mrzixi / sentiprompt Goto Github PK
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License: MIT License
您好,以下是我使用本仓库代码替换随机种子重复5次跑出的实验结果,感觉实验结果与论文中汇报的结果差距有点大,请问能提供一下详细的训练命令代码吗?
task="penga/14lap penga/14res penga/15res penga/16res pengb/14lap pengb/14res pengb/15res pengb/16res lcx/14lap lcx/14res lcx/15res lcx/16res"
s="544 3210 8 5678 744"
for SEED in $s
do
for dataset in $task
do
LOG_DIR=/mnt/f/yj/senti/logs_prompt_type3bs16/$dataset/$SEED
python train.py --dataset_name $dataset \
--log_dir $LOG_DIR \
--batch_size 32 \
--lr 5e-5 \
--n_epochs 50 \
--save_model $LOG_DIR/models \
--prompt type3 \
--seed $SEED
done
done
ds | test_aesc_f1_mean | test_pair_f1_mean | test_f1_mean | test_precision_mean | test_recall_mean |
---|---|---|---|---|---|
penga_14lap | 65.3 | 58.59 | 54.48 | 64.43 | 47.2 |
penga_14res | 76.19 | 74.02 | 69.96 | 72.69 | 67.42 |
penga_15res | 66.32 | 64.76 | 55.55 | 61.7 | 50.51 |
penga_16res | 74.94 | 72.99 | 67.89 | 70.64 | 65.36 |
ds | test_aesc_f1_mean | test_pair_f1_mean | test_f1_mean | test_precision_mean | test_recall_mean |
---|---|---|---|---|---|
pengb_14lap | 68.42 | 61.09 | 55.25 | 60.22 | 51.04 |
pengb_14res | 78.59 | 72.26 | 69.01 | 70.61 | 67.49 |
pengb_15res | 67.19 | 62.19 | 53.49 | 59.96 | 48.29 |
pengb_16res | 73.11 | 70.07 | 65.8 | 70.94 | 61.37 |
ds | test_aesc_f1_mean | test_pair_f1_mean | test_f1_mean | test_precision_mean | test_recall_mean |
---|---|---|---|---|---|
lcx_14lap | 67.12 | 55.59 | 51.64 | 57.75 | 46.7 |
lcx_14res | 75.56 | 64.84 | 60.99 | 63.45 | 58.73 |
lcx_15res | 68.63 | 56.04 | 51.25 | 55.15 | 47.88 |
lcx_16res | 76.25 | 68.69 | 64.06 | 67.66 | 60.83 |
您好,我阅读了您的论文,觉得您的想法十分具有创造性和启发性,所以我想深入阅读您的具体代码。但是我运行了代码,发现跑出来的结果和论文中报告的差距比较大。希望您可以公布最新版本的代码。感谢您的工作。
Congratulations!I found only data used in paper in this repository but no source code, I cannot wait to learn from your code for more information~
您好,请问什么时候可以把代码开源呢?
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