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Summer2022
221cb0332
Commits
0cf6bb30
Commit
0cf6bb30
authored
3 years ago
by
Shawny
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fix duconv eval part readme
parent
cc249a8a
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official/nlp/duconv/README_CN.md
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0cf6bb30
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@@ -266,14 +266,14 @@ Proactive Conversation模型包含四个部分:
-
在Ascend环境运行时评估DuConv数据集
在运行以下命令之前,请检查用于评估的检查点路径。请将检查点路径设置为绝对全路径,例如“
username/googlenet/train_googlenet_cifar10-125_390.
ckpt”。
在运行以下命令之前,请检查用于评估的检查点路径。请将检查点路径设置为绝对全路径,例如“
/home/DuConv_mindspore/save_model/
ckpt
0
”。
```
bash
##example for evaluate model
bash run_preict.sh match_kn_gene /DuConv_mindspore/data/test.mindrecord
.
/save_model/ckpt
_
0 predict1p
bash run_preict.sh match_kn_gene /DuConv_mindspore/data/test.mindrecord
/home/DuConv_mindspore
/save_model/ckpt0 predict1p
```
上述python命令将在后台运行,您可以通过predict/predict_match_kn_gene_rank_?_ckpt.log文件查看结果。测试数据集的准确性如下:
上述python命令将在后台运行,您可以通过
./
predict/predict_match_kn_gene_rank_?_ckpt.log文件查看结果。测试数据集的准确性如下:
```
bash
F1: 31.50%
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@@ -283,7 +283,7 @@ Proactive Conversation模型包含四个部分:
DISTINCT2: 0.399%
```
注:对于分布式训练后评估,请将checkpoint_path设置为
最后保存的检查点文件,如“userna
me/DuConv_mindspore/output8p/
match_kn_gene_rank_7-1_7023.
ckpt
”。
测试数据集的准确性如下:
注:对于分布式训练后评估,请将checkpoint_path设置为
所有检查点文件的目录,如“/ho
me/DuConv_mindspore/output8p/
save_model/ckpt0”。一个
ckpt
对应的
测试数据集的准确性如下:
```
bash
F1: 31.17%
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@@ -293,6 +293,8 @@ Proactive Conversation模型包含四个部分:
DISTINCT2: 0.405%
```
在predict文件夹含有所有ckpt目录中权重文件的的评估log,每个log的文件名与ckpt文件名对应,需要遍历所有log找到最优精度,通过对应文件名确认ckpt文件,或根据自己的需求,使用所需精度的ckpt。
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