEL_STR} Run"
CUDA_VISIBLE_DEVICES=${GPU_SCOPE} ./scripts/train_multi.sh ${NUM_GPUS} --resume_latest_cpt ${RESUME_TRAIN} --save_cpt_flag ${SAVE_CPT} \
--data_dir ${DATA_DIR} --exp_dir ${EXP_DIR} --task_name ${COMMON_TASK_NAME} --num_train_epochs ${N_EPOCH} \
--train_batch_size ${TRAIN_BS} --gradient_accumulation_steps ${GRAD_ACC_STEP} --eval_batch_size ${EVAL_BS} \
--model_type ${MODEL_TYPE} --cpt_file_name ${MODEL_STR}

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miraitowa9 avatar miraitowa9 commented on July 3, 2024 1

好滴谢谢!祝您科研顺利!
15E3DA4D

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Spico197 avatar Spico197 commented on July 3, 2024

嗨您好,这两种指标不太一样。

一般事件论元抽取任务会假设事件检测的正确率为100%,而论文中的F1值是整个事件表的F1,可以理解为包含了事件类型的判断,是一个任务的整体F1值。在DocEE任务中,通常不会考虑单一的论元抽取性能。输出结果中的实体部分是指实体抽取结果,而非论元(不是所有的实体都是论元)。

overall下的overall指的是事件表填充的整体F1值。

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miraitowa9 avatar miraitowa9 commented on July 3, 2024

好滴!非常感谢!
我观察到您的代码中包含DCFEE的代码(dee/models/dcfee.py),请问是否可以运行这个模型(DCFEE)的代码呢?

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miraitowa9 avatar miraitowa9 commented on July 3, 2024

好滴!
31行中提到“DCFEE Baselines: DCFEE-O, DCFEE-M ”,请问这个脚本也是运行一次,就可以得到这两个模型(DCFEE-O, DCFEE-M)的结果吗

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Spico197 avatar Spico197 commented on July 3, 2024

抱歉时间太久有些记不得了,可以跑一轮试试看,我印象中是的。

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miraitowa9 avatar miraitowa9 commented on July 3, 2024

好滴!非常感谢!

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miraitowa9 avatar miraitowa9 commented on July 3, 2024

我尝试换了一个数据集运行了deppn模型,但最终得分非常低,只有百分之十几,请问这是正常的嘛?

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Spico197 avatar Spico197 commented on July 3, 2024

抱歉,当前代码对DEPPN的支持有问题,还是需要使用他们原生的代码跑实验:https://github.com/HangYang-NLP/DE-PPN

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miraitowa9 avatar miraitowa9 commented on July 3, 2024

我复现他的原生代码好像存在一些问题呀?您有没有复现成功过他的代码呀?

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Spico197 avatar Spico197 commented on July 3, 2024

您可以联系DE-PPN的作者帮忙解决该模型的复现问题

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