MBuechel commited on
Commit
0eb8bc6
·
verified ·
1 Parent(s): c512993

Upload folder using huggingface_hub

Browse files
This view is limited to 50 files because it contains too many changes.   See raw diff
Files changed (50) hide show
  1. .gitattributes +29 -0
  2. Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/classification_reports.txt +513 -0
  3. Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/config.json +51 -0
  4. Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/loss.pdf +0 -0
  5. Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/model.safetensors +3 -0
  6. Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/model_params.json +7 -0
  7. Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/special_tokens_map.json +7 -0
  8. Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/stats.json +245 -0
  9. Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/tokenizer.json +0 -0
  10. Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/tokenizer_config.json +55 -0
  11. Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/vocab.txt +0 -0
  12. Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/classification_reports.txt +0 -0
  13. Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/config.json +131 -0
  14. Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/loss.pdf +0 -0
  15. Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/model.safetensors +3 -0
  16. Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/model_params.json +8 -0
  17. Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/special_tokens_map.json +7 -0
  18. Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/stats.json +380 -0
  19. Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/tokenizer.json +0 -0
  20. Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/tokenizer_config.json +55 -0
  21. Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/vocab.txt +0 -0
  22. Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/classification_reports.txt +0 -0
  23. Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/config.json +131 -0
  24. Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/loss.pdf +0 -0
  25. Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/model.safetensors +3 -0
  26. Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/model_params.json +8 -0
  27. Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/special_tokens_map.json +7 -0
  28. Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/stats.json +335 -0
  29. Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/tokenizer.json +0 -0
  30. Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/tokenizer_config.json +55 -0
  31. Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/vocab.txt +0 -0
  32. Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/classification_reports.txt +0 -0
  33. Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/config.json +131 -0
  34. Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/loss.pdf +0 -0
  35. Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/model.safetensors +3 -0
  36. Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/model_params.json +8 -0
  37. Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/special_tokens_map.json +7 -0
  38. Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/stats.json +416 -0
  39. Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/tokenizer.json +0 -0
  40. Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/tokenizer_config.json +55 -0
  41. Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/vocab.txt +0 -0
  42. Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/classification_reports.txt +1888 -0
  43. Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/config.json +131 -0
  44. Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/loss.pdf +0 -0
  45. Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/model.safetensors +3 -0
  46. Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/model_params.json +8 -0
  47. Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/special_tokens_map.json +7 -0
  48. Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/stats.json +290 -0
  49. Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/tokenizer.json +0 -0
  50. Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/tokenizer_config.json +55 -0
.gitattributes CHANGED
@@ -33,3 +33,32 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ Classification/bosch_swipe/1012_bosch_t50_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
37
+ Classification/bosch_swipe/1013_bosch_t50_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
38
+ Classification/bosch_swipe/1020_bosch_t50_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
39
+ Classification/bosch_swipe/1021_bosch_t50_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
40
+ Classification/bosch_swipe/15_bosch_t10_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
41
+ Classification/bosch_swipe/16_bosch_t25_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
42
+ Classification/bosch_swipe/17_bosch_t_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
43
+ Classification/bosch_swipe/215_bosch_t10_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
44
+ Classification/bosch_swipe/216_bosch_t25_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
45
+ Classification/bosch_swipe/217_bosch_t_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
46
+ Classification/bosch_swipe/225_bosch_t10_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
47
+ Classification/bosch_swipe/226_bosch_t25_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
48
+ Classification/bosch_swipe/227_bosch_t_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
49
+ Classification/bosch_swipe/25_bosch_t10_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
50
+ Classification/bosch_swipe/26_bosch_t25_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
51
+ Classification/bosch_swipe/27_bosch_t_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
52
+ Classification/bosch_swipe/extra_patience/667_bosch_t_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
53
+ Classification/tram_swipe/18_tram_10_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
54
+ Classification/tram_swipe/19_tram_10_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
55
+ Classification/tram_swipe/20_tram_25_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
56
+ Classification/tram_swipe/21_tram_25_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
57
+ Classification/tram_swipe/22_tram_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
58
+ Classification/tram_swipe/23_tram_FacebookAI-xlm-roberta-base/tokenizer.json filter=lfs diff=lfs merge=lfs -text
59
+ Classification/tram_swipe/30_tram_10_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
60
+ Classification/tram_swipe/31_tram_10_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
61
+ Classification/tram_swipe/32_tram_25_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
62
+ Classification/tram_swipe/33_tram_25_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
63
+ Classification/tram_swipe/34_tram_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
64
+ Classification/tram_swipe/35_tram_FacebookAI-xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/classification_reports.txt ADDED
@@ -0,0 +1,513 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ > epoch: 0
2
+ precision recall f1-score support
3
+
4
+ 0 0.00 0.00 0.00 13
5
+ 1 0.00 0.00 0.00 10
6
+ 2 0.00 0.00 0.00 8
7
+ 3 0.00 0.00 0.00 25
8
+ 4 0.00 0.00 0.00 18
9
+ 5 0.00 0.00 0.00 14
10
+ 6 0.00 0.00 0.00 10
11
+ 7 0.00 0.00 0.00 8
12
+ 8 0.00 0.00 0.00 32
13
+ 9 0.00 0.00 0.00 47
14
+
15
+ micro avg 0.00 0.00 0.00 185
16
+ macro avg 0.00 0.00 0.00 185
17
+ weighted avg 0.00 0.00 0.00 185
18
+ samples avg 0.00 0.00 0.00 185
19
+
20
+ > epoch: 1
21
+ precision recall f1-score support
22
+
23
+ 0 0.00 0.00 0.00 13
24
+ 1 0.00 0.00 0.00 10
25
+ 2 0.00 0.00 0.00 8
26
+ 3 0.00 0.00 0.00 25
27
+ 4 0.00 0.00 0.00 18
28
+ 5 0.00 0.00 0.00 14
29
+ 6 0.00 0.00 0.00 10
30
+ 7 0.00 0.00 0.00 8
31
+ 8 1.00 0.03 0.06 32
32
+ 9 0.00 0.00 0.00 47
33
+
34
+ micro avg 1.00 0.01 0.01 185
35
+ macro avg 0.10 0.00 0.01 185
36
+ weighted avg 0.17 0.01 0.01 185
37
+ samples avg 0.00 0.00 0.00 185
38
+
39
+ > epoch: 2
40
+ precision recall f1-score support
41
+
42
+ 0 0.00 0.00 0.00 13
43
+ 1 0.00 0.00 0.00 10
44
+ 2 0.00 0.00 0.00 8
45
+ 3 0.60 0.24 0.34 25
46
+ 4 0.00 0.00 0.00 18
47
+ 5 0.00 0.00 0.00 14
48
+ 6 0.00 0.00 0.00 10
49
+ 7 0.00 0.00 0.00 8
50
+ 8 0.89 0.78 0.83 32
51
+ 9 0.73 0.81 0.77 47
52
+
53
+ micro avg 0.77 0.37 0.50 185
54
+ macro avg 0.22 0.18 0.19 185
55
+ weighted avg 0.42 0.37 0.39 185
56
+ samples avg 0.09 0.09 0.09 185
57
+
58
+ > epoch: 3
59
+ precision recall f1-score support
60
+
61
+ 0 0.00 0.00 0.00 13
62
+ 1 0.00 0.00 0.00 10
63
+ 2 0.00 0.00 0.00 8
64
+ 3 0.65 0.52 0.58 25
65
+ 4 0.00 0.00 0.00 18
66
+ 5 0.00 0.00 0.00 14
67
+ 6 0.00 0.00 0.00 10
68
+ 7 0.00 0.00 0.00 8
69
+ 8 0.96 0.78 0.86 32
70
+ 9 0.75 0.83 0.79 47
71
+
72
+ micro avg 0.79 0.42 0.54 185
73
+ macro avg 0.24 0.21 0.22 185
74
+ weighted avg 0.44 0.42 0.43 185
75
+ samples avg 0.10 0.10 0.10 185
76
+
77
+ > epoch: 4
78
+ precision recall f1-score support
79
+
80
+ 0 0.00 0.00 0.00 13
81
+ 1 0.00 0.00 0.00 10
82
+ 2 0.00 0.00 0.00 8
83
+ 3 0.62 0.72 0.67 25
84
+ 4 0.50 0.06 0.10 18
85
+ 5 0.75 0.21 0.33 14
86
+ 6 0.80 0.40 0.53 10
87
+ 7 0.00 0.00 0.00 8
88
+ 8 0.96 0.75 0.84 32
89
+ 9 0.73 0.77 0.75 47
90
+
91
+ micro avg 0.75 0.46 0.58 185
92
+ macro avg 0.44 0.29 0.32 185
93
+ weighted avg 0.59 0.46 0.49 185
94
+ samples avg 0.11 0.11 0.11 185
95
+
96
+ > epoch: 5
97
+ precision recall f1-score support
98
+
99
+ 0 1.00 0.15 0.27 13
100
+ 1 0.00 0.00 0.00 10
101
+ 2 0.00 0.00 0.00 8
102
+ 3 0.62 0.72 0.67 25
103
+ 4 0.50 0.06 0.10 18
104
+ 5 0.64 0.50 0.56 14
105
+ 6 0.70 0.70 0.70 10
106
+ 7 0.00 0.00 0.00 8
107
+ 8 0.96 0.84 0.90 32
108
+ 9 0.79 0.79 0.79 47
109
+
110
+ micro avg 0.77 0.54 0.63 185
111
+ macro avg 0.52 0.38 0.40 185
112
+ weighted avg 0.66 0.54 0.55 185
113
+ samples avg 0.13 0.13 0.13 185
114
+
115
+ > epoch: 6
116
+ precision recall f1-score support
117
+
118
+ 0 0.67 0.15 0.25 13
119
+ 1 0.00 0.00 0.00 10
120
+ 2 0.00 0.00 0.00 8
121
+ 3 0.59 0.68 0.63 25
122
+ 4 0.57 0.22 0.32 18
123
+ 5 0.54 0.50 0.52 14
124
+ 6 0.62 0.50 0.56 10
125
+ 7 0.00 0.00 0.00 8
126
+ 8 0.90 0.84 0.87 32
127
+ 9 0.79 0.70 0.74 47
128
+
129
+ micro avg 0.72 0.51 0.60 185
130
+ macro avg 0.47 0.36 0.39 185
131
+ weighted avg 0.61 0.51 0.54 185
132
+ samples avg 0.13 0.12 0.12 185
133
+
134
+ > epoch: 7
135
+ precision recall f1-score support
136
+
137
+ 0 0.33 0.08 0.12 13
138
+ 1 0.00 0.00 0.00 10
139
+ 2 1.00 0.12 0.22 8
140
+ 3 0.68 0.68 0.68 25
141
+ 4 0.67 0.11 0.19 18
142
+ 5 0.60 0.43 0.50 14
143
+ 6 0.71 0.50 0.59 10
144
+ 7 0.67 0.50 0.57 8
145
+ 8 1.00 0.78 0.88 32
146
+ 9 0.80 0.70 0.75 47
147
+
148
+ micro avg 0.76 0.51 0.61 185
149
+ macro avg 0.65 0.39 0.45 185
150
+ weighted avg 0.71 0.51 0.57 185
151
+ samples avg 0.13 0.12 0.12 185
152
+
153
+ > epoch: 8
154
+ precision recall f1-score support
155
+
156
+ 0 0.44 0.31 0.36 13
157
+ 1 0.50 0.70 0.58 10
158
+ 2 1.00 0.62 0.77 8
159
+ 3 0.62 0.80 0.70 25
160
+ 4 0.62 0.44 0.52 18
161
+ 5 0.41 0.64 0.50 14
162
+ 6 0.73 0.80 0.76 10
163
+ 7 0.54 0.88 0.67 8
164
+ 8 0.90 0.88 0.89 32
165
+ 9 0.76 0.81 0.78 47
166
+
167
+ micro avg 0.67 0.72 0.70 185
168
+ macro avg 0.65 0.69 0.65 185
169
+ weighted avg 0.69 0.72 0.70 185
170
+ samples avg 0.18 0.17 0.17 185
171
+
172
+ > epoch: 9
173
+ precision recall f1-score support
174
+
175
+ 0 0.42 0.38 0.40 13
176
+ 1 0.60 0.30 0.40 10
177
+ 2 1.00 0.75 0.86 8
178
+ 3 0.59 0.80 0.68 25
179
+ 4 0.55 0.33 0.41 18
180
+ 5 0.62 0.36 0.45 14
181
+ 6 0.67 0.60 0.63 10
182
+ 7 0.83 0.62 0.71 8
183
+ 8 0.93 0.88 0.90 32
184
+ 9 0.76 0.68 0.72 47
185
+
186
+ micro avg 0.71 0.63 0.67 185
187
+ macro avg 0.70 0.57 0.62 185
188
+ weighted avg 0.71 0.63 0.66 185
189
+ samples avg 0.15 0.15 0.15 185
190
+
191
+ > epoch: 10
192
+ precision recall f1-score support
193
+
194
+ 0 0.47 0.62 0.53 13
195
+ 1 0.57 0.40 0.47 10
196
+ 2 1.00 0.75 0.86 8
197
+ 3 0.56 0.80 0.66 25
198
+ 4 0.60 0.33 0.43 18
199
+ 5 0.44 0.50 0.47 14
200
+ 6 0.60 0.60 0.60 10
201
+ 7 0.83 0.62 0.71 8
202
+ 8 0.93 0.88 0.90 32
203
+ 9 0.77 0.79 0.78 47
204
+
205
+ micro avg 0.68 0.69 0.68 185
206
+ macro avg 0.68 0.63 0.64 185
207
+ weighted avg 0.70 0.69 0.68 185
208
+ samples avg 0.17 0.16 0.16 185
209
+
210
+ > epoch: 11
211
+ precision recall f1-score support
212
+
213
+ 0 0.40 0.31 0.35 13
214
+ 1 0.43 0.30 0.35 10
215
+ 2 0.78 0.88 0.82 8
216
+ 3 0.59 0.80 0.68 25
217
+ 4 0.67 0.33 0.44 18
218
+ 5 0.64 0.50 0.56 14
219
+ 6 0.70 0.70 0.70 10
220
+ 7 0.64 0.88 0.74 8
221
+ 8 0.96 0.84 0.90 32
222
+ 9 0.76 0.74 0.75 47
223
+
224
+ micro avg 0.70 0.66 0.68 185
225
+ macro avg 0.66 0.63 0.63 185
226
+ weighted avg 0.70 0.66 0.67 185
227
+ samples avg 0.16 0.16 0.16 185
228
+
229
+ > epoch: 12
230
+ precision recall f1-score support
231
+
232
+ 0 0.50 0.38 0.43 13
233
+ 1 0.55 0.60 0.57 10
234
+ 2 1.00 0.75 0.86 8
235
+ 3 0.54 0.80 0.65 25
236
+ 4 0.58 0.39 0.47 18
237
+ 5 0.53 0.57 0.55 14
238
+ 6 0.73 0.80 0.76 10
239
+ 7 0.71 0.62 0.67 8
240
+ 8 0.90 0.88 0.89 32
241
+ 9 0.67 0.89 0.76 47
242
+
243
+ micro avg 0.67 0.73 0.70 185
244
+ macro avg 0.67 0.67 0.66 185
245
+ weighted avg 0.67 0.73 0.69 185
246
+ samples avg 0.18 0.17 0.17 185
247
+
248
+ > epoch: 13
249
+ precision recall f1-score support
250
+
251
+ 0 0.35 0.54 0.42 13
252
+ 1 0.50 0.60 0.55 10
253
+ 2 1.00 0.88 0.93 8
254
+ 3 0.59 0.80 0.68 25
255
+ 4 0.62 0.44 0.52 18
256
+ 5 0.38 0.64 0.47 14
257
+ 6 0.64 0.70 0.67 10
258
+ 7 0.67 0.50 0.57 8
259
+ 8 0.93 0.88 0.90 32
260
+ 9 0.74 0.83 0.78 47
261
+
262
+ micro avg 0.64 0.73 0.68 185
263
+ macro avg 0.64 0.68 0.65 185
264
+ weighted avg 0.67 0.73 0.69 185
265
+ samples avg 0.17 0.17 0.17 185
266
+
267
+ > epoch: 14
268
+ precision recall f1-score support
269
+
270
+ 0 0.44 0.54 0.48 13
271
+ 1 0.42 0.50 0.45 10
272
+ 2 1.00 0.88 0.93 8
273
+ 3 0.62 0.80 0.70 25
274
+ 4 0.56 0.28 0.37 18
275
+ 5 0.53 0.57 0.55 14
276
+ 6 0.67 0.60 0.63 10
277
+ 7 0.67 0.50 0.57 8
278
+ 8 0.97 0.88 0.92 32
279
+ 9 0.75 0.77 0.76 47
280
+
281
+ micro avg 0.69 0.68 0.68 185
282
+ macro avg 0.66 0.63 0.64 185
283
+ weighted avg 0.70 0.68 0.68 185
284
+ samples avg 0.17 0.16 0.16 185
285
+
286
+ > epoch: 15
287
+ precision recall f1-score support
288
+
289
+ 0 0.36 0.62 0.46 13
290
+ 1 0.50 0.40 0.44 10
291
+ 2 1.00 0.88 0.93 8
292
+ 3 0.64 0.84 0.72 25
293
+ 4 0.88 0.39 0.54 18
294
+ 5 0.64 0.50 0.56 14
295
+ 6 0.57 0.40 0.47 10
296
+ 7 0.67 0.50 0.57 8
297
+ 8 0.97 0.88 0.92 32
298
+ 9 0.74 0.74 0.74 47
299
+
300
+ micro avg 0.70 0.68 0.69 185
301
+ macro avg 0.70 0.61 0.64 185
302
+ weighted avg 0.73 0.68 0.69 185
303
+ samples avg 0.16 0.16 0.16 185
304
+
305
+ > epoch: 16
306
+ precision recall f1-score support
307
+
308
+ 0 0.58 0.54 0.56 13
309
+ 1 0.40 0.20 0.27 10
310
+ 2 1.00 0.88 0.93 8
311
+ 3 0.61 0.80 0.69 25
312
+ 4 0.60 0.33 0.43 18
313
+ 5 0.54 0.50 0.52 14
314
+ 6 0.62 0.50 0.56 10
315
+ 7 0.71 0.62 0.67 8
316
+ 8 0.90 0.88 0.89 32
317
+ 9 0.69 0.89 0.78 47
318
+
319
+ micro avg 0.69 0.70 0.69 185
320
+ macro avg 0.67 0.61 0.63 185
321
+ weighted avg 0.68 0.70 0.68 185
322
+ samples avg 0.17 0.17 0.17 185
323
+
324
+ > epoch: 17
325
+ precision recall f1-score support
326
+
327
+ 0 0.50 0.23 0.32 13
328
+ 1 0.62 0.50 0.56 10
329
+ 2 0.78 0.88 0.82 8
330
+ 3 0.60 0.84 0.70 25
331
+ 4 0.78 0.39 0.52 18
332
+ 5 0.54 0.50 0.52 14
333
+ 6 0.57 0.40 0.47 10
334
+ 7 0.00 0.00 0.00 8
335
+ 8 0.96 0.84 0.90 32
336
+ 9 0.74 0.83 0.78 47
337
+
338
+ micro avg 0.71 0.65 0.68 185
339
+ macro avg 0.61 0.54 0.56 185
340
+ weighted avg 0.68 0.65 0.65 185
341
+ samples avg 0.16 0.15 0.15 185
342
+
343
+ > epoch: 18
344
+ precision recall f1-score support
345
+
346
+ 0 0.42 0.62 0.50 13
347
+ 1 0.50 0.60 0.55 10
348
+ 2 0.78 0.88 0.82 8
349
+ 3 0.57 0.84 0.68 25
350
+ 4 0.67 0.44 0.53 18
351
+ 5 0.45 0.64 0.53 14
352
+ 6 0.62 0.80 0.70 10
353
+ 7 0.67 0.75 0.71 8
354
+ 8 0.93 0.84 0.89 32
355
+ 9 0.69 0.81 0.75 47
356
+
357
+ micro avg 0.64 0.75 0.69 185
358
+ macro avg 0.63 0.72 0.66 185
359
+ weighted avg 0.66 0.75 0.69 185
360
+ samples avg 0.18 0.17 0.17 185
361
+
362
+ > epoch: 19
363
+ precision recall f1-score support
364
+
365
+ 0 0.60 0.46 0.52 13
366
+ 1 0.43 0.60 0.50 10
367
+ 2 0.86 0.75 0.80 8
368
+ 3 0.58 0.76 0.66 25
369
+ 4 0.53 0.44 0.48 18
370
+ 5 0.46 0.79 0.58 14
371
+ 6 0.70 0.70 0.70 10
372
+ 7 0.55 0.75 0.63 8
373
+ 8 0.88 0.88 0.88 32
374
+ 9 0.69 0.85 0.76 47
375
+
376
+ micro avg 0.64 0.74 0.69 185
377
+ macro avg 0.63 0.70 0.65 185
378
+ weighted avg 0.65 0.74 0.69 185
379
+ samples avg 0.18 0.17 0.17 185
380
+
381
+ > epoch: 20
382
+ precision recall f1-score support
383
+
384
+ 0 0.50 0.23 0.32 13
385
+ 1 0.67 0.60 0.63 10
386
+ 2 1.00 0.88 0.93 8
387
+ 3 0.59 0.76 0.67 25
388
+ 4 0.54 0.39 0.45 18
389
+ 5 0.50 0.64 0.56 14
390
+ 6 0.55 0.60 0.57 10
391
+ 7 0.71 0.62 0.67 8
392
+ 8 0.85 0.88 0.86 32
393
+ 9 0.77 0.85 0.81 47
394
+
395
+ micro avg 0.69 0.70 0.70 185
396
+ macro avg 0.67 0.64 0.65 185
397
+ weighted avg 0.69 0.70 0.69 185
398
+ samples avg 0.17 0.17 0.16 185
399
+
400
+ > epoch: 21
401
+ precision recall f1-score support
402
+
403
+ 0 0.40 0.46 0.43 13
404
+ 1 0.62 0.50 0.56 10
405
+ 2 1.00 0.88 0.93 8
406
+ 3 0.56 0.76 0.64 25
407
+ 4 0.57 0.44 0.50 18
408
+ 5 0.50 0.50 0.50 14
409
+ 6 0.50 0.40 0.44 10
410
+ 7 0.71 0.62 0.67 8
411
+ 8 0.90 0.81 0.85 32
412
+ 9 0.80 0.83 0.81 47
413
+
414
+ micro avg 0.68 0.68 0.68 185
415
+ macro avg 0.66 0.62 0.63 185
416
+ weighted avg 0.69 0.68 0.68 185
417
+ samples avg 0.16 0.16 0.16 185
418
+
419
+ > epoch: 22
420
+ precision recall f1-score support
421
+
422
+ 0 0.40 0.46 0.43 13
423
+ 1 0.62 0.50 0.56 10
424
+ 2 1.00 0.88 0.93 8
425
+ 3 0.59 0.76 0.67 25
426
+ 4 0.57 0.44 0.50 18
427
+ 5 0.58 0.50 0.54 14
428
+ 6 0.56 0.50 0.53 10
429
+ 7 0.71 0.62 0.67 8
430
+ 8 0.93 0.81 0.87 32
431
+ 9 0.75 0.85 0.80 47
432
+
433
+ micro avg 0.69 0.69 0.69 185
434
+ macro avg 0.67 0.63 0.65 185
435
+ weighted avg 0.70 0.69 0.69 185
436
+ samples avg 0.17 0.16 0.16 185
437
+
438
+ > epoch: 23
439
+ precision recall f1-score support
440
+
441
+ 0 0.40 0.46 0.43 13
442
+ 1 0.62 0.50 0.56 10
443
+ 2 1.00 0.88 0.93 8
444
+ 3 0.58 0.76 0.66 25
445
+ 4 0.57 0.44 0.50 18
446
+ 5 0.62 0.57 0.59 14
447
+ 6 0.60 0.60 0.60 10
448
+ 7 0.71 0.62 0.67 8
449
+ 8 0.90 0.81 0.85 32
450
+ 9 0.73 0.85 0.78 47
451
+
452
+ micro avg 0.68 0.70 0.69 185
453
+ macro avg 0.67 0.65 0.66 185
454
+ weighted avg 0.69 0.70 0.69 185
455
+ samples avg 0.17 0.17 0.17 185
456
+
457
+ > epoch: 24
458
+ precision recall f1-score support
459
+
460
+ 0 0.44 0.54 0.48 13
461
+ 1 0.60 0.60 0.60 10
462
+ 2 1.00 0.88 0.93 8
463
+ 3 0.61 0.76 0.68 25
464
+ 4 0.50 0.44 0.47 18
465
+ 5 0.57 0.57 0.57 14
466
+ 6 0.60 0.60 0.60 10
467
+ 7 0.62 0.62 0.62 8
468
+ 8 0.87 0.81 0.84 32
469
+ 9 0.62 0.87 0.73 47
470
+
471
+ micro avg 0.64 0.72 0.68 185
472
+ macro avg 0.64 0.67 0.65 185
473
+ weighted avg 0.65 0.72 0.68 185
474
+ samples avg 0.17 0.17 0.17 185
475
+
476
+ > epoch: 25
477
+ precision recall f1-score support
478
+
479
+ 0 0.45 0.38 0.42 13
480
+ 1 0.62 0.50 0.56 10
481
+ 2 1.00 0.88 0.93 8
482
+ 3 0.61 0.76 0.68 25
483
+ 4 0.67 0.22 0.33 18
484
+ 5 0.46 0.43 0.44 14
485
+ 6 0.64 0.70 0.67 10
486
+ 7 0.78 0.88 0.82 8
487
+ 8 0.90 0.88 0.89 32
488
+ 9 0.73 0.70 0.72 47
489
+
490
+ micro avg 0.70 0.65 0.68 185
491
+ macro avg 0.69 0.63 0.65 185
492
+ weighted avg 0.70 0.65 0.67 185
493
+ samples avg 0.16 0.15 0.15 185
494
+
495
+ > epoch: 26
496
+ precision recall f1-score support
497
+
498
+ 0 0.38 0.46 0.41 13
499
+ 1 0.62 0.50 0.56 10
500
+ 2 1.00 0.88 0.93 8
501
+ 3 0.54 0.76 0.63 25
502
+ 4 0.86 0.33 0.48 18
503
+ 5 0.67 0.43 0.52 14
504
+ 6 0.70 0.70 0.70 10
505
+ 7 0.67 0.75 0.71 8
506
+ 8 0.90 0.88 0.89 32
507
+ 9 0.71 0.83 0.76 47
508
+
509
+ micro avg 0.69 0.70 0.69 185
510
+ macro avg 0.70 0.65 0.66 185
511
+ weighted avg 0.71 0.70 0.69 185
512
+ samples avg 0.17 0.17 0.17 185
513
+
Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/config.json ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "bert-base-uncased",
3
+ "architectures": [
4
+ "BertForSequenceClassification"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "gradient_checkpointing": false,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 768,
12
+ "id2label": {
13
+ "0": "T1027",
14
+ "1": "T1041",
15
+ "2": "T1056",
16
+ "3": "T1059",
17
+ "4": "T1071",
18
+ "5": "T1105",
19
+ "6": "T1140",
20
+ "7": "T1190",
21
+ "8": "T1486",
22
+ "9": "T1566"
23
+ },
24
+ "initializer_range": 0.02,
25
+ "intermediate_size": 3072,
26
+ "label2id": {
27
+ "T1027": 0,
28
+ "T1041": 1,
29
+ "T1056": 2,
30
+ "T1059": 3,
31
+ "T1071": 4,
32
+ "T1105": 5,
33
+ "T1140": 6,
34
+ "T1190": 7,
35
+ "T1486": 8,
36
+ "T1566": 9
37
+ },
38
+ "layer_norm_eps": 1e-12,
39
+ "max_position_embeddings": 512,
40
+ "model_type": "bert",
41
+ "num_attention_heads": 12,
42
+ "num_hidden_layers": 12,
43
+ "pad_token_id": 0,
44
+ "position_embedding_type": "absolute",
45
+ "problem_type": "multi_label_classification",
46
+ "torch_dtype": "float32",
47
+ "transformers_version": "4.45.2",
48
+ "type_vocab_size": 2,
49
+ "use_cache": true,
50
+ "vocab_size": 30522
51
+ }
Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/loss.pdf ADDED
Binary file (15.4 kB). View file
 
Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d455bad4728cc318eae98c8acb04160ac530ae52d14135701382f23d81904152
3
+ size 437983256
Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/model_params.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "epochs": 100,
3
+ "batch_size": 16,
4
+ "freeze_layers": 0,
5
+ "learning_rate": 2e-05,
6
+ "pos_weight": 0
7
+ }
Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/special_tokens_map.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": "[CLS]",
3
+ "mask_token": "[MASK]",
4
+ "pad_token": "[PAD]",
5
+ "sep_token": "[SEP]",
6
+ "unk_token": "[UNK]"
7
+ }
Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/stats.json ADDED
@@ -0,0 +1,245 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "epoch": 1,
4
+ "Training Loss": 0.21524116381212502,
5
+ "Valid. Loss": 0.116888977975949,
6
+ "Valid. Accur.": 0.7697981366459627,
7
+ "Valid. F1": 0.0,
8
+ "Training Time": "0:00:53",
9
+ "Validation Time": "0:00:08"
10
+ },
11
+ {
12
+ "epoch": 2,
13
+ "Training Loss": 0.11854389417727572,
14
+ "Valid. Loss": 0.09760210386922827,
15
+ "Valid. Accur.": 0.7711568322981366,
16
+ "Valid. F1": 0.003623188405797101,
17
+ "Training Time": "0:01:15",
18
+ "Validation Time": "0:00:08"
19
+ },
20
+ {
21
+ "epoch": 3,
22
+ "Training Loss": 0.09009838699693433,
23
+ "Valid. Loss": 0.08016903681969971,
24
+ "Valid. Accur.": 0.8365683229813664,
25
+ "Valid. F1": 0.3541235334713595,
26
+ "Training Time": "0:01:18",
27
+ "Validation Time": "0:00:06"
28
+ },
29
+ {
30
+ "epoch": 4,
31
+ "Training Loss": 0.07018067179516847,
32
+ "Valid. Loss": 0.06911124651918986,
33
+ "Valid. Accur.": 0.8433618012422359,
34
+ "Valid. F1": 0.39663561076604553,
35
+ "Training Time": "0:01:17",
36
+ "Validation Time": "0:00:08"
37
+ },
38
+ {
39
+ "epoch": 5,
40
+ "Training Loss": 0.0573402114256274,
41
+ "Valid. Loss": 0.06624160225579794,
42
+ "Valid. Accur.": 0.8489906832298137,
43
+ "Valid. F1": 0.4524355877616748,
44
+ "Training Time": "0:01:19",
45
+ "Validation Time": "0:00:08"
46
+ },
47
+ {
48
+ "epoch": 6,
49
+ "Training Loss": 0.04298209146363397,
50
+ "Valid. Loss": 0.05682169973370038,
51
+ "Valid. Accur.": 0.8585015527950312,
52
+ "Valid. F1": 0.5444300667126754,
53
+ "Training Time": "0:01:19",
54
+ "Validation Time": "0:00:08"
55
+ },
56
+ {
57
+ "epoch": 7,
58
+ "Training Loss": 0.03236084048439281,
59
+ "Valid. Loss": 0.05451186575380194,
60
+ "Valid. Accur.": 0.8435559006211181,
61
+ "Valid. F1": 0.5010553255118472,
62
+ "Training Time": "0:01:19",
63
+ "Validation Time": "0:00:08"
64
+ },
65
+ {
66
+ "epoch": 8,
67
+ "Training Loss": 0.024404446804080666,
68
+ "Valid. Loss": 0.0578358889725898,
69
+ "Valid. Accur.": 0.8571428571428572,
70
+ "Valid. F1": 0.5063520818955601,
71
+ "Training Time": "0:01:19",
72
+ "Validation Time": "0:00:08"
73
+ },
74
+ {
75
+ "epoch": 9,
76
+ "Training Loss": 0.019143750726568428,
77
+ "Valid. Loss": 0.05462993376981249,
78
+ "Valid. Accur.": 0.8623835403726707,
79
+ "Valid. F1": 0.698317805383023,
80
+ "Training Time": "0:01:16",
81
+ "Validation Time": "0:00:08"
82
+ },
83
+ {
84
+ "epoch": 10,
85
+ "Training Loss": 0.01583783999462407,
86
+ "Valid. Loss": 0.05901449345146603,
87
+ "Valid. Accur.": 0.8652950310559007,
88
+ "Valid. F1": 0.6235967333793422,
89
+ "Training Time": "0:01:16",
90
+ "Validation Time": "0:00:08"
91
+ },
92
+ {
93
+ "epoch": 11,
94
+ "Training Loss": 0.012464373384379246,
95
+ "Valid. Loss": 0.05474822152547791,
96
+ "Valid. Accur.": 0.8625776397515529,
97
+ "Valid. F1": 0.6711899010812056,
98
+ "Training Time": "0:01:18",
99
+ "Validation Time": "0:00:08"
100
+ },
101
+ {
102
+ "epoch": 12,
103
+ "Training Loss": 0.010523827983896531,
104
+ "Valid. Loss": 0.057532220802422436,
105
+ "Valid. Accur.": 0.8680124223602484,
106
+ "Valid. F1": 0.6472250977685761,
107
+ "Training Time": "0:01:18",
108
+ "Validation Time": "0:00:08"
109
+ },
110
+ {
111
+ "epoch": 13,
112
+ "Training Loss": 0.007744736150182335,
113
+ "Valid. Loss": 0.05750957896802085,
114
+ "Valid. Accur.": 0.8623835403726707,
115
+ "Valid. F1": 0.6965044858523121,
116
+ "Training Time": "0:01:18",
117
+ "Validation Time": "0:00:08"
118
+ },
119
+ {
120
+ "epoch": 14,
121
+ "Training Loss": 0.006714850923853572,
122
+ "Valid. Loss": 0.060021618840006966,
123
+ "Valid. Accur.": 0.859472049689441,
124
+ "Valid. F1": 0.6993357487922707,
125
+ "Training Time": "0:01:18",
126
+ "Validation Time": "0:00:08"
127
+ },
128
+ {
129
+ "epoch": 15,
130
+ "Training Loss": 0.0053229971303654314,
131
+ "Valid. Loss": 0.06096832856289343,
132
+ "Valid. Accur.": 0.8652950310559007,
133
+ "Valid. F1": 0.6561680469289167,
134
+ "Training Time": "0:01:18",
135
+ "Validation Time": "0:00:08"
136
+ },
137
+ {
138
+ "epoch": 16,
139
+ "Training Loss": 0.005114505972480823,
140
+ "Valid. Loss": 0.061880902108871545,
141
+ "Valid. Accur.": 0.8693711180124224,
142
+ "Valid. F1": 0.6568524269611228,
143
+ "Training Time": "0:01:18",
144
+ "Validation Time": "0:00:08"
145
+ },
146
+ {
147
+ "epoch": 17,
148
+ "Training Loss": 0.004806136092810301,
149
+ "Valid. Loss": 0.06361331760652218,
150
+ "Valid. Accur.": 0.8652950310559007,
151
+ "Valid. F1": 0.6736174373130897,
152
+ "Training Time": "0:01:18",
153
+ "Validation Time": "0:00:08"
154
+ },
155
+ {
156
+ "epoch": 18,
157
+ "Training Loss": 0.0043766915872344,
158
+ "Valid. Loss": 0.0712894881119039,
159
+ "Valid. Accur.": 0.8693711180124224,
160
+ "Valid. F1": 0.6050115021854152,
161
+ "Training Time": "0:01:18",
162
+ "Validation Time": "0:00:08"
163
+ },
164
+ {
165
+ "epoch": 19,
166
+ "Training Loss": 0.004599793666903164,
167
+ "Valid. Loss": 0.06791899474814023,
168
+ "Valid. Accur.": 0.8517080745341615,
169
+ "Valid. F1": 0.7165200138026225,
170
+ "Training Time": "0:01:18",
171
+ "Validation Time": "0:00:08"
172
+ },
173
+ {
174
+ "epoch": 20,
175
+ "Training Loss": 0.005551775648121331,
176
+ "Valid. Loss": 0.06937517707075634,
177
+ "Valid. Accur.": 0.8476319875776398,
178
+ "Valid. F1": 0.7097394755003453,
179
+ "Training Time": "0:01:18",
180
+ "Validation Time": "0:00:08"
181
+ },
182
+ {
183
+ "epoch": 21,
184
+ "Training Loss": 0.0033375171549290634,
185
+ "Valid. Loss": 0.07097872209740458,
186
+ "Valid. Accur.": 0.8680124223602484,
187
+ "Valid. F1": 0.6886870255348518,
188
+ "Training Time": "0:01:18",
189
+ "Validation Time": "0:00:07"
190
+ },
191
+ {
192
+ "epoch": 22,
193
+ "Training Loss": 0.0027612900512882536,
194
+ "Valid. Loss": 0.0727502658542215,
195
+ "Valid. Accur.": 0.8652950310559007,
196
+ "Valid. F1": 0.661012767425811,
197
+ "Training Time": "0:01:11",
198
+ "Validation Time": "0:00:08"
199
+ },
200
+ {
201
+ "epoch": 23,
202
+ "Training Loss": 0.0020060102644440148,
203
+ "Valid. Loss": 0.07429445223339674,
204
+ "Valid. Accur.": 0.8666537267080746,
205
+ "Valid. F1": 0.6740044858523121,
206
+ "Training Time": "0:01:16",
207
+ "Validation Time": "0:00:06"
208
+ },
209
+ {
210
+ "epoch": 24,
211
+ "Training Loss": 0.0020951755726486415,
212
+ "Valid. Loss": 0.07359603367118515,
213
+ "Valid. Accur.": 0.8652950310559007,
214
+ "Valid. F1": 0.6837870945479644,
215
+ "Training Time": "0:01:16",
216
+ "Validation Time": "0:00:08"
217
+ },
218
+ {
219
+ "epoch": 25,
220
+ "Training Loss": 0.0017958812340678543,
221
+ "Valid. Loss": 0.08150516410324085,
222
+ "Valid. Accur.": 0.8530667701863355,
223
+ "Valid. F1": 0.679981021394065,
224
+ "Training Time": "0:01:13",
225
+ "Validation Time": "0:00:08"
226
+ },
227
+ {
228
+ "epoch": 26,
229
+ "Training Loss": 0.0030339173731557595,
230
+ "Valid. Loss": 0.07327716414685755,
231
+ "Valid. Accur.": 0.8666537267080746,
232
+ "Valid. F1": 0.6457988267770876,
233
+ "Training Time": "0:01:16",
234
+ "Validation Time": "0:00:08"
235
+ },
236
+ {
237
+ "epoch": 27,
238
+ "Training Loss": 0.002632833944873736,
239
+ "Valid. Loss": 0.07526306226888287,
240
+ "Valid. Accur.": 0.8680124223602484,
241
+ "Valid. F1": 0.6943161950770648,
242
+ "Training Time": "0:01:13",
243
+ "Validation Time": "0:00:08"
244
+ }
245
+ ]
Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/tokenizer_config.json ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "[PAD]",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "100": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "101": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "102": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "103": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": false,
45
+ "cls_token": "[CLS]",
46
+ "do_lower_case": true,
47
+ "mask_token": "[MASK]",
48
+ "model_max_length": 512,
49
+ "pad_token": "[PAD]",
50
+ "sep_token": "[SEP]",
51
+ "strip_accents": null,
52
+ "tokenize_chinese_chars": true,
53
+ "tokenizer_class": "BertTokenizer",
54
+ "unk_token": "[UNK]"
55
+ }
Classification/bosch_swipe/0_bosch_t10_bert-base-uncased/vocab.txt ADDED
The diff for this file is too large to render. See raw diff
 
Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/classification_reports.txt ADDED
The diff for this file is too large to render. See raw diff
 
Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/config.json ADDED
@@ -0,0 +1,131 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "bert-base-uncased",
3
+ "architectures": [
4
+ "BertForSequenceClassification"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "gradient_checkpointing": false,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 768,
12
+ "id2label": {
13
+ "0": "T1005",
14
+ "1": "T1014",
15
+ "2": "T1021",
16
+ "3": "T1027",
17
+ "4": "T1033",
18
+ "5": "T1036",
19
+ "6": "T1041",
20
+ "7": "T1053",
21
+ "8": "T1055",
22
+ "9": "T1056",
23
+ "10": "T1057",
24
+ "11": "T1059",
25
+ "12": "T1070",
26
+ "13": "T1071",
27
+ "14": "T1078",
28
+ "15": "T1082",
29
+ "16": "T1083",
30
+ "17": "T1105",
31
+ "18": "T1110",
32
+ "19": "T1112",
33
+ "20": "T1113",
34
+ "21": "T1115",
35
+ "22": "T1125",
36
+ "23": "T1132",
37
+ "24": "T1137",
38
+ "25": "T1140",
39
+ "26": "T1189",
40
+ "27": "T1190",
41
+ "28": "T1195",
42
+ "29": "T1203",
43
+ "30": "T1204",
44
+ "31": "T1218",
45
+ "32": "T1486",
46
+ "33": "T1496",
47
+ "34": "T1497",
48
+ "35": "T1499",
49
+ "36": "T1528",
50
+ "37": "T1539",
51
+ "38": "T1543",
52
+ "39": "T1547",
53
+ "40": "T1555",
54
+ "41": "T1557",
55
+ "42": "T1562",
56
+ "43": "T1566",
57
+ "44": "T1571",
58
+ "45": "T1573",
59
+ "46": "T1583",
60
+ "47": "T1587",
61
+ "48": "T1589",
62
+ "49": "T1606"
63
+ },
64
+ "initializer_range": 0.02,
65
+ "intermediate_size": 3072,
66
+ "label2id": {
67
+ "T1005": 0,
68
+ "T1014": 1,
69
+ "T1021": 2,
70
+ "T1027": 3,
71
+ "T1033": 4,
72
+ "T1036": 5,
73
+ "T1041": 6,
74
+ "T1053": 7,
75
+ "T1055": 8,
76
+ "T1056": 9,
77
+ "T1057": 10,
78
+ "T1059": 11,
79
+ "T1070": 12,
80
+ "T1071": 13,
81
+ "T1078": 14,
82
+ "T1082": 15,
83
+ "T1083": 16,
84
+ "T1105": 17,
85
+ "T1110": 18,
86
+ "T1112": 19,
87
+ "T1113": 20,
88
+ "T1115": 21,
89
+ "T1125": 22,
90
+ "T1132": 23,
91
+ "T1137": 24,
92
+ "T1140": 25,
93
+ "T1189": 26,
94
+ "T1190": 27,
95
+ "T1195": 28,
96
+ "T1203": 29,
97
+ "T1204": 30,
98
+ "T1218": 31,
99
+ "T1486": 32,
100
+ "T1496": 33,
101
+ "T1497": 34,
102
+ "T1499": 35,
103
+ "T1528": 36,
104
+ "T1539": 37,
105
+ "T1543": 38,
106
+ "T1547": 39,
107
+ "T1555": 40,
108
+ "T1557": 41,
109
+ "T1562": 42,
110
+ "T1566": 43,
111
+ "T1571": 44,
112
+ "T1573": 45,
113
+ "T1583": 46,
114
+ "T1587": 47,
115
+ "T1589": 48,
116
+ "T1606": 49
117
+ },
118
+ "layer_norm_eps": 1e-12,
119
+ "max_position_embeddings": 512,
120
+ "model_type": "bert",
121
+ "num_attention_heads": 12,
122
+ "num_hidden_layers": 12,
123
+ "pad_token_id": 0,
124
+ "position_embedding_type": "absolute",
125
+ "problem_type": "multi_label_classification",
126
+ "torch_dtype": "float32",
127
+ "transformers_version": "4.45.2",
128
+ "type_vocab_size": 2,
129
+ "use_cache": true,
130
+ "vocab_size": 30522
131
+ }
Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/loss.pdf ADDED
Binary file (16.2 kB). View file
 
Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ec4f41bcb29b6b15cde3de00c860e86a1d8dd607fd0fd0280df565bf02c4cca3
3
+ size 438106296
Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/model_params.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "epochs": 100,
3
+ "batch_size": 16,
4
+ "freeze_layers": 0,
5
+ "learning_rate": 2e-05,
6
+ "pos_weight": 0,
7
+ "end_factor": 0.7
8
+ }
Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/special_tokens_map.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": "[CLS]",
3
+ "mask_token": "[MASK]",
4
+ "pad_token": "[PAD]",
5
+ "sep_token": "[SEP]",
6
+ "unk_token": "[UNK]"
7
+ }
Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/stats.json ADDED
@@ -0,0 +1,380 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "epoch": 1,
4
+ "Training Loss": 0.2546680272327737,
5
+ "Valid. Loss": 0.08957959780134031,
6
+ "Valid. Accur.": 0.640722049689441,
7
+ "Valid. F1": 0.0,
8
+ "Training Time": "0:00:57",
9
+ "Validation Time": "0:00:05"
10
+ },
11
+ {
12
+ "epoch": 2,
13
+ "Training Loss": 0.06851049929938244,
14
+ "Valid. Loss": 0.05408126538214475,
15
+ "Valid. Accur.": 0.640722049689441,
16
+ "Valid. F1": 0.0,
17
+ "Training Time": "0:00:50",
18
+ "Validation Time": "0:00:05"
19
+ },
20
+ {
21
+ "epoch": 3,
22
+ "Training Loss": 0.05146002894159461,
23
+ "Valid. Loss": 0.04752182235628923,
24
+ "Valid. Accur.": 0.640722049689441,
25
+ "Valid. F1": 0.0,
26
+ "Training Time": "0:00:50",
27
+ "Validation Time": "0:00:05"
28
+ },
29
+ {
30
+ "epoch": 4,
31
+ "Training Loss": 0.04743223195139695,
32
+ "Valid. Loss": 0.045408009211098915,
33
+ "Valid. Accur.": 0.640722049689441,
34
+ "Valid. F1": 0.0,
35
+ "Training Time": "0:00:50",
36
+ "Validation Time": "0:00:05"
37
+ },
38
+ {
39
+ "epoch": 5,
40
+ "Training Loss": 0.045818793181653925,
41
+ "Valid. Loss": 0.04438303922459205,
42
+ "Valid. Accur.": 0.640722049689441,
43
+ "Valid. F1": 0.0,
44
+ "Training Time": "0:00:49",
45
+ "Validation Time": "0:00:05"
46
+ },
47
+ {
48
+ "epoch": 6,
49
+ "Training Loss": 0.044649833569816055,
50
+ "Valid. Loss": 0.042448038676556964,
51
+ "Valid. Accur.": 0.640722049689441,
52
+ "Valid. F1": 0.0,
53
+ "Training Time": "0:00:50",
54
+ "Validation Time": "0:00:05"
55
+ },
56
+ {
57
+ "epoch": 7,
58
+ "Training Loss": 0.04278184549493269,
59
+ "Valid. Loss": 0.041154061105125435,
60
+ "Valid. Accur.": 0.640722049689441,
61
+ "Valid. F1": 0.0,
62
+ "Training Time": "0:00:50",
63
+ "Validation Time": "0:00:05"
64
+ },
65
+ {
66
+ "epoch": 8,
67
+ "Training Loss": 0.04025338003244438,
68
+ "Valid. Loss": 0.039450054115141076,
69
+ "Valid. Accur.": 0.6803183229813665,
70
+ "Valid. F1": 0.12076745718050065,
71
+ "Training Time": "0:00:49",
72
+ "Validation Time": "0:00:05"
73
+ },
74
+ {
75
+ "epoch": 9,
76
+ "Training Loss": 0.03726925131058057,
77
+ "Valid. Loss": 0.03739111495171009,
78
+ "Valid. Accur.": 0.6678959627329192,
79
+ "Valid. F1": 0.08695338477947172,
80
+ "Training Time": "0:00:49",
81
+ "Validation Time": "0:00:05"
82
+ },
83
+ {
84
+ "epoch": 10,
85
+ "Training Loss": 0.034272493572489765,
86
+ "Valid. Loss": 0.034722870842019325,
87
+ "Valid. Accur.": 0.700698757763975,
88
+ "Valid. F1": 0.18437205910031998,
89
+ "Training Time": "0:00:49",
90
+ "Validation Time": "0:00:05"
91
+ },
92
+ {
93
+ "epoch": 11,
94
+ "Training Loss": 0.03166333704320478,
95
+ "Valid. Loss": 0.033165830457612,
96
+ "Valid. Accur.": 0.7117624223602484,
97
+ "Valid. F1": 0.24010995043603736,
98
+ "Training Time": "0:00:49",
99
+ "Validation Time": "0:00:05"
100
+ },
101
+ {
102
+ "epoch": 12,
103
+ "Training Loss": 0.02886174059033891,
104
+ "Valid. Loss": 0.03203265296269977,
105
+ "Valid. Accur.": 0.7183618012422359,
106
+ "Valid. F1": 0.25256564088085826,
107
+ "Training Time": "0:00:49",
108
+ "Validation Time": "0:00:05"
109
+ },
110
+ {
111
+ "epoch": 13,
112
+ "Training Loss": 0.026711223863573087,
113
+ "Valid. Loss": 0.030649934109323505,
114
+ "Valid. Accur.": 0.7226319875776398,
115
+ "Valid. F1": 0.2850574847857456,
116
+ "Training Time": "0:00:49",
117
+ "Validation Time": "0:00:05"
118
+ },
119
+ {
120
+ "epoch": 14,
121
+ "Training Loss": 0.024793394398048813,
122
+ "Valid. Loss": 0.029565782346354187,
123
+ "Valid. Accur.": 0.7212732919254659,
124
+ "Valid. F1": 0.29107887885061806,
125
+ "Training Time": "0:00:49",
126
+ "Validation Time": "0:00:05"
127
+ },
128
+ {
129
+ "epoch": 15,
130
+ "Training Loss": 0.022329593308744903,
131
+ "Valid. Loss": 0.028349834637635435,
132
+ "Valid. Accur.": 0.7267080745341615,
133
+ "Valid. F1": 0.3107711431080996,
134
+ "Training Time": "0:00:49",
135
+ "Validation Time": "0:00:05"
136
+ },
137
+ {
138
+ "epoch": 16,
139
+ "Training Loss": 0.020452742532648123,
140
+ "Valid. Loss": 0.027469802622376773,
141
+ "Valid. Accur.": 0.7253493788819876,
142
+ "Valid. F1": 0.30464622937449026,
143
+ "Training Time": "0:00:49",
144
+ "Validation Time": "0:00:05"
145
+ },
146
+ {
147
+ "epoch": 17,
148
+ "Training Loss": 0.01874537663142014,
149
+ "Valid. Loss": 0.02696396363103728,
150
+ "Valid. Accur.": 0.7226319875776398,
151
+ "Valid. F1": 0.31445471798732666,
152
+ "Training Time": "0:00:49",
153
+ "Validation Time": "0:00:05"
154
+ },
155
+ {
156
+ "epoch": 18,
157
+ "Training Loss": 0.017107816414835456,
158
+ "Valid. Loss": 0.026308181849901243,
159
+ "Valid. Accur.": 0.7389363354037267,
160
+ "Valid. F1": 0.3883591348265261,
161
+ "Training Time": "0:00:49",
162
+ "Validation Time": "0:00:05"
163
+ },
164
+ {
165
+ "epoch": 19,
166
+ "Training Loss": 0.015581695526056715,
167
+ "Valid. Loss": 0.025860232923061277,
168
+ "Valid. Accur.": 0.7389363354037267,
169
+ "Valid. F1": 0.3647229280381455,
170
+ "Training Time": "0:00:50",
171
+ "Validation Time": "0:00:05"
172
+ },
173
+ {
174
+ "epoch": 20,
175
+ "Training Loss": 0.014133917096272763,
176
+ "Valid. Loss": 0.024845572070795997,
177
+ "Valid. Accur.": 0.7470885093167702,
178
+ "Valid. F1": 0.4413447361816927,
179
+ "Training Time": "0:00:49",
180
+ "Validation Time": "0:00:05"
181
+ },
182
+ {
183
+ "epoch": 21,
184
+ "Training Loss": 0.013130152139851086,
185
+ "Valid. Loss": 0.024865670861504242,
186
+ "Valid. Accur.": 0.7606754658385093,
187
+ "Valid. F1": 0.46506877784051703,
188
+ "Training Time": "0:00:49",
189
+ "Validation Time": "0:00:05"
190
+ },
191
+ {
192
+ "epoch": 22,
193
+ "Training Loss": 0.011960581618247711,
194
+ "Valid. Loss": 0.023633249986422196,
195
+ "Valid. Accur.": 0.7552406832298137,
196
+ "Valid. F1": 0.502253905514775,
197
+ "Training Time": "0:00:49",
198
+ "Validation Time": "0:00:05"
199
+ },
200
+ {
201
+ "epoch": 23,
202
+ "Training Loss": 0.01088608542765133,
203
+ "Valid. Loss": 0.023515737826159722,
204
+ "Valid. Accur.": 0.766110248447205,
205
+ "Valid. F1": 0.497172030867683,
206
+ "Training Time": "0:00:49",
207
+ "Validation Time": "0:00:05"
208
+ },
209
+ {
210
+ "epoch": 24,
211
+ "Training Loss": 0.009827784619473789,
212
+ "Valid. Loss": 0.02238780806035596,
213
+ "Valid. Accur.": 0.766110248447205,
214
+ "Valid. F1": 0.5375763326850284,
215
+ "Training Time": "0:00:50",
216
+ "Validation Time": "0:00:05"
217
+ },
218
+ {
219
+ "epoch": 25,
220
+ "Training Loss": 0.009220747145239103,
221
+ "Valid. Loss": 0.022424084202586004,
222
+ "Valid. Accur.": 0.7905667701863355,
223
+ "Valid. F1": 0.5634124631407242,
224
+ "Training Time": "0:00:49",
225
+ "Validation Time": "0:00:05"
226
+ },
227
+ {
228
+ "epoch": 26,
229
+ "Training Loss": 0.008366035897125729,
230
+ "Valid. Loss": 0.02272323459416472,
231
+ "Valid. Accur.": 0.7674689440993789,
232
+ "Valid. F1": 0.5109358334901812,
233
+ "Training Time": "0:00:50",
234
+ "Validation Time": "0:00:05"
235
+ },
236
+ {
237
+ "epoch": 27,
238
+ "Training Loss": 0.007660172312211271,
239
+ "Valid. Loss": 0.021775153353058307,
240
+ "Valid. Accur.": 0.7946428571428572,
241
+ "Valid. F1": 0.5954345473367212,
242
+ "Training Time": "0:00:50",
243
+ "Validation Time": "0:00:05"
244
+ },
245
+ {
246
+ "epoch": 28,
247
+ "Training Loss": 0.007025993147849541,
248
+ "Valid. Loss": 0.022096331525732833,
249
+ "Valid. Accur.": 0.7878493788819876,
250
+ "Valid. F1": 0.5478974527887572,
251
+ "Training Time": "0:00:49",
252
+ "Validation Time": "0:00:05"
253
+ },
254
+ {
255
+ "epoch": 29,
256
+ "Training Loss": 0.00642754322312691,
257
+ "Valid. Loss": 0.022264615781435324,
258
+ "Valid. Accur.": 0.7905667701863355,
259
+ "Valid. F1": 0.5565844783236089,
260
+ "Training Time": "0:00:49",
261
+ "Validation Time": "0:00:05"
262
+ },
263
+ {
264
+ "epoch": 30,
265
+ "Training Loss": 0.0060036920084731065,
266
+ "Valid. Loss": 0.024807475964898126,
267
+ "Valid. Accur.": 0.7822204968944099,
268
+ "Valid. F1": 0.5288325804630153,
269
+ "Training Time": "0:00:49",
270
+ "Validation Time": "0:00:05"
271
+ },
272
+ {
273
+ "epoch": 31,
274
+ "Training Loss": 0.006230262431014069,
275
+ "Valid. Loss": 0.023448199803350923,
276
+ "Valid. Accur.": 0.7864906832298137,
277
+ "Valid. F1": 0.5432131877784052,
278
+ "Training Time": "0:00:49",
279
+ "Validation Time": "0:00:05"
280
+ },
281
+ {
282
+ "epoch": 32,
283
+ "Training Loss": 0.0054360214413084204,
284
+ "Valid. Loss": 0.023477769910821747,
285
+ "Valid. Accur.": 0.782414596273292,
286
+ "Valid. F1": 0.5543559298994083,
287
+ "Training Time": "0:00:50",
288
+ "Validation Time": "0:00:05"
289
+ },
290
+ {
291
+ "epoch": 33,
292
+ "Training Loss": 0.004909269173185224,
293
+ "Valid. Loss": 0.023074312843053356,
294
+ "Valid. Accur.": 0.7946428571428572,
295
+ "Valid. F1": 0.5675719409415063,
296
+ "Training Time": "0:00:49",
297
+ "Validation Time": "0:00:05"
298
+ },
299
+ {
300
+ "epoch": 34,
301
+ "Training Loss": 0.004490565658445538,
302
+ "Valid. Loss": 0.022579318701498565,
303
+ "Valid. Accur.": 0.797360248447205,
304
+ "Valid. F1": 0.5664447267708138,
305
+ "Training Time": "0:00:49",
306
+ "Validation Time": "0:00:05"
307
+ },
308
+ {
309
+ "epoch": 35,
310
+ "Training Loss": 0.004014178937907581,
311
+ "Valid. Loss": 0.021814040484339624,
312
+ "Valid. Accur.": 0.7892080745341615,
313
+ "Valid. F1": 0.5912894472677083,
314
+ "Training Time": "0:00:49",
315
+ "Validation Time": "0:00:05"
316
+ },
317
+ {
318
+ "epoch": 36,
319
+ "Training Loss": 0.0037434031768804037,
320
+ "Valid. Loss": 0.024734150599214674,
321
+ "Valid. Accur.": 0.7932841614906833,
322
+ "Valid. F1": 0.5646733902168686,
323
+ "Training Time": "0:00:49",
324
+ "Validation Time": "0:00:05"
325
+ },
326
+ {
327
+ "epoch": 37,
328
+ "Training Loss": 0.003671119770057715,
329
+ "Valid. Loss": 0.024742319330969688,
330
+ "Valid. Accur.": 0.7878493788819876,
331
+ "Valid. F1": 0.5720189733776693,
332
+ "Training Time": "0:00:50",
333
+ "Validation Time": "0:00:05"
334
+ },
335
+ {
336
+ "epoch": 38,
337
+ "Training Loss": 0.0034556743491028076,
338
+ "Valid. Loss": 0.02317205364713847,
339
+ "Valid. Accur.": 0.7905667701863355,
340
+ "Valid. F1": 0.5925273699730221,
341
+ "Training Time": "0:00:50",
342
+ "Validation Time": "0:00:05"
343
+ },
344
+ {
345
+ "epoch": 39,
346
+ "Training Loss": 0.00317796662476779,
347
+ "Valid. Loss": 0.025284077657140087,
348
+ "Valid. Accur.": 0.7835791925465838,
349
+ "Valid. F1": 0.5693130842587364,
350
+ "Training Time": "0:00:49",
351
+ "Validation Time": "0:00:05"
352
+ },
353
+ {
354
+ "epoch": 40,
355
+ "Training Loss": 0.0028334314120877307,
356
+ "Valid. Loss": 0.025292831399235435,
357
+ "Valid. Accur.": 0.7878493788819876,
358
+ "Valid. F1": 0.5673030773574252,
359
+ "Training Time": "0:00:50",
360
+ "Validation Time": "0:00:05"
361
+ },
362
+ {
363
+ "epoch": 41,
364
+ "Training Loss": 0.0026329465949833245,
365
+ "Valid. Loss": 0.024424386845342306,
366
+ "Valid. Accur.": 0.7932841614906833,
367
+ "Valid. F1": 0.5960039055147751,
368
+ "Training Time": "0:00:49",
369
+ "Validation Time": "0:00:05"
370
+ },
371
+ {
372
+ "epoch": 42,
373
+ "Training Loss": 0.002521713031954648,
374
+ "Valid. Loss": 0.022990894444948083,
375
+ "Valid. Accur.": 0.7919254658385093,
376
+ "Valid. F1": 0.6179673285651548,
377
+ "Training Time": "0:00:50",
378
+ "Validation Time": "0:00:05"
379
+ }
380
+ ]
Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/tokenizer_config.json ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "[PAD]",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "100": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "101": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "102": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "103": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": false,
45
+ "cls_token": "[CLS]",
46
+ "do_lower_case": true,
47
+ "mask_token": "[MASK]",
48
+ "model_max_length": 512,
49
+ "pad_token": "[PAD]",
50
+ "sep_token": "[SEP]",
51
+ "strip_accents": null,
52
+ "tokenize_chinese_chars": true,
53
+ "tokenizer_class": "BertTokenizer",
54
+ "unk_token": "[UNK]"
55
+ }
Classification/bosch_swipe/1000_bosch_t50_bert-base-uncased/vocab.txt ADDED
The diff for this file is too large to render. See raw diff
 
Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/classification_reports.txt ADDED
The diff for this file is too large to render. See raw diff
 
Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/config.json ADDED
@@ -0,0 +1,131 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "bert-base-uncased",
3
+ "architectures": [
4
+ "BertForSequenceClassification"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "gradient_checkpointing": false,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 768,
12
+ "id2label": {
13
+ "0": "T1005",
14
+ "1": "T1014",
15
+ "2": "T1021",
16
+ "3": "T1027",
17
+ "4": "T1033",
18
+ "5": "T1036",
19
+ "6": "T1041",
20
+ "7": "T1053",
21
+ "8": "T1055",
22
+ "9": "T1056",
23
+ "10": "T1057",
24
+ "11": "T1059",
25
+ "12": "T1070",
26
+ "13": "T1071",
27
+ "14": "T1078",
28
+ "15": "T1082",
29
+ "16": "T1083",
30
+ "17": "T1105",
31
+ "18": "T1110",
32
+ "19": "T1112",
33
+ "20": "T1113",
34
+ "21": "T1115",
35
+ "22": "T1125",
36
+ "23": "T1132",
37
+ "24": "T1137",
38
+ "25": "T1140",
39
+ "26": "T1189",
40
+ "27": "T1190",
41
+ "28": "T1195",
42
+ "29": "T1203",
43
+ "30": "T1204",
44
+ "31": "T1218",
45
+ "32": "T1486",
46
+ "33": "T1496",
47
+ "34": "T1497",
48
+ "35": "T1499",
49
+ "36": "T1528",
50
+ "37": "T1539",
51
+ "38": "T1543",
52
+ "39": "T1547",
53
+ "40": "T1555",
54
+ "41": "T1557",
55
+ "42": "T1562",
56
+ "43": "T1566",
57
+ "44": "T1571",
58
+ "45": "T1573",
59
+ "46": "T1583",
60
+ "47": "T1587",
61
+ "48": "T1589",
62
+ "49": "T1606"
63
+ },
64
+ "initializer_range": 0.02,
65
+ "intermediate_size": 3072,
66
+ "label2id": {
67
+ "T1005": 0,
68
+ "T1014": 1,
69
+ "T1021": 2,
70
+ "T1027": 3,
71
+ "T1033": 4,
72
+ "T1036": 5,
73
+ "T1041": 6,
74
+ "T1053": 7,
75
+ "T1055": 8,
76
+ "T1056": 9,
77
+ "T1057": 10,
78
+ "T1059": 11,
79
+ "T1070": 12,
80
+ "T1071": 13,
81
+ "T1078": 14,
82
+ "T1082": 15,
83
+ "T1083": 16,
84
+ "T1105": 17,
85
+ "T1110": 18,
86
+ "T1112": 19,
87
+ "T1113": 20,
88
+ "T1115": 21,
89
+ "T1125": 22,
90
+ "T1132": 23,
91
+ "T1137": 24,
92
+ "T1140": 25,
93
+ "T1189": 26,
94
+ "T1190": 27,
95
+ "T1195": 28,
96
+ "T1203": 29,
97
+ "T1204": 30,
98
+ "T1218": 31,
99
+ "T1486": 32,
100
+ "T1496": 33,
101
+ "T1497": 34,
102
+ "T1499": 35,
103
+ "T1528": 36,
104
+ "T1539": 37,
105
+ "T1543": 38,
106
+ "T1547": 39,
107
+ "T1555": 40,
108
+ "T1557": 41,
109
+ "T1562": 42,
110
+ "T1566": 43,
111
+ "T1571": 44,
112
+ "T1573": 45,
113
+ "T1583": 46,
114
+ "T1587": 47,
115
+ "T1589": 48,
116
+ "T1606": 49
117
+ },
118
+ "layer_norm_eps": 1e-12,
119
+ "max_position_embeddings": 512,
120
+ "model_type": "bert",
121
+ "num_attention_heads": 12,
122
+ "num_hidden_layers": 12,
123
+ "pad_token_id": 0,
124
+ "position_embedding_type": "absolute",
125
+ "problem_type": "multi_label_classification",
126
+ "torch_dtype": "float32",
127
+ "transformers_version": "4.45.2",
128
+ "type_vocab_size": 2,
129
+ "use_cache": true,
130
+ "vocab_size": 30522
131
+ }
Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/loss.pdf ADDED
Binary file (15.8 kB). View file
 
Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7ac6fe78488a6ab2d488070be315a27a53d08534a5a30219a3897b559c1baf44
3
+ size 438106296
Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/model_params.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "epochs": 100,
3
+ "batch_size": 16,
4
+ "freeze_layers": 0,
5
+ "learning_rate": 2e-05,
6
+ "pos_weight": 20,
7
+ "end_factor": 0.7
8
+ }
Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/special_tokens_map.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": "[CLS]",
3
+ "mask_token": "[MASK]",
4
+ "pad_token": "[PAD]",
5
+ "sep_token": "[SEP]",
6
+ "unk_token": "[UNK]"
7
+ }
Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/stats.json ADDED
@@ -0,0 +1,335 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "epoch": 1,
4
+ "Training Loss": 0.5143901018646709,
5
+ "Valid. Loss": 0.40204828206139803,
6
+ "Valid. Accur.": 0.04503105590062112,
7
+ "Valid. F1": 0.15503687043727968,
8
+ "Training Time": "0:00:50",
9
+ "Validation Time": "0:00:05"
10
+ },
11
+ {
12
+ "epoch": 2,
13
+ "Training Loss": 0.36579259857058216,
14
+ "Valid. Loss": 0.31684083218040443,
15
+ "Valid. Accur.": 0.6133540372670808,
16
+ "Valid. F1": 0.31098292709705755,
17
+ "Training Time": "0:00:49",
18
+ "Validation Time": "0:00:05"
19
+ },
20
+ {
21
+ "epoch": 3,
22
+ "Training Loss": 0.2898514890040936,
23
+ "Valid. Loss": 0.2695042870176836,
24
+ "Valid. Accur.": 0.6230590062111802,
25
+ "Valid. F1": 0.4303042253352811,
26
+ "Training Time": "0:00:49",
27
+ "Validation Time": "0:00:05"
28
+ },
29
+ {
30
+ "epoch": 4,
31
+ "Training Loss": 0.23258883877391145,
32
+ "Valid. Loss": 0.23650076415514948,
33
+ "Valid. Accur.": 0.5972437888198757,
34
+ "Valid. F1": 0.5588218289072325,
35
+ "Training Time": "0:00:50",
36
+ "Validation Time": "0:00:05"
37
+ },
38
+ {
39
+ "epoch": 5,
40
+ "Training Loss": 0.1871818120286978,
41
+ "Valid. Loss": 0.20332793823097542,
42
+ "Valid. Accur.": 0.6310170807453417,
43
+ "Valid. F1": 0.6113058826956341,
44
+ "Training Time": "0:00:49",
45
+ "Validation Time": "0:00:05"
46
+ },
47
+ {
48
+ "epoch": 6,
49
+ "Training Loss": 0.1531792699309728,
50
+ "Valid. Loss": 0.19157535676362938,
51
+ "Valid. Accur.": 0.6651785714285714,
52
+ "Valid. F1": 0.6214126438893518,
53
+ "Training Time": "0:00:50",
54
+ "Validation Time": "0:00:05"
55
+ },
56
+ {
57
+ "epoch": 7,
58
+ "Training Loss": 0.12948008162755972,
59
+ "Valid. Loss": 0.17973209241117347,
60
+ "Valid. Accur.": 0.6570263975155279,
61
+ "Valid. F1": 0.6193232980500061,
62
+ "Training Time": "0:00:50",
63
+ "Validation Time": "0:00:05"
64
+ },
65
+ {
66
+ "epoch": 8,
67
+ "Training Loss": 0.11158353298837395,
68
+ "Valid. Loss": 0.1796026082864188,
69
+ "Valid. Accur.": 0.7142857142857142,
70
+ "Valid. F1": 0.6196939821163421,
71
+ "Training Time": "0:00:49",
72
+ "Validation Time": "0:00:05"
73
+ },
74
+ {
75
+ "epoch": 9,
76
+ "Training Loss": 0.09642519927874346,
77
+ "Valid. Loss": 0.17228706567179813,
78
+ "Valid. Accur.": 0.7061335403726707,
79
+ "Valid. F1": 0.6497689554521852,
80
+ "Training Time": "0:00:50",
81
+ "Validation Time": "0:00:05"
82
+ },
83
+ {
84
+ "epoch": 10,
85
+ "Training Loss": 0.08283422336609214,
86
+ "Valid. Loss": 0.1615073127039475,
87
+ "Valid. Accur.": 0.7045807453416149,
88
+ "Valid. F1": 0.6902644905129377,
89
+ "Training Time": "0:00:50",
90
+ "Validation Time": "0:00:05"
91
+ },
92
+ {
93
+ "epoch": 11,
94
+ "Training Loss": 0.07388428420554748,
95
+ "Valid. Loss": 0.16873314360134062,
96
+ "Valid. Accur.": 0.7183618012422359,
97
+ "Valid. F1": 0.6626945363125486,
98
+ "Training Time": "0:00:50",
99
+ "Validation Time": "0:00:05"
100
+ },
101
+ {
102
+ "epoch": 12,
103
+ "Training Loss": 0.06633381736927507,
104
+ "Valid. Loss": 0.17092151760414892,
105
+ "Valid. Accur.": 0.7237965838509316,
106
+ "Valid. F1": 0.6457656459597451,
107
+ "Training Time": "0:00:49",
108
+ "Validation Time": "0:00:05"
109
+ },
110
+ {
111
+ "epoch": 13,
112
+ "Training Loss": 0.05921326912498035,
113
+ "Valid. Loss": 0.17360287066356184,
114
+ "Valid. Accur.": 0.7509704968944099,
115
+ "Valid. F1": 0.6648044030963284,
116
+ "Training Time": "0:00:49",
117
+ "Validation Time": "0:00:05"
118
+ },
119
+ {
120
+ "epoch": 14,
121
+ "Training Loss": 0.053061706588694885,
122
+ "Valid. Loss": 0.16841152149373348,
123
+ "Valid. Accur.": 0.7237965838509316,
124
+ "Valid. F1": 0.6747981030356804,
125
+ "Training Time": "0:00:49",
126
+ "Validation Time": "0:00:05"
127
+ },
128
+ {
129
+ "epoch": 15,
130
+ "Training Loss": 0.047712771313681915,
131
+ "Valid. Loss": 0.17714479046567524,
132
+ "Valid. Accur.": 0.7387422360248447,
133
+ "Valid. F1": 0.6654700920772347,
134
+ "Training Time": "0:00:50",
135
+ "Validation Time": "0:00:05"
136
+ },
137
+ {
138
+ "epoch": 16,
139
+ "Training Loss": 0.04335880595863695,
140
+ "Valid. Loss": 0.16769457267218063,
141
+ "Valid. Accur.": 0.7455357142857142,
142
+ "Valid. F1": 0.6790872418356889,
143
+ "Training Time": "0:00:49",
144
+ "Validation Time": "0:00:05"
145
+ },
146
+ {
147
+ "epoch": 17,
148
+ "Training Loss": 0.040387465410994056,
149
+ "Valid. Loss": 0.16459463905725302,
150
+ "Valid. Accur.": 0.7428183229813664,
151
+ "Valid. F1": 0.6823714817503636,
152
+ "Training Time": "0:00:49",
153
+ "Validation Time": "0:00:05"
154
+ },
155
+ {
156
+ "epoch": 18,
157
+ "Training Loss": 0.03955248594162793,
158
+ "Valid. Loss": 0.18072665686197786,
159
+ "Valid. Accur.": 0.7426242236024845,
160
+ "Valid. F1": 0.6644308248112598,
161
+ "Training Time": "0:00:49",
162
+ "Validation Time": "0:00:05"
163
+ },
164
+ {
165
+ "epoch": 19,
166
+ "Training Loss": 0.036450642034899866,
167
+ "Valid. Loss": 0.19461521281264413,
168
+ "Valid. Accur.": 0.7618400621118012,
169
+ "Valid. F1": 0.6511027906990641,
170
+ "Training Time": "0:00:49",
171
+ "Validation Time": "0:00:05"
172
+ },
173
+ {
174
+ "epoch": 20,
175
+ "Training Loss": 0.03210331993747273,
176
+ "Valid. Loss": 0.20520198240206,
177
+ "Valid. Accur.": 0.7727096273291925,
178
+ "Valid. F1": 0.6668367160214987,
179
+ "Training Time": "0:00:49",
180
+ "Validation Time": "0:00:05"
181
+ },
182
+ {
183
+ "epoch": 21,
184
+ "Training Loss": 0.029488289849712433,
185
+ "Valid. Loss": 0.20960609081970616,
186
+ "Valid. Accur.": 0.7808618012422359,
187
+ "Valid. F1": 0.6608792375096725,
188
+ "Training Time": "0:00:49",
189
+ "Validation Time": "0:00:05"
190
+ },
191
+ {
192
+ "epoch": 22,
193
+ "Training Loss": 0.02791638105710276,
194
+ "Valid. Loss": 0.160058174248354,
195
+ "Valid. Accur.": 0.7385481366459627,
196
+ "Valid. F1": 0.6937122705911526,
197
+ "Training Time": "0:00:49",
198
+ "Validation Time": "0:00:05"
199
+ },
200
+ {
201
+ "epoch": 23,
202
+ "Training Loss": 0.025383777069644925,
203
+ "Valid. Loss": 0.18360062280506118,
204
+ "Valid. Accur.": 0.764557453416149,
205
+ "Valid. F1": 0.7056220852416503,
206
+ "Training Time": "0:00:49",
207
+ "Validation Time": "0:00:05"
208
+ },
209
+ {
210
+ "epoch": 24,
211
+ "Training Loss": 0.023433650266100732,
212
+ "Valid. Loss": 0.1914035714514896,
213
+ "Valid. Accur.": 0.7699922360248447,
214
+ "Valid. F1": 0.6763856212225778,
215
+ "Training Time": "0:00:49",
216
+ "Validation Time": "0:00:05"
217
+ },
218
+ {
219
+ "epoch": 25,
220
+ "Training Loss": 0.02122675720240796,
221
+ "Valid. Loss": 0.20948572524742168,
222
+ "Valid. Accur.": 0.7781444099378881,
223
+ "Valid. F1": 0.6587355177029092,
224
+ "Training Time": "0:00:49",
225
+ "Validation Time": "0:00:05"
226
+ },
227
+ {
228
+ "epoch": 26,
229
+ "Training Loss": 0.020370427849580215,
230
+ "Valid. Loss": 0.23490834921822046,
231
+ "Valid. Accur.": 0.7808618012422359,
232
+ "Valid. F1": 0.638497422464814,
233
+ "Training Time": "0:00:49",
234
+ "Validation Time": "0:00:05"
235
+ },
236
+ {
237
+ "epoch": 27,
238
+ "Training Loss": 0.01910172003197237,
239
+ "Valid. Loss": 0.23306808914066945,
240
+ "Valid. Accur.": 0.7781444099378881,
241
+ "Valid. F1": 0.6389071878202316,
242
+ "Training Time": "0:00:49",
243
+ "Validation Time": "0:00:05"
244
+ },
245
+ {
246
+ "epoch": 28,
247
+ "Training Loss": 0.01842709349753484,
248
+ "Valid. Loss": 0.2211135066030263,
249
+ "Valid. Accur.": 0.7672748447204968,
250
+ "Valid. F1": 0.6613450760189892,
251
+ "Training Time": "0:00:49",
252
+ "Validation Time": "0:00:05"
253
+ },
254
+ {
255
+ "epoch": 29,
256
+ "Training Loss": 0.01649225821672176,
257
+ "Valid. Loss": 0.24418543944918764,
258
+ "Valid. Accur.": 0.7822204968944099,
259
+ "Valid. F1": 0.6450450415124329,
260
+ "Training Time": "0:00:49",
261
+ "Validation Time": "0:00:05"
262
+ },
263
+ {
264
+ "epoch": 30,
265
+ "Training Loss": 0.016077998951486594,
266
+ "Valid. Loss": 0.2090348182451244,
267
+ "Valid. Accur.": 0.7672748447204968,
268
+ "Valid. F1": 0.6652719221197482,
269
+ "Training Time": "0:00:49",
270
+ "Validation Time": "0:00:05"
271
+ },
272
+ {
273
+ "epoch": 31,
274
+ "Training Loss": 0.015994327703156157,
275
+ "Valid. Loss": 0.21698476039620002,
276
+ "Valid. Accur.": 0.7496118012422359,
277
+ "Valid. F1": 0.6574872430307213,
278
+ "Training Time": "0:00:49",
279
+ "Validation Time": "0:00:05"
280
+ },
281
+ {
282
+ "epoch": 32,
283
+ "Training Loss": 0.013778808323537919,
284
+ "Valid. Loss": 0.23175018121226448,
285
+ "Valid. Accur.": 0.7699922360248447,
286
+ "Valid. F1": 0.6708990787795135,
287
+ "Training Time": "0:00:49",
288
+ "Validation Time": "0:00:05"
289
+ },
290
+ {
291
+ "epoch": 33,
292
+ "Training Loss": 0.012361955808193437,
293
+ "Valid. Loss": 0.22527438814070758,
294
+ "Valid. Accur.": 0.7727096273291925,
295
+ "Valid. F1": 0.6661710531819228,
296
+ "Training Time": "0:00:49",
297
+ "Validation Time": "0:00:05"
298
+ },
299
+ {
300
+ "epoch": 34,
301
+ "Training Loss": 0.013152264201455465,
302
+ "Valid. Loss": 0.16880043273960244,
303
+ "Valid. Accur.": 0.7286490683229815,
304
+ "Valid. F1": 0.682829354622833,
305
+ "Training Time": "0:00:49",
306
+ "Validation Time": "0:00:05"
307
+ },
308
+ {
309
+ "epoch": 35,
310
+ "Training Loss": 0.012765101279911125,
311
+ "Valid. Loss": 0.23160128495202376,
312
+ "Valid. Accur.": 0.7618400621118012,
313
+ "Valid. F1": 0.6537028148984675,
314
+ "Training Time": "0:00:49",
315
+ "Validation Time": "0:00:05"
316
+ },
317
+ {
318
+ "epoch": 36,
319
+ "Training Loss": 0.01079817485090789,
320
+ "Valid. Loss": 0.22925546324492374,
321
+ "Valid. Accur.": 0.7699922360248447,
322
+ "Valid. F1": 0.6722465598009078,
323
+ "Training Time": "0:00:49",
324
+ "Validation Time": "0:00:05"
325
+ },
326
+ {
327
+ "epoch": 37,
328
+ "Training Loss": 0.00985983520911364,
329
+ "Valid. Loss": 0.23159883465636838,
330
+ "Valid. Accur.": 0.7713509316770185,
331
+ "Valid. F1": 0.6804179214505303,
332
+ "Training Time": "0:00:49",
333
+ "Validation Time": "0:00:05"
334
+ }
335
+ ]
Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/tokenizer_config.json ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "[PAD]",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "100": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "101": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "102": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "103": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": false,
45
+ "cls_token": "[CLS]",
46
+ "do_lower_case": true,
47
+ "mask_token": "[MASK]",
48
+ "model_max_length": 512,
49
+ "pad_token": "[PAD]",
50
+ "sep_token": "[SEP]",
51
+ "strip_accents": null,
52
+ "tokenize_chinese_chars": true,
53
+ "tokenizer_class": "BertTokenizer",
54
+ "unk_token": "[UNK]"
55
+ }
Classification/bosch_swipe/1001_bosch_t50_bert-base-uncased/vocab.txt ADDED
The diff for this file is too large to render. See raw diff
 
Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/classification_reports.txt ADDED
The diff for this file is too large to render. See raw diff
 
Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/config.json ADDED
@@ -0,0 +1,131 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "bert-base-cased",
3
+ "architectures": [
4
+ "BertForSequenceClassification"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "gradient_checkpointing": false,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 768,
12
+ "id2label": {
13
+ "0": "T1005",
14
+ "1": "T1014",
15
+ "2": "T1021",
16
+ "3": "T1027",
17
+ "4": "T1033",
18
+ "5": "T1036",
19
+ "6": "T1041",
20
+ "7": "T1053",
21
+ "8": "T1055",
22
+ "9": "T1056",
23
+ "10": "T1057",
24
+ "11": "T1059",
25
+ "12": "T1070",
26
+ "13": "T1071",
27
+ "14": "T1078",
28
+ "15": "T1082",
29
+ "16": "T1083",
30
+ "17": "T1105",
31
+ "18": "T1110",
32
+ "19": "T1112",
33
+ "20": "T1113",
34
+ "21": "T1115",
35
+ "22": "T1125",
36
+ "23": "T1132",
37
+ "24": "T1137",
38
+ "25": "T1140",
39
+ "26": "T1189",
40
+ "27": "T1190",
41
+ "28": "T1195",
42
+ "29": "T1203",
43
+ "30": "T1204",
44
+ "31": "T1218",
45
+ "32": "T1486",
46
+ "33": "T1496",
47
+ "34": "T1497",
48
+ "35": "T1499",
49
+ "36": "T1528",
50
+ "37": "T1539",
51
+ "38": "T1543",
52
+ "39": "T1547",
53
+ "40": "T1555",
54
+ "41": "T1557",
55
+ "42": "T1562",
56
+ "43": "T1566",
57
+ "44": "T1571",
58
+ "45": "T1573",
59
+ "46": "T1583",
60
+ "47": "T1587",
61
+ "48": "T1589",
62
+ "49": "T1606"
63
+ },
64
+ "initializer_range": 0.02,
65
+ "intermediate_size": 3072,
66
+ "label2id": {
67
+ "T1005": 0,
68
+ "T1014": 1,
69
+ "T1021": 2,
70
+ "T1027": 3,
71
+ "T1033": 4,
72
+ "T1036": 5,
73
+ "T1041": 6,
74
+ "T1053": 7,
75
+ "T1055": 8,
76
+ "T1056": 9,
77
+ "T1057": 10,
78
+ "T1059": 11,
79
+ "T1070": 12,
80
+ "T1071": 13,
81
+ "T1078": 14,
82
+ "T1082": 15,
83
+ "T1083": 16,
84
+ "T1105": 17,
85
+ "T1110": 18,
86
+ "T1112": 19,
87
+ "T1113": 20,
88
+ "T1115": 21,
89
+ "T1125": 22,
90
+ "T1132": 23,
91
+ "T1137": 24,
92
+ "T1140": 25,
93
+ "T1189": 26,
94
+ "T1190": 27,
95
+ "T1195": 28,
96
+ "T1203": 29,
97
+ "T1204": 30,
98
+ "T1218": 31,
99
+ "T1486": 32,
100
+ "T1496": 33,
101
+ "T1497": 34,
102
+ "T1499": 35,
103
+ "T1528": 36,
104
+ "T1539": 37,
105
+ "T1543": 38,
106
+ "T1547": 39,
107
+ "T1555": 40,
108
+ "T1557": 41,
109
+ "T1562": 42,
110
+ "T1566": 43,
111
+ "T1571": 44,
112
+ "T1573": 45,
113
+ "T1583": 46,
114
+ "T1587": 47,
115
+ "T1589": 48,
116
+ "T1606": 49
117
+ },
118
+ "layer_norm_eps": 1e-12,
119
+ "max_position_embeddings": 512,
120
+ "model_type": "bert",
121
+ "num_attention_heads": 12,
122
+ "num_hidden_layers": 12,
123
+ "pad_token_id": 0,
124
+ "position_embedding_type": "absolute",
125
+ "problem_type": "multi_label_classification",
126
+ "torch_dtype": "float32",
127
+ "transformers_version": "4.45.2",
128
+ "type_vocab_size": 2,
129
+ "use_cache": true,
130
+ "vocab_size": 28996
131
+ }
Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/loss.pdf ADDED
Binary file (16.3 kB). View file
 
Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:698629ad37b81d9f4703be24aa2d5a77bb4b0af415a082d792f5fa6ba6c9a215
3
+ size 433418416
Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/model_params.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "epochs": 100,
3
+ "batch_size": 16,
4
+ "freeze_layers": 0,
5
+ "learning_rate": 2e-05,
6
+ "pos_weight": 0,
7
+ "end_factor": 0.7
8
+ }
Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/special_tokens_map.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": "[CLS]",
3
+ "mask_token": "[MASK]",
4
+ "pad_token": "[PAD]",
5
+ "sep_token": "[SEP]",
6
+ "unk_token": "[UNK]"
7
+ }
Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/stats.json ADDED
@@ -0,0 +1,416 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "epoch": 1,
4
+ "Training Loss": 0.23967277184777472,
5
+ "Valid. Loss": 0.08773300389417947,
6
+ "Valid. Accur.": 0.640722049689441,
7
+ "Valid. F1": 0.0,
8
+ "Training Time": "0:00:49",
9
+ "Validation Time": "0:00:05"
10
+ },
11
+ {
12
+ "epoch": 2,
13
+ "Training Loss": 0.06972368106461956,
14
+ "Valid. Loss": 0.05565312712088873,
15
+ "Valid. Accur.": 0.640722049689441,
16
+ "Valid. F1": 0.0,
17
+ "Training Time": "0:00:48",
18
+ "Validation Time": "0:00:05"
19
+ },
20
+ {
21
+ "epoch": 3,
22
+ "Training Loss": 0.05296487258153333,
23
+ "Valid. Loss": 0.04844841642131716,
24
+ "Valid. Accur.": 0.640722049689441,
25
+ "Valid. F1": 0.0,
26
+ "Training Time": "0:00:49",
27
+ "Validation Time": "0:00:05"
28
+ },
29
+ {
30
+ "epoch": 4,
31
+ "Training Loss": 0.048239972136806746,
32
+ "Valid. Loss": 0.045896086182838636,
33
+ "Valid. Accur.": 0.640722049689441,
34
+ "Valid. F1": 0.0,
35
+ "Training Time": "0:00:49",
36
+ "Validation Time": "0:00:05"
37
+ },
38
+ {
39
+ "epoch": 5,
40
+ "Training Loss": 0.04631268307944568,
41
+ "Valid. Loss": 0.04459588322302569,
42
+ "Valid. Accur.": 0.640722049689441,
43
+ "Valid. F1": 0.0,
44
+ "Training Time": "0:00:49",
45
+ "Validation Time": "0:00:05"
46
+ },
47
+ {
48
+ "epoch": 6,
49
+ "Training Loss": 0.044907993921821106,
50
+ "Valid. Loss": 0.04331482727149999,
51
+ "Valid. Accur.": 0.640722049689441,
52
+ "Valid. F1": 0.0,
53
+ "Training Time": "0:00:49",
54
+ "Validation Time": "0:00:05"
55
+ },
56
+ {
57
+ "epoch": 7,
58
+ "Training Loss": 0.04349562685194333,
59
+ "Valid. Loss": 0.04221217991541262,
60
+ "Valid. Accur.": 0.640722049689441,
61
+ "Valid. F1": 0.0,
62
+ "Training Time": "0:00:49",
63
+ "Validation Time": "0:00:05"
64
+ },
65
+ {
66
+ "epoch": 8,
67
+ "Training Loss": 0.0414049620164872,
68
+ "Valid. Loss": 0.04001450351375786,
69
+ "Valid. Accur.": 0.640722049689441,
70
+ "Valid. F1": 0.0,
71
+ "Training Time": "0:00:49",
72
+ "Validation Time": "0:00:05"
73
+ },
74
+ {
75
+ "epoch": 9,
76
+ "Training Loss": 0.038246758781825665,
77
+ "Valid. Loss": 0.038292086402555194,
78
+ "Valid. Accur.": 0.6651785714285714,
79
+ "Valid. F1": 0.07074314574314573,
80
+ "Training Time": "0:00:49",
81
+ "Validation Time": "0:00:05"
82
+ },
83
+ {
84
+ "epoch": 10,
85
+ "Training Loss": 0.03513314171717315,
86
+ "Valid. Loss": 0.03574676018581012,
87
+ "Valid. Accur.": 0.7115683229813664,
88
+ "Valid. F1": 0.21435145973189448,
89
+ "Training Time": "0:00:49",
90
+ "Validation Time": "0:00:05"
91
+ },
92
+ {
93
+ "epoch": 11,
94
+ "Training Loss": 0.03246234346246745,
95
+ "Valid. Loss": 0.03431426599759157,
96
+ "Valid. Accur.": 0.7090450310559007,
97
+ "Valid. F1": 0.280031421879248,
98
+ "Training Time": "0:00:49",
99
+ "Validation Time": "0:00:05"
100
+ },
101
+ {
102
+ "epoch": 12,
103
+ "Training Loss": 0.029123301149136725,
104
+ "Valid. Loss": 0.0331896996731734,
105
+ "Valid. Accur.": 0.7156444099378881,
106
+ "Valid. F1": 0.2828269757617584,
107
+ "Training Time": "0:00:49",
108
+ "Validation Time": "0:00:05"
109
+ },
110
+ {
111
+ "epoch": 13,
112
+ "Training Loss": 0.026752796080566604,
113
+ "Valid. Loss": 0.03100722765784294,
114
+ "Valid. Accur.": 0.7226319875776398,
115
+ "Valid. F1": 0.28359907863013456,
116
+ "Training Time": "0:00:49",
117
+ "Validation Time": "0:00:05"
118
+ },
119
+ {
120
+ "epoch": 14,
121
+ "Training Loss": 0.024683260231399478,
122
+ "Valid. Loss": 0.029963613197739637,
123
+ "Valid. Accur.": 0.7170031055900621,
124
+ "Valid. F1": 0.2730009724574943,
125
+ "Training Time": "0:00:49",
126
+ "Validation Time": "0:00:05"
127
+ },
128
+ {
129
+ "epoch": 15,
130
+ "Training Loss": 0.02265358168844377,
131
+ "Valid. Loss": 0.030352245649837694,
132
+ "Valid. Accur.": 0.7183618012422359,
133
+ "Valid. F1": 0.27573117301378175,
134
+ "Training Time": "0:00:49",
135
+ "Validation Time": "0:00:05"
136
+ },
137
+ {
138
+ "epoch": 16,
139
+ "Training Loss": 0.020833584574258723,
140
+ "Valid. Loss": 0.0289781651782705,
141
+ "Valid. Accur.": 0.7156444099378881,
142
+ "Valid. F1": 0.30879080661689357,
143
+ "Training Time": "0:00:49",
144
+ "Validation Time": "0:00:05"
145
+ },
146
+ {
147
+ "epoch": 17,
148
+ "Training Loss": 0.019239316153516345,
149
+ "Valid. Loss": 0.028311015030532582,
150
+ "Valid. Accur.": 0.7090450310559007,
151
+ "Valid. F1": 0.30823059790451096,
152
+ "Training Time": "0:00:49",
153
+ "Validation Time": "0:00:05"
154
+ },
155
+ {
156
+ "epoch": 18,
157
+ "Training Loss": 0.01774260778488983,
158
+ "Valid. Loss": 0.027603315717733386,
159
+ "Valid. Accur.": 0.7362189440993789,
160
+ "Valid. F1": 0.3634650072150073,
161
+ "Training Time": "0:00:49",
162
+ "Validation Time": "0:00:05"
163
+ },
164
+ {
165
+ "epoch": 19,
166
+ "Training Loss": 0.016284606990877252,
167
+ "Valid. Loss": 0.026275369822729513,
168
+ "Valid. Accur.": 0.7387422360248447,
169
+ "Valid. F1": 0.38656335612857357,
170
+ "Training Time": "0:00:49",
171
+ "Validation Time": "0:00:05"
172
+ },
173
+ {
174
+ "epoch": 20,
175
+ "Training Loss": 0.014831473556224018,
176
+ "Valid. Loss": 0.02635271540361924,
177
+ "Valid. Accur.": 0.734860248447205,
178
+ "Valid. F1": 0.36361649622519193,
179
+ "Training Time": "0:00:49",
180
+ "Validation Time": "0:00:05"
181
+ },
182
+ {
183
+ "epoch": 21,
184
+ "Training Loss": 0.013676380747437822,
185
+ "Valid. Loss": 0.02600000864979435,
186
+ "Valid. Accur.": 0.751164596273292,
187
+ "Valid. F1": 0.40841894619068525,
188
+ "Training Time": "0:00:49",
189
+ "Validation Time": "0:00:05"
190
+ },
191
+ {
192
+ "epoch": 22,
193
+ "Training Loss": 0.012554420935444743,
194
+ "Valid. Loss": 0.025073354455724832,
195
+ "Valid. Accur.": 0.7538819875776398,
196
+ "Valid. F1": 0.45623431520170643,
197
+ "Training Time": "0:00:49",
198
+ "Validation Time": "0:00:05"
199
+ },
200
+ {
201
+ "epoch": 23,
202
+ "Training Loss": 0.01136879031045954,
203
+ "Valid. Loss": 0.025763617354949142,
204
+ "Valid. Accur.": 0.7618400621118012,
205
+ "Valid. F1": 0.4385175512892904,
206
+ "Training Time": "0:00:49",
207
+ "Validation Time": "0:00:05"
208
+ },
209
+ {
210
+ "epoch": 24,
211
+ "Training Loss": 0.010348055467226346,
212
+ "Valid. Loss": 0.024666395642959523,
213
+ "Valid. Accur.": 0.7604813664596273,
214
+ "Valid. F1": 0.49180913691783246,
215
+ "Training Time": "0:00:49",
216
+ "Validation Time": "0:00:05"
217
+ },
218
+ {
219
+ "epoch": 25,
220
+ "Training Loss": 0.009579673389825323,
221
+ "Valid. Loss": 0.02464732097126312,
222
+ "Valid. Accur.": 0.764557453416149,
223
+ "Valid. F1": 0.480180427463036,
224
+ "Training Time": "0:00:49",
225
+ "Validation Time": "0:00:05"
226
+ },
227
+ {
228
+ "epoch": 26,
229
+ "Training Loss": 0.008837044477765187,
230
+ "Valid. Loss": 0.023708919064251713,
231
+ "Valid. Accur.": 0.7699922360248447,
232
+ "Valid. F1": 0.503363997113997,
233
+ "Training Time": "0:00:49",
234
+ "Validation Time": "0:00:05"
235
+ },
236
+ {
237
+ "epoch": 27,
238
+ "Training Loss": 0.008085103340317668,
239
+ "Valid. Loss": 0.02492591440099905,
240
+ "Valid. Accur.": 0.7618400621118012,
241
+ "Valid. F1": 0.47350053328314184,
242
+ "Training Time": "0:00:49",
243
+ "Validation Time": "0:00:05"
244
+ },
245
+ {
246
+ "epoch": 28,
247
+ "Training Loss": 0.0075078105112703075,
248
+ "Valid. Loss": 0.02508351766987001,
249
+ "Valid. Accur.": 0.7643633540372671,
250
+ "Valid. F1": 0.5107307547524937,
251
+ "Training Time": "0:00:49",
252
+ "Validation Time": "0:00:05"
253
+ },
254
+ {
255
+ "epoch": 29,
256
+ "Training Loss": 0.006939045551740836,
257
+ "Valid. Loss": 0.02374452424337053,
258
+ "Valid. Accur.": 0.7670807453416149,
259
+ "Valid. F1": 0.5360370004391742,
260
+ "Training Time": "0:00:49",
261
+ "Validation Time": "0:00:05"
262
+ },
263
+ {
264
+ "epoch": 30,
265
+ "Training Loss": 0.0065233793430767625,
266
+ "Valid. Loss": 0.023198857193511568,
267
+ "Valid. Accur.": 0.7740683229813664,
268
+ "Valid. F1": 0.5580974078256686,
269
+ "Training Time": "0:00:49",
270
+ "Validation Time": "0:00:05"
271
+ },
272
+ {
273
+ "epoch": 31,
274
+ "Training Loss": 0.006095627320701961,
275
+ "Valid. Loss": 0.022687453222837047,
276
+ "Valid. Accur.": 0.7565993788819876,
277
+ "Valid. F1": 0.5464004694982955,
278
+ "Training Time": "0:00:49",
279
+ "Validation Time": "0:00:05"
280
+ },
281
+ {
282
+ "epoch": 32,
283
+ "Training Loss": 0.005555237763608442,
284
+ "Valid. Loss": 0.02274993333786669,
285
+ "Valid. Accur.": 0.7864906832298137,
286
+ "Valid. F1": 0.5688081644603383,
287
+ "Training Time": "0:00:49",
288
+ "Validation Time": "0:00:05"
289
+ },
290
+ {
291
+ "epoch": 33,
292
+ "Training Loss": 0.004991700114879295,
293
+ "Valid. Loss": 0.024241284690953692,
294
+ "Valid. Accur.": 0.7742624223602484,
295
+ "Valid. F1": 0.5417291444465355,
296
+ "Training Time": "0:00:49",
297
+ "Validation Time": "0:00:05"
298
+ },
299
+ {
300
+ "epoch": 34,
301
+ "Training Loss": 0.00457181571495338,
302
+ "Valid. Loss": 0.0236992985621906,
303
+ "Valid. Accur.": 0.7810559006211181,
304
+ "Valid. F1": 0.5499764728025598,
305
+ "Training Time": "0:00:49",
306
+ "Validation Time": "0:00:05"
307
+ },
308
+ {
309
+ "epoch": 35,
310
+ "Training Loss": 0.0042910504346999,
311
+ "Valid. Loss": 0.024967902445257678,
312
+ "Valid. Accur.": 0.7756211180124224,
313
+ "Valid. F1": 0.5464036064579542,
314
+ "Training Time": "0:00:49",
315
+ "Validation Time": "0:00:05"
316
+ },
317
+ {
318
+ "epoch": 36,
319
+ "Training Loss": 0.0039008854475300254,
320
+ "Valid. Loss": 0.024476702256271556,
321
+ "Valid. Accur.": 0.7851319875776398,
322
+ "Valid. F1": 0.5706600163121902,
323
+ "Training Time": "0:00:49",
324
+ "Validation Time": "0:00:05"
325
+ },
326
+ {
327
+ "epoch": 37,
328
+ "Training Loss": 0.0038158257209911627,
329
+ "Valid. Loss": 0.024399026073967333,
330
+ "Valid. Accur.": 0.7713509316770185,
331
+ "Valid. F1": 0.5570083599974904,
332
+ "Training Time": "0:00:49",
333
+ "Validation Time": "0:00:05"
334
+ },
335
+ {
336
+ "epoch": 38,
337
+ "Training Loss": 0.003392752745745331,
338
+ "Valid. Loss": 0.025053668496926907,
339
+ "Valid. Accur.": 0.782414596273292,
340
+ "Valid. F1": 0.549797404688709,
341
+ "Training Time": "0:00:49",
342
+ "Validation Time": "0:00:05"
343
+ },
344
+ {
345
+ "epoch": 39,
346
+ "Training Loss": 0.003160932413753156,
347
+ "Valid. Loss": 0.024947267900046068,
348
+ "Valid. Accur.": 0.7851319875776398,
349
+ "Valid. F1": 0.575313434552565,
350
+ "Training Time": "0:00:49",
351
+ "Validation Time": "0:00:05"
352
+ },
353
+ {
354
+ "epoch": 40,
355
+ "Training Loss": 0.0028699425179603047,
356
+ "Valid. Loss": 0.02512070179137396,
357
+ "Valid. Accur.": 0.7810559006211181,
358
+ "Valid. F1": 0.585742256937909,
359
+ "Training Time": "0:00:49",
360
+ "Validation Time": "0:00:05"
361
+ },
362
+ {
363
+ "epoch": 41,
364
+ "Training Loss": 0.002737723764433779,
365
+ "Valid. Loss": 0.024856783991270285,
366
+ "Valid. Accur.": 0.782414596273292,
367
+ "Valid. F1": 0.5895703933747413,
368
+ "Training Time": "0:00:49",
369
+ "Validation Time": "0:00:05"
370
+ },
371
+ {
372
+ "epoch": 42,
373
+ "Training Loss": 0.002586882398821257,
374
+ "Valid. Loss": 0.024950452045590318,
375
+ "Valid. Accur.": 0.7917313664596273,
376
+ "Valid. F1": 0.6178614561766738,
377
+ "Training Time": "0:00:49",
378
+ "Validation Time": "0:00:05"
379
+ },
380
+ {
381
+ "epoch": 43,
382
+ "Training Loss": 0.0030256632417866424,
383
+ "Valid. Loss": 0.026610686337587612,
384
+ "Valid. Accur.": 0.782414596273292,
385
+ "Valid. F1": 0.5556773741556351,
386
+ "Training Time": "0:00:49",
387
+ "Validation Time": "0:00:05"
388
+ },
389
+ {
390
+ "epoch": 44,
391
+ "Training Loss": 0.0029102383662733635,
392
+ "Valid. Loss": 0.02793561936213771,
393
+ "Valid. Accur.": 0.7810559006211181,
394
+ "Valid. F1": 0.5385451303929564,
395
+ "Training Time": "0:00:49",
396
+ "Validation Time": "0:00:05"
397
+ },
398
+ {
399
+ "epoch": 45,
400
+ "Training Loss": 0.002323069330547919,
401
+ "Valid. Loss": 0.025877745469624645,
402
+ "Valid. Accur.": 0.794448757763975,
403
+ "Valid. F1": 0.6051061860844469,
404
+ "Training Time": "0:00:49",
405
+ "Validation Time": "0:00:05"
406
+ },
407
+ {
408
+ "epoch": 46,
409
+ "Training Loss": 0.002150733596802415,
410
+ "Valid. Loss": 0.02624247839974363,
411
+ "Valid. Accur.": 0.795807453416149,
412
+ "Valid. F1": 0.6236397358679968,
413
+ "Training Time": "0:00:49",
414
+ "Validation Time": "0:00:05"
415
+ }
416
+ ]
Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/tokenizer_config.json ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "[PAD]",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "100": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "101": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "102": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "103": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": false,
45
+ "cls_token": "[CLS]",
46
+ "do_lower_case": false,
47
+ "mask_token": "[MASK]",
48
+ "model_max_length": 512,
49
+ "pad_token": "[PAD]",
50
+ "sep_token": "[SEP]",
51
+ "strip_accents": null,
52
+ "tokenize_chinese_chars": true,
53
+ "tokenizer_class": "BertTokenizer",
54
+ "unk_token": "[UNK]"
55
+ }
Classification/bosch_swipe/1004_bosch_t50_bert-base-cased/vocab.txt ADDED
The diff for this file is too large to render. See raw diff
 
Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/classification_reports.txt ADDED
@@ -0,0 +1,1888 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ > epoch: 0
2
+ precision recall f1-score support
3
+
4
+ 0 0.00 0.00 0.00 6
5
+ 1 0.00 0.00 0.00 1
6
+ 2 0.00 0.00 0.00 2
7
+ 3 0.00 0.00 0.00 13
8
+ 4 0.00 0.00 0.00 2
9
+ 5 0.00 0.00 0.00 8
10
+ 6 0.00 0.00 0.00 10
11
+ 7 0.00 0.00 0.00 2
12
+ 8 0.00 0.00 0.00 7
13
+ 9 0.00 0.00 0.00 8
14
+ 10 0.00 0.00 0.00 1
15
+ 11 0.09 0.96 0.17 25
16
+ 12 0.00 0.00 0.00 2
17
+ 13 0.00 0.00 0.00 18
18
+ 14 0.00 0.00 0.00 3
19
+ 15 0.00 0.00 0.00 3
20
+ 16 0.00 0.00 0.00 5
21
+ 17 0.11 0.29 0.16 14
22
+ 18 0.00 0.00 0.00 4
23
+ 19 0.00 0.00 0.00 1
24
+ 20 0.00 0.00 0.00 1
25
+ 21 0.00 0.00 0.00 1
26
+ 22 0.00 0.00 0.00 4
27
+ 23 0.00 0.00 0.00 1
28
+ 24 0.00 0.00 0.00 2
29
+ 25 0.50 0.10 0.17 10
30
+ 26 0.00 0.00 0.00 7
31
+ 27 0.00 0.00 0.00 8
32
+ 28 0.00 0.00 0.00 8
33
+ 29 0.00 0.00 0.00 1
34
+ 30 0.00 0.00 0.00 4
35
+ 31 0.00 0.00 0.00 2
36
+ 32 0.96 0.75 0.84 32
37
+ 33 0.00 0.00 0.00 4
38
+ 34 0.00 0.00 0.00 3
39
+ 35 0.00 0.00 0.00 7
40
+ 36 0.00 0.00 0.00 0
41
+ 37 0.00 0.00 0.00 2
42
+ 38 0.00 0.00 0.00 0
43
+ 39 0.00 0.00 0.00 4
44
+ 40 0.00 0.00 0.00 6
45
+ 41 0.00 0.00 0.00 0
46
+ 42 0.00 0.00 0.00 4
47
+ 43 0.06 1.00 0.12 47
48
+ 44 0.00 0.00 0.00 1
49
+ 45 0.00 0.00 0.00 8
50
+ 46 0.00 0.00 0.00 0
51
+ 47 0.00 0.00 0.00 2
52
+ 48 0.00 0.00 0.00 3
53
+ 49 0.00 0.00 0.00 0
54
+
55
+ micro avg 0.10 0.33 0.15 307
56
+ macro avg 0.03 0.06 0.03 307
57
+ weighted avg 0.14 0.33 0.13 307
58
+ samples avg 0.08 0.12 0.09 307
59
+
60
+ > epoch: 1
61
+ precision recall f1-score support
62
+
63
+ 0 0.00 0.00 0.00 6
64
+ 1 0.00 0.00 0.00 1
65
+ 2 0.00 0.00 0.00 2
66
+ 3 0.00 0.00 0.00 13
67
+ 4 0.00 0.00 0.00 2
68
+ 5 0.00 0.00 0.00 8
69
+ 6 0.00 0.00 0.00 10
70
+ 7 0.00 0.00 0.00 2
71
+ 8 0.29 0.57 0.38 7
72
+ 9 0.22 0.25 0.24 8
73
+ 10 0.00 0.00 0.00 1
74
+ 11 0.20 0.96 0.34 25
75
+ 12 0.00 0.00 0.00 2
76
+ 13 0.07 0.06 0.06 18
77
+ 14 0.00 0.00 0.00 3
78
+ 15 0.10 0.33 0.15 3
79
+ 16 0.00 0.00 0.00 5
80
+ 17 0.11 0.86 0.20 14
81
+ 18 0.00 0.00 0.00 4
82
+ 19 0.00 0.00 0.00 1
83
+ 20 0.00 0.00 0.00 1
84
+ 21 0.00 0.00 0.00 1
85
+ 22 1.00 0.25 0.40 4
86
+ 23 0.00 0.00 0.00 1
87
+ 24 0.00 0.00 0.00 2
88
+ 25 0.23 1.00 0.38 10
89
+ 26 0.00 0.00 0.00 7
90
+ 27 0.00 0.00 0.00 8
91
+ 28 0.00 0.00 0.00 8
92
+ 29 0.00 0.00 0.00 1
93
+ 30 0.00 0.00 0.00 4
94
+ 31 0.00 0.00 0.00 2
95
+ 32 0.47 0.88 0.61 32
96
+ 33 0.00 0.00 0.00 4
97
+ 34 0.00 0.00 0.00 3
98
+ 35 0.00 0.00 0.00 7
99
+ 36 0.00 0.00 0.00 0
100
+ 37 0.00 0.00 0.00 2
101
+ 38 0.00 0.00 0.00 0
102
+ 39 0.00 0.00 0.00 4
103
+ 40 0.25 0.17 0.20 6
104
+ 41 0.00 0.00 0.00 0
105
+ 42 0.00 0.00 0.00 4
106
+ 43 0.10 1.00 0.18 47
107
+ 44 0.00 0.00 0.00 1
108
+ 45 0.50 0.12 0.20 8
109
+ 46 0.00 0.00 0.00 0
110
+ 47 0.00 0.00 0.00 2
111
+ 48 0.00 0.00 0.00 3
112
+ 49 0.00 0.00 0.00 0
113
+
114
+ micro avg 0.15 0.43 0.22 307
115
+ macro avg 0.07 0.13 0.07 307
116
+ weighted avg 0.14 0.43 0.17 307
117
+ samples avg 0.09 0.16 0.11 307
118
+
119
+ > epoch: 2
120
+ precision recall f1-score support
121
+
122
+ 0 0.00 0.00 0.00 6
123
+ 1 0.00 0.00 0.00 1
124
+ 2 0.00 0.00 0.00 2
125
+ 3 0.12 0.46 0.19 13
126
+ 4 0.00 0.00 0.00 2
127
+ 5 0.00 0.00 0.00 8
128
+ 6 0.17 0.10 0.12 10
129
+ 7 0.00 0.00 0.00 2
130
+ 8 0.33 0.86 0.48 7
131
+ 9 0.16 0.38 0.22 8
132
+ 10 0.20 1.00 0.33 1
133
+ 11 0.26 1.00 0.42 25
134
+ 12 0.00 0.00 0.00 2
135
+ 13 0.25 0.44 0.32 18
136
+ 14 0.00 0.00 0.00 3
137
+ 15 0.14 0.67 0.24 3
138
+ 16 0.00 0.00 0.00 5
139
+ 17 0.13 0.79 0.23 14
140
+ 18 0.00 0.00 0.00 4
141
+ 19 0.00 0.00 0.00 1
142
+ 20 0.00 0.00 0.00 1
143
+ 21 0.00 0.00 0.00 1
144
+ 22 0.75 0.75 0.75 4
145
+ 23 0.00 0.00 0.00 1
146
+ 24 0.00 0.00 0.00 2
147
+ 25 0.16 1.00 0.28 10
148
+ 26 0.00 0.00 0.00 7
149
+ 27 0.50 0.38 0.43 8
150
+ 28 0.00 0.00 0.00 8
151
+ 29 0.00 0.00 0.00 1
152
+ 30 0.00 0.00 0.00 4
153
+ 31 0.00 0.00 0.00 2
154
+ 32 0.79 0.84 0.82 32
155
+ 33 0.00 0.00 0.00 4
156
+ 34 0.00 0.00 0.00 3
157
+ 35 1.00 0.86 0.92 7
158
+ 36 0.00 0.00 0.00 0
159
+ 37 0.00 0.00 0.00 2
160
+ 38 0.00 0.00 0.00 0
161
+ 39 0.00 0.00 0.00 4
162
+ 40 0.22 0.33 0.27 6
163
+ 41 0.00 0.00 0.00 0
164
+ 42 0.00 0.00 0.00 4
165
+ 43 0.25 0.98 0.40 47
166
+ 44 0.00 0.00 0.00 1
167
+ 45 0.25 0.75 0.38 8
168
+ 46 0.00 0.00 0.00 0
169
+ 47 0.00 0.00 0.00 2
170
+ 48 0.00 0.00 0.00 3
171
+ 49 0.00 0.00 0.00 0
172
+
173
+ micro avg 0.24 0.54 0.33 307
174
+ macro avg 0.11 0.23 0.14 307
175
+ weighted avg 0.25 0.54 0.31 307
176
+ samples avg 0.12 0.20 0.14 307
177
+
178
+ > epoch: 3
179
+ precision recall f1-score support
180
+
181
+ 0 0.10 0.83 0.18 6
182
+ 1 0.00 0.00 0.00 1
183
+ 2 0.00 0.00 0.00 2
184
+ 3 0.17 1.00 0.29 13
185
+ 4 0.22 1.00 0.36 2
186
+ 5 0.17 0.25 0.20 8
187
+ 6 0.11 0.30 0.16 10
188
+ 7 0.00 0.00 0.00 2
189
+ 8 0.16 0.86 0.27 7
190
+ 9 0.15 0.88 0.25 8
191
+ 10 0.00 0.00 0.00 1
192
+ 11 0.30 0.88 0.45 25
193
+ 12 0.00 0.00 0.00 2
194
+ 13 0.14 0.61 0.23 18
195
+ 14 0.00 0.00 0.00 3
196
+ 15 0.08 0.67 0.14 3
197
+ 16 0.00 0.00 0.00 5
198
+ 17 0.16 0.86 0.27 14
199
+ 18 0.00 0.00 0.00 4
200
+ 19 0.00 0.00 0.00 1
201
+ 20 0.07 1.00 0.12 1
202
+ 21 0.12 1.00 0.22 1
203
+ 22 0.19 0.75 0.30 4
204
+ 23 0.00 0.00 0.00 1
205
+ 24 0.00 0.00 0.00 2
206
+ 25 0.11 1.00 0.19 10
207
+ 26 0.00 0.00 0.00 7
208
+ 27 0.38 1.00 0.55 8
209
+ 28 0.00 0.00 0.00 8
210
+ 29 0.00 0.00 0.00 1
211
+ 30 0.14 0.50 0.22 4
212
+ 31 0.00 0.00 0.00 2
213
+ 32 0.81 0.94 0.87 32
214
+ 33 0.00 0.00 0.00 4
215
+ 34 0.06 0.33 0.11 3
216
+ 35 0.60 0.86 0.71 7
217
+ 36 0.00 0.00 0.00 0
218
+ 37 0.00 0.00 0.00 2
219
+ 38 0.00 0.00 0.00 0
220
+ 39 0.00 0.00 0.00 4
221
+ 40 0.12 0.50 0.19 6
222
+ 41 0.00 0.00 0.00 0
223
+ 42 0.08 0.25 0.12 4
224
+ 43 0.39 1.00 0.56 47
225
+ 44 0.00 0.00 0.00 1
226
+ 45 0.13 1.00 0.23 8
227
+ 46 0.00 0.00 0.00 0
228
+ 47 0.00 0.00 0.00 2
229
+ 48 0.20 0.67 0.31 3
230
+ 49 0.00 0.00 0.00 0
231
+
232
+ micro avg 0.20 0.68 0.31 307
233
+ macro avg 0.10 0.38 0.15 307
234
+ weighted avg 0.25 0.68 0.34 307
235
+ samples avg 0.16 0.24 0.17 307
236
+
237
+ > epoch: 4
238
+ precision recall f1-score support
239
+
240
+ 0 0.19 0.83 0.30 6
241
+ 1 1.00 1.00 1.00 1
242
+ 2 0.00 0.00 0.00 2
243
+ 3 0.19 0.69 0.30 13
244
+ 4 0.25 0.50 0.33 2
245
+ 5 0.18 0.25 0.21 8
246
+ 6 0.12 0.50 0.19 10
247
+ 7 0.67 1.00 0.80 2
248
+ 8 0.30 1.00 0.47 7
249
+ 9 0.30 0.75 0.43 8
250
+ 10 0.00 0.00 0.00 1
251
+ 11 0.29 0.92 0.44 25
252
+ 12 0.00 0.00 0.00 2
253
+ 13 0.25 0.56 0.34 18
254
+ 14 0.00 0.00 0.00 3
255
+ 15 0.11 0.67 0.19 3
256
+ 16 1.00 0.20 0.33 5
257
+ 17 0.14 0.86 0.25 14
258
+ 18 0.00 0.00 0.00 4
259
+ 19 0.00 0.00 0.00 1
260
+ 20 0.11 1.00 0.20 1
261
+ 21 0.00 0.00 0.00 1
262
+ 22 0.60 0.75 0.67 4
263
+ 23 0.00 0.00 0.00 1
264
+ 24 0.00 0.00 0.00 2
265
+ 25 0.20 0.90 0.32 10
266
+ 26 0.83 0.71 0.77 7
267
+ 27 0.36 1.00 0.53 8
268
+ 28 0.00 0.00 0.00 8
269
+ 29 0.00 0.00 0.00 1
270
+ 30 0.10 0.75 0.18 4
271
+ 31 0.00 0.00 0.00 2
272
+ 32 0.85 0.88 0.86 32
273
+ 33 0.00 0.00 0.00 4
274
+ 34 0.05 0.33 0.09 3
275
+ 35 0.50 0.86 0.63 7
276
+ 36 0.00 0.00 0.00 0
277
+ 37 0.00 0.00 0.00 2
278
+ 38 0.00 0.00 0.00 0
279
+ 39 0.33 0.25 0.29 4
280
+ 40 0.18 0.50 0.26 6
281
+ 41 0.00 0.00 0.00 0
282
+ 42 0.12 0.25 0.17 4
283
+ 43 0.36 0.96 0.53 47
284
+ 44 0.00 0.00 0.00 1
285
+ 45 0.22 0.88 0.35 8
286
+ 46 0.00 0.00 0.00 0
287
+ 47 0.17 0.50 0.25 2
288
+ 48 0.50 0.67 0.57 3
289
+ 49 0.00 0.00 0.00 0
290
+
291
+ micro avg 0.26 0.68 0.38 307
292
+ macro avg 0.21 0.42 0.25 307
293
+ weighted avg 0.33 0.68 0.40 307
294
+ samples avg 0.16 0.25 0.18 307
295
+
296
+ > epoch: 5
297
+ precision recall f1-score support
298
+
299
+ 0 0.17 0.83 0.29 6
300
+ 1 1.00 1.00 1.00 1
301
+ 2 0.00 0.00 0.00 2
302
+ 3 0.21 0.85 0.33 13
303
+ 4 0.67 1.00 0.80 2
304
+ 5 0.30 0.38 0.33 8
305
+ 6 0.13 0.30 0.18 10
306
+ 7 0.40 1.00 0.57 2
307
+ 8 0.29 1.00 0.45 7
308
+ 9 0.26 0.62 0.37 8
309
+ 10 1.00 1.00 1.00 1
310
+ 11 0.33 0.88 0.48 25
311
+ 12 0.00 0.00 0.00 2
312
+ 13 0.23 0.50 0.32 18
313
+ 14 0.00 0.00 0.00 3
314
+ 15 0.13 0.67 0.22 3
315
+ 16 0.50 0.20 0.29 5
316
+ 17 0.23 0.86 0.36 14
317
+ 18 0.00 0.00 0.00 4
318
+ 19 0.00 0.00 0.00 1
319
+ 20 0.07 1.00 0.13 1
320
+ 21 0.20 1.00 0.33 1
321
+ 22 0.50 1.00 0.67 4
322
+ 23 0.00 0.00 0.00 1
323
+ 24 0.00 0.00 0.00 2
324
+ 25 0.20 0.90 0.33 10
325
+ 26 0.62 0.71 0.67 7
326
+ 27 0.29 1.00 0.44 8
327
+ 28 0.20 0.12 0.15 8
328
+ 29 0.00 0.00 0.00 1
329
+ 30 0.17 0.75 0.27 4
330
+ 31 0.00 0.00 0.00 2
331
+ 32 0.73 0.84 0.78 32
332
+ 33 0.25 0.25 0.25 4
333
+ 34 0.05 0.33 0.08 3
334
+ 35 0.50 0.86 0.63 7
335
+ 36 0.00 0.00 0.00 0
336
+ 37 0.00 0.00 0.00 2
337
+ 38 0.00 0.00 0.00 0
338
+ 39 0.25 0.25 0.25 4
339
+ 40 0.15 0.50 0.23 6
340
+ 41 0.00 0.00 0.00 0
341
+ 42 0.00 0.00 0.00 4
342
+ 43 0.53 0.87 0.66 47
343
+ 44 0.00 0.00 0.00 1
344
+ 45 0.27 0.75 0.40 8
345
+ 46 0.00 0.00 0.00 0
346
+ 47 0.10 0.50 0.17 2
347
+ 48 0.50 0.67 0.57 3
348
+ 49 0.00 0.00 0.00 0
349
+
350
+ micro avg 0.29 0.67 0.40 307
351
+ macro avg 0.23 0.47 0.28 307
352
+ weighted avg 0.34 0.67 0.43 307
353
+ samples avg 0.17 0.24 0.19 307
354
+
355
+ > epoch: 6
356
+ precision recall f1-score support
357
+
358
+ 0 0.18 0.83 0.29 6
359
+ 1 1.00 1.00 1.00 1
360
+ 2 0.00 0.00 0.00 2
361
+ 3 0.23 0.85 0.37 13
362
+ 4 0.33 0.50 0.40 2
363
+ 5 0.33 0.38 0.35 8
364
+ 6 0.20 0.40 0.27 10
365
+ 7 0.67 1.00 0.80 2
366
+ 8 0.32 1.00 0.48 7
367
+ 9 0.35 0.88 0.50 8
368
+ 10 0.00 0.00 0.00 1
369
+ 11 0.48 0.88 0.62 25
370
+ 12 0.00 0.00 0.00 2
371
+ 13 0.27 0.50 0.35 18
372
+ 14 0.00 0.00 0.00 3
373
+ 15 0.22 0.67 0.33 3
374
+ 16 1.00 0.20 0.33 5
375
+ 17 0.22 0.86 0.35 14
376
+ 18 0.00 0.00 0.00 4
377
+ 19 0.00 0.00 0.00 1
378
+ 20 0.08 1.00 0.15 1
379
+ 21 0.20 1.00 0.33 1
380
+ 22 0.57 1.00 0.73 4
381
+ 23 0.00 0.00 0.00 1
382
+ 24 0.00 0.00 0.00 2
383
+ 25 0.21 0.90 0.34 10
384
+ 26 1.00 0.57 0.73 7
385
+ 27 0.44 0.88 0.58 8
386
+ 28 0.00 0.00 0.00 8
387
+ 29 0.00 0.00 0.00 1
388
+ 30 0.20 0.75 0.32 4
389
+ 31 0.00 0.00 0.00 2
390
+ 32 0.85 0.88 0.86 32
391
+ 33 0.50 0.25 0.33 4
392
+ 34 0.05 0.33 0.09 3
393
+ 35 0.55 0.86 0.67 7
394
+ 36 0.00 0.00 0.00 0
395
+ 37 0.00 0.00 0.00 2
396
+ 38 0.00 0.00 0.00 0
397
+ 39 0.33 0.25 0.29 4
398
+ 40 0.21 0.50 0.30 6
399
+ 41 0.00 0.00 0.00 0
400
+ 42 0.00 0.00 0.00 4
401
+ 43 0.45 0.96 0.62 47
402
+ 44 0.00 0.00 0.00 1
403
+ 45 0.29 0.88 0.44 8
404
+ 46 0.00 0.00 0.00 0
405
+ 47 0.50 0.50 0.50 2
406
+ 48 0.75 1.00 0.86 3
407
+ 49 0.00 0.00 0.00 0
408
+
409
+ micro avg 0.33 0.69 0.45 307
410
+ macro avg 0.26 0.45 0.29 307
411
+ weighted avg 0.39 0.69 0.47 307
412
+ samples avg 0.19 0.25 0.20 307
413
+
414
+ > epoch: 7
415
+ precision recall f1-score support
416
+
417
+ 0 0.22 0.67 0.33 6
418
+ 1 1.00 1.00 1.00 1
419
+ 2 0.00 0.00 0.00 2
420
+ 3 0.21 0.54 0.30 13
421
+ 4 0.20 0.50 0.29 2
422
+ 5 0.36 0.62 0.45 8
423
+ 6 0.23 0.60 0.33 10
424
+ 7 0.22 1.00 0.36 2
425
+ 8 0.32 0.86 0.46 7
426
+ 9 0.44 0.88 0.58 8
427
+ 10 0.00 0.00 0.00 1
428
+ 11 0.44 0.84 0.58 25
429
+ 12 0.00 0.00 0.00 2
430
+ 13 0.34 0.56 0.43 18
431
+ 14 0.00 0.00 0.00 3
432
+ 15 0.17 0.67 0.27 3
433
+ 16 1.00 0.20 0.33 5
434
+ 17 0.22 0.86 0.35 14
435
+ 18 0.50 0.25 0.33 4
436
+ 19 0.00 0.00 0.00 1
437
+ 20 0.09 1.00 0.17 1
438
+ 21 0.20 1.00 0.33 1
439
+ 22 0.67 1.00 0.80 4
440
+ 23 0.00 0.00 0.00 1
441
+ 24 0.00 0.00 0.00 2
442
+ 25 0.32 0.70 0.44 10
443
+ 26 0.60 0.86 0.71 7
444
+ 27 0.44 1.00 0.62 8
445
+ 28 0.33 0.25 0.29 8
446
+ 29 0.00 0.00 0.00 1
447
+ 30 0.15 0.75 0.25 4
448
+ 31 0.00 0.00 0.00 2
449
+ 32 0.88 0.91 0.89 32
450
+ 33 0.40 0.50 0.44 4
451
+ 34 0.06 0.33 0.10 3
452
+ 35 0.50 0.86 0.63 7
453
+ 36 0.00 0.00 0.00 0
454
+ 37 0.40 1.00 0.57 2
455
+ 38 0.00 0.00 0.00 0
456
+ 39 0.33 0.25 0.29 4
457
+ 40 0.13 0.33 0.19 6
458
+ 41 0.00 0.00 0.00 0
459
+ 42 0.00 0.00 0.00 4
460
+ 43 0.59 0.81 0.68 47
461
+ 44 0.00 0.00 0.00 1
462
+ 45 0.35 0.75 0.48 8
463
+ 46 0.00 0.00 0.00 0
464
+ 47 0.14 0.50 0.22 2
465
+ 48 0.75 1.00 0.86 3
466
+ 49 0.00 0.00 0.00 0
467
+
468
+ micro avg 0.34 0.68 0.46 307
469
+ macro avg 0.26 0.48 0.31 307
470
+ weighted avg 0.43 0.68 0.50 307
471
+ samples avg 0.18 0.25 0.19 307
472
+
473
+ > epoch: 8
474
+ precision recall f1-score support
475
+
476
+ 0 0.19 0.50 0.27 6
477
+ 1 1.00 1.00 1.00 1
478
+ 2 0.00 0.00 0.00 2
479
+ 3 0.22 0.62 0.33 13
480
+ 4 0.00 0.00 0.00 2
481
+ 5 0.33 0.62 0.43 8
482
+ 6 0.21 0.30 0.25 10
483
+ 7 0.67 1.00 0.80 2
484
+ 8 0.55 0.86 0.67 7
485
+ 9 0.42 0.62 0.50 8
486
+ 10 0.00 0.00 0.00 1
487
+ 11 0.49 0.92 0.64 25
488
+ 12 0.00 0.00 0.00 2
489
+ 13 0.42 0.56 0.48 18
490
+ 14 0.00 0.00 0.00 3
491
+ 15 0.38 1.00 0.55 3
492
+ 16 1.00 0.20 0.33 5
493
+ 17 0.29 0.86 0.44 14
494
+ 18 0.50 0.25 0.33 4
495
+ 19 0.00 0.00 0.00 1
496
+ 20 0.17 1.00 0.29 1
497
+ 21 0.00 0.00 0.00 1
498
+ 22 0.80 1.00 0.89 4
499
+ 23 0.00 0.00 0.00 1
500
+ 24 0.00 0.00 0.00 2
501
+ 25 0.38 0.90 0.53 10
502
+ 26 0.71 0.71 0.71 7
503
+ 27 0.42 1.00 0.59 8
504
+ 28 0.67 0.50 0.57 8
505
+ 29 0.00 0.00 0.00 1
506
+ 30 0.21 0.75 0.33 4
507
+ 31 0.00 0.00 0.00 2
508
+ 32 0.74 0.91 0.82 32
509
+ 33 0.50 0.75 0.60 4
510
+ 34 0.12 0.33 0.18 3
511
+ 35 0.60 0.86 0.71 7
512
+ 36 0.00 0.00 0.00 0
513
+ 37 0.00 0.00 0.00 2
514
+ 38 0.00 0.00 0.00 0
515
+ 39 0.50 0.25 0.33 4
516
+ 40 0.18 0.33 0.24 6
517
+ 41 0.00 0.00 0.00 0
518
+ 42 0.00 0.00 0.00 4
519
+ 43 0.52 0.83 0.64 47
520
+ 44 0.00 0.00 0.00 1
521
+ 45 0.40 0.75 0.52 8
522
+ 46 0.00 0.00 0.00 0
523
+ 47 0.25 0.50 0.33 2
524
+ 48 0.60 1.00 0.75 3
525
+ 49 0.00 0.00 0.00 0
526
+
527
+ micro avg 0.41 0.68 0.51 307
528
+ macro avg 0.29 0.43 0.32 307
529
+ weighted avg 0.44 0.68 0.51 307
530
+ samples avg 0.20 0.25 0.21 307
531
+
532
+ > epoch: 9
533
+ precision recall f1-score support
534
+
535
+ 0 0.27 0.67 0.38 6
536
+ 1 1.00 1.00 1.00 1
537
+ 2 0.00 0.00 0.00 2
538
+ 3 0.32 0.77 0.45 13
539
+ 4 0.00 0.00 0.00 2
540
+ 5 0.33 0.50 0.40 8
541
+ 6 0.24 0.40 0.30 10
542
+ 7 0.67 1.00 0.80 2
543
+ 8 0.55 0.86 0.67 7
544
+ 9 0.50 0.62 0.56 8
545
+ 10 0.00 0.00 0.00 1
546
+ 11 0.43 0.92 0.59 25
547
+ 12 0.00 0.00 0.00 2
548
+ 13 0.34 0.56 0.43 18
549
+ 14 0.00 0.00 0.00 3
550
+ 15 0.38 1.00 0.55 3
551
+ 16 1.00 0.40 0.57 5
552
+ 17 0.25 0.86 0.39 14
553
+ 18 0.50 0.25 0.33 4
554
+ 19 0.00 0.00 0.00 1
555
+ 20 0.17 1.00 0.29 1
556
+ 21 0.00 0.00 0.00 1
557
+ 22 1.00 1.00 1.00 4
558
+ 23 0.00 0.00 0.00 1
559
+ 24 0.00 0.00 0.00 2
560
+ 25 0.33 0.70 0.45 10
561
+ 26 0.83 0.71 0.77 7
562
+ 27 0.40 1.00 0.57 8
563
+ 28 0.80 0.50 0.62 8
564
+ 29 0.00 0.00 0.00 1
565
+ 30 0.16 0.75 0.26 4
566
+ 31 0.00 0.00 0.00 2
567
+ 32 0.77 0.84 0.81 32
568
+ 33 0.50 0.75 0.60 4
569
+ 34 0.25 0.33 0.29 3
570
+ 35 0.64 1.00 0.78 7
571
+ 36 0.00 0.00 0.00 0
572
+ 37 1.00 0.50 0.67 2
573
+ 38 0.00 0.00 0.00 0
574
+ 39 0.33 0.25 0.29 4
575
+ 40 0.17 0.33 0.22 6
576
+ 41 0.00 0.00 0.00 0
577
+ 42 0.00 0.00 0.00 4
578
+ 43 0.51 0.94 0.66 47
579
+ 44 0.00 0.00 0.00 1
580
+ 45 0.38 0.75 0.50 8
581
+ 46 0.00 0.00 0.00 0
582
+ 47 0.11 0.50 0.18 2
583
+ 48 0.67 0.67 0.67 3
584
+ 49 0.00 0.00 0.00 0
585
+
586
+ micro avg 0.41 0.70 0.52 307
587
+ macro avg 0.32 0.45 0.34 307
588
+ weighted avg 0.45 0.70 0.52 307
589
+ samples avg 0.20 0.25 0.22 307
590
+
591
+ > epoch: 10
592
+ precision recall f1-score support
593
+
594
+ 0 0.27 0.67 0.38 6
595
+ 1 1.00 1.00 1.00 1
596
+ 2 0.00 0.00 0.00 2
597
+ 3 0.31 0.85 0.45 13
598
+ 4 0.00 0.00 0.00 2
599
+ 5 0.43 0.75 0.55 8
600
+ 6 0.26 0.50 0.34 10
601
+ 7 0.50 1.00 0.67 2
602
+ 8 0.47 1.00 0.64 7
603
+ 9 0.60 0.75 0.67 8
604
+ 10 0.00 0.00 0.00 1
605
+ 11 0.40 0.92 0.55 25
606
+ 12 0.00 0.00 0.00 2
607
+ 13 0.38 0.61 0.47 18
608
+ 14 0.00 0.00 0.00 3
609
+ 15 0.27 1.00 0.43 3
610
+ 16 1.00 0.20 0.33 5
611
+ 17 0.28 0.86 0.42 14
612
+ 18 0.50 0.25 0.33 4
613
+ 19 0.00 0.00 0.00 1
614
+ 20 0.17 1.00 0.29 1
615
+ 21 0.33 1.00 0.50 1
616
+ 22 0.80 1.00 0.89 4
617
+ 23 0.00 0.00 0.00 1
618
+ 24 0.00 0.00 0.00 2
619
+ 25 0.32 0.80 0.46 10
620
+ 26 0.71 0.71 0.71 7
621
+ 27 0.36 1.00 0.53 8
622
+ 28 0.80 0.50 0.62 8
623
+ 29 0.00 0.00 0.00 1
624
+ 30 0.18 0.75 0.29 4
625
+ 31 0.00 0.00 0.00 2
626
+ 32 0.82 0.84 0.83 32
627
+ 33 0.57 1.00 0.73 4
628
+ 34 0.20 0.67 0.31 3
629
+ 35 0.58 1.00 0.74 7
630
+ 36 0.00 0.00 0.00 0
631
+ 37 0.33 0.50 0.40 2
632
+ 38 0.00 0.00 0.00 0
633
+ 39 0.50 0.50 0.50 4
634
+ 40 0.22 0.33 0.27 6
635
+ 41 0.00 0.00 0.00 0
636
+ 42 0.00 0.00 0.00 4
637
+ 43 0.55 0.87 0.67 47
638
+ 44 0.00 0.00 0.00 1
639
+ 45 0.40 0.75 0.52 8
640
+ 46 0.00 0.00 0.00 0
641
+ 47 0.10 0.50 0.17 2
642
+ 48 0.67 0.67 0.67 3
643
+ 49 0.00 0.00 0.00 0
644
+
645
+ micro avg 0.40 0.72 0.52 307
646
+ macro avg 0.31 0.49 0.35 307
647
+ weighted avg 0.46 0.72 0.53 307
648
+ samples avg 0.21 0.26 0.22 307
649
+
650
+ > epoch: 11
651
+ precision recall f1-score support
652
+
653
+ 0 0.36 0.83 0.50 6
654
+ 1 1.00 1.00 1.00 1
655
+ 2 0.00 0.00 0.00 2
656
+ 3 0.28 0.69 0.40 13
657
+ 4 0.00 0.00 0.00 2
658
+ 5 0.50 0.50 0.50 8
659
+ 6 0.25 0.50 0.33 10
660
+ 7 0.67 1.00 0.80 2
661
+ 8 0.60 0.86 0.71 7
662
+ 9 0.50 0.62 0.56 8
663
+ 10 0.00 0.00 0.00 1
664
+ 11 0.50 0.88 0.64 25
665
+ 12 0.00 0.00 0.00 2
666
+ 13 0.41 0.50 0.45 18
667
+ 14 0.00 0.00 0.00 3
668
+ 15 0.40 0.67 0.50 3
669
+ 16 1.00 0.40 0.57 5
670
+ 17 0.31 0.86 0.45 14
671
+ 18 0.50 0.25 0.33 4
672
+ 19 0.00 0.00 0.00 1
673
+ 20 0.17 1.00 0.29 1
674
+ 21 0.00 0.00 0.00 1
675
+ 22 1.00 1.00 1.00 4
676
+ 23 0.00 0.00 0.00 1
677
+ 24 0.00 0.00 0.00 2
678
+ 25 0.35 0.80 0.48 10
679
+ 26 1.00 0.71 0.83 7
680
+ 27 0.53 1.00 0.70 8
681
+ 28 0.67 0.50 0.57 8
682
+ 29 0.00 0.00 0.00 1
683
+ 30 0.19 0.75 0.30 4
684
+ 31 0.00 0.00 0.00 2
685
+ 32 0.85 0.88 0.86 32
686
+ 33 0.50 0.75 0.60 4
687
+ 34 0.33 0.33 0.33 3
688
+ 35 0.64 1.00 0.78 7
689
+ 36 0.00 0.00 0.00 0
690
+ 37 1.00 0.50 0.67 2
691
+ 38 0.00 0.00 0.00 0
692
+ 39 0.33 0.25 0.29 4
693
+ 40 0.21 0.50 0.30 6
694
+ 41 0.00 0.00 0.00 0
695
+ 42 0.00 0.00 0.00 4
696
+ 43 0.60 0.81 0.69 47
697
+ 44 0.00 0.00 0.00 1
698
+ 45 0.32 0.75 0.44 8
699
+ 46 0.00 0.00 0.00 0
700
+ 47 0.20 0.50 0.29 2
701
+ 48 0.50 0.33 0.40 3
702
+ 49 0.00 0.00 0.00 0
703
+
704
+ micro avg 0.45 0.68 0.54 307
705
+ macro avg 0.33 0.44 0.35 307
706
+ weighted avg 0.50 0.68 0.55 307
707
+ samples avg 0.21 0.25 0.22 307
708
+
709
+ > epoch: 12
710
+ precision recall f1-score support
711
+
712
+ 0 0.24 0.83 0.37 6
713
+ 1 1.00 1.00 1.00 1
714
+ 2 0.00 0.00 0.00 2
715
+ 3 0.32 0.69 0.44 13
716
+ 4 0.00 0.00 0.00 2
717
+ 5 0.67 0.75 0.71 8
718
+ 6 0.45 0.50 0.48 10
719
+ 7 0.67 1.00 0.80 2
720
+ 8 0.50 1.00 0.67 7
721
+ 9 0.55 0.75 0.63 8
722
+ 10 1.00 1.00 1.00 1
723
+ 11 0.48 0.88 0.62 25
724
+ 12 0.00 0.00 0.00 2
725
+ 13 0.46 0.61 0.52 18
726
+ 14 0.00 0.00 0.00 3
727
+ 15 0.25 1.00 0.40 3
728
+ 16 1.00 0.40 0.57 5
729
+ 17 0.43 0.86 0.57 14
730
+ 18 0.33 0.25 0.29 4
731
+ 19 0.00 0.00 0.00 1
732
+ 20 0.17 1.00 0.29 1
733
+ 21 0.00 0.00 0.00 1
734
+ 22 0.80 1.00 0.89 4
735
+ 23 0.00 0.00 0.00 1
736
+ 24 0.00 0.00 0.00 2
737
+ 25 0.40 0.80 0.53 10
738
+ 26 1.00 0.71 0.83 7
739
+ 27 0.43 0.75 0.55 8
740
+ 28 0.83 0.62 0.71 8
741
+ 29 0.00 0.00 0.00 1
742
+ 30 0.33 0.75 0.46 4
743
+ 31 0.00 0.00 0.00 2
744
+ 32 0.82 0.88 0.85 32
745
+ 33 0.60 0.75 0.67 4
746
+ 34 0.29 0.67 0.40 3
747
+ 35 1.00 1.00 1.00 7
748
+ 36 0.00 0.00 0.00 0
749
+ 37 1.00 0.50 0.67 2
750
+ 38 0.00 0.00 0.00 0
751
+ 39 0.50 0.50 0.50 4
752
+ 40 0.25 0.50 0.33 6
753
+ 41 0.00 0.00 0.00 0
754
+ 42 0.00 0.00 0.00 4
755
+ 43 0.60 0.83 0.70 47
756
+ 44 0.00 0.00 0.00 1
757
+ 45 0.43 0.75 0.55 8
758
+ 46 0.00 0.00 0.00 0
759
+ 47 0.17 0.50 0.25 2
760
+ 48 0.67 0.67 0.67 3
761
+ 49 0.00 0.00 0.00 0
762
+
763
+ micro avg 0.48 0.71 0.57 307
764
+ macro avg 0.37 0.49 0.40 307
765
+ weighted avg 0.53 0.71 0.59 307
766
+ samples avg 0.23 0.26 0.23 307
767
+
768
+ > epoch: 13
769
+ precision recall f1-score support
770
+
771
+ 0 0.29 0.67 0.40 6
772
+ 1 1.00 1.00 1.00 1
773
+ 2 0.00 0.00 0.00 2
774
+ 3 0.26 0.77 0.38 13
775
+ 4 0.00 0.00 0.00 2
776
+ 5 0.57 0.50 0.53 8
777
+ 6 0.43 0.30 0.35 10
778
+ 7 0.67 1.00 0.80 2
779
+ 8 0.55 0.86 0.67 7
780
+ 9 0.54 0.88 0.67 8
781
+ 10 0.00 0.00 0.00 1
782
+ 11 0.40 0.92 0.55 25
783
+ 12 0.00 0.00 0.00 2
784
+ 13 0.40 0.56 0.47 18
785
+ 14 0.00 0.00 0.00 3
786
+ 15 0.30 1.00 0.46 3
787
+ 16 1.00 0.40 0.57 5
788
+ 17 0.43 0.86 0.57 14
789
+ 18 0.50 0.25 0.33 4
790
+ 19 0.00 0.00 0.00 1
791
+ 20 0.11 1.00 0.20 1
792
+ 21 0.33 1.00 0.50 1
793
+ 22 0.80 1.00 0.89 4
794
+ 23 0.00 0.00 0.00 1
795
+ 24 0.00 0.00 0.00 2
796
+ 25 0.38 0.80 0.52 10
797
+ 26 1.00 0.71 0.83 7
798
+ 27 0.50 0.75 0.60 8
799
+ 28 0.67 0.25 0.36 8
800
+ 29 0.00 0.00 0.00 1
801
+ 30 0.27 0.75 0.40 4
802
+ 31 0.00 0.00 0.00 2
803
+ 32 0.90 0.84 0.87 32
804
+ 33 0.50 1.00 0.67 4
805
+ 34 0.33 0.67 0.44 3
806
+ 35 1.00 0.86 0.92 7
807
+ 36 0.00 0.00 0.00 0
808
+ 37 1.00 1.00 1.00 2
809
+ 38 0.00 0.00 0.00 0
810
+ 39 0.50 0.50 0.50 4
811
+ 40 0.21 0.50 0.30 6
812
+ 41 0.00 0.00 0.00 0
813
+ 42 0.00 0.00 0.00 4
814
+ 43 0.69 0.77 0.73 47
815
+ 44 0.00 0.00 0.00 1
816
+ 45 0.41 0.88 0.56 8
817
+ 46 0.00 0.00 0.00 0
818
+ 47 0.33 0.50 0.40 2
819
+ 48 0.50 0.33 0.40 3
820
+ 49 0.00 0.00 0.00 0
821
+
822
+ micro avg 0.47 0.68 0.56 307
823
+ macro avg 0.36 0.48 0.38 307
824
+ weighted avg 0.53 0.68 0.56 307
825
+ samples avg 0.21 0.25 0.21 307
826
+
827
+ > epoch: 14
828
+ precision recall f1-score support
829
+
830
+ 0 0.27 0.67 0.38 6
831
+ 1 1.00 1.00 1.00 1
832
+ 2 0.00 0.00 0.00 2
833
+ 3 0.32 0.62 0.42 13
834
+ 4 0.50 0.50 0.50 2
835
+ 5 0.57 0.50 0.53 8
836
+ 6 0.55 0.60 0.57 10
837
+ 7 0.67 1.00 0.80 2
838
+ 8 0.54 1.00 0.70 7
839
+ 9 0.60 0.75 0.67 8
840
+ 10 0.33 1.00 0.50 1
841
+ 11 0.45 0.92 0.61 25
842
+ 12 0.00 0.00 0.00 2
843
+ 13 0.42 0.44 0.43 18
844
+ 14 0.00 0.00 0.00 3
845
+ 15 0.30 1.00 0.46 3
846
+ 16 1.00 0.40 0.57 5
847
+ 17 0.30 0.86 0.44 14
848
+ 18 0.50 0.25 0.33 4
849
+ 19 0.00 0.00 0.00 1
850
+ 20 0.17 1.00 0.29 1
851
+ 21 0.00 0.00 0.00 1
852
+ 22 1.00 1.00 1.00 4
853
+ 23 0.00 0.00 0.00 1
854
+ 24 0.00 0.00 0.00 2
855
+ 25 0.47 0.80 0.59 10
856
+ 26 0.83 0.71 0.77 7
857
+ 27 0.58 0.88 0.70 8
858
+ 28 0.75 0.75 0.75 8
859
+ 29 0.00 0.00 0.00 1
860
+ 30 0.16 0.75 0.26 4
861
+ 31 0.00 0.00 0.00 2
862
+ 32 0.84 0.84 0.84 32
863
+ 33 0.50 0.75 0.60 4
864
+ 34 0.25 0.67 0.36 3
865
+ 35 1.00 0.86 0.92 7
866
+ 36 0.00 0.00 0.00 0
867
+ 37 1.00 1.00 1.00 2
868
+ 38 0.00 0.00 0.00 0
869
+ 39 0.50 0.50 0.50 4
870
+ 40 0.27 0.67 0.38 6
871
+ 41 0.00 0.00 0.00 0
872
+ 42 0.00 0.00 0.00 4
873
+ 43 0.59 0.81 0.68 47
874
+ 44 0.00 0.00 0.00 1
875
+ 45 0.50 0.75 0.60 8
876
+ 46 0.00 0.00 0.00 0
877
+ 47 0.20 0.50 0.29 2
878
+ 48 0.75 1.00 0.86 3
879
+ 49 0.00 0.00 0.00 0
880
+
881
+ micro avg 0.47 0.71 0.57 307
882
+ macro avg 0.37 0.51 0.41 307
883
+ weighted avg 0.53 0.71 0.58 307
884
+ samples avg 0.23 0.26 0.23 307
885
+
886
+ > epoch: 15
887
+ precision recall f1-score support
888
+
889
+ 0 0.33 0.67 0.44 6
890
+ 1 1.00 1.00 1.00 1
891
+ 2 0.33 0.50 0.40 2
892
+ 3 0.35 0.54 0.42 13
893
+ 4 0.00 0.00 0.00 2
894
+ 5 0.67 0.50 0.57 8
895
+ 6 0.50 0.40 0.44 10
896
+ 7 0.50 1.00 0.67 2
897
+ 8 0.54 1.00 0.70 7
898
+ 9 0.64 0.88 0.74 8
899
+ 10 1.00 1.00 1.00 1
900
+ 11 0.51 0.84 0.64 25
901
+ 12 0.00 0.00 0.00 2
902
+ 13 0.47 0.50 0.49 18
903
+ 14 0.00 0.00 0.00 3
904
+ 15 0.50 1.00 0.67 3
905
+ 16 1.00 0.40 0.57 5
906
+ 17 0.40 0.86 0.55 14
907
+ 18 0.33 0.25 0.29 4
908
+ 19 0.00 0.00 0.00 1
909
+ 20 0.17 1.00 0.29 1
910
+ 21 0.00 0.00 0.00 1
911
+ 22 0.80 1.00 0.89 4
912
+ 23 0.00 0.00 0.00 1
913
+ 24 0.00 0.00 0.00 2
914
+ 25 0.53 0.80 0.64 10
915
+ 26 0.86 0.86 0.86 7
916
+ 27 0.58 0.88 0.70 8
917
+ 28 0.71 0.62 0.67 8
918
+ 29 0.00 0.00 0.00 1
919
+ 30 0.27 0.75 0.40 4
920
+ 31 0.00 0.00 0.00 2
921
+ 32 0.90 0.84 0.87 32
922
+ 33 0.60 0.75 0.67 4
923
+ 34 0.17 0.33 0.22 3
924
+ 35 0.60 0.86 0.71 7
925
+ 36 0.00 0.00 0.00 0
926
+ 37 1.00 1.00 1.00 2
927
+ 38 0.00 0.00 0.00 0
928
+ 39 0.50 0.50 0.50 4
929
+ 40 0.25 0.67 0.36 6
930
+ 41 0.00 0.00 0.00 0
931
+ 42 0.00 0.00 0.00 4
932
+ 43 0.60 0.79 0.68 47
933
+ 44 0.00 0.00 0.00 1
934
+ 45 0.43 0.75 0.55 8
935
+ 46 0.00 0.00 0.00 0
936
+ 47 0.25 0.50 0.33 2
937
+ 48 0.67 0.67 0.67 3
938
+ 49 0.00 0.00 0.00 0
939
+
940
+ micro avg 0.50 0.69 0.58 307
941
+ macro avg 0.38 0.50 0.41 307
942
+ weighted avg 0.54 0.69 0.59 307
943
+ samples avg 0.22 0.25 0.22 307
944
+
945
+ > epoch: 16
946
+ precision recall f1-score support
947
+
948
+ 0 0.31 0.67 0.42 6
949
+ 1 1.00 1.00 1.00 1
950
+ 2 0.00 0.00 0.00 2
951
+ 3 0.27 0.62 0.37 13
952
+ 4 0.50 1.00 0.67 2
953
+ 5 0.50 0.50 0.50 8
954
+ 6 0.25 0.30 0.27 10
955
+ 7 0.67 1.00 0.80 2
956
+ 8 0.50 1.00 0.67 7
957
+ 9 0.54 0.88 0.67 8
958
+ 10 0.00 0.00 0.00 1
959
+ 11 0.44 0.92 0.60 25
960
+ 12 0.00 0.00 0.00 2
961
+ 13 0.35 0.61 0.45 18
962
+ 14 0.00 0.00 0.00 3
963
+ 15 0.23 1.00 0.38 3
964
+ 16 1.00 0.40 0.57 5
965
+ 17 0.44 0.86 0.59 14
966
+ 18 0.25 0.25 0.25 4
967
+ 19 0.00 0.00 0.00 1
968
+ 20 0.17 1.00 0.29 1
969
+ 21 0.20 1.00 0.33 1
970
+ 22 0.67 1.00 0.80 4
971
+ 23 0.00 0.00 0.00 1
972
+ 24 0.00 0.00 0.00 2
973
+ 25 0.53 0.90 0.67 10
974
+ 26 1.00 0.71 0.83 7
975
+ 27 0.42 1.00 0.59 8
976
+ 28 0.71 0.62 0.67 8
977
+ 29 0.00 0.00 0.00 1
978
+ 30 0.33 0.75 0.46 4
979
+ 31 0.00 0.00 0.00 2
980
+ 32 0.82 0.84 0.83 32
981
+ 33 0.67 1.00 0.80 4
982
+ 34 0.25 0.67 0.36 3
983
+ 35 0.88 1.00 0.93 7
984
+ 36 0.00 0.00 0.00 0
985
+ 37 1.00 1.00 1.00 2
986
+ 38 0.00 0.00 0.00 0
987
+ 39 0.50 0.50 0.50 4
988
+ 40 0.27 0.67 0.38 6
989
+ 41 0.00 0.00 0.00 0
990
+ 42 0.00 0.00 0.00 4
991
+ 43 0.56 0.87 0.68 47
992
+ 44 0.00 0.00 0.00 1
993
+ 45 0.38 0.75 0.50 8
994
+ 46 0.00 0.00 0.00 0
995
+ 47 0.33 0.50 0.40 2
996
+ 48 0.67 0.67 0.67 3
997
+ 49 0.00 0.00 0.00 0
998
+
999
+ micro avg 0.46 0.73 0.56 307
1000
+ macro avg 0.35 0.53 0.40 307
1001
+ weighted avg 0.50 0.73 0.57 307
1002
+ samples avg 0.23 0.26 0.24 307
1003
+
1004
+ > epoch: 17
1005
+ precision recall f1-score support
1006
+
1007
+ 0 0.50 0.83 0.62 6
1008
+ 1 1.00 1.00 1.00 1
1009
+ 2 0.00 0.00 0.00 2
1010
+ 3 0.41 0.54 0.47 13
1011
+ 4 0.33 0.50 0.40 2
1012
+ 5 0.50 0.50 0.50 8
1013
+ 6 0.50 0.50 0.50 10
1014
+ 7 0.67 1.00 0.80 2
1015
+ 8 0.55 0.86 0.67 7
1016
+ 9 0.70 0.88 0.78 8
1017
+ 10 0.00 0.00 0.00 1
1018
+ 11 0.53 0.84 0.65 25
1019
+ 12 0.00 0.00 0.00 2
1020
+ 13 0.44 0.61 0.51 18
1021
+ 14 0.00 0.00 0.00 3
1022
+ 15 0.25 1.00 0.40 3
1023
+ 16 1.00 0.60 0.75 5
1024
+ 17 0.44 0.86 0.59 14
1025
+ 18 0.50 0.25 0.33 4
1026
+ 19 0.00 0.00 0.00 1
1027
+ 20 0.20 1.00 0.33 1
1028
+ 21 0.00 0.00 0.00 1
1029
+ 22 1.00 1.00 1.00 4
1030
+ 23 0.00 0.00 0.00 1
1031
+ 24 0.00 0.00 0.00 2
1032
+ 25 0.58 0.70 0.64 10
1033
+ 26 1.00 0.71 0.83 7
1034
+ 27 0.46 0.75 0.57 8
1035
+ 28 0.60 0.38 0.46 8
1036
+ 29 0.00 0.00 0.00 1
1037
+ 30 0.27 0.75 0.40 4
1038
+ 31 0.00 0.00 0.00 2
1039
+ 32 0.84 0.84 0.84 32
1040
+ 33 0.67 1.00 0.80 4
1041
+ 34 0.67 0.67 0.67 3
1042
+ 35 1.00 1.00 1.00 7
1043
+ 36 0.00 0.00 0.00 0
1044
+ 37 1.00 1.00 1.00 2
1045
+ 38 0.00 0.00 0.00 0
1046
+ 39 0.50 0.50 0.50 4
1047
+ 40 0.25 0.33 0.29 6
1048
+ 41 0.00 0.00 0.00 0
1049
+ 42 0.00 0.00 0.00 4
1050
+ 43 0.57 0.85 0.68 47
1051
+ 44 0.00 0.00 0.00 1
1052
+ 45 0.46 0.75 0.57 8
1053
+ 46 0.00 0.00 0.00 0
1054
+ 47 0.50 0.50 0.50 2
1055
+ 48 0.50 0.33 0.40 3
1056
+ 49 0.00 0.00 0.00 0
1057
+
1058
+ micro avg 0.54 0.69 0.60 307
1059
+ macro avg 0.39 0.48 0.41 307
1060
+ weighted avg 0.55 0.69 0.60 307
1061
+ samples avg 0.24 0.25 0.24 307
1062
+
1063
+ > epoch: 18
1064
+ precision recall f1-score support
1065
+
1066
+ 0 0.40 0.67 0.50 6
1067
+ 1 1.00 1.00 1.00 1
1068
+ 2 0.00 0.00 0.00 2
1069
+ 3 0.47 0.54 0.50 13
1070
+ 4 0.50 0.50 0.50 2
1071
+ 5 0.67 0.50 0.57 8
1072
+ 6 0.40 0.40 0.40 10
1073
+ 7 0.50 1.00 0.67 2
1074
+ 8 0.45 0.71 0.56 7
1075
+ 9 0.64 0.88 0.74 8
1076
+ 10 0.00 0.00 0.00 1
1077
+ 11 0.54 0.84 0.66 25
1078
+ 12 0.00 0.00 0.00 2
1079
+ 13 0.52 0.67 0.59 18
1080
+ 14 0.00 0.00 0.00 3
1081
+ 15 0.33 1.00 0.50 3
1082
+ 16 1.00 0.20 0.33 5
1083
+ 17 0.46 0.86 0.60 14
1084
+ 18 0.50 0.25 0.33 4
1085
+ 19 0.00 0.00 0.00 1
1086
+ 20 0.20 1.00 0.33 1
1087
+ 21 0.00 0.00 0.00 1
1088
+ 22 1.00 1.00 1.00 4
1089
+ 23 0.00 0.00 0.00 1
1090
+ 24 0.00 0.00 0.00 2
1091
+ 25 0.44 0.70 0.54 10
1092
+ 26 1.00 0.71 0.83 7
1093
+ 27 0.55 0.75 0.63 8
1094
+ 28 0.60 0.38 0.46 8
1095
+ 29 0.00 0.00 0.00 1
1096
+ 30 0.38 0.75 0.50 4
1097
+ 31 0.00 0.00 0.00 2
1098
+ 32 0.82 0.88 0.85 32
1099
+ 33 0.67 1.00 0.80 4
1100
+ 34 0.25 0.33 0.29 3
1101
+ 35 1.00 1.00 1.00 7
1102
+ 36 0.00 0.00 0.00 0
1103
+ 37 1.00 1.00 1.00 2
1104
+ 38 0.00 0.00 0.00 0
1105
+ 39 1.00 0.50 0.67 4
1106
+ 40 0.36 0.67 0.47 6
1107
+ 41 0.00 0.00 0.00 0
1108
+ 42 0.00 0.00 0.00 4
1109
+ 43 0.66 0.81 0.72 47
1110
+ 44 0.00 0.00 0.00 1
1111
+ 45 0.50 0.75 0.60 8
1112
+ 46 0.00 0.00 0.00 0
1113
+ 47 0.50 0.50 0.50 2
1114
+ 48 0.60 1.00 0.75 3
1115
+ 49 0.00 0.00 0.00 0
1116
+
1117
+ micro avg 0.56 0.68 0.61 307
1118
+ macro avg 0.40 0.47 0.41 307
1119
+ weighted avg 0.57 0.68 0.60 307
1120
+ samples avg 0.23 0.25 0.23 307
1121
+
1122
+ > epoch: 19
1123
+ precision recall f1-score support
1124
+
1125
+ 0 0.50 0.83 0.62 6
1126
+ 1 1.00 1.00 1.00 1
1127
+ 2 0.00 0.00 0.00 2
1128
+ 3 0.44 0.54 0.48 13
1129
+ 4 1.00 0.50 0.67 2
1130
+ 5 0.40 0.50 0.44 8
1131
+ 6 0.50 0.50 0.50 10
1132
+ 7 0.50 1.00 0.67 2
1133
+ 8 0.54 1.00 0.70 7
1134
+ 9 0.70 0.88 0.78 8
1135
+ 10 0.00 0.00 0.00 1
1136
+ 11 0.56 0.88 0.69 25
1137
+ 12 0.00 0.00 0.00 2
1138
+ 13 0.48 0.67 0.56 18
1139
+ 14 0.00 0.00 0.00 3
1140
+ 15 0.43 1.00 0.60 3
1141
+ 16 1.00 0.60 0.75 5
1142
+ 17 0.38 0.86 0.52 14
1143
+ 18 0.50 0.25 0.33 4
1144
+ 19 0.00 0.00 0.00 1
1145
+ 20 0.17 1.00 0.29 1
1146
+ 21 0.00 0.00 0.00 1
1147
+ 22 0.80 1.00 0.89 4
1148
+ 23 0.00 0.00 0.00 1
1149
+ 24 0.00 0.00 0.00 2
1150
+ 25 0.54 0.70 0.61 10
1151
+ 26 1.00 0.71 0.83 7
1152
+ 27 0.60 0.75 0.67 8
1153
+ 28 0.62 0.62 0.62 8
1154
+ 29 0.00 0.00 0.00 1
1155
+ 30 0.27 0.75 0.40 4
1156
+ 31 0.00 0.00 0.00 2
1157
+ 32 0.82 0.88 0.85 32
1158
+ 33 0.57 1.00 0.73 4
1159
+ 34 0.33 0.67 0.44 3
1160
+ 35 1.00 1.00 1.00 7
1161
+ 36 0.00 0.00 0.00 0
1162
+ 37 1.00 1.00 1.00 2
1163
+ 38 0.00 0.00 0.00 0
1164
+ 39 0.50 0.50 0.50 4
1165
+ 40 0.44 0.67 0.53 6
1166
+ 41 0.00 0.00 0.00 0
1167
+ 42 0.00 0.00 0.00 4
1168
+ 43 0.67 0.81 0.73 47
1169
+ 44 0.00 0.00 0.00 1
1170
+ 45 0.46 0.75 0.57 8
1171
+ 46 0.00 0.00 0.00 0
1172
+ 47 0.50 0.50 0.50 2
1173
+ 48 0.33 0.33 0.33 3
1174
+ 49 0.00 0.00 0.00 0
1175
+
1176
+ micro avg 0.55 0.71 0.62 307
1177
+ macro avg 0.39 0.49 0.42 307
1178
+ weighted avg 0.57 0.71 0.62 307
1179
+ samples avg 0.24 0.26 0.24 307
1180
+
1181
+ > epoch: 20
1182
+ precision recall f1-score support
1183
+
1184
+ 0 0.50 0.83 0.62 6
1185
+ 1 1.00 1.00 1.00 1
1186
+ 2 0.33 0.50 0.40 2
1187
+ 3 0.44 0.54 0.48 13
1188
+ 4 0.00 0.00 0.00 2
1189
+ 5 0.50 0.50 0.50 8
1190
+ 6 0.39 0.70 0.50 10
1191
+ 7 0.50 1.00 0.67 2
1192
+ 8 0.47 1.00 0.64 7
1193
+ 9 0.70 0.88 0.78 8
1194
+ 10 0.00 0.00 0.00 1
1195
+ 11 0.60 0.84 0.70 25
1196
+ 12 0.00 0.00 0.00 2
1197
+ 13 0.48 0.61 0.54 18
1198
+ 14 0.00 0.00 0.00 3
1199
+ 15 0.33 1.00 0.50 3
1200
+ 16 0.75 0.60 0.67 5
1201
+ 17 0.41 0.86 0.56 14
1202
+ 18 0.33 0.25 0.29 4
1203
+ 19 0.00 0.00 0.00 1
1204
+ 20 0.20 1.00 0.33 1
1205
+ 21 0.00 0.00 0.00 1
1206
+ 22 0.80 1.00 0.89 4
1207
+ 23 0.00 0.00 0.00 1
1208
+ 24 0.00 0.00 0.00 2
1209
+ 25 0.58 0.70 0.64 10
1210
+ 26 0.83 0.71 0.77 7
1211
+ 27 0.57 1.00 0.73 8
1212
+ 28 0.71 0.62 0.67 8
1213
+ 29 0.00 0.00 0.00 1
1214
+ 30 0.25 0.75 0.38 4
1215
+ 31 0.00 0.00 0.00 2
1216
+ 32 0.85 0.88 0.86 32
1217
+ 33 0.67 1.00 0.80 4
1218
+ 34 0.50 0.67 0.57 3
1219
+ 35 1.00 1.00 1.00 7
1220
+ 36 0.00 0.00 0.00 0
1221
+ 37 1.00 1.00 1.00 2
1222
+ 38 0.00 0.00 0.00 0
1223
+ 39 0.67 0.50 0.57 4
1224
+ 40 0.36 0.67 0.47 6
1225
+ 41 0.00 0.00 0.00 0
1226
+ 42 0.00 0.00 0.00 4
1227
+ 43 0.62 0.81 0.70 47
1228
+ 44 0.00 0.00 0.00 1
1229
+ 45 0.43 0.75 0.55 8
1230
+ 46 0.00 0.00 0.00 0
1231
+ 47 0.50 0.50 0.50 2
1232
+ 48 0.50 0.67 0.57 3
1233
+ 49 0.00 0.00 0.00 0
1234
+
1235
+ micro avg 0.54 0.72 0.62 307
1236
+ macro avg 0.38 0.51 0.42 307
1237
+ weighted avg 0.55 0.72 0.62 307
1238
+ samples avg 0.24 0.26 0.25 307
1239
+
1240
+ > epoch: 21
1241
+ precision recall f1-score support
1242
+
1243
+ 0 0.38 1.00 0.55 6
1244
+ 1 1.00 1.00 1.00 1
1245
+ 2 0.00 0.00 0.00 2
1246
+ 3 0.42 0.62 0.50 13
1247
+ 4 0.00 0.00 0.00 2
1248
+ 5 0.71 0.62 0.67 8
1249
+ 6 0.40 0.60 0.48 10
1250
+ 7 0.67 1.00 0.80 2
1251
+ 8 0.64 1.00 0.78 7
1252
+ 9 0.64 0.88 0.74 8
1253
+ 10 1.00 1.00 1.00 1
1254
+ 11 0.53 0.84 0.65 25
1255
+ 12 0.00 0.00 0.00 2
1256
+ 13 0.50 0.67 0.57 18
1257
+ 14 0.00 0.00 0.00 3
1258
+ 15 0.27 1.00 0.43 3
1259
+ 16 0.75 0.60 0.67 5
1260
+ 17 0.43 0.86 0.57 14
1261
+ 18 0.33 0.25 0.29 4
1262
+ 19 0.00 0.00 0.00 1
1263
+ 20 0.20 1.00 0.33 1
1264
+ 21 0.00 0.00 0.00 1
1265
+ 22 0.80 1.00 0.89 4
1266
+ 23 0.00 0.00 0.00 1
1267
+ 24 0.00 0.00 0.00 2
1268
+ 25 0.50 0.80 0.62 10
1269
+ 26 0.83 0.71 0.77 7
1270
+ 27 0.38 0.75 0.50 8
1271
+ 28 0.71 0.62 0.67 8
1272
+ 29 0.00 0.00 0.00 1
1273
+ 30 0.43 0.75 0.55 4
1274
+ 31 0.00 0.00 0.00 2
1275
+ 32 0.84 0.84 0.84 32
1276
+ 33 0.67 1.00 0.80 4
1277
+ 34 0.33 0.67 0.44 3
1278
+ 35 1.00 1.00 1.00 7
1279
+ 36 0.00 0.00 0.00 0
1280
+ 37 1.00 1.00 1.00 2
1281
+ 38 0.00 0.00 0.00 0
1282
+ 39 0.67 0.50 0.57 4
1283
+ 40 0.36 0.67 0.47 6
1284
+ 41 0.00 0.00 0.00 0
1285
+ 42 0.00 0.00 0.00 4
1286
+ 43 0.60 0.81 0.69 47
1287
+ 44 0.00 0.00 0.00 1
1288
+ 45 0.40 0.75 0.52 8
1289
+ 46 0.00 0.00 0.00 0
1290
+ 47 0.67 1.00 0.80 2
1291
+ 48 0.50 0.33 0.40 3
1292
+ 49 0.00 0.00 0.00 0
1293
+
1294
+ micro avg 0.52 0.72 0.61 307
1295
+ macro avg 0.39 0.52 0.43 307
1296
+ weighted avg 0.55 0.72 0.61 307
1297
+ samples avg 0.24 0.26 0.25 307
1298
+
1299
+ > epoch: 22
1300
+ precision recall f1-score support
1301
+
1302
+ 0 0.56 0.83 0.67 6
1303
+ 1 1.00 1.00 1.00 1
1304
+ 2 0.00 0.00 0.00 2
1305
+ 3 0.50 0.54 0.52 13
1306
+ 4 0.00 0.00 0.00 2
1307
+ 5 1.00 0.50 0.67 8
1308
+ 6 0.30 0.30 0.30 10
1309
+ 7 0.67 1.00 0.80 2
1310
+ 8 0.56 0.71 0.62 7
1311
+ 9 0.70 0.88 0.78 8
1312
+ 10 0.50 1.00 0.67 1
1313
+ 11 0.56 0.88 0.69 25
1314
+ 12 0.00 0.00 0.00 2
1315
+ 13 0.59 0.56 0.57 18
1316
+ 14 0.00 0.00 0.00 3
1317
+ 15 0.33 1.00 0.50 3
1318
+ 16 1.00 0.60 0.75 5
1319
+ 17 0.32 0.86 0.46 14
1320
+ 18 0.33 0.25 0.29 4
1321
+ 19 0.00 0.00 0.00 1
1322
+ 20 0.20 1.00 0.33 1
1323
+ 21 1.00 1.00 1.00 1
1324
+ 22 1.00 1.00 1.00 4
1325
+ 23 0.00 0.00 0.00 1
1326
+ 24 0.00 0.00 0.00 2
1327
+ 25 0.58 0.70 0.64 10
1328
+ 26 1.00 0.71 0.83 7
1329
+ 27 0.64 0.88 0.74 8
1330
+ 28 0.71 0.62 0.67 8
1331
+ 29 0.00 0.00 0.00 1
1332
+ 30 0.43 0.75 0.55 4
1333
+ 31 0.00 0.00 0.00 2
1334
+ 32 0.87 0.84 0.86 32
1335
+ 33 0.67 1.00 0.80 4
1336
+ 34 0.33 0.33 0.33 3
1337
+ 35 1.00 1.00 1.00 7
1338
+ 36 0.00 0.00 0.00 0
1339
+ 37 1.00 1.00 1.00 2
1340
+ 38 0.00 0.00 0.00 0
1341
+ 39 0.33 0.25 0.29 4
1342
+ 40 0.50 0.67 0.57 6
1343
+ 41 0.00 0.00 0.00 0
1344
+ 42 0.00 0.00 0.00 4
1345
+ 43 0.64 0.79 0.70 47
1346
+ 44 0.00 0.00 0.00 1
1347
+ 45 0.50 0.75 0.60 8
1348
+ 46 0.00 0.00 0.00 0
1349
+ 47 0.50 0.50 0.50 2
1350
+ 48 0.75 1.00 0.86 3
1351
+ 49 0.00 0.00 0.00 0
1352
+
1353
+ micro avg 0.58 0.69 0.63 307
1354
+ macro avg 0.43 0.51 0.45 307
1355
+ weighted avg 0.59 0.69 0.62 307
1356
+ samples avg 0.24 0.25 0.24 307
1357
+
1358
+ > epoch: 23
1359
+ precision recall f1-score support
1360
+
1361
+ 0 0.56 0.83 0.67 6
1362
+ 1 1.00 1.00 1.00 1
1363
+ 2 0.25 0.50 0.33 2
1364
+ 3 0.54 0.54 0.54 13
1365
+ 4 0.00 0.00 0.00 2
1366
+ 5 0.50 0.62 0.56 8
1367
+ 6 0.29 0.40 0.33 10
1368
+ 7 0.67 1.00 0.80 2
1369
+ 8 0.58 1.00 0.74 7
1370
+ 9 0.70 0.88 0.78 8
1371
+ 10 0.00 0.00 0.00 1
1372
+ 11 0.57 0.84 0.68 25
1373
+ 12 0.00 0.00 0.00 2
1374
+ 13 0.55 0.61 0.58 18
1375
+ 14 0.00 0.00 0.00 3
1376
+ 15 0.25 0.67 0.36 3
1377
+ 16 0.75 0.60 0.67 5
1378
+ 17 0.38 0.79 0.51 14
1379
+ 18 0.33 0.25 0.29 4
1380
+ 19 0.00 0.00 0.00 1
1381
+ 20 0.17 1.00 0.29 1
1382
+ 21 0.25 1.00 0.40 1
1383
+ 22 1.00 1.00 1.00 4
1384
+ 23 0.00 0.00 0.00 1
1385
+ 24 0.00 0.00 0.00 2
1386
+ 25 0.73 0.80 0.76 10
1387
+ 26 0.83 0.71 0.77 7
1388
+ 27 0.67 0.75 0.71 8
1389
+ 28 0.71 0.62 0.67 8
1390
+ 29 0.00 0.00 0.00 1
1391
+ 30 0.30 0.75 0.43 4
1392
+ 31 0.00 0.00 0.00 2
1393
+ 32 0.84 0.84 0.84 32
1394
+ 33 0.67 1.00 0.80 4
1395
+ 34 0.33 0.67 0.44 3
1396
+ 35 1.00 1.00 1.00 7
1397
+ 36 0.00 0.00 0.00 0
1398
+ 37 0.67 1.00 0.80 2
1399
+ 38 0.00 0.00 0.00 0
1400
+ 39 0.67 0.50 0.57 4
1401
+ 40 0.40 0.67 0.50 6
1402
+ 41 0.00 0.00 0.00 0
1403
+ 42 0.00 0.00 0.00 4
1404
+ 43 0.64 0.77 0.70 47
1405
+ 44 0.00 0.00 0.00 1
1406
+ 45 0.46 0.75 0.57 8
1407
+ 46 0.00 0.00 0.00 0
1408
+ 47 0.50 0.50 0.50 2
1409
+ 48 0.67 0.67 0.67 3
1410
+ 49 0.00 0.00 0.00 0
1411
+
1412
+ micro avg 0.56 0.70 0.62 307
1413
+ macro avg 0.39 0.51 0.42 307
1414
+ weighted avg 0.57 0.70 0.62 307
1415
+ samples avg 0.23 0.25 0.24 307
1416
+
1417
+ > epoch: 24
1418
+ precision recall f1-score support
1419
+
1420
+ 0 0.50 0.67 0.57 6
1421
+ 1 1.00 1.00 1.00 1
1422
+ 2 0.00 0.00 0.00 2
1423
+ 3 0.47 0.62 0.53 13
1424
+ 4 0.00 0.00 0.00 2
1425
+ 5 0.67 0.50 0.57 8
1426
+ 6 0.40 0.40 0.40 10
1427
+ 7 0.67 1.00 0.80 2
1428
+ 8 0.47 1.00 0.64 7
1429
+ 9 0.70 0.88 0.78 8
1430
+ 10 0.00 0.00 0.00 1
1431
+ 11 0.49 0.92 0.64 25
1432
+ 12 0.00 0.00 0.00 2
1433
+ 13 0.52 0.67 0.59 18
1434
+ 14 0.00 0.00 0.00 3
1435
+ 15 0.33 0.67 0.44 3
1436
+ 16 1.00 0.40 0.57 5
1437
+ 17 0.40 0.71 0.51 14
1438
+ 18 0.33 0.25 0.29 4
1439
+ 19 0.00 0.00 0.00 1
1440
+ 20 0.20 1.00 0.33 1
1441
+ 21 0.50 1.00 0.67 1
1442
+ 22 0.80 1.00 0.89 4
1443
+ 23 0.00 0.00 0.00 1
1444
+ 24 0.00 0.00 0.00 2
1445
+ 25 0.67 0.80 0.73 10
1446
+ 26 0.83 0.71 0.77 7
1447
+ 27 0.53 1.00 0.70 8
1448
+ 28 0.71 0.62 0.67 8
1449
+ 29 0.00 0.00 0.00 1
1450
+ 30 0.33 0.75 0.46 4
1451
+ 31 0.00 0.00 0.00 2
1452
+ 32 0.90 0.84 0.87 32
1453
+ 33 0.80 1.00 0.89 4
1454
+ 34 0.50 0.67 0.57 3
1455
+ 35 1.00 1.00 1.00 7
1456
+ 36 0.00 0.00 0.00 0
1457
+ 37 1.00 1.00 1.00 2
1458
+ 38 0.00 0.00 0.00 0
1459
+ 39 1.00 0.50 0.67 4
1460
+ 40 0.33 0.50 0.40 6
1461
+ 41 0.00 0.00 0.00 0
1462
+ 42 0.00 0.00 0.00 4
1463
+ 43 0.67 0.77 0.71 47
1464
+ 44 0.00 0.00 0.00 1
1465
+ 45 0.55 0.75 0.63 8
1466
+ 46 0.00 0.00 0.00 0
1467
+ 47 0.67 1.00 0.80 2
1468
+ 48 0.50 0.67 0.57 3
1469
+ 49 0.00 0.00 0.00 0
1470
+
1471
+ micro avg 0.57 0.70 0.63 307
1472
+ macro avg 0.41 0.51 0.43 307
1473
+ weighted avg 0.58 0.70 0.62 307
1474
+ samples avg 0.24 0.25 0.24 307
1475
+
1476
+ > epoch: 25
1477
+ precision recall f1-score support
1478
+
1479
+ 0 0.71 0.83 0.77 6
1480
+ 1 1.00 1.00 1.00 1
1481
+ 2 0.00 0.00 0.00 2
1482
+ 3 0.53 0.62 0.57 13
1483
+ 4 0.00 0.00 0.00 2
1484
+ 5 0.80 0.50 0.62 8
1485
+ 6 0.58 0.70 0.64 10
1486
+ 7 0.67 1.00 0.80 2
1487
+ 8 0.64 1.00 0.78 7
1488
+ 9 0.70 0.88 0.78 8
1489
+ 10 0.00 0.00 0.00 1
1490
+ 11 0.55 0.84 0.67 25
1491
+ 12 0.00 0.00 0.00 2
1492
+ 13 0.56 0.50 0.53 18
1493
+ 14 0.00 0.00 0.00 3
1494
+ 15 0.50 1.00 0.67 3
1495
+ 16 0.75 0.60 0.67 5
1496
+ 17 0.40 0.86 0.55 14
1497
+ 18 0.50 0.25 0.33 4
1498
+ 19 0.00 0.00 0.00 1
1499
+ 20 0.20 1.00 0.33 1
1500
+ 21 0.00 0.00 0.00 1
1501
+ 22 1.00 1.00 1.00 4
1502
+ 23 0.00 0.00 0.00 1
1503
+ 24 0.00 0.00 0.00 2
1504
+ 25 0.50 0.70 0.58 10
1505
+ 26 1.00 0.71 0.83 7
1506
+ 27 0.55 0.75 0.63 8
1507
+ 28 0.71 0.62 0.67 8
1508
+ 29 0.00 0.00 0.00 1
1509
+ 30 0.38 0.75 0.50 4
1510
+ 31 0.00 0.00 0.00 2
1511
+ 32 0.90 0.84 0.87 32
1512
+ 33 0.67 1.00 0.80 4
1513
+ 34 0.67 0.67 0.67 3
1514
+ 35 1.00 1.00 1.00 7
1515
+ 36 0.00 0.00 0.00 0
1516
+ 37 1.00 1.00 1.00 2
1517
+ 38 0.00 0.00 0.00 0
1518
+ 39 0.67 0.50 0.57 4
1519
+ 40 0.40 0.33 0.36 6
1520
+ 41 0.00 0.00 0.00 0
1521
+ 42 0.00 0.00 0.00 4
1522
+ 43 0.56 0.81 0.66 47
1523
+ 44 0.00 0.00 0.00 1
1524
+ 45 0.46 0.75 0.57 8
1525
+ 46 0.00 0.00 0.00 0
1526
+ 47 0.50 0.50 0.50 2
1527
+ 48 0.50 0.67 0.57 3
1528
+ 49 0.00 0.00 0.00 0
1529
+
1530
+ micro avg 0.59 0.70 0.64 307
1531
+ macro avg 0.41 0.48 0.43 307
1532
+ weighted avg 0.59 0.70 0.62 307
1533
+ samples avg 0.24 0.26 0.24 307
1534
+
1535
+ > epoch: 26
1536
+ precision recall f1-score support
1537
+
1538
+ 0 0.62 0.83 0.71 6
1539
+ 1 1.00 1.00 1.00 1
1540
+ 2 0.33 0.50 0.40 2
1541
+ 3 0.40 0.77 0.53 13
1542
+ 4 0.00 0.00 0.00 2
1543
+ 5 0.80 0.50 0.62 8
1544
+ 6 0.50 0.30 0.38 10
1545
+ 7 0.67 1.00 0.80 2
1546
+ 8 0.43 0.86 0.57 7
1547
+ 9 0.70 0.88 0.78 8
1548
+ 10 0.00 0.00 0.00 1
1549
+ 11 0.66 0.76 0.70 25
1550
+ 12 0.00 0.00 0.00 2
1551
+ 13 0.56 0.56 0.56 18
1552
+ 14 0.00 0.00 0.00 3
1553
+ 15 0.43 1.00 0.60 3
1554
+ 16 0.75 0.60 0.67 5
1555
+ 17 0.41 0.79 0.54 14
1556
+ 18 0.33 0.25 0.29 4
1557
+ 19 0.00 0.00 0.00 1
1558
+ 20 0.20 1.00 0.33 1
1559
+ 21 0.00 0.00 0.00 1
1560
+ 22 0.80 1.00 0.89 4
1561
+ 23 0.00 0.00 0.00 1
1562
+ 24 0.00 0.00 0.00 2
1563
+ 25 0.58 0.70 0.64 10
1564
+ 26 1.00 0.71 0.83 7
1565
+ 27 0.64 0.88 0.74 8
1566
+ 28 0.67 0.75 0.71 8
1567
+ 29 0.00 0.00 0.00 1
1568
+ 30 0.75 0.75 0.75 4
1569
+ 31 0.00 0.00 0.00 2
1570
+ 32 0.84 0.84 0.84 32
1571
+ 33 0.67 1.00 0.80 4
1572
+ 34 0.67 0.67 0.67 3
1573
+ 35 1.00 1.00 1.00 7
1574
+ 36 0.00 0.00 0.00 0
1575
+ 37 1.00 1.00 1.00 2
1576
+ 38 0.00 0.00 0.00 0
1577
+ 39 0.50 0.25 0.33 4
1578
+ 40 0.33 0.50 0.40 6
1579
+ 41 0.00 0.00 0.00 0
1580
+ 42 0.00 0.00 0.00 4
1581
+ 43 0.73 0.74 0.74 47
1582
+ 44 0.00 0.00 0.00 1
1583
+ 45 0.55 0.75 0.63 8
1584
+ 46 0.00 0.00 0.00 0
1585
+ 47 0.50 0.50 0.50 2
1586
+ 48 0.60 1.00 0.75 3
1587
+ 49 0.00 0.00 0.00 0
1588
+
1589
+ micro avg 0.61 0.68 0.64 307
1590
+ macro avg 0.41 0.49 0.43 307
1591
+ weighted avg 0.61 0.68 0.63 307
1592
+ samples avg 0.24 0.25 0.24 307
1593
+
1594
+ > epoch: 27
1595
+ precision recall f1-score support
1596
+
1597
+ 0 0.56 0.83 0.67 6
1598
+ 1 1.00 1.00 1.00 1
1599
+ 2 0.25 0.50 0.33 2
1600
+ 3 0.50 0.54 0.52 13
1601
+ 4 0.00 0.00 0.00 2
1602
+ 5 1.00 0.38 0.55 8
1603
+ 6 0.50 0.60 0.55 10
1604
+ 7 0.67 1.00 0.80 2
1605
+ 8 0.55 0.86 0.67 7
1606
+ 9 0.70 0.88 0.78 8
1607
+ 10 0.00 0.00 0.00 1
1608
+ 11 0.59 0.88 0.71 25
1609
+ 12 0.00 0.00 0.00 2
1610
+ 13 0.53 0.56 0.54 18
1611
+ 14 0.00 0.00 0.00 3
1612
+ 15 0.38 1.00 0.55 3
1613
+ 16 0.75 0.60 0.67 5
1614
+ 17 0.41 0.86 0.56 14
1615
+ 18 0.33 0.25 0.29 4
1616
+ 19 0.00 0.00 0.00 1
1617
+ 20 0.20 1.00 0.33 1
1618
+ 21 0.25 1.00 0.40 1
1619
+ 22 1.00 1.00 1.00 4
1620
+ 23 0.00 0.00 0.00 1
1621
+ 24 0.00 0.00 0.00 2
1622
+ 25 0.45 0.50 0.48 10
1623
+ 26 1.00 0.71 0.83 7
1624
+ 27 0.55 0.75 0.63 8
1625
+ 28 0.71 0.62 0.67 8
1626
+ 29 0.00 0.00 0.00 1
1627
+ 30 0.33 0.75 0.46 4
1628
+ 31 0.00 0.00 0.00 2
1629
+ 32 0.82 0.88 0.85 32
1630
+ 33 0.67 1.00 0.80 4
1631
+ 34 0.67 0.67 0.67 3
1632
+ 35 1.00 1.00 1.00 7
1633
+ 36 0.00 0.00 0.00 0
1634
+ 37 1.00 1.00 1.00 2
1635
+ 38 0.00 0.00 0.00 0
1636
+ 39 0.50 0.25 0.33 4
1637
+ 40 0.36 0.67 0.47 6
1638
+ 41 0.00 0.00 0.00 0
1639
+ 42 0.00 0.00 0.00 4
1640
+ 43 0.68 0.81 0.74 47
1641
+ 44 0.00 0.00 0.00 1
1642
+ 45 0.70 0.88 0.78 8
1643
+ 46 0.00 0.00 0.00 0
1644
+ 47 0.50 0.50 0.50 2
1645
+ 48 0.60 1.00 0.75 3
1646
+ 49 0.00 0.00 0.00 0
1647
+
1648
+ micro avg 0.59 0.70 0.64 307
1649
+ macro avg 0.41 0.51 0.44 307
1650
+ weighted avg 0.60 0.70 0.63 307
1651
+ samples avg 0.24 0.26 0.24 307
1652
+
1653
+ > epoch: 28
1654
+ precision recall f1-score support
1655
+
1656
+ 0 0.62 0.83 0.71 6
1657
+ 1 1.00 1.00 1.00 1
1658
+ 2 0.00 0.00 0.00 2
1659
+ 3 0.54 0.54 0.54 13
1660
+ 4 0.00 0.00 0.00 2
1661
+ 5 0.80 0.50 0.62 8
1662
+ 6 0.40 0.40 0.40 10
1663
+ 7 0.67 1.00 0.80 2
1664
+ 8 0.60 0.86 0.71 7
1665
+ 9 0.70 0.88 0.78 8
1666
+ 10 0.00 0.00 0.00 1
1667
+ 11 0.55 0.88 0.68 25
1668
+ 12 0.00 0.00 0.00 2
1669
+ 13 0.56 0.56 0.56 18
1670
+ 14 0.00 0.00 0.00 3
1671
+ 15 0.60 1.00 0.75 3
1672
+ 16 0.75 0.60 0.67 5
1673
+ 17 0.42 0.79 0.55 14
1674
+ 18 0.33 0.25 0.29 4
1675
+ 19 0.00 0.00 0.00 1
1676
+ 20 0.20 1.00 0.33 1
1677
+ 21 0.00 0.00 0.00 1
1678
+ 22 1.00 1.00 1.00 4
1679
+ 23 0.00 0.00 0.00 1
1680
+ 24 0.00 0.00 0.00 2
1681
+ 25 0.50 0.40 0.44 10
1682
+ 26 1.00 0.71 0.83 7
1683
+ 27 0.60 0.75 0.67 8
1684
+ 28 0.71 0.62 0.67 8
1685
+ 29 0.00 0.00 0.00 1
1686
+ 30 0.38 0.75 0.50 4
1687
+ 31 0.00 0.00 0.00 2
1688
+ 32 0.84 0.84 0.84 32
1689
+ 33 0.67 1.00 0.80 4
1690
+ 34 0.50 0.67 0.57 3
1691
+ 35 1.00 1.00 1.00 7
1692
+ 36 0.00 0.00 0.00 0
1693
+ 37 1.00 1.00 1.00 2
1694
+ 38 0.00 0.00 0.00 0
1695
+ 39 1.00 0.25 0.40 4
1696
+ 40 0.33 0.50 0.40 6
1697
+ 41 0.00 0.00 0.00 0
1698
+ 42 0.00 0.00 0.00 4
1699
+ 43 0.66 0.81 0.72 47
1700
+ 44 0.00 0.00 0.00 1
1701
+ 45 0.58 0.88 0.70 8
1702
+ 46 0.00 0.00 0.00 0
1703
+ 47 0.50 0.50 0.50 2
1704
+ 48 0.67 0.67 0.67 3
1705
+ 49 0.00 0.00 0.00 0
1706
+
1707
+ micro avg 0.60 0.68 0.64 307
1708
+ macro avg 0.41 0.47 0.42 307
1709
+ weighted avg 0.60 0.68 0.62 307
1710
+ samples avg 0.24 0.25 0.24 307
1711
+
1712
+ > epoch: 29
1713
+ precision recall f1-score support
1714
+
1715
+ 0 0.56 0.83 0.67 6
1716
+ 1 1.00 1.00 1.00 1
1717
+ 2 0.20 0.50 0.29 2
1718
+ 3 0.50 0.54 0.52 13
1719
+ 4 0.00 0.00 0.00 2
1720
+ 5 1.00 0.25 0.40 8
1721
+ 6 0.40 0.40 0.40 10
1722
+ 7 0.67 1.00 0.80 2
1723
+ 8 0.55 0.86 0.67 7
1724
+ 9 0.58 0.88 0.70 8
1725
+ 10 0.00 0.00 0.00 1
1726
+ 11 0.59 0.88 0.71 25
1727
+ 12 0.00 0.00 0.00 2
1728
+ 13 0.48 0.61 0.54 18
1729
+ 14 0.00 0.00 0.00 3
1730
+ 15 0.38 1.00 0.55 3
1731
+ 16 0.75 0.60 0.67 5
1732
+ 17 0.39 0.79 0.52 14
1733
+ 18 0.40 0.50 0.44 4
1734
+ 19 0.00 0.00 0.00 1
1735
+ 20 0.20 1.00 0.33 1
1736
+ 21 0.00 0.00 0.00 1
1737
+ 22 0.80 1.00 0.89 4
1738
+ 23 0.00 0.00 0.00 1
1739
+ 24 0.00 0.00 0.00 2
1740
+ 25 0.50 0.40 0.44 10
1741
+ 26 1.00 0.71 0.83 7
1742
+ 27 0.47 0.88 0.61 8
1743
+ 28 0.71 0.62 0.67 8
1744
+ 29 0.00 0.00 0.00 1
1745
+ 30 0.30 0.75 0.43 4
1746
+ 31 0.00 0.00 0.00 2
1747
+ 32 0.84 0.84 0.84 32
1748
+ 33 0.80 1.00 0.89 4
1749
+ 34 0.50 0.67 0.57 3
1750
+ 35 1.00 1.00 1.00 7
1751
+ 36 0.00 0.00 0.00 0
1752
+ 37 1.00 1.00 1.00 2
1753
+ 38 0.00 0.00 0.00 0
1754
+ 39 1.00 0.25 0.40 4
1755
+ 40 0.40 0.67 0.50 6
1756
+ 41 0.00 0.00 0.00 0
1757
+ 42 1.00 0.25 0.40 4
1758
+ 43 0.64 0.79 0.70 47
1759
+ 44 0.00 0.00 0.00 1
1760
+ 45 0.60 0.75 0.67 8
1761
+ 46 0.00 0.00 0.00 0
1762
+ 47 0.33 0.50 0.40 2
1763
+ 48 0.75 1.00 0.86 3
1764
+ 49 0.00 0.00 0.00 0
1765
+
1766
+ micro avg 0.57 0.69 0.62 307
1767
+ macro avg 0.43 0.49 0.43 307
1768
+ weighted avg 0.60 0.69 0.61 307
1769
+ samples avg 0.24 0.25 0.24 307
1770
+
1771
+ > epoch: 30
1772
+ precision recall f1-score support
1773
+
1774
+ 0 0.62 0.83 0.71 6
1775
+ 1 1.00 1.00 1.00 1
1776
+ 2 0.25 0.50 0.33 2
1777
+ 3 0.54 0.54 0.54 13
1778
+ 4 0.00 0.00 0.00 2
1779
+ 5 1.00 0.25 0.40 8
1780
+ 6 0.50 0.60 0.55 10
1781
+ 7 0.67 1.00 0.80 2
1782
+ 8 0.55 0.86 0.67 7
1783
+ 9 0.70 0.88 0.78 8
1784
+ 10 1.00 1.00 1.00 1
1785
+ 11 0.55 0.84 0.67 25
1786
+ 12 0.00 0.00 0.00 2
1787
+ 13 0.52 0.61 0.56 18
1788
+ 14 0.00 0.00 0.00 3
1789
+ 15 0.43 1.00 0.60 3
1790
+ 16 0.75 0.60 0.67 5
1791
+ 17 0.46 0.86 0.60 14
1792
+ 18 0.33 0.25 0.29 4
1793
+ 19 0.00 0.00 0.00 1
1794
+ 20 0.20 1.00 0.33 1
1795
+ 21 0.50 1.00 0.67 1
1796
+ 22 0.80 1.00 0.89 4
1797
+ 23 0.00 0.00 0.00 1
1798
+ 24 0.00 0.00 0.00 2
1799
+ 25 0.62 0.80 0.70 10
1800
+ 26 0.71 0.71 0.71 7
1801
+ 27 0.54 0.88 0.67 8
1802
+ 28 0.71 0.62 0.67 8
1803
+ 29 0.00 0.00 0.00 1
1804
+ 30 0.30 0.75 0.43 4
1805
+ 31 0.00 0.00 0.00 2
1806
+ 32 0.82 0.84 0.83 32
1807
+ 33 0.80 1.00 0.89 4
1808
+ 34 0.40 0.67 0.50 3
1809
+ 35 1.00 1.00 1.00 7
1810
+ 36 0.00 0.00 0.00 0
1811
+ 37 0.67 1.00 0.80 2
1812
+ 38 0.00 0.00 0.00 0
1813
+ 39 0.33 0.25 0.29 4
1814
+ 40 0.36 0.67 0.47 6
1815
+ 41 0.00 0.00 0.00 0
1816
+ 42 0.00 0.00 0.00 4
1817
+ 43 0.65 0.83 0.73 47
1818
+ 44 0.00 0.00 0.00 1
1819
+ 45 0.60 0.75 0.67 8
1820
+ 46 0.00 0.00 0.00 0
1821
+ 47 0.50 0.50 0.50 2
1822
+ 48 0.75 1.00 0.86 3
1823
+ 49 0.00 0.00 0.00 0
1824
+
1825
+ micro avg 0.58 0.71 0.64 307
1826
+ macro avg 0.42 0.54 0.45 307
1827
+ weighted avg 0.59 0.71 0.63 307
1828
+ samples avg 0.24 0.26 0.25 307
1829
+
1830
+ > epoch: 31
1831
+ precision recall f1-score support
1832
+
1833
+ 0 0.62 0.83 0.71 6
1834
+ 1 1.00 1.00 1.00 1
1835
+ 2 0.50 0.50 0.50 2
1836
+ 3 0.62 0.62 0.62 13
1837
+ 4 0.00 0.00 0.00 2
1838
+ 5 1.00 0.38 0.55 8
1839
+ 6 0.50 0.60 0.55 10
1840
+ 7 0.67 1.00 0.80 2
1841
+ 8 0.55 0.86 0.67 7
1842
+ 9 0.70 0.88 0.78 8
1843
+ 10 0.00 0.00 0.00 1
1844
+ 11 0.54 0.84 0.66 25
1845
+ 12 0.00 0.00 0.00 2
1846
+ 13 0.59 0.56 0.57 18
1847
+ 14 0.00 0.00 0.00 3
1848
+ 15 0.33 1.00 0.50 3
1849
+ 16 0.75 0.60 0.67 5
1850
+ 17 0.41 0.86 0.56 14
1851
+ 18 0.33 0.25 0.29 4
1852
+ 19 0.00 0.00 0.00 1
1853
+ 20 0.20 1.00 0.33 1
1854
+ 21 0.00 0.00 0.00 1
1855
+ 22 1.00 1.00 1.00 4
1856
+ 23 0.00 0.00 0.00 1
1857
+ 24 0.00 0.00 0.00 2
1858
+ 25 0.67 0.80 0.73 10
1859
+ 26 0.83 0.71 0.77 7
1860
+ 27 0.55 0.75 0.63 8
1861
+ 28 0.71 0.62 0.67 8
1862
+ 29 0.00 0.00 0.00 1
1863
+ 30 0.27 0.75 0.40 4
1864
+ 31 0.00 0.00 0.00 2
1865
+ 32 0.87 0.84 0.86 32
1866
+ 33 0.80 1.00 0.89 4
1867
+ 34 0.50 0.67 0.57 3
1868
+ 35 1.00 1.00 1.00 7
1869
+ 36 0.00 0.00 0.00 0
1870
+ 37 0.67 1.00 0.80 2
1871
+ 38 0.00 0.00 0.00 0
1872
+ 39 0.50 0.25 0.33 4
1873
+ 40 0.44 0.67 0.53 6
1874
+ 41 0.00 0.00 0.00 0
1875
+ 42 0.00 0.00 0.00 4
1876
+ 43 0.65 0.83 0.73 47
1877
+ 44 0.00 0.00 0.00 1
1878
+ 45 0.54 0.88 0.67 8
1879
+ 46 0.00 0.00 0.00 0
1880
+ 47 0.50 0.50 0.50 2
1881
+ 48 0.75 1.00 0.86 3
1882
+ 49 0.00 0.00 0.00 0
1883
+
1884
+ micro avg 0.59 0.71 0.64 307
1885
+ macro avg 0.41 0.50 0.43 307
1886
+ weighted avg 0.60 0.71 0.64 307
1887
+ samples avg 0.24 0.26 0.24 307
1888
+
Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/config.json ADDED
@@ -0,0 +1,131 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "bert-base-cased",
3
+ "architectures": [
4
+ "BertForSequenceClassification"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "gradient_checkpointing": false,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 768,
12
+ "id2label": {
13
+ "0": "T1005",
14
+ "1": "T1014",
15
+ "2": "T1021",
16
+ "3": "T1027",
17
+ "4": "T1033",
18
+ "5": "T1036",
19
+ "6": "T1041",
20
+ "7": "T1053",
21
+ "8": "T1055",
22
+ "9": "T1056",
23
+ "10": "T1057",
24
+ "11": "T1059",
25
+ "12": "T1070",
26
+ "13": "T1071",
27
+ "14": "T1078",
28
+ "15": "T1082",
29
+ "16": "T1083",
30
+ "17": "T1105",
31
+ "18": "T1110",
32
+ "19": "T1112",
33
+ "20": "T1113",
34
+ "21": "T1115",
35
+ "22": "T1125",
36
+ "23": "T1132",
37
+ "24": "T1137",
38
+ "25": "T1140",
39
+ "26": "T1189",
40
+ "27": "T1190",
41
+ "28": "T1195",
42
+ "29": "T1203",
43
+ "30": "T1204",
44
+ "31": "T1218",
45
+ "32": "T1486",
46
+ "33": "T1496",
47
+ "34": "T1497",
48
+ "35": "T1499",
49
+ "36": "T1528",
50
+ "37": "T1539",
51
+ "38": "T1543",
52
+ "39": "T1547",
53
+ "40": "T1555",
54
+ "41": "T1557",
55
+ "42": "T1562",
56
+ "43": "T1566",
57
+ "44": "T1571",
58
+ "45": "T1573",
59
+ "46": "T1583",
60
+ "47": "T1587",
61
+ "48": "T1589",
62
+ "49": "T1606"
63
+ },
64
+ "initializer_range": 0.02,
65
+ "intermediate_size": 3072,
66
+ "label2id": {
67
+ "T1005": 0,
68
+ "T1014": 1,
69
+ "T1021": 2,
70
+ "T1027": 3,
71
+ "T1033": 4,
72
+ "T1036": 5,
73
+ "T1041": 6,
74
+ "T1053": 7,
75
+ "T1055": 8,
76
+ "T1056": 9,
77
+ "T1057": 10,
78
+ "T1059": 11,
79
+ "T1070": 12,
80
+ "T1071": 13,
81
+ "T1078": 14,
82
+ "T1082": 15,
83
+ "T1083": 16,
84
+ "T1105": 17,
85
+ "T1110": 18,
86
+ "T1112": 19,
87
+ "T1113": 20,
88
+ "T1115": 21,
89
+ "T1125": 22,
90
+ "T1132": 23,
91
+ "T1137": 24,
92
+ "T1140": 25,
93
+ "T1189": 26,
94
+ "T1190": 27,
95
+ "T1195": 28,
96
+ "T1203": 29,
97
+ "T1204": 30,
98
+ "T1218": 31,
99
+ "T1486": 32,
100
+ "T1496": 33,
101
+ "T1497": 34,
102
+ "T1499": 35,
103
+ "T1528": 36,
104
+ "T1539": 37,
105
+ "T1543": 38,
106
+ "T1547": 39,
107
+ "T1555": 40,
108
+ "T1557": 41,
109
+ "T1562": 42,
110
+ "T1566": 43,
111
+ "T1571": 44,
112
+ "T1573": 45,
113
+ "T1583": 46,
114
+ "T1587": 47,
115
+ "T1589": 48,
116
+ "T1606": 49
117
+ },
118
+ "layer_norm_eps": 1e-12,
119
+ "max_position_embeddings": 512,
120
+ "model_type": "bert",
121
+ "num_attention_heads": 12,
122
+ "num_hidden_layers": 12,
123
+ "pad_token_id": 0,
124
+ "position_embedding_type": "absolute",
125
+ "problem_type": "multi_label_classification",
126
+ "torch_dtype": "float32",
127
+ "transformers_version": "4.45.2",
128
+ "type_vocab_size": 2,
129
+ "use_cache": true,
130
+ "vocab_size": 28996
131
+ }
Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/loss.pdf ADDED
Binary file (15.6 kB). View file
 
Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4bd6189131e13472ff996201e3958a320b3919656398b9328539641222698de4
3
+ size 433418416
Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/model_params.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "epochs": 100,
3
+ "batch_size": 16,
4
+ "freeze_layers": 0,
5
+ "learning_rate": 2e-05,
6
+ "pos_weight": 20,
7
+ "end_factor": 0.7
8
+ }
Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/special_tokens_map.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": "[CLS]",
3
+ "mask_token": "[MASK]",
4
+ "pad_token": "[PAD]",
5
+ "sep_token": "[SEP]",
6
+ "unk_token": "[UNK]"
7
+ }
Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/stats.json ADDED
@@ -0,0 +1,290 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "epoch": 1,
4
+ "Training Loss": 0.5218500771836673,
5
+ "Valid. Loss": 0.40171955847363505,
6
+ "Valid. Accur.": 0.04910714285714286,
7
+ "Valid. F1": 0.15062380269199266,
8
+ "Training Time": "0:00:49",
9
+ "Validation Time": "0:00:05"
10
+ },
11
+ {
12
+ "epoch": 2,
13
+ "Training Loss": 0.3720079886238812,
14
+ "Valid. Loss": 0.3295426036986084,
15
+ "Valid. Accur.": 0.2531055900621118,
16
+ "Valid. F1": 0.2601924358853706,
17
+ "Training Time": "0:00:49",
18
+ "Validation Time": "0:00:05"
19
+ },
20
+ {
21
+ "epoch": 3,
22
+ "Training Loss": 0.29761856266562253,
23
+ "Valid. Loss": 0.2801070085359754,
24
+ "Valid. Accur.": 0.5737577639751553,
25
+ "Valid. F1": 0.4190712302707645,
26
+ "Training Time": "0:00:49",
27
+ "Validation Time": "0:00:05"
28
+ },
29
+ {
30
+ "epoch": 4,
31
+ "Training Loss": 0.2419349273832157,
32
+ "Valid. Loss": 0.24740627887336647,
33
+ "Valid. Accur.": 0.5916149068322981,
34
+ "Valid. F1": 0.5308531484618441,
35
+ "Training Time": "0:00:49",
36
+ "Validation Time": "0:00:05"
37
+ },
38
+ {
39
+ "epoch": 5,
40
+ "Training Loss": 0.19702210121254804,
41
+ "Valid. Loss": 0.22003652065789261,
42
+ "Valid. Accur.": 0.6269409937888198,
43
+ "Valid. F1": 0.5649495733734863,
44
+ "Training Time": "0:00:49",
45
+ "Validation Time": "0:00:05"
46
+ },
47
+ {
48
+ "epoch": 6,
49
+ "Training Loss": 0.16230859189806554,
50
+ "Valid. Loss": 0.20799052046479133,
51
+ "Valid. Accur.": 0.6706133540372671,
52
+ "Valid. F1": 0.5834491655687306,
53
+ "Training Time": "0:00:49",
54
+ "Validation Time": "0:00:05"
55
+ },
56
+ {
57
+ "epoch": 7,
58
+ "Training Loss": 0.1368219768992558,
59
+ "Valid. Loss": 0.1954012633883946,
60
+ "Valid. Accur.": 0.6653726708074534,
61
+ "Valid. F1": 0.6013530752661188,
62
+ "Training Time": "0:00:49",
63
+ "Validation Time": "0:00:05"
64
+ },
65
+ {
66
+ "epoch": 8,
67
+ "Training Loss": 0.11852534369972477,
68
+ "Valid. Loss": 0.1927645795099308,
69
+ "Valid. Accur.": 0.6966226708074533,
70
+ "Valid. F1": 0.6225721500721502,
71
+ "Training Time": "0:00:49",
72
+ "Validation Time": "0:00:05"
73
+ },
74
+ {
75
+ "epoch": 9,
76
+ "Training Loss": 0.10167272198402863,
77
+ "Valid. Loss": 0.1844042306704363,
78
+ "Valid. Accur.": 0.7156444099378881,
79
+ "Valid. F1": 0.6248214547127591,
80
+ "Training Time": "0:00:49",
81
+ "Validation Time": "0:00:05"
82
+ },
83
+ {
84
+ "epoch": 10,
85
+ "Training Loss": 0.08908803074246477,
86
+ "Valid. Loss": 0.18183399693016278,
87
+ "Valid. Accur.": 0.6964285714285714,
88
+ "Valid. F1": 0.6383935106761194,
89
+ "Training Time": "0:00:49",
90
+ "Validation Time": "0:00:05"
91
+ },
92
+ {
93
+ "epoch": 11,
94
+ "Training Loss": 0.08006644867314346,
95
+ "Valid. Loss": 0.17036882454018468,
96
+ "Valid. Accur.": 0.7059394409937888,
97
+ "Valid. F1": 0.6556197325219065,
98
+ "Training Time": "0:00:49",
99
+ "Validation Time": "0:00:05"
100
+ },
101
+ {
102
+ "epoch": 12,
103
+ "Training Loss": 0.07067212085263844,
104
+ "Valid. Loss": 0.18620774599293652,
105
+ "Valid. Accur.": 0.7317546583850931,
106
+ "Valid. F1": 0.6271198191617445,
107
+ "Training Time": "0:00:48",
108
+ "Validation Time": "0:00:05"
109
+ },
110
+ {
111
+ "epoch": 13,
112
+ "Training Loss": 0.0655433302959421,
113
+ "Valid. Loss": 0.16807802218246068,
114
+ "Valid. Accur.": 0.7441770186335404,
115
+ "Valid. F1": 0.6736384549428027,
116
+ "Training Time": "0:00:49",
117
+ "Validation Time": "0:00:05"
118
+ },
119
+ {
120
+ "epoch": 14,
121
+ "Training Loss": 0.05897777275476508,
122
+ "Valid. Loss": 0.1907085384528629,
123
+ "Valid. Accur.": 0.7373835403726707,
124
+ "Valid. F1": 0.6306907062341844,
125
+ "Training Time": "0:00:49",
126
+ "Validation Time": "0:00:05"
127
+ },
128
+ {
129
+ "epoch": 15,
130
+ "Training Loss": 0.05394657232918636,
131
+ "Valid. Loss": 0.17009701644232872,
132
+ "Valid. Accur.": 0.7358307453416149,
133
+ "Valid. F1": 0.6642862632536545,
134
+ "Training Time": "0:00:49",
135
+ "Validation Time": "0:00:05"
136
+ },
137
+ {
138
+ "epoch": 16,
139
+ "Training Loss": 0.04921937914631112,
140
+ "Valid. Loss": 0.190280746773915,
141
+ "Valid. Accur.": 0.7523291925465838,
142
+ "Valid. F1": 0.6551878777422254,
143
+ "Training Time": "0:00:49",
144
+ "Validation Time": "0:00:05"
145
+ },
146
+ {
147
+ "epoch": 17,
148
+ "Training Loss": 0.04611475241216441,
149
+ "Valid. Loss": 0.16397053235931025,
150
+ "Valid. Accur.": 0.7237965838509316,
151
+ "Valid. F1": 0.6782041690193864,
152
+ "Training Time": "0:00:49",
153
+ "Validation Time": "0:00:05"
154
+ },
155
+ {
156
+ "epoch": 18,
157
+ "Training Loss": 0.040081878506748894,
158
+ "Valid. Loss": 0.18078628079694464,
159
+ "Valid. Accur.": 0.7577639751552795,
160
+ "Valid. F1": 0.6608601804797458,
161
+ "Training Time": "0:00:48",
162
+ "Validation Time": "0:00:05"
163
+ },
164
+ {
165
+ "epoch": 19,
166
+ "Training Loss": 0.036441725886872285,
167
+ "Valid. Loss": 0.1858899235909598,
168
+ "Valid. Accur.": 0.763198757763975,
169
+ "Valid. F1": 0.6592565405608884,
170
+ "Training Time": "0:00:49",
171
+ "Validation Time": "0:00:05"
172
+ },
173
+ {
174
+ "epoch": 20,
175
+ "Training Loss": 0.03337661182218838,
176
+ "Valid. Loss": 0.17944210783233455,
177
+ "Valid. Accur.": 0.7684394409937888,
178
+ "Valid. F1": 0.6817748133509004,
179
+ "Training Time": "0:00:48",
180
+ "Validation Time": "0:00:05"
181
+ },
182
+ {
183
+ "epoch": 21,
184
+ "Training Loss": 0.03148973163411651,
185
+ "Valid. Loss": 0.177766373033208,
186
+ "Valid. Accur.": 0.7686335403726707,
187
+ "Valid. F1": 0.7013429062885584,
188
+ "Training Time": "0:00:49",
189
+ "Validation Time": "0:00:05"
190
+ },
191
+ {
192
+ "epoch": 22,
193
+ "Training Loss": 0.029215847490505324,
194
+ "Valid. Loss": 0.17316711951059477,
195
+ "Valid. Accur.": 0.7509704968944099,
196
+ "Valid. F1": 0.6872536179601396,
197
+ "Training Time": "0:00:49",
198
+ "Validation Time": "0:00:05"
199
+ },
200
+ {
201
+ "epoch": 23,
202
+ "Training Loss": 0.02650427256296835,
203
+ "Valid. Loss": 0.20725420704263578,
204
+ "Valid. Accur.": 0.7740683229813664,
205
+ "Valid. F1": 0.658734184495054,
206
+ "Training Time": "0:00:49",
207
+ "Validation Time": "0:00:05"
208
+ },
209
+ {
210
+ "epoch": 24,
211
+ "Training Loss": 0.02484516504426922,
212
+ "Valid. Loss": 0.19325256733577698,
213
+ "Valid. Accur.": 0.7699922360248447,
214
+ "Valid. F1": 0.6744383796557711,
215
+ "Training Time": "0:00:49",
216
+ "Validation Time": "0:00:05"
217
+ },
218
+ {
219
+ "epoch": 25,
220
+ "Training Loss": 0.023950603223509958,
221
+ "Valid. Loss": 0.19618402767121898,
222
+ "Valid. Accur.": 0.764557453416149,
223
+ "Valid. F1": 0.6791030125269257,
224
+ "Training Time": "0:00:49",
225
+ "Validation Time": "0:00:05"
226
+ },
227
+ {
228
+ "epoch": 26,
229
+ "Training Loss": 0.022427819713796617,
230
+ "Valid. Loss": 0.20332816418509253,
231
+ "Valid. Accur.": 0.7672748447204968,
232
+ "Valid. F1": 0.6638797394232177,
233
+ "Training Time": "0:00:49",
234
+ "Validation Time": "0:00:05"
235
+ },
236
+ {
237
+ "epoch": 27,
238
+ "Training Loss": 0.024106613525150554,
239
+ "Valid. Loss": 0.20077860895189076,
240
+ "Valid. Accur.": 0.7754270186335404,
241
+ "Valid. F1": 0.6762204341552168,
242
+ "Training Time": "0:00:49",
243
+ "Validation Time": "0:00:05"
244
+ },
245
+ {
246
+ "epoch": 28,
247
+ "Training Loss": 0.0212038847478212,
248
+ "Valid. Loss": 0.19677124346440944,
249
+ "Valid. Accur.": 0.7699922360248447,
250
+ "Valid. F1": 0.6682743846330804,
251
+ "Training Time": "0:00:49",
252
+ "Validation Time": "0:00:05"
253
+ },
254
+ {
255
+ "epoch": 29,
256
+ "Training Loss": 0.018433858075206998,
257
+ "Valid. Loss": 0.20629438151009663,
258
+ "Valid. Accur.": 0.765916149068323,
259
+ "Valid. F1": 0.649617317062969,
260
+ "Training Time": "0:00:49",
261
+ "Validation Time": "0:00:05"
262
+ },
263
+ {
264
+ "epoch": 30,
265
+ "Training Loss": 0.016911809441356836,
266
+ "Valid. Loss": 0.198827445730097,
267
+ "Valid. Accur.": 0.7618400621118012,
268
+ "Valid. F1": 0.6616474266474265,
269
+ "Training Time": "0:00:49",
270
+ "Validation Time": "0:00:05"
271
+ },
272
+ {
273
+ "epoch": 31,
274
+ "Training Loss": 0.015451187509959023,
275
+ "Valid. Loss": 0.1943195323818147,
276
+ "Valid. Accur.": 0.7754270186335404,
277
+ "Valid. F1": 0.6826742058263797,
278
+ "Training Time": "0:00:49",
279
+ "Validation Time": "0:00:05"
280
+ },
281
+ {
282
+ "epoch": 32,
283
+ "Training Loss": 0.014025402849032271,
284
+ "Valid. Loss": 0.19630328044895892,
285
+ "Valid. Accur.": 0.7672748447204968,
286
+ "Valid. F1": 0.6790942551812116,
287
+ "Training Time": "0:00:49",
288
+ "Validation Time": "0:00:05"
289
+ }
290
+ ]
Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
Classification/bosch_swipe/1005_bosch_t50_bert-base-cased/tokenizer_config.json ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "[PAD]",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "100": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "101": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "102": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "103": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": false,
45
+ "cls_token": "[CLS]",
46
+ "do_lower_case": false,
47
+ "mask_token": "[MASK]",
48
+ "model_max_length": 512,
49
+ "pad_token": "[PAD]",
50
+ "sep_token": "[SEP]",
51
+ "strip_accents": null,
52
+ "tokenize_chinese_chars": true,
53
+ "tokenizer_class": "BertTokenizer",
54
+ "unk_token": "[UNK]"
55
+ }