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[Docs] Fix CharMetric P/R wrong definition (#1740)

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Kevin Wang 2023-02-22 10:29:10 +08:00 committed by GitHub
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4
docs/en/basic_concepts/evaluation.md
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@ -125,7 +125,7 @@ val_evaluator = [dict(type='CharMetric')]
Specifically, `CharMetric` will output two evaluation metrics, namely `char_precision` and `char_recall`. Let the number of correctly predicted characters (True Positive) be {math}`\sigma_{tp}`, then the precision *P* and recall *R* can be calculated by the following equation:
```{math}
P=\frac{\sigma_{tp}}{\sigma_{gt}}, R = \frac{\sigma_{tp}}{\sigma_{pred}}
P=\frac{\sigma_{tp}}{\sigma_{pred}}, R = \frac{\sigma_{tp}}{\sigma_{gt}}
```
where {math}`\sigma_{gt}` and {math}`\sigma_{pred}` represent the total number of characters in the label text and the predicted text, respectively.
@ -133,7 +133,7 @@ where {math}`\sigma_{gt}` and {math}`\sigma_{pred}` represent the total number o
For example, assume that the label text is "MM**O**CR" and the predicted text is "mm**0**cR**1**". The score of the `CharMetric` is:
```{math}
P=\frac{4}{5}, R=\frac{4}{6}
P=\frac{4}{6}, R=\frac{4}{5}
```
### OneMinusNEDMetric

4
docs/zh_cn/basic_concepts/evaluation.md
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@ -125,7 +125,7 @@ val_evaluator = [dict(type='CharMetric')]
具体而言，`CharMetric` 会输出两个评测评测指标，即字符精度 `char_precision` 和字符召回率 `char_recall`。设正确预测的字符（True Positive）数量为 {math}`\sigma_{tp}`，则精度 *P* 和召回率 *R* 可由下式计算取得：
```{math}
P=\frac{\sigma_{tp}}{\sigma_{gt}}, R = \frac{\sigma_{tp}}{\sigma_{pred}}
P=\frac{\sigma_{tp}}{\sigma_{pred}}, R = \frac{\sigma_{tp}}{\sigma_{gt}}
```
其中，{math}`\sigma_{gt}` 与 {math}`\sigma_{pred}` 分别为标签文本与预测文本所包含的字符总数。
@ -133,7 +133,7 @@ P=\frac{\sigma_{tp}}{\sigma_{gt}}, R = \frac{\sigma_{tp}}{\sigma_{pred}}
例如，假设标签文本为 "MM**O**CR"，预测文本为 "mm**0**cR**1**"，则使用 `CharMetric` 评测指标的得分为：
```{math}
P=\frac{4}{5}, R=\frac{4}{6}
P=\frac{4}{6}, R=\frac{4}{5}
```
### OneMinusNEDMetric