---
# System prepended metadata

title: Untitled

---

```
fold 0  model: text_cnn_best_99.7172859450727_LR0.001_BATCH100_EPOCH100
Test data(20%)
test fold data: 1196
0.0    507
1.0    290
3.0    256
4.0     84
2.0     57
5.0      2

Predict    0    1   2    3   4  5   6   All
label                                      
0.0      495    6   1    3   0  1   1   507
1.0      109  163   0    8   5  1   4   290
2.0        7    3  16    1   0  0  30    57
3.0       33    8   0  202   1  2  10   256
4.0       18    7   3    5  31  0  20    84
5.0        1    0   0    0   0  0   1     2
All      663  187  20  219  37  4  66  1196
fold 0  20%_Accuracy 0.8135451505016722
fold 0  20%_Error amount: 223 / 1196
Test data(80%)
test fold data: 4782
0.0    1814
1.0    1293
3.0    1100
4.0     329
2.0     242
5.0       3
Name: label, dtype: int64
4782it [01:59, 40.14it/s]
Predict     0     1   2     3    4  5    6   All
label                                           
0.0      1793    15   0     3    0  1    2  1814
1.0        38  1146   1    74    0  3   31  1293
2.0        22     3  92     0   10  0  115   242
3.0        14    13   0  1033    1  4   35  1100
4.0         8     1   2    21  243  0   54   329
5.0         2     0   0     0    0  0    1     3
All      1877  1178  95  1131  254  8  238  4781
fold 0  80%_Accuracy 0.9504391468005019
fold 0  80%_Error amount: 237 / 4782

```

```
add train amount: 1696
remove amount: 806
train_merge fold data: 61872
0.0    51956
1.0     3523
3.0     2952
4.0     2270
2.0      662
5.0      509

fold 1  model:text_cnn_best_99.80775444264944_LR0.001_BATCH100_EPOCH100
Test data(20%)
test fold data: 1196
0.0    447
1.0    339
3.0    260
4.0     90
2.0     58
5.0      1
Predict    0    1   2    3   4  5   6   All
label                                      
0.0      441    4   0    2   0  0   0   447
1.0      132  179   0   15   1  4   8   339
2.0       12    2  13    0   0  0  31    58
3.0       43    5   3  193   0  5  11   260
4.0       26    1   5    6  35  0  17    90
5.0        1    0   0    0   0  0   0     1
All      655  191  21  216  36  9  67  1195
fold 1  20%_Accuracy 0.7759197324414716
fold 1  20%_Error amount: 268 / 1196
Test data(80%)
test fold data: 4782
0.0    1874
1.0    1244
3.0    1096
4.0     323
2.0     241
5.0       4
Name: label, dtype: int64
4782it [01:58, 40.50it/s]
Predict     0     1    2     3    4   5    6   All
label                                             
0.0      1854    12    0     3    0   2    3  1874
1.0        27  1112    1    73    0   4   27  1244
2.0        13     4  103     0    7   0  114   241
3.0        11    10    0  1031    2   8   34  1096
4.0         7     0    1    18  240   0   57   323
5.0         2     0    0     0    0   0    2     4
All      1914  1138  105  1125  249  14  237  4782
fold 1  80%_Accuracy 0.9571309075700544
fold 1  80%_Error amount: 205 / 4782
```

```
add train amount: 1657
remove amount: 773
train_merge fold data: 61866
0.0    51930
1.0     3523
3.0     2947
4.0     2299
2.0      658
5.0      508
dtype: int64

fold 2  model :text_cnn_best_99.65589660743134_LR0.001_BATCH100_EPOCH100
Test data(20%)
test fold data: 1196
0.0    471
1.0    328
3.0    275
2.0     62
4.0     60

Predict    0    1   2    3   4   5   6   All
label                                       
0.0      459    5   0    4   2   1   0   471
1.0      140  150   3   12  15   2   6   328
2.0        8    0  19    0   3   0  32    62
3.0       33    5   3  203  12   7  12   275
4.0       13    1   0    5  31   0  10    60
All      653  161  25  224  63  10  60  1196
fold 2  20%_Accuracy 0.7709030100334449
fold 2  20%_Error amount: 274 / 1196

Test data(80%)
test fold data: 4782
0.0    1850
1.0    1255
3.0    1081
4.0     353
2.0     237
5.0       5

Predict     0     1    2     3    4   5    6   All
label                                             
0.0      1824    15    0     6    0   2    3  1850
1.0        30  1111    0    75    5   5   29  1255
2.0        11     6   99     0    7   1  113   237
3.0        13    12    0  1015    4   4   33  1081
4.0         7     0    2    21  259   0   64   353
5.0         3     0    0     0    0   0    2     5
All      1888  1144  101  1117  275  12  244  4781
fold 2  80%_Accuracy 0.9519029694688415
fold 2  80%_Error amount: 230 / 4782
```

```
add train amount: 1678
remove amount: 799
train_merge fold data: 61861
0.0    51946
1.0     3543
3.0     2928
4.0     2277
2.0      657
5.0      509

fold 3  model :text_cnn_best_99.78352180936996_LR0.001_BATCH100_EPOCH100

Test data(20%)
test fold data: 1195
0.0    453
1.0    311
3.0    284
4.0     83
2.0     63
5.0      1

Predict    0    1   2    3   4  5   6   All
label                                      
0.0      441    9   0    0   1  2   0   453
1.0      135  144   2   17   5  1   7   311
2.0        9    1  23    0   3  0  27    63
3.0       41    8   0  225   0  5   5   284
4.0       20    2   2    9  37  0  13    83
5.0        1    0   0    0   0  0   0     1
All      647  164  27  251  46  8  52  1195
fold 3  20%_Accuracy 0.7715481171548118
fold 3  20%_Error amount: 273 / 1195

Test data(80%)
test fold data: 4783
0.0    1868
1.0    1272
3.0    1072
4.0     330
2.0     236
5.0       4

Predict     0     1   2     3    4   5    6   All
label                                            
0.0      1836    21   0     7    0   1    3  1868
1.0        30  1132   1    75    0   6   28  1272
2.0        17     5  89     0    7   0  118   236
3.0         8    13   0  1006    2   3   40  1072
4.0         7     0   1    16  245   0   61   330
5.0         2     0   0     0    0   0    2     4
All      1900  1171  91  1104  254  10  252  4782
fold 3  80%_Accuracy 0.9533765419192975
fold 3  80%_Error amount: 223 / 4783
```
```
4 embedding
add train amount: 1677
remove amount: 794
train_merge fold data: 61865
0.0    51956
1.0     3535
3.0     2937
4.0     2261
2.0      666
5.0      509

fold 4  model :text_cnn_best_99.77705977382875_LR0.001_BATCH100_EPOCH100
Test data(20%)
test fold data: 1195
0.0    443
1.0    315
3.0    281
4.0     96
2.0     59
5.0      1

Predict    0    1   2    3   4  5   6   All
label                                      
0.0      431    7   0    3   0  0   2   443
1.0      129  162   0   12   1  1  10   315
2.0        9    0  23    0   2  0  25    59
3.0       44    7   0  219   1  3   7   281
4.0       24    3   3   10  41  1  14    96
5.0        0    0   0    0   0  0   1     1
All      637  179  26  244  45  5  59  1195
fold 4  20%_Accuracy 0.7824267782426778
fold 4  20%_Error amount: 260 / 1195

Test data(80%)
test fold data: 4783
0.0    1878
1.0    1268
3.0    1075
4.0     317
2.0     240
5.0       4

Predict     0     1   2     3    4   5    6   All
label                                            
0.0      1858    12   0     4    0   3    1  1878
1.0        43  1123   1    71    1   4   25  1268
2.0        17     6  90     0    7   0  120   240
3.0        14    12   0  1003    1   7   38  1075
4.0         6     1   2    15  233   0   60   317
5.0         3     0   0     0    0   0    1     4
All      1941  1154  93  1093  242  14  245  4782
fold 4  80%_Accuracy 0.9517039514948777
fold 4  80%_Error amount: 231 / 4783

```

## 不斷詞
```
Test data(20%)
Predict    0    1   2    3   4  5   All
label                                  
0.0      485   10   1    7   1  3   507
1.0      118  155   0   10   5  0   288
2.0       18    3  28    2   5  1    57
3.0       35    7   1  210   3  0   256
4.0       18    8   3    8  47  0    84
5.0        2    0   0    0   0  0     2
All      676  183  33  237  61  4  1194
fold 0  20%_Accuracy: 0.7747068676716918
fold 0  20%_Error amount: 269 / 1194

Test data(80%)
Predict     0     1    2     3    4   5   All
label                                        
0.0      1770    16    0    17    8   3  1814
1.0        24  1176    1    83    1   2  1287
2.0         2     1  228     0   10   1   242
3.0         6     8    0  1079    2   1  1096
4.0         2     0    2    22  303   0   329
5.0         0     0    0     0    0   3     3
All      1804  1201  231  1201  324  10  4771
fold 0  80%_Accuracy 0.9553646269907795
fold 0  80%_Error amount: 213 / 4772
```

```
Test data(20%)
Predict    0    1   2    3   4  5   All
label                                  
0.0      441    2   2    2   0  0   447
1.0      143  173   0   20   1  2   339
2.0       19    1  28    3   6  1    58
3.0       49    5   4  194   0  5   257
4.0       27    2   8    6  47  0    90
5.0        0    1   0    0   0  0     1
All      679  184  42  225  54  8  1192
fold 1  20%_Accuracy: 0.7401508801341157
fold 1  20%_Error amount: 310 / 1193

Test data(80%) 
Predict     0     1    2     3    4  5   All
label                                       
0.0      1852     8    1     6    5  2  1874
1.0        24  1139    1    70    0  2  1236
2.0         2     0  229     0   10  0   241
3.0         5     6    0  1082    2  0  1095
4.0         0     0    1    19  303  0   323
5.0         0     0    0     0    0  4     4
All      1883  1153  232  1177  320  8  4773
fold 1  80%_Accuracy 0.9656400586633145
fold 1  80%_Error amount: 164 / 4773
```

```
Test data(20%)
Predict    0    1   2    3   4   5   All
label                                   
0.0      456    3   2    8   1   1   471
1.0      146  143   5   11  17   2   324
2.0       21    0  27    5   7   2    62
3.0       36    4   4  209  14   7   274
4.0       14    1   0    8  37   0    60
All      673  151  38  241  76  12  1191
fold 2  20%_Accuracy: 0.7321578505457599
fold 2  20%_Error amount: 319 / 1191

Test data(80%)
Predict     0     1    2     3    4   5   All
label                                        
0.0      1824     4    1    13    5   3  1850
1.0        23  1144    0    79    5   0  1251
2.0         4     1  220     1   10   1   237
3.0         9     6    0  1057    5   1  1078
4.0         1     0    2    21  329   0   353
5.0         0     0    0     0    0   5     5
All      1861  1155  223  1171  354  10  4774
fold 2  80%_Accuracy 0.9589528795811518
fold 2  80%_Error amount: 196 / 4775
```

```

Test data(20%)
Predict    0    1   2    3   4  5   All
label                                  
0.0      444    2   0    1   5  1   453
1.0      137  146   2   19   5  0   309
2.0       13    0  43    2   5  0    63
3.0       41    6   1  234   0  2   284
4.0       23    2   2    9  46  1    83
5.0        0    0   0    0   0  1     1
All      658  156  48  265  61  5  1193
fold 3  20%_Accuracy: 0.7661357921207042
fold 3  20%_Error amount: 279 / 1193

Test data(80%)
48it [00:00, 99.58it/s]
Predict     0     1    2     3    4  5   All
label                                       
0.0      1836    11    1     6   12  2  1868
1.0        25  1156    1    79    2  3  1266
2.0         1     0  224     0   11  0   236
3.0         5     7    0  1053    2  1  1068
4.0         0     0    1    18  311  0   330
5.0         0     0    0     2    0  2     4
All      1867  1174  227  1158  338  8  4772
fold 3  80%_Accuracy 0.9599832390530065
fold 3  80%_Error amount: 191 / 4773
```

```
Test data(20%)
Predict    0    1   2    3   4   5   All
label                                   
0.0      438    4   1    0   0   0   443
1.0      138  159   0   15   1   2   315
2.0       23    0  28    0   6   2    59
3.0       46    5   0  224   2   4   281
4.0       22    3   5   13  51   2    96
5.0        0    0   0    1   0   0     1
All      667  171  34  253  60  10  1195
fold 4  20%_Accuracy: 0.7531380753138075
fold 4  20%_Error amount: 295 / 1195

Test data(80%)
Predict     0     1    2     3    4  5   All
label                                       
0.0      1867     5    1     3    0  2  1878
1.0        33  1144    1    80    0  2  1260
2.0         3     0  230     0    7  0   240
3.0         6     7    0  1057    1  0  1071
4.0         0     0    2    14  301  0   317
5.0         0     0    0     0    0  4     4
All      1909  1156  234  1154  309  8  4770
fold 4  80%_Accuracy 0.9647872563403899
fold 4  80%_Error amount: 168 / 4771
```