---
# System prepended metadata

title: Update 2021.04.26

---

# Update 2021.04.26
## 作法
CensorWords_Ground Truth_precise_210419_1316.csv 拿去斷詞 predict
## 結果
**CensorWords_Ground Truth_precise_210419_1316.csv**

```
斷詞前
Predict Accuracy :  0.829118460120815
Model
correct      0     1    2     3    4   5
predict                                 
0        12877   692   34  1337  432  85
1            8  1923    2   166   14   0
2            2     2  108    12   55   0
3            1    31    0   446    5   0
4            6     5   12   123  746   2
5            1     1    2   304    4  96
```

```
CensorWords_Ground Truth: 19534
0    12895
1     2654
3     2388
4     1256
5      183
2      158

Label Accuracy :  0.8237432169550527
數美
correct      0     1   2     3    4    5
label                                   
0        12894  1116  59  1125  801  179
1            1  1536   1    96    0    0
2            0     0  91     0   27    0
3            0     2   0  1146    6    0
4            0     0   7    20  421    1
5            0     0   0     1    1    3

Predict Accuracy :  0.8374116924337053
Model
correct      0     1   2     3    4    5
predict                                 
0        12753   911  65   987  450  111
1           92  1703   2   130   14    1
2            3     1  79     0   41    0
3           29    35   1  1020   14    0
4           14     3   9    29  734    2
5            4     1   2   222    3   69
```

https://docs.google.com/spreadsheets/d/1E_jU-_UOfK1xrCzh0k7mrVtD28-bhn544JED-BsMdWU/edit?usp=sharing

---
# Ground truth 
## Train:
new word dataset_add label_normal name_default
## Test:
Ground truth
## Model
text_cnn_best_99.47767857142857_LR0.001_BATCH100_EPOCH100
## result(有斷詞)
```
CensorWords_Ground Truth_precise_210425_1810.csv
Predict      0     1    2    3    4    5    All
correct                                        
0        12877     8    2    1    6    1  12895
1          692  1923    2   31    5    1   2654
2           34     2  108    0   12    2    158
3         1337   166   12  446  123  304   2388
4          432    14   55    5  746    4   1256
5           85     0    0    0    2   96    183
All      15457  2113  179  483  894  408  19534

Accuracy: 0.829118460120815
Error amount: 3338 / 19534
Type 0 Accuracy: 12877 / 12895 = 0.998604
Type 1 Accuracy: 1923 / 2654 = 0.724567
Type 2 Accuracy: 108 / 158 = 0.683544
Type 3 Accuracy: 446 / 2388 = 0.186767
Type 4 Accuracy: 746 / 1256 = 0.593949
Type 5 Accuracy: 96 / 183 = 0.524590
```

## result (沒斷詞)

```
predict correct number:  15190 accuracy:  0.7781762295081968
predict      0     1    2    3    4   5
correct                                
0        12500   274   12    7   92   9
1          869  1726    3   21   28   0
2           23     4  110    1   19   1
3         1573   279   13  180  269  68
4          505    47   55    8  640   1
5          136     6    1    1    5  34
```


---

# ULR detect
### 作法
![](https://i.imgur.com/UmbbfxH.png)
 
https://www.notion.so/0ea0785783454d2fb05b7afc2f0d7401?v=4ac201766ff1426eb514b67750643558&p=b6ffd73c1c1a4404bfe56b539033c99a
## 結果
```
Predict   0  4     5   All
label                     
5        56  2  5728  5786
All      56  2  5728  5786

Accuracy: 0.9899758036640166
Error amount: 58 / 5786

Type 0 Accuracy: 56 / 5786 = 0.009679
```

```
/content/drive/Shared drives/DataScience/1091_Spam_Detect/nickname_predict_label_result/CensorWords_Ground Truth_precise_210427_1723.csv
Predict      0     1    2     3     4    5    All
label                                            
0        13368  1171   43   635   744  213  16174
1          263  1298    0    69     2    2   1634
2           18     0   67     0    32    1    118
3          201    63    0   761    25  104   1154
4          127    10   13     6   292    1    449
5            3     0    0     0     2    0      5
All      13980  2542  123  1471  1097  321  19534

斷詞
Accuracy: 0.8081294153783147
Error amount: 3748 / 19534

Type 0 Accuracy: 13368 / 16174 = 0.826512
Type 1 Accuracy: 1298 / 1634 = 0.794370
Type 2 Accuracy: 67 / 118 = 0.567797
Type 3 Accuracy: 761 / 1154 = 0.659445
Type 4 Accuracy: 292 / 449 = 0.650334
Type 5 Accuracy: 0 / 5 = 0.000000

```