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hamedkhaledi
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persain-flair-upos

Token Classification
Flair
PyTorch
Persian
sequence-tagger-model
Model card Files Community
hamedkhaledi commited on
Commit
3364279
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1 Parent(s): c56b4aa

Update Model for 150 epochs

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Files changed (3) hide show
  1. loss.tsv +22 -0
  2. pytorch_model.bin +1 -1
  3. training.log +441 -217
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training.log CHANGED
@@ -1,5 +1,5 @@
1
- 2022-03-26 18:28:28,396 ----------------------------------------------------------------------------------------------------
2
- 2022-03-26 18:28:28,403 Model: "SequenceTagger(
3
  (embeddings): StackedEmbeddings(
4
  (list_embedding_0): WordEmbeddings('fa')
5
  (list_embedding_1): FlairEmbeddings(
@@ -28,227 +28,451 @@
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  (weights): None
29
  (weight_tensor) None
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  )"
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- 2022-03-26 18:28:28,408 ----------------------------------------------------------------------------------------------------
32
- 2022-03-26 18:28:28,413 Corpus: "Corpus: 4798 train + 599 dev + 600 test sentences"
33
- 2022-03-26 18:28:28,417 ----------------------------------------------------------------------------------------------------
34
- 2022-03-26 18:28:28,421 Parameters:
35
- 2022-03-26 18:28:28,425 - learning_rate: "0.1"
36
- 2022-03-26 18:28:28,429 - mini_batch_size: "16"
37
- 2022-03-26 18:28:28,433 - patience: "3"
38
- 2022-03-26 18:28:28,437 - anneal_factor: "0.5"
39
- 2022-03-26 18:28:28,441 - max_epochs: "10"
40
- 2022-03-26 18:28:28,446 - shuffle: "True"
41
- 2022-03-26 18:28:28,450 - train_with_dev: "False"
42
- 2022-03-26 18:28:28,454 - batch_growth_annealing: "False"
43
- 2022-03-26 18:28:28,459 ----------------------------------------------------------------------------------------------------
44
- 2022-03-26 18:28:28,463 Model training base path: "/content/drive/MyDrive/project/data/upos/model"
45
- 2022-03-26 18:28:28,467 ----------------------------------------------------------------------------------------------------
46
- 2022-03-26 18:28:28,471 Device: cpu
47
- 2022-03-26 18:28:28,475 ----------------------------------------------------------------------------------------------------
48
- 2022-03-26 18:28:28,479 Embeddings storage mode: gpu
49
- 2022-03-26 18:28:28,493 ----------------------------------------------------------------------------------------------------
50
- 2022-03-26 18:29:26,634 epoch 3 - iter 30/300 - loss 0.21780701 - samples/sec: 8.26 - lr: 0.100000
51
- 2022-03-26 18:30:25,604 epoch 3 - iter 60/300 - loss 0.21508316 - samples/sec: 8.25 - lr: 0.100000
52
- 2022-03-26 18:31:25,244 epoch 3 - iter 90/300 - loss 0.21826493 - samples/sec: 8.15 - lr: 0.100000
53
- 2022-03-26 18:32:23,342 epoch 3 - iter 120/300 - loss 0.21892146 - samples/sec: 8.37 - lr: 0.100000
54
- 2022-03-26 18:33:21,014 epoch 3 - iter 150/300 - loss 0.21730343 - samples/sec: 8.43 - lr: 0.100000
55
- 2022-03-26 18:34:14,599 epoch 3 - iter 180/300 - loss 0.21702189 - samples/sec: 9.08 - lr: 0.100000
56
- 2022-03-26 18:35:12,356 epoch 3 - iter 210/300 - loss 0.21630755 - samples/sec: 8.42 - lr: 0.100000
57
- 2022-03-26 18:36:06,856 epoch 3 - iter 240/300 - loss 0.21400964 - samples/sec: 8.93 - lr: 0.100000
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- 2022-03-26 18:37:08,687 epoch 3 - iter 270/300 - loss 0.21247675 - samples/sec: 7.86 - lr: 0.100000
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- 2022-03-26 18:38:01,564 epoch 3 - iter 300/300 - loss 0.21083426 - samples/sec: 9.21 - lr: 0.100000
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- 2022-03-26 18:38:02,290 ----------------------------------------------------------------------------------------------------
61
- 2022-03-26 18:38:02,296 EPOCH 3 done: loss 0.2108 - lr 0.1000000
62
- 2022-03-26 18:38:37,055 DEV : loss 0.10177797079086304 - f1-score (micro avg) 0.9632
63
- 2022-03-26 18:38:37,066 BAD EPOCHS (no improvement): 0
64
- 2022-03-26 18:38:39,211 saving best model
65
- 2022-03-26 18:38:41,788 ----------------------------------------------------------------------------------------------------
66
- 2022-03-26 18:39:44,831 epoch 4 - iter 30/300 - loss 0.18688576 - samples/sec: 7.62 - lr: 0.100000
67
- 2022-03-26 18:40:41,400 epoch 4 - iter 60/300 - loss 0.19300362 - samples/sec: 8.60 - lr: 0.100000
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- 2022-03-26 18:41:35,550 epoch 4 - iter 90/300 - loss 0.18890585 - samples/sec: 8.99 - lr: 0.100000
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- 2022-03-26 18:42:35,593 epoch 4 - iter 120/300 - loss 0.18624418 - samples/sec: 8.10 - lr: 0.100000
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- 2022-03-26 18:43:33,546 epoch 4 - iter 150/300 - loss 0.18554402 - samples/sec: 8.40 - lr: 0.100000
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- 2022-03-26 18:44:33,914 epoch 4 - iter 180/300 - loss 0.18584095 - samples/sec: 8.05 - lr: 0.100000
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- 2022-03-26 18:45:29,917 epoch 4 - iter 210/300 - loss 0.18589026 - samples/sec: 8.69 - lr: 0.100000
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- 2022-03-26 18:46:32,292 epoch 4 - iter 240/300 - loss 0.18559392 - samples/sec: 7.79 - lr: 0.100000
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- 2022-03-26 18:47:30,182 epoch 4 - iter 270/300 - loss 0.18575698 - samples/sec: 8.40 - lr: 0.100000
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- 2022-03-26 18:48:26,907 epoch 4 - iter 300/300 - loss 0.18421186 - samples/sec: 8.57 - lr: 0.100000
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- 2022-03-26 18:48:27,666 ----------------------------------------------------------------------------------------------------
77
- 2022-03-26 18:48:27,668 EPOCH 4 done: loss 0.1842 - lr 0.1000000
78
- 2022-03-26 18:49:02,130 DEV : loss 0.09465233236551285 - f1-score (micro avg) 0.9669
79
- 2022-03-26 18:49:02,144 BAD EPOCHS (no improvement): 0
80
- 2022-03-26 18:49:04,090 saving best model
81
- 2022-03-26 18:49:05,985 ----------------------------------------------------------------------------------------------------
82
- 2022-03-26 18:50:06,015 epoch 5 - iter 30/300 - loss 0.16421632 - samples/sec: 8.00 - lr: 0.100000
83
- 2022-03-26 18:51:05,990 epoch 5 - iter 60/300 - loss 0.17351129 - samples/sec: 8.11 - lr: 0.100000
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- 2022-03-26 18:52:04,378 epoch 5 - iter 90/300 - loss 0.17586507 - samples/sec: 8.33 - lr: 0.100000
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- 2022-03-26 18:53:05,501 epoch 5 - iter 120/300 - loss 0.17394961 - samples/sec: 7.95 - lr: 0.100000
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- 2022-03-26 18:54:00,916 epoch 5 - iter 150/300 - loss 0.17300689 - samples/sec: 8.78 - lr: 0.100000
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- 2022-03-26 18:54:57,715 epoch 5 - iter 180/300 - loss 0.17261280 - samples/sec: 8.57 - lr: 0.100000
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- 2022-03-26 18:55:57,744 epoch 5 - iter 210/300 - loss 0.17164017 - samples/sec: 8.10 - lr: 0.100000
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- 2022-03-26 18:56:56,119 epoch 5 - iter 240/300 - loss 0.17097431 - samples/sec: 8.33 - lr: 0.100000
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- 2022-03-26 18:57:55,783 epoch 5 - iter 270/300 - loss 0.16989670 - samples/sec: 8.15 - lr: 0.100000
91
- 2022-03-26 18:58:51,241 epoch 5 - iter 300/300 - loss 0.16877445 - samples/sec: 8.78 - lr: 0.100000
92
- 2022-03-26 18:58:51,971 ----------------------------------------------------------------------------------------------------
93
- 2022-03-26 18:58:51,977 EPOCH 5 done: loss 0.1688 - lr 0.1000000
94
- 2022-03-26 18:59:26,003 DEV : loss 0.0927693173289299 - f1-score (micro avg) 0.9685
95
- 2022-03-26 18:59:26,016 BAD EPOCHS (no improvement): 0
96
- 2022-03-26 18:59:27,881 saving best model
97
- 2022-03-26 18:59:29,894 ----------------------------------------------------------------------------------------------------
98
- 2022-03-26 19:00:33,149 epoch 6 - iter 30/300 - loss 0.14432260 - samples/sec: 7.63 - lr: 0.100000
99
- 2022-03-26 19:01:33,485 epoch 6 - iter 60/300 - loss 0.15103870 - samples/sec: 8.06 - lr: 0.100000
100
- 2022-03-26 19:02:31,837 epoch 6 - iter 90/300 - loss 0.15365191 - samples/sec: 8.34 - lr: 0.100000
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- 2022-03-26 19:03:32,414 epoch 6 - iter 120/300 - loss 0.15042872 - samples/sec: 8.03 - lr: 0.100000
102
- 2022-03-26 19:04:26,597 epoch 6 - iter 150/300 - loss 0.15368852 - samples/sec: 8.98 - lr: 0.100000
103
- 2022-03-26 19:05:24,107 epoch 6 - iter 180/300 - loss 0.15555481 - samples/sec: 8.46 - lr: 0.100000
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- 2022-03-26 19:06:19,500 epoch 6 - iter 210/300 - loss 0.15523396 - samples/sec: 8.79 - lr: 0.100000
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- 2022-03-26 19:07:17,219 epoch 6 - iter 240/300 - loss 0.15404900 - samples/sec: 8.43 - lr: 0.100000
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- 2022-03-26 19:08:14,604 epoch 6 - iter 270/300 - loss 0.15404996 - samples/sec: 8.47 - lr: 0.100000
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- 2022-03-26 19:09:13,802 epoch 6 - iter 300/300 - loss 0.15441547 - samples/sec: 8.21 - lr: 0.100000
108
- 2022-03-26 19:09:14,566 ----------------------------------------------------------------------------------------------------
109
- 2022-03-26 19:09:14,572 EPOCH 6 done: loss 0.1544 - lr 0.1000000
110
- 2022-03-26 19:09:49,953 DEV : loss 0.08772891014814377 - f1-score (micro avg) 0.9694
111
- 2022-03-26 19:09:49,964 BAD EPOCHS (no improvement): 0
112
- 2022-03-26 19:09:51,889 saving best model
113
- 2022-03-26 19:09:54,018 ----------------------------------------------------------------------------------------------------
114
- 2022-03-26 19:10:52,509 epoch 7 - iter 30/300 - loss 0.15358505 - samples/sec: 8.21 - lr: 0.100000
115
- 2022-03-26 19:11:48,362 epoch 7 - iter 60/300 - loss 0.14751687 - samples/sec: 8.71 - lr: 0.100000
116
- 2022-03-26 19:12:41,652 epoch 7 - iter 90/300 - loss 0.14377441 - samples/sec: 9.14 - lr: 0.100000
117
- 2022-03-26 19:13:35,573 epoch 7 - iter 120/300 - loss 0.14545907 - samples/sec: 9.02 - lr: 0.100000
118
- 2022-03-26 19:14:34,920 epoch 7 - iter 150/300 - loss 0.14192736 - samples/sec: 8.19 - lr: 0.100000
119
- 2022-03-26 19:15:33,403 epoch 7 - iter 180/300 - loss 0.14151937 - samples/sec: 8.32 - lr: 0.100000
120
- 2022-03-26 19:16:29,738 epoch 7 - iter 210/300 - loss 0.14126257 - samples/sec: 8.64 - lr: 0.100000
121
- 2022-03-26 19:17:31,054 epoch 7 - iter 240/300 - loss 0.14257588 - samples/sec: 7.92 - lr: 0.100000
122
- 2022-03-26 19:18:29,561 epoch 7 - iter 270/300 - loss 0.14341101 - samples/sec: 8.31 - lr: 0.100000
123
- 2022-03-26 19:19:27,476 epoch 7 - iter 300/300 - loss 0.14375555 - samples/sec: 8.40 - lr: 0.100000
124
- 2022-03-26 19:19:28,260 ----------------------------------------------------------------------------------------------------
125
- 2022-03-26 19:19:28,265 EPOCH 7 done: loss 0.1438 - lr 0.1000000
126
- 2022-03-26 19:20:02,111 DEV : loss 0.07945315539836884 - f1-score (micro avg) 0.9726
127
- 2022-03-26 19:20:02,123 BAD EPOCHS (no improvement): 0
128
- 2022-03-26 19:20:04,028 saving best model
129
- 2022-03-26 19:20:06,346 ----------------------------------------------------------------------------------------------------
130
- 2022-03-26 19:21:11,691 epoch 8 - iter 30/300 - loss 0.13364310 - samples/sec: 7.35 - lr: 0.100000
131
- 2022-03-26 19:22:12,558 epoch 8 - iter 60/300 - loss 0.13855684 - samples/sec: 7.99 - lr: 0.100000
132
- 2022-03-26 19:23:09,829 epoch 8 - iter 90/300 - loss 0.13394536 - samples/sec: 8.49 - lr: 0.100000
133
- 2022-03-26 19:24:08,887 epoch 8 - iter 120/300 - loss 0.13508951 - samples/sec: 8.23 - lr: 0.100000
134
- 2022-03-26 19:25:06,880 epoch 8 - iter 150/300 - loss 0.13515692 - samples/sec: 8.38 - lr: 0.100000
135
- 2022-03-26 19:26:08,197 epoch 8 - iter 180/300 - loss 0.13378825 - samples/sec: 7.92 - lr: 0.100000
136
- 2022-03-26 19:27:00,020 epoch 8 - iter 210/300 - loss 0.13404063 - samples/sec: 9.40 - lr: 0.100000
137
- 2022-03-26 19:27:53,870 epoch 8 - iter 240/300 - loss 0.13436135 - samples/sec: 9.04 - lr: 0.100000
138
- 2022-03-26 19:28:49,337 epoch 8 - iter 270/300 - loss 0.13531524 - samples/sec: 8.78 - lr: 0.100000
139
- 2022-03-26 19:29:42,873 epoch 8 - iter 300/300 - loss 0.13536904 - samples/sec: 9.09 - lr: 0.100000
140
- 2022-03-26 19:29:43,631 ----------------------------------------------------------------------------------------------------
141
- 2022-03-26 19:29:43,637 EPOCH 8 done: loss 0.1354 - lr 0.1000000
142
- 2022-03-26 19:30:17,094 DEV : loss 0.08174271136522293 - f1-score (micro avg) 0.9722
143
- 2022-03-26 19:30:17,107 BAD EPOCHS (no improvement): 1
144
- 2022-03-26 19:30:18,985 ----------------------------------------------------------------------------------------------------
145
- 2022-03-26 19:31:16,984 epoch 9 - iter 30/300 - loss 0.13113106 - samples/sec: 8.28 - lr: 0.100000
146
- 2022-03-26 19:32:17,088 epoch 9 - iter 60/300 - loss 0.12762148 - samples/sec: 8.09 - lr: 0.100000
147
- 2022-03-26 19:33:13,388 epoch 9 - iter 90/300 - loss 0.12918592 - samples/sec: 8.64 - lr: 0.100000
148
- 2022-03-26 19:34:09,153 epoch 9 - iter 120/300 - loss 0.12757417 - samples/sec: 8.72 - lr: 0.100000
149
- 2022-03-26 19:35:06,603 epoch 9 - iter 150/300 - loss 0.12579949 - samples/sec: 8.46 - lr: 0.100000
150
- 2022-03-26 19:36:04,584 epoch 9 - iter 180/300 - loss 0.12581085 - samples/sec: 8.39 - lr: 0.100000
151
- 2022-03-26 19:36:57,995 epoch 9 - iter 210/300 - loss 0.12706659 - samples/sec: 9.11 - lr: 0.100000
152
- 2022-03-26 19:38:00,292 epoch 9 - iter 240/300 - loss 0.12693307 - samples/sec: 7.80 - lr: 0.100000
153
- 2022-03-26 19:38:53,107 epoch 9 - iter 270/300 - loss 0.12635006 - samples/sec: 9.22 - lr: 0.100000
154
- 2022-03-26 19:39:48,616 epoch 9 - iter 300/300 - loss 0.12747396 - samples/sec: 8.77 - lr: 0.100000
155
- 2022-03-26 19:39:49,345 ----------------------------------------------------------------------------------------------------
156
- 2022-03-26 19:39:49,350 EPOCH 9 done: loss 0.1275 - lr 0.1000000
157
- 2022-03-26 19:40:23,288 DEV : loss 0.07768776267766953 - f1-score (micro avg) 0.9723
158
- 2022-03-26 19:40:23,299 BAD EPOCHS (no improvement): 2
159
- 2022-03-26 19:40:25,255 ----------------------------------------------------------------------------------------------------
160
- 2022-03-26 19:41:20,498 epoch 10 - iter 30/300 - loss 0.12360019 - samples/sec: 8.70 - lr: 0.100000
161
- 2022-03-26 19:42:19,947 epoch 10 - iter 60/300 - loss 0.12434835 - samples/sec: 8.18 - lr: 0.100000
162
- 2022-03-26 19:43:17,427 epoch 10 - iter 90/300 - loss 0.12278036 - samples/sec: 8.46 - lr: 0.100000
163
- 2022-03-26 19:44:17,158 epoch 10 - iter 120/300 - loss 0.12117075 - samples/sec: 8.14 - lr: 0.100000
164
- 2022-03-26 19:45:17,232 epoch 10 - iter 150/300 - loss 0.12210352 - samples/sec: 8.09 - lr: 0.100000
165
- 2022-03-26 19:46:19,236 epoch 10 - iter 180/300 - loss 0.12216010 - samples/sec: 7.84 - lr: 0.100000
166
- 2022-03-26 19:47:13,956 epoch 10 - iter 210/300 - loss 0.12109513 - samples/sec: 8.90 - lr: 0.100000
167
- 2022-03-26 19:48:10,196 epoch 10 - iter 240/300 - loss 0.12159190 - samples/sec: 8.65 - lr: 0.100000
168
- 2022-03-26 19:49:07,814 epoch 10 - iter 270/300 - loss 0.12185842 - samples/sec: 8.44 - lr: 0.100000
169
- 2022-03-26 19:50:05,726 epoch 10 - iter 300/300 - loss 0.12154911 - samples/sec: 8.40 - lr: 0.100000
170
- 2022-03-26 19:50:06,487 ----------------------------------------------------------------------------------------------------
171
- 2022-03-26 19:50:06,493 EPOCH 10 done: loss 0.1215 - lr 0.1000000
172
- 2022-03-26 19:50:42,216 DEV : loss 0.0763716846704483 - f1-score (micro avg) 0.9749
173
- 2022-03-26 19:50:42,228 BAD EPOCHS (no improvement): 0
174
- 2022-03-26 19:50:44,090 saving best model
175
- 2022-03-26 19:50:48,236 ----------------------------------------------------------------------------------------------------
176
- 2022-03-26 19:50:48,294 loading file /content/drive/MyDrive/project/data/upos/model/best-model.pt
177
- 2022-03-26 19:58:45,547 0.9732 0.9732 0.9732 0.9732
178
- 2022-03-26 19:58:45,553
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
179
  Results:
180
- - F-score (micro) 0.9732
181
- - F-score (macro) 0.9036
182
- - Accuracy 0.9732
183
 
184
  By class:
185
  precision recall f1-score support
186
 
187
- NOUN 0.9705 0.9850 0.9777 6420
188
- ADP 0.9921 0.9900 0.9911 1909
189
- ADJ 0.9293 0.8964 0.9126 1525
190
  PUNCT 1.0000 1.0000 1.0000 1365
191
- VERB 0.9855 0.9553 0.9702 1141
192
- CCONJ 0.9950 0.9937 0.9943 794
193
- AUX 0.9402 0.9799 0.9596 546
194
- PRON 0.9586 0.9865 0.9724 517
195
- SCONJ 0.9857 0.9737 0.9796 494
196
- NUM 0.9948 0.9844 0.9896 385
197
- ADV 0.9277 0.8867 0.9068 362
198
- DET 0.9836 0.9614 0.9724 311
199
  PART 0.9916 1.0000 0.9958 237
200
- INTJ 1.0000 0.6000 0.7500 10
201
- X 0.3333 0.1250 0.1818 8
202
 
203
- micro avg 0.9732 0.9732 0.9732 16024
204
- macro avg 0.9325 0.8879 0.9036 16024
205
- weighted avg 0.9729 0.9732 0.9729 16024
206
- samples avg 0.9732 0.9732 0.9732 16024
207
 
208
- 2022-03-26 19:58:45,558 ----------------------------------------------------------------------------------------------------
209
- iter 120/300 - loss 0.12117075 - samples/sec: 8.14 - lr: 0.100000
210
- 2022-03-26 19:45:17,232 epoch 10 - iter 150/300 - loss 0.12210352 - samples/sec: 8.09 - lr: 0.100000
211
- 2022-03-26 19:46:19,236 epoch 10 - iter 180/300 - loss 0.12216010 - samples/sec: 7.84 - lr: 0.100000
212
- 2022-03-26 19:47:13,956 epoch 10 - iter 210/300 - loss 0.12109513 - samples/sec: 8.90 - lr: 0.100000
213
- 2022-03-26 19:48:10,196 epoch 10 - iter 240/300 - loss 0.12159190 - samples/sec: 8.65 - lr: 0.100000
214
- 2022-03-26 19:49:07,814 epoch 10 - iter 270/300 - loss 0.12185842 - samples/sec: 8.44 - lr: 0.100000
215
- 2022-03-26 19:50:05,726 epoch 10 - iter 300/300 - loss 0.12154911 - samples/sec: 8.40 - lr: 0.100000
216
- 2022-03-26 19:50:06,487 ----------------------------------------------------------------------------------------------------
217
- 2022-03-26 19:50:06,493 EPOCH 10 done: loss 0.1215 - lr 0.1000000
218
- 2022-03-26 19:50:42,216 DEV : loss 0.0763716846704483 - f1-score (micro avg) 0.9749
219
- 2022-03-26 19:50:42,228 BAD EPOCHS (no improvement): 0
220
- 2022-03-26 19:50:44,090 saving best model
221
- 2022-03-26 19:50:48,236 ----------------------------------------------------------------------------------------------------
222
- 2022-03-26 19:50:48,294 loading file /content/drive/MyDrive/project/data/upos/model/best-model.pt
223
- 2022-03-26 19:58:45,547 0.9732 0.9732 0.9732 0.9732
224
- 2022-03-26 19:58:45,553
225
- Results:
226
- - F-score (micro) 0.9732
227
- - F-score (macro) 0.9036
228
- - Accuracy 0.9732
229
-
230
- By class:
231
- precision recall f1-score support
232
-
233
- NOUN 0.9705 0.9850 0.9777 6420
234
- ADP 0.9921 0.9900 0.9911 1909
235
- ADJ 0.9293 0.8964 0.9126 1525
236
- PUNCT 1.0000 1.0000 1.0000 1365
237
- VERB 0.9855 0.9553 0.9702 1141
238
- CCONJ 0.9950 0.9937 0.9943 794
239
- AUX 0.9402 0.9799 0.9596 546
240
- PRON 0.9586 0.9865 0.9724 517
241
- SCONJ 0.9857 0.9737 0.9796 494
242
- NUM 0.9948 0.9844 0.9896 385
243
- ADV 0.9277 0.8867 0.9068 362
244
- DET 0.9836 0.9614 0.9724 311
245
- PART 0.9916 1.0000 0.9958 237
246
- INTJ 1.0000 0.6000 0.7500 10
247
- X 0.3333 0.1250 0.1818 8
248
-
249
- micro avg 0.9732 0.9732 0.9732 16024
250
- macro avg 0.9325 0.8879 0.9036 16024
251
- weighted avg 0.9729 0.9732 0.9729 16024
252
- samples avg 0.9732 0.9732 0.9732 16024
253
-
254
- 2022-03-26 19:58:45,558 ----------------------------------------------------------------------------------------------------
1
+ 2022-03-30 07:52:04,950 ----------------------------------------------------------------------------------------------------
2
+ 2022-03-30 07:52:04,958 Model: "SequenceTagger(
3
  (embeddings): StackedEmbeddings(
4
  (list_embedding_0): WordEmbeddings('fa')
5
  (list_embedding_1): FlairEmbeddings(
28
  (weights): None
29
  (weight_tensor) None
30
  )"
31
+ 2022-03-30 07:52:04,960 ----------------------------------------------------------------------------------------------------
32
+ 2022-03-30 07:52:04,967 Corpus: "Corpus: 4798 train + 599 dev + 600 test sentences"
33
+ 2022-03-30 07:52:04,970 ----------------------------------------------------------------------------------------------------
34
+ 2022-03-30 07:52:04,973 Parameters:
35
+ 2022-03-30 07:52:04,977 - learning_rate: "0.00625"
36
+ 2022-03-30 07:52:04,986 - mini_batch_size: "16"
37
+ 2022-03-30 07:52:04,993 - patience: "3"
38
+ 2022-03-30 07:52:04,995 - anneal_factor: "0.5"
39
+ 2022-03-30 07:52:04,999 - max_epochs: "200"
40
+ 2022-03-30 07:52:05,001 - shuffle: "True"
41
+ 2022-03-30 07:52:05,004 - train_with_dev: "False"
42
+ 2022-03-30 07:52:05,016 - batch_growth_annealing: "False"
43
+ 2022-03-30 07:52:05,019 ----------------------------------------------------------------------------------------------------
44
+ 2022-03-30 07:52:05,021 Model training base path: "/content/drive/MyDrive/project/data/upos/model"
45
+ 2022-03-30 07:52:05,023 ----------------------------------------------------------------------------------------------------
46
+ 2022-03-30 07:52:05,027 Device: cpu
47
+ 2022-03-30 07:52:05,029 ----------------------------------------------------------------------------------------------------
48
+ 2022-03-30 07:52:05,030 Embeddings storage mode: gpu
49
+ 2022-03-30 07:52:05,718 ----------------------------------------------------------------------------------------------------
50
+ 2022-03-30 08:00:20,625 epoch 130 - iter 30/300 - loss 0.03267748 - samples/sec: 0.97 - lr: 0.006250
51
+ 2022-03-30 08:08:05,510 epoch 130 - iter 60/300 - loss 0.03183758 - samples/sec: 1.03 - lr: 0.006250
52
+ 2022-03-30 08:16:39,338 epoch 130 - iter 90/300 - loss 0.03310138 - samples/sec: 0.94 - lr: 0.006250
53
+ 2022-03-30 08:24:54,316 epoch 130 - iter 120/300 - loss 0.03337116 - samples/sec: 0.97 - lr: 0.006250
54
+ 2022-03-30 08:32:46,929 epoch 130 - iter 150/300 - loss 0.03255506 - samples/sec: 1.02 - lr: 0.006250
55
+ 2022-03-30 08:41:23,256 epoch 130 - iter 180/300 - loss 0.03223376 - samples/sec: 0.93 - lr: 0.006250
56
+ 2022-03-30 08:49:42,419 epoch 130 - iter 210/300 - loss 0.03220594 - samples/sec: 0.96 - lr: 0.006250
57
+ 2022-03-30 08:57:44,963 epoch 130 - iter 240/300 - loss 0.03212158 - samples/sec: 1.00 - lr: 0.006250
58
+ 2022-03-30 09:05:42,177 epoch 130 - iter 270/300 - loss 0.03242742 - samples/sec: 1.01 - lr: 0.006250
59
+ 2022-03-30 09:13:37,521 epoch 130 - iter 300/300 - loss 0.03227379 - samples/sec: 1.01 - lr: 0.006250
60
+ 2022-03-30 09:13:38,432 ----------------------------------------------------------------------------------------------------
61
+ 2022-03-30 09:13:38,441 EPOCH 130 done: loss 0.0323 - lr 0.0062500
62
+ 2022-03-30 09:22:28,190 DEV : loss 0.10123619437217712 - f1-score (micro avg) 0.9805
63
+ 2022-03-30 09:22:28,203 BAD EPOCHS (no improvement): 0
64
+ 2022-03-30 09:22:30,400 saving best model
65
+ 2022-03-30 09:22:32,790 ----------------------------------------------------------------------------------------------------
66
+ 2022-03-30 09:23:33,426 epoch 131 - iter 30/300 - loss 0.03217341 - samples/sec: 7.92 - lr: 0.006250
67
+ 2022-03-30 09:24:34,156 epoch 131 - iter 60/300 - loss 0.03280988 - samples/sec: 8.05 - lr: 0.006250
68
+ 2022-03-30 09:25:36,132 epoch 131 - iter 90/300 - loss 0.03307191 - samples/sec: 7.90 - lr: 0.006250
69
+ 2022-03-30 09:26:37,730 epoch 131 - iter 120/300 - loss 0.03208308 - samples/sec: 7.92 - lr: 0.006250
70
+ 2022-03-30 09:27:42,746 epoch 131 - iter 150/300 - loss 0.03185314 - samples/sec: 7.50 - lr: 0.006250
71
+ 2022-03-30 09:28:51,373 epoch 131 - iter 180/300 - loss 0.03166563 - samples/sec: 7.12 - lr: 0.006250
72
+ 2022-03-30 09:29:55,576 epoch 131 - iter 210/300 - loss 0.03182935 - samples/sec: 7.62 - lr: 0.006250
73
+ 2022-03-30 09:31:03,605 epoch 131 - iter 240/300 - loss 0.03156167 - samples/sec: 7.18 - lr: 0.006250
74
+ 2022-03-30 09:32:15,124 epoch 131 - iter 270/300 - loss 0.03182382 - samples/sec: 6.84 - lr: 0.006250
75
+ 2022-03-30 09:33:17,573 epoch 131 - iter 300/300 - loss 0.03189648 - samples/sec: 7.84 - lr: 0.006250
76
+ 2022-03-30 09:33:18,844 ----------------------------------------------------------------------------------------------------
77
+ 2022-03-30 09:33:18,855 EPOCH 131 done: loss 0.0319 - lr 0.0062500
78
+ 2022-03-30 09:33:55,554 DEV : loss 0.10127950459718704 - f1-score (micro avg) 0.9806
79
+ 2022-03-30 09:33:55,573 BAD EPOCHS (no improvement): 0
80
+ 2022-03-30 09:33:57,817 saving best model
81
+ 2022-03-30 09:34:00,204 ----------------------------------------------------------------------------------------------------
82
+ 2022-03-30 09:35:12,825 epoch 132 - iter 30/300 - loss 0.03402971 - samples/sec: 6.61 - lr: 0.006250
83
+ 2022-03-30 09:36:22,700 epoch 132 - iter 60/300 - loss 0.03264956 - samples/sec: 7.02 - lr: 0.006250
84
+ 2022-03-30 09:37:32,308 epoch 132 - iter 90/300 - loss 0.03345275 - samples/sec: 7.05 - lr: 0.006250
85
+ 2022-03-30 09:38:38,115 epoch 132 - iter 120/300 - loss 0.03354812 - samples/sec: 7.46 - lr: 0.006250
86
+ 2022-03-30 09:39:44,471 epoch 132 - iter 150/300 - loss 0.03282504 - samples/sec: 7.36 - lr: 0.006250
87
+ 2022-03-30 09:40:49,418 epoch 132 - iter 180/300 - loss 0.03263641 - samples/sec: 7.53 - lr: 0.006250
88
+ 2022-03-30 09:41:55,844 epoch 132 - iter 210/300 - loss 0.03231144 - samples/sec: 7.37 - lr: 0.006250
89
+ 2022-03-30 09:42:55,650 epoch 132 - iter 240/300 - loss 0.03227446 - samples/sec: 8.18 - lr: 0.006250
90
+ 2022-03-30 09:43:58,522 epoch 132 - iter 270/300 - loss 0.03245053 - samples/sec: 7.81 - lr: 0.006250
91
+ 2022-03-30 09:45:03,752 epoch 132 - iter 300/300 - loss 0.03224173 - samples/sec: 7.49 - lr: 0.006250
92
+ 2022-03-30 09:45:05,008 ----------------------------------------------------------------------------------------------------
93
+ 2022-03-30 09:45:05,017 EPOCH 132 done: loss 0.0322 - lr 0.0062500
94
+ 2022-03-30 09:45:42,784 DEV : loss 0.10122731328010559 - f1-score (micro avg) 0.9802
95
+ 2022-03-30 09:45:42,797 BAD EPOCHS (no improvement): 1
96
+ 2022-03-30 09:45:44,948 ----------------------------------------------------------------------------------------------------
97
+ 2022-03-30 09:46:55,002 epoch 133 - iter 30/300 - loss 0.03269861 - samples/sec: 6.85 - lr: 0.006250
98
+ 2022-03-30 09:47:56,852 epoch 133 - iter 60/300 - loss 0.03223739 - samples/sec: 7.96 - lr: 0.006250
99
+ 2022-03-30 09:48:58,825 epoch 133 - iter 90/300 - loss 0.03190079 - samples/sec: 7.90 - lr: 0.006250
100
+ 2022-03-30 09:50:06,466 epoch 133 - iter 120/300 - loss 0.03202131 - samples/sec: 7.22 - lr: 0.006250
101
+ 2022-03-30 09:51:13,277 epoch 133 - iter 150/300 - loss 0.03154350 - samples/sec: 7.38 - lr: 0.006250
102
+ 2022-03-30 09:52:18,131 epoch 133 - iter 180/300 - loss 0.03188183 - samples/sec: 7.56 - lr: 0.006250
103
+ 2022-03-30 09:53:23,838 epoch 133 - iter 210/300 - loss 0.03109959 - samples/sec: 7.44 - lr: 0.006250
104
+ 2022-03-30 09:54:30,950 epoch 133 - iter 240/300 - loss 0.03185256 - samples/sec: 7.28 - lr: 0.006250
105
+ 2022-03-30 09:55:36,895 epoch 133 - iter 270/300 - loss 0.03213568 - samples/sec: 7.44 - lr: 0.006250
106
+ 2022-03-30 09:56:44,302 epoch 133 - iter 300/300 - loss 0.03195716 - samples/sec: 7.26 - lr: 0.006250
107
+ 2022-03-30 09:56:45,573 ----------------------------------------------------------------------------------------------------
108
+ 2022-03-30 09:56:45,584 EPOCH 133 done: loss 0.0320 - lr 0.0062500
109
+ 2022-03-30 09:57:21,079 DEV : loss 0.10153035819530487 - f1-score (micro avg) 0.9801
110
+ 2022-03-30 09:57:21,095 BAD EPOCHS (no improvement): 2
111
+ 2022-03-30 09:57:23,256 ----------------------------------------------------------------------------------------------------
112
+ 2022-03-30 09:58:25,099 epoch 134 - iter 30/300 - loss 0.03314154 - samples/sec: 7.76 - lr: 0.006250
113
+ 2022-03-30 09:59:30,588 epoch 134 - iter 60/300 - loss 0.03131403 - samples/sec: 7.47 - lr: 0.006250
114
+ 2022-03-30 10:00:32,373 epoch 134 - iter 90/300 - loss 0.03143065 - samples/sec: 7.92 - lr: 0.006250
115
+ 2022-03-30 10:01:36,218 epoch 134 - iter 120/300 - loss 0.03178706 - samples/sec: 7.69 - lr: 0.006250
116
+ 2022-03-30 10:02:44,074 epoch 134 - iter 150/300 - loss 0.03166911 - samples/sec: 7.23 - lr: 0.006250
117
+ 2022-03-30 10:03:53,060 epoch 134 - iter 180/300 - loss 0.03108413 - samples/sec: 7.10 - lr: 0.006250
118
+ 2022-03-30 10:04:57,710 epoch 134 - iter 210/300 - loss 0.03049568 - samples/sec: 7.56 - lr: 0.006250
119
+ 2022-03-30 10:05:58,539 epoch 134 - iter 240/300 - loss 0.03053009 - samples/sec: 8.13 - lr: 0.006250
120
+ 2022-03-30 10:06:59,522 epoch 134 - iter 270/300 - loss 0.03073424 - samples/sec: 8.01 - lr: 0.006250
121
+ 2022-03-30 10:08:03,948 epoch 134 - iter 300/300 - loss 0.03153425 - samples/sec: 7.65 - lr: 0.006250
122
+ 2022-03-30 10:08:05,164 ----------------------------------------------------------------------------------------------------
123
+ 2022-03-30 10:08:05,172 EPOCH 134 done: loss 0.0315 - lr 0.0062500
124
+ 2022-03-30 10:08:40,529 DEV : loss 0.10191945731639862 - f1-score (micro avg) 0.9799
125
+ 2022-03-30 10:08:40,545 BAD EPOCHS (no improvement): 3
126
+ 2022-03-30 10:08:42,752 ----------------------------------------------------------------------------------------------------
127
+ 2022-03-30 10:09:49,186 epoch 135 - iter 30/300 - loss 0.02855664 - samples/sec: 7.23 - lr: 0.006250
128
+ 2022-03-30 10:10:49,961 epoch 135 - iter 60/300 - loss 0.02798751 - samples/sec: 8.03 - lr: 0.006250
129
+ 2022-03-30 10:11:48,628 epoch 135 - iter 90/300 - loss 0.02862530 - samples/sec: 8.36 - lr: 0.006250
130
+ 2022-03-30 10:12:46,050 epoch 135 - iter 120/300 - loss 0.02734978 - samples/sec: 8.50 - lr: 0.006250
131
+ 2022-03-30 10:13:48,289 epoch 135 - iter 150/300 - loss 0.02787561 - samples/sec: 7.83 - lr: 0.006250
132
+ 2022-03-30 10:14:48,840 epoch 135 - iter 180/300 - loss 0.02805375 - samples/sec: 8.05 - lr: 0.006250
133
+ 2022-03-30 10:15:53,142 epoch 135 - iter 210/300 - loss 0.02819357 - samples/sec: 7.57 - lr: 0.006250
134
+ 2022-03-30 10:16:55,614 epoch 135 - iter 240/300 - loss 0.02857102 - samples/sec: 7.81 - lr: 0.006250
135
+ 2022-03-30 10:17:57,771 epoch 135 - iter 270/300 - loss 0.02854189 - samples/sec: 7.90 - lr: 0.006250
136
+ 2022-03-30 10:18:56,900 epoch 135 - iter 300/300 - loss 0.02924464 - samples/sec: 8.32 - lr: 0.006250
137
+ 2022-03-30 10:18:58,001 ----------------------------------------------------------------------------------------------------
138
+ 2022-03-30 10:18:58,011 EPOCH 135 done: loss 0.0292 - lr 0.0062500
139
+ 2022-03-30 10:19:37,487 DEV : loss 0.10203799605369568 - f1-score (micro avg) 0.9799
140
+ 2022-03-30 10:19:37,508 BAD EPOCHS (no improvement): 4
141
+ 2022-03-30 10:19:40,464 ----------------------------------------------------------------------------------------------------
142
+ 2022-03-30 10:20:42,433 epoch 136 - iter 30/300 - loss 0.02678492 - samples/sec: 7.75 - lr: 0.003125
143
+ 2022-03-30 10:21:45,796 epoch 136 - iter 60/300 - loss 0.02964621 - samples/sec: 7.75 - lr: 0.003125
144
+ 2022-03-30 10:22:53,636 epoch 136 - iter 90/300 - loss 0.02966682 - samples/sec: 7.32 - lr: 0.003125
145
+ 2022-03-30 10:23:51,242 epoch 136 - iter 120/300 - loss 0.02938922 - samples/sec: 8.49 - lr: 0.003125
146
+ 2022-03-30 10:24:52,074 epoch 136 - iter 150/300 - loss 0.02991657 - samples/sec: 8.06 - lr: 0.003125
147
+ 2022-03-30 10:25:55,338 epoch 136 - iter 180/300 - loss 0.03012840 - samples/sec: 7.71 - lr: 0.003125
148
+ 2022-03-30 10:26:58,329 epoch 136 - iter 210/300 - loss 0.03004874 - samples/sec: 7.74 - lr: 0.003125
149
+ 2022-03-30 10:27:57,399 epoch 136 - iter 240/300 - loss 0.03035409 - samples/sec: 8.26 - lr: 0.003125
150
+ 2022-03-30 10:28:56,834 epoch 136 - iter 270/300 - loss 0.03021945 - samples/sec: 8.20 - lr: 0.003125
151
+ 2022-03-30 10:29:56,059 epoch 136 - iter 300/300 - loss 0.02976912 - samples/sec: 8.25 - lr: 0.003125
152
+ 2022-03-30 10:29:56,997 ----------------------------------------------------------------------------------------------------
153
+ 2022-03-30 10:29:57,005 EPOCH 136 done: loss 0.0298 - lr 0.0031250
154
+ 2022-03-30 10:30:32,284 DEV : loss 0.10185939818620682 - f1-score (micro avg) 0.9799
155
+ 2022-03-30 10:30:32,301 BAD EPOCHS (no improvement): 1
156
+ 2022-03-30 10:30:34,702 ----------------------------------------------------------------------------------------------------
157
+ 2022-03-30 10:31:34,245 epoch 137 - iter 30/300 - loss 0.03121086 - samples/sec: 8.06 - lr: 0.003125
158
+ 2022-03-30 10:32:42,985 epoch 137 - iter 60/300 - loss 0.02851138 - samples/sec: 7.12 - lr: 0.003125
159
+ 2022-03-30 10:33:46,476 epoch 137 - iter 90/300 - loss 0.02891198 - samples/sec: 7.71 - lr: 0.003125
160
+ 2022-03-30 10:34:53,147 epoch 137 - iter 120/300 - loss 0.02967460 - samples/sec: 7.33 - lr: 0.003125
161
+ 2022-03-30 10:35:53,437 epoch 137 - iter 150/300 - loss 0.02960777 - samples/sec: 8.11 - lr: 0.003125
162
+ 2022-03-30 10:36:53,995 epoch 137 - iter 180/300 - loss 0.03064080 - samples/sec: 8.06 - lr: 0.003125
163
+ 2022-03-30 10:38:02,423 epoch 137 - iter 210/300 - loss 0.03087131 - samples/sec: 7.15 - lr: 0.003125
164
+ 2022-03-30 10:39:13,159 epoch 137 - iter 240/300 - loss 0.03080292 - samples/sec: 6.91 - lr: 0.003125
165
+ 2022-03-30 10:40:17,139 epoch 137 - iter 270/300 - loss 0.03084099 - samples/sec: 7.66 - lr: 0.003125
166
+ 2022-03-30 10:41:20,762 epoch 137 - iter 300/300 - loss 0.03082571 - samples/sec: 7.69 - lr: 0.003125
167
+ 2022-03-30 10:41:22,012 ----------------------------------------------------------------------------------------------------
168
+ 2022-03-30 10:41:22,022 EPOCH 137 done: loss 0.0308 - lr 0.0031250
169
+ 2022-03-30 10:41:57,389 DEV : loss 0.10173739492893219 - f1-score (micro avg) 0.9797
170
+ 2022-03-30 10:41:57,403 BAD EPOCHS (no improvement): 2
171
+ 2022-03-30 10:41:59,737 ----------------------------------------------------------------------------------------------------
172
+ 2022-03-30 10:43:02,290 epoch 138 - iter 30/300 - loss 0.02868595 - samples/sec: 7.67 - lr: 0.003125
173
+ 2022-03-30 10:44:10,101 epoch 138 - iter 60/300 - loss 0.03005261 - samples/sec: 7.23 - lr: 0.003125
174
+ 2022-03-30 10:45:18,374 epoch 138 - iter 90/300 - loss 0.03051957 - samples/sec: 7.17 - lr: 0.003125
175
+ 2022-03-30 10:46:16,512 epoch 138 - iter 120/300 - loss 0.03062131 - samples/sec: 8.39 - lr: 0.003125
176
+ 2022-03-30 10:47:20,487 epoch 138 - iter 150/300 - loss 0.03084338 - samples/sec: 7.63 - lr: 0.003125
177
+ 2022-03-30 10:48:18,416 epoch 138 - iter 180/300 - loss 0.03006383 - samples/sec: 8.46 - lr: 0.003125
178
+ 2022-03-30 10:49:21,648 epoch 138 - iter 210/300 - loss 0.03021354 - samples/sec: 7.71 - lr: 0.003125
179
+ 2022-03-30 10:50:20,510 epoch 138 - iter 240/300 - loss 0.02932483 - samples/sec: 8.30 - lr: 0.003125
180
+ 2022-03-30 10:51:20,095 epoch 138 - iter 270/300 - loss 0.02939289 - samples/sec: 8.19 - lr: 0.003125
181
+ 2022-03-30 10:52:17,869 epoch 138 - iter 300/300 - loss 0.02959066 - samples/sec: 8.56 - lr: 0.003125
182
+ 2022-03-30 10:52:18,790 ----------------------------------------------------------------------------------------------------
183
+ 2022-03-30 10:52:18,798 EPOCH 138 done: loss 0.0296 - lr 0.0031250
184
+ 2022-03-30 10:52:54,449 DEV : loss 0.10195963829755783 - f1-score (micro avg) 0.9799
185
+ 2022-03-30 10:52:54,462 BAD EPOCHS (no improvement): 3
186
+ 2022-03-30 10:52:56,763 ----------------------------------------------------------------------------------------------------
187
+ 2022-03-30 10:53:56,820 epoch 139 - iter 30/300 - loss 0.02870452 - samples/sec: 8.00 - lr: 0.003125
188
+ 2022-03-30 10:54:55,650 epoch 139 - iter 60/300 - loss 0.02865684 - samples/sec: 8.29 - lr: 0.003125
189
+ 2022-03-30 10:55:57,327 epoch 139 - iter 90/300 - loss 0.03033846 - samples/sec: 7.92 - lr: 0.003125
190
+ 2022-03-30 10:57:00,485 epoch 139 - iter 120/300 - loss 0.03033128 - samples/sec: 7.72 - lr: 0.003125
191
+ 2022-03-30 10:58:00,955 epoch 139 - iter 150/300 - loss 0.03097701 - samples/sec: 8.09 - lr: 0.003125
192
+ 2022-03-30 10:58:57,771 epoch 139 - iter 180/300 - loss 0.03067534 - samples/sec: 8.62 - lr: 0.003125
193
+ 2022-03-30 10:59:56,571 epoch 139 - iter 210/300 - loss 0.03043512 - samples/sec: 8.30 - lr: 0.003125
194
+ 2022-03-30 11:00:56,944 epoch 139 - iter 240/300 - loss 0.03097712 - samples/sec: 8.08 - lr: 0.003125
195
+ 2022-03-30 11:01:54,372 epoch 139 - iter 270/300 - loss 0.03147405 - samples/sec: 8.53 - lr: 0.003125
196
+ 2022-03-30 11:03:02,721 epoch 139 - iter 300/300 - loss 0.03130255 - samples/sec: 7.17 - lr: 0.003125
197
+ 2022-03-30 11:03:04,039 ----------------------------------------------------------------------------------------------------
198
+ 2022-03-30 11:03:04,047 EPOCH 139 done: loss 0.0313 - lr 0.0031250
199
+ 2022-03-30 11:03:40,583 DEV : loss 0.10206855833530426 - f1-score (micro avg) 0.98
200
+ 2022-03-30 11:03:40,600 BAD EPOCHS (no improvement): 4
201
+ 2022-03-30 11:03:42,934 ----------------------------------------------------------------------------------------------------
202
+ 2022-03-30 11:04:43,474 epoch 140 - iter 30/300 - loss 0.02956418 - samples/sec: 7.93 - lr: 0.001563
203
+ 2022-03-30 11:05:46,895 epoch 140 - iter 60/300 - loss 0.03269747 - samples/sec: 7.70 - lr: 0.001563
204
+ 2022-03-30 11:06:54,734 epoch 140 - iter 90/300 - loss 0.03185046 - samples/sec: 7.18 - lr: 0.001563
205
+ 2022-03-30 11:07:59,429 epoch 140 - iter 120/300 - loss 0.03156745 - samples/sec: 7.54 - lr: 0.001563
206
+ 2022-03-30 11:09:03,178 epoch 140 - iter 150/300 - loss 0.03111944 - samples/sec: 7.67 - lr: 0.001563
207
+ 2022-03-30 11:10:03,574 epoch 140 - iter 180/300 - loss 0.03137674 - samples/sec: 8.08 - lr: 0.001563
208
+ 2022-03-30 11:11:08,571 epoch 140 - iter 210/300 - loss 0.03057508 - samples/sec: 7.50 - lr: 0.001563
209
+ 2022-03-30 11:12:07,984 epoch 140 - iter 240/300 - loss 0.03026252 - samples/sec: 8.21 - lr: 0.001563
210
+ 2022-03-30 11:13:10,011 epoch 140 - iter 270/300 - loss 0.03010044 - samples/sec: 7.86 - lr: 0.001563
211
+ 2022-03-30 11:14:13,319 epoch 140 - iter 300/300 - loss 0.02984354 - samples/sec: 7.87 - lr: 0.001563
212
+ 2022-03-30 11:14:15,239 ----------------------------------------------------------------------------------------------------
213
+ 2022-03-30 11:14:15,254 EPOCH 140 done: loss 0.0298 - lr 0.0015625
214
+ 2022-03-30 11:14:53,914 DEV : loss 0.10188718885183334 - f1-score (micro avg) 0.9799
215
+ 2022-03-30 11:14:53,932 BAD EPOCHS (no improvement): 1
216
+ 2022-03-30 11:14:56,051 ----------------------------------------------------------------------------------------------------
217
+ 2022-03-30 11:15:56,071 epoch 141 - iter 30/300 - loss 0.03055940 - samples/sec: 8.00 - lr: 0.001563
218
+ 2022-03-30 11:17:03,620 epoch 141 - iter 60/300 - loss 0.03027722 - samples/sec: 7.22 - lr: 0.001563
219
+ 2022-03-30 11:18:05,512 epoch 141 - iter 90/300 - loss 0.02871502 - samples/sec: 7.90 - lr: 0.001563
220
+ 2022-03-30 11:19:09,247 epoch 141 - iter 120/300 - loss 0.02972079 - samples/sec: 7.67 - lr: 0.001563
221
+ 2022-03-30 11:20:06,221 epoch 141 - iter 150/300 - loss 0.02927190 - samples/sec: 8.59 - lr: 0.001563
222
+ 2022-03-30 11:21:09,274 epoch 141 - iter 180/300 - loss 0.02953372 - samples/sec: 7.73 - lr: 0.001563
223
+ 2022-03-30 11:22:12,010 epoch 141 - iter 210/300 - loss 0.02986717 - samples/sec: 7.78 - lr: 0.001563
224
+ 2022-03-30 11:23:27,048 epoch 141 - iter 240/300 - loss 0.02962978 - samples/sec: 6.50 - lr: 0.001563
225
+ 2022-03-30 11:24:31,510 epoch 141 - iter 270/300 - loss 0.02956472 - samples/sec: 7.58 - lr: 0.001563
226
+ 2022-03-30 11:25:38,381 epoch 141 - iter 300/300 - loss 0.02905854 - samples/sec: 7.34 - lr: 0.001563
227
+ 2022-03-30 11:25:39,523 ----------------------------------------------------------------------------------------------------
228
+ 2022-03-30 11:25:39,534 EPOCH 141 done: loss 0.0291 - lr 0.0015625
229
+ 2022-03-30 11:26:18,182 DEV : loss 0.10185949504375458 - f1-score (micro avg) 0.98
230
+ 2022-03-30 11:26:18,196 BAD EPOCHS (no improvement): 2
231
+ 2022-03-30 11:26:20,410 ----------------------------------------------------------------------------------------------------
232
+ 2022-03-30 11:27:21,964 epoch 142 - iter 30/300 - loss 0.03034100 - samples/sec: 7.80 - lr: 0.001563
233
+ 2022-03-30 11:28:33,021 epoch 142 - iter 60/300 - loss 0.02986344 - samples/sec: 6.90 - lr: 0.001563
234
+ 2022-03-30 11:29:40,667 epoch 142 - iter 90/300 - loss 0.03023673 - samples/sec: 7.24 - lr: 0.001563
235
+ 2022-03-30 11:30:46,660 epoch 142 - iter 120/300 - loss 0.03055494 - samples/sec: 7.43 - lr: 0.001563
236
+ 2022-03-30 11:31:57,441 epoch 142 - iter 150/300 - loss 0.03014855 - samples/sec: 6.89 - lr: 0.001563
237
+ 2022-03-30 11:33:04,374 epoch 142 - iter 180/300 - loss 0.02997817 - samples/sec: 7.29 - lr: 0.001563
238
+ 2022-03-30 11:34:11,717 epoch 142 - iter 210/300 - loss 0.02960975 - samples/sec: 7.28 - lr: 0.001563
239
+ 2022-03-30 11:35:18,891 epoch 142 - iter 240/300 - loss 0.02960418 - samples/sec: 7.30 - lr: 0.001563
240
+ 2022-03-30 11:36:26,640 epoch 142 - iter 270/300 - loss 0.02951040 - samples/sec: 7.20 - lr: 0.001563
241
+ 2022-03-30 11:37:30,673 epoch 142 - iter 300/300 - loss 0.02959805 - samples/sec: 7.66 - lr: 0.001563
242
+ 2022-03-30 11:37:32,055 ----------------------------------------------------------------------------------------------------
243
+ 2022-03-30 11:37:32,065 EPOCH 142 done: loss 0.0296 - lr 0.0015625
244
+ 2022-03-30 11:38:10,602 DEV : loss 0.1019764393568039 - f1-score (micro avg) 0.98
245
+ 2022-03-30 11:38:10,618 BAD EPOCHS (no improvement): 3
246
+ 2022-03-30 11:38:12,899 ----------------------------------------------------------------------------------------------------
247
+ 2022-03-30 11:39:18,069 epoch 143 - iter 30/300 - loss 0.03082201 - samples/sec: 7.37 - lr: 0.001563
248
+ 2022-03-30 11:40:24,720 epoch 143 - iter 60/300 - loss 0.03049819 - samples/sec: 7.32 - lr: 0.001563
249
+ 2022-03-30 11:41:28,402 epoch 143 - iter 90/300 - loss 0.03046947 - samples/sec: 7.69 - lr: 0.001563
250
+ 2022-03-30 11:42:34,764 epoch 143 - iter 120/300 - loss 0.03107469 - samples/sec: 7.36 - lr: 0.001563
251
+ 2022-03-30 11:43:33,723 epoch 143 - iter 150/300 - loss 0.03117679 - samples/sec: 8.30 - lr: 0.001563
252
+ 2022-03-30 11:44:38,979 epoch 143 - iter 180/300 - loss 0.03124911 - samples/sec: 7.50 - lr: 0.001563
253
+ 2022-03-30 11:45:38,207 epoch 143 - iter 210/300 - loss 0.03069054 - samples/sec: 8.26 - lr: 0.001563
254
+ 2022-03-30 11:46:38,216 epoch 143 - iter 240/300 - loss 0.03057702 - samples/sec: 8.13 - lr: 0.001563
255
+ 2022-03-30 11:47:42,725 epoch 143 - iter 270/300 - loss 0.03075338 - samples/sec: 7.55 - lr: 0.001563
256
+ 2022-03-30 11:48:51,739 epoch 143 - iter 300/300 - loss 0.03080276 - samples/sec: 7.09 - lr: 0.001563
257
+ 2022-03-30 11:48:52,851 ----------------------------------------------------------------------------------------------------
258
+ 2022-03-30 11:48:52,859 EPOCH 143 done: loss 0.0308 - lr 0.0015625
259
+ 2022-03-30 11:49:28,228 DEV : loss 0.10188134014606476 - f1-score (micro avg) 0.9799
260
+ 2022-03-30 11:49:28,244 BAD EPOCHS (no improvement): 4
261
+ 2022-03-30 11:49:30,299 ----------------------------------------------------------------------------------------------------
262
+ 2022-03-30 11:50:34,600 epoch 144 - iter 30/300 - loss 0.03093159 - samples/sec: 7.47 - lr: 0.000781
263
+ 2022-03-30 11:51:31,933 epoch 144 - iter 60/300 - loss 0.03006009 - samples/sec: 8.62 - lr: 0.000781
264
+ 2022-03-30 11:52:36,364 epoch 144 - iter 90/300 - loss 0.03038329 - samples/sec: 7.62 - lr: 0.000781
265
+ 2022-03-30 11:53:39,169 epoch 144 - iter 120/300 - loss 0.03019530 - samples/sec: 7.79 - lr: 0.000781
266
+ 2022-03-30 11:54:43,057 epoch 144 - iter 150/300 - loss 0.03019717 - samples/sec: 7.63 - lr: 0.000781
267
+ 2022-03-30 11:55:44,578 epoch 144 - iter 180/300 - loss 0.02965347 - samples/sec: 7.95 - lr: 0.000781
268
+ 2022-03-30 11:56:45,040 epoch 144 - iter 210/300 - loss 0.02932736 - samples/sec: 8.10 - lr: 0.000781
269
+ 2022-03-30 11:57:47,657 epoch 144 - iter 240/300 - loss 0.02934119 - samples/sec: 7.79 - lr: 0.000781
270
+ 2022-03-30 11:58:51,712 epoch 144 - iter 270/300 - loss 0.02864624 - samples/sec: 7.62 - lr: 0.000781
271
+ 2022-03-30 11:59:54,247 epoch 144 - iter 300/300 - loss 0.02886004 - samples/sec: 7.84 - lr: 0.000781
272
+ 2022-03-30 11:59:55,421 ----------------------------------------------------------------------------------------------------
273
+ 2022-03-30 11:59:55,428 EPOCH 144 done: loss 0.0289 - lr 0.0007813
274
+ 2022-03-30 12:00:31,510 DEV : loss 0.10193286091089249 - f1-score (micro avg) 0.9799
275
+ 2022-03-30 12:00:31,529 BAD EPOCHS (no improvement): 1
276
+ 2022-03-30 12:00:33,530 ----------------------------------------------------------------------------------------------------
277
+ 2022-03-30 12:01:34,070 epoch 145 - iter 30/300 - loss 0.03054470 - samples/sec: 7.93 - lr: 0.000781
278
+ 2022-03-30 12:02:37,077 epoch 145 - iter 60/300 - loss 0.02925298 - samples/sec: 7.75 - lr: 0.000781
279
+ 2022-03-30 12:03:38,400 epoch 145 - iter 90/300 - loss 0.03073912 - samples/sec: 7.95 - lr: 0.000781
280
+ 2022-03-30 12:04:39,348 epoch 145 - iter 120/300 - loss 0.03068456 - samples/sec: 8.00 - lr: 0.000781
281
+ 2022-03-30 12:05:39,921 epoch 145 - iter 150/300 - loss 0.03031453 - samples/sec: 8.05 - lr: 0.000781
282
+ 2022-03-30 12:06:41,034 epoch 145 - iter 180/300 - loss 0.02958307 - samples/sec: 7.98 - lr: 0.000781
283
+ 2022-03-30 12:07:43,244 epoch 145 - iter 210/300 - loss 0.02954896 - samples/sec: 7.84 - lr: 0.000781
284
+ 2022-03-30 12:08:42,598 epoch 145 - iter 240/300 - loss 0.03014911 - samples/sec: 8.22 - lr: 0.000781
285
+ 2022-03-30 12:09:41,007 epoch 145 - iter 270/300 - loss 0.03031660 - samples/sec: 8.37 - lr: 0.000781
286
+ 2022-03-30 12:10:40,278 epoch 145 - iter 300/300 - loss 0.03040646 - samples/sec: 8.25 - lr: 0.000781
287
+ 2022-03-30 12:10:41,359 ----------------------------------------------------------------------------------------------------
288
+ 2022-03-30 12:10:41,369 EPOCH 145 done: loss 0.0304 - lr 0.0007813
289
+ 2022-03-30 12:11:16,524 DEV : loss 0.1020410880446434 - f1-score (micro avg) 0.9799
290
+ 2022-03-30 12:11:16,537 BAD EPOCHS (no improvement): 2
291
+ 2022-03-30 12:11:18,468 ----------------------------------------------------------------------------------------------------
292
+ 2022-03-30 12:12:17,736 epoch 146 - iter 30/300 - loss 0.03388915 - samples/sec: 8.10 - lr: 0.000781
293
+ 2022-03-30 12:13:16,442 epoch 146 - iter 60/300 - loss 0.03019310 - samples/sec: 8.31 - lr: 0.000781
294
+ 2022-03-30 12:14:24,567 epoch 146 - iter 90/300 - loss 0.02995728 - samples/sec: 7.15 - lr: 0.000781
295
+ 2022-03-30 12:15:20,711 epoch 146 - iter 120/300 - loss 0.03055739 - samples/sec: 8.70 - lr: 0.000781
296
+ 2022-03-30 12:16:19,853 epoch 146 - iter 150/300 - loss 0.03013465 - samples/sec: 8.26 - lr: 0.000781
297
+ 2022-03-30 12:17:19,384 epoch 146 - iter 180/300 - loss 0.03001331 - samples/sec: 8.20 - lr: 0.000781
298
+ 2022-03-30 12:18:22,009 epoch 146 - iter 210/300 - loss 0.03033218 - samples/sec: 7.78 - lr: 0.000781
299
+ 2022-03-30 12:19:18,662 epoch 146 - iter 240/300 - loss 0.03027508 - samples/sec: 8.62 - lr: 0.000781
300
+ 2022-03-30 12:20:16,122 epoch 146 - iter 270/300 - loss 0.02978917 - samples/sec: 8.49 - lr: 0.000781
301
+ 2022-03-30 12:21:17,243 epoch 146 - iter 300/300 - loss 0.02969052 - samples/sec: 7.98 - lr: 0.000781
302
+ 2022-03-30 12:21:18,187 ----------------------------------------------------------------------------------------------------
303
+ 2022-03-30 12:21:18,195 EPOCH 146 done: loss 0.0297 - lr 0.0007813
304
+ 2022-03-30 12:21:52,094 DEV : loss 0.10200724005699158 - f1-score (micro avg) 0.9799
305
+ 2022-03-30 12:21:52,110 BAD EPOCHS (no improvement): 3
306
+ 2022-03-30 12:21:54,193 ----------------------------------------------------------------------------------------------------
307
+ 2022-03-30 12:22:55,160 epoch 147 - iter 30/300 - loss 0.03017420 - samples/sec: 7.87 - lr: 0.000781
308
+ 2022-03-30 12:23:50,715 epoch 147 - iter 60/300 - loss 0.03011640 - samples/sec: 8.82 - lr: 0.000781
309
+ 2022-03-30 12:24:46,161 epoch 147 - iter 90/300 - loss 0.02814870 - samples/sec: 8.81 - lr: 0.000781
310
+ 2022-03-30 12:25:49,615 epoch 147 - iter 120/300 - loss 0.02833966 - samples/sec: 7.68 - lr: 0.000781
311
+ 2022-03-30 12:26:49,911 epoch 147 - iter 150/300 - loss 0.02799142 - samples/sec: 8.09 - lr: 0.000781
312
+ 2022-03-30 12:27:51,843 epoch 147 - iter 180/300 - loss 0.02847355 - samples/sec: 7.88 - lr: 0.000781
313
+ 2022-03-30 12:28:57,284 epoch 147 - iter 210/300 - loss 0.02890269 - samples/sec: 7.45 - lr: 0.000781
314
+ 2022-03-30 12:29:53,822 epoch 147 - iter 240/300 - loss 0.02913940 - samples/sec: 8.64 - lr: 0.000781
315
+ 2022-03-30 12:30:51,413 epoch 147 - iter 270/300 - loss 0.02966032 - samples/sec: 8.48 - lr: 0.000781
316
+ 2022-03-30 12:31:48,559 epoch 147 - iter 300/300 - loss 0.03015249 - samples/sec: 8.57 - lr: 0.000781
317
+ 2022-03-30 12:31:49,495 ----------------------------------------------------------------------------------------------------
318
+ 2022-03-30 12:31:49,502 EPOCH 147 done: loss 0.0302 - lr 0.0007813
319
+ 2022-03-30 12:32:24,767 DEV : loss 0.10197500139474869 - f1-score (micro avg) 0.9799
320
+ 2022-03-30 12:32:24,780 BAD EPOCHS (no improvement): 4
321
+ 2022-03-30 12:32:27,012 ----------------------------------------------------------------------------------------------------
322
+ 2022-03-30 12:33:24,651 epoch 148 - iter 30/300 - loss 0.02956941 - samples/sec: 8.33 - lr: 0.000391
323
+ 2022-03-30 12:34:22,801 epoch 148 - iter 60/300 - loss 0.02827974 - samples/sec: 8.39 - lr: 0.000391
324
+ 2022-03-30 12:35:19,846 epoch 148 - iter 90/300 - loss 0.02906290 - samples/sec: 8.56 - lr: 0.000391
325
+ 2022-03-30 12:36:19,972 epoch 148 - iter 120/300 - loss 0.02973210 - samples/sec: 8.13 - lr: 0.000391
326
+ 2022-03-30 12:37:20,722 epoch 148 - iter 150/300 - loss 0.03000164 - samples/sec: 8.11 - lr: 0.000391
327
+ 2022-03-30 12:38:21,387 epoch 148 - iter 180/300 - loss 0.03013482 - samples/sec: 8.06 - lr: 0.000391
328
+ 2022-03-30 12:39:29,775 epoch 148 - iter 210/300 - loss 0.02972903 - samples/sec: 7.15 - lr: 0.000391
329
+ 2022-03-30 12:40:30,565 epoch 148 - iter 240/300 - loss 0.02919740 - samples/sec: 8.04 - lr: 0.000391
330
+ 2022-03-30 12:41:40,602 epoch 148 - iter 270/300 - loss 0.02951950 - samples/sec: 6.97 - lr: 0.000391
331
+ 2022-03-30 12:42:43,341 epoch 148 - iter 300/300 - loss 0.02951220 - samples/sec: 7.80 - lr: 0.000391
332
+ 2022-03-30 12:42:44,430 ----------------------------------------------------------------------------------------------------
333
+ 2022-03-30 12:42:44,439 EPOCH 148 done: loss 0.0295 - lr 0.0003906
334
+ 2022-03-30 12:43:19,991 DEV : loss 0.1020146831870079 - f1-score (micro avg) 0.9799
335
+ 2022-03-30 12:43:20,004 BAD EPOCHS (no improvement): 1
336
+ 2022-03-30 12:43:22,042 ----------------------------------------------------------------------------------------------------
337
+ 2022-03-30 12:44:17,873 epoch 149 - iter 30/300 - loss 0.03481397 - samples/sec: 8.60 - lr: 0.000391
338
+ 2022-03-30 12:45:25,311 epoch 149 - iter 60/300 - loss 0.02951263 - samples/sec: 7.22 - lr: 0.000391
339
+ 2022-03-30 12:46:28,256 epoch 149 - iter 90/300 - loss 0.03115284 - samples/sec: 7.76 - lr: 0.000391
340
+ 2022-03-30 12:47:26,637 epoch 149 - iter 120/300 - loss 0.03026986 - samples/sec: 8.36 - lr: 0.000391
341
+ 2022-03-30 12:48:27,732 epoch 149 - iter 150/300 - loss 0.02926616 - samples/sec: 7.99 - lr: 0.000391
342
+ 2022-03-30 12:49:28,983 epoch 149 - iter 180/300 - loss 0.02904276 - samples/sec: 7.96 - lr: 0.000391
343
+ 2022-03-30 12:50:37,366 epoch 149 - iter 210/300 - loss 0.02906074 - samples/sec: 7.12 - lr: 0.000391
344
+ 2022-03-30 12:51:40,166 epoch 149 - iter 240/300 - loss 0.02931871 - samples/sec: 7.76 - lr: 0.000391
345
+ 2022-03-30 12:52:44,553 epoch 149 - iter 270/300 - loss 0.02949797 - samples/sec: 7.60 - lr: 0.000391
346
+ 2022-03-30 12:53:43,279 epoch 149 - iter 300/300 - loss 0.02966499 - samples/sec: 8.33 - lr: 0.000391
347
+ 2022-03-30 12:53:44,358 ----------------------------------------------------------------------------------------------------
348
+ 2022-03-30 12:53:44,368 EPOCH 149 done: loss 0.0297 - lr 0.0003906
349
+ 2022-03-30 12:54:20,685 DEV : loss 0.10201691836118698 - f1-score (micro avg) 0.9799
350
+ 2022-03-30 12:54:20,700 BAD EPOCHS (no improvement): 2
351
+ 2022-03-30 12:54:22,923 ----------------------------------------------------------------------------------------------------
352
+ 2022-03-30 12:55:26,769 epoch 150 - iter 30/300 - loss 0.02921641 - samples/sec: 7.52 - lr: 0.000391
353
+ 2022-03-30 12:56:30,124 epoch 150 - iter 60/300 - loss 0.03017024 - samples/sec: 7.70 - lr: 0.000391
354
+ 2022-03-30 12:57:37,174 epoch 150 - iter 90/300 - loss 0.02976986 - samples/sec: 7.29 - lr: 0.000391
355
+ 2022-03-30 12:58:37,123 epoch 150 - iter 120/300 - loss 0.02963135 - samples/sec: 8.13 - lr: 0.000391
356
+ 2022-03-30 12:59:34,900 epoch 150 - iter 150/300 - loss 0.02946543 - samples/sec: 8.46 - lr: 0.000391
357
+ 2022-03-30 13:00:38,262 epoch 150 - iter 180/300 - loss 0.02918791 - samples/sec: 7.72 - lr: 0.000391
358
+ 2022-03-30 13:01:36,418 epoch 150 - iter 210/300 - loss 0.02878193 - samples/sec: 8.39 - lr: 0.000391
359
+ 2022-03-30 13:02:37,434 epoch 150 - iter 240/300 - loss 0.02897084 - samples/sec: 8.00 - lr: 0.000391
360
+ 2022-03-30 13:03:38,183 epoch 150 - iter 270/300 - loss 0.02925266 - samples/sec: 8.03 - lr: 0.000391
361
+ 2022-03-30 13:04:38,412 epoch 150 - iter 300/300 - loss 0.02904189 - samples/sec: 8.09 - lr: 0.000391
362
+ 2022-03-30 13:04:39,315 ----------------------------------------------------------------------------------------------------
363
+ 2022-03-30 13:04:39,324 EPOCH 150 done: loss 0.0290 - lr 0.0003906
364
+ 2022-03-30 13:05:16,273 DEV : loss 0.10202094167470932 - f1-score (micro avg) 0.9799
365
+ 2022-03-30 13:05:16,286 BAD EPOCHS (no improvement): 3
366
+ 2022-03-30 13:05:18,224 ----------------------------------------------------------------------------------------------------
367
+ 2022-03-30 13:06:20,779 epoch 151 - iter 30/300 - loss 0.02923949 - samples/sec: 7.68 - lr: 0.000391
368
+ 2022-03-30 13:07:20,679 epoch 151 - iter 60/300 - loss 0.02844942 - samples/sec: 8.15 - lr: 0.000391
369
+ 2022-03-30 13:08:15,320 epoch 151 - iter 90/300 - loss 0.02703875 - samples/sec: 8.94 - lr: 0.000391
370
+ 2022-03-30 13:09:18,118 epoch 151 - iter 120/300 - loss 0.02737682 - samples/sec: 7.77 - lr: 0.000391
371
+ 2022-03-30 13:10:22,493 epoch 151 - iter 150/300 - loss 0.02725408 - samples/sec: 7.57 - lr: 0.000391
372
+ 2022-03-30 13:11:18,619 epoch 151 - iter 180/300 - loss 0.02774154 - samples/sec: 8.70 - lr: 0.000391
373
+ 2022-03-30 13:12:16,135 epoch 151 - iter 210/300 - loss 0.02828949 - samples/sec: 8.48 - lr: 0.000391
374
+ 2022-03-30 13:13:20,885 epoch 151 - iter 240/300 - loss 0.02853759 - samples/sec: 7.53 - lr: 0.000391
375
+ 2022-03-30 13:14:20,337 epoch 151 - iter 270/300 - loss 0.02806431 - samples/sec: 8.21 - lr: 0.000391
376
+ 2022-03-30 13:15:18,141 epoch 151 - iter 300/300 - loss 0.02838301 - samples/sec: 8.44 - lr: 0.000391
377
+ 2022-03-30 13:15:19,109 ----------------------------------------------------------------------------------------------------
378
+ 2022-03-30 13:15:19,118 EPOCH 151 done: loss 0.0284 - lr 0.0003906
379
+ 2022-03-30 13:15:55,729 DEV : loss 0.10201210528612137 - f1-score (micro avg) 0.98
380
+ 2022-03-30 13:15:55,743 BAD EPOCHS (no improvement): 4
381
+ 2022-03-30 13:15:57,761 ----------------------------------------------------------------------------------------------------
382
+ 2022-03-30 13:16:51,190 epoch 152 - iter 30/300 - loss 0.03240213 - samples/sec: 8.99 - lr: 0.000195
383
+ 2022-03-30 13:17:52,520 epoch 152 - iter 60/300 - loss 0.02845009 - samples/sec: 7.94 - lr: 0.000195
384
+ 2022-03-30 13:18:51,431 epoch 152 - iter 90/300 - loss 0.02996368 - samples/sec: 8.27 - lr: 0.000195
385
+ 2022-03-30 13:19:51,886 epoch 152 - iter 120/300 - loss 0.02991149 - samples/sec: 8.06 - lr: 0.000195
386
+ 2022-03-30 13:20:55,106 epoch 152 - iter 150/300 - loss 0.02958199 - samples/sec: 7.70 - lr: 0.000195
387
+ 2022-03-30 13:21:53,509 epoch 152 - iter 180/300 - loss 0.02972192 - samples/sec: 8.35 - lr: 0.000195
388
+ 2022-03-30 13:22:52,257 epoch 152 - iter 210/300 - loss 0.03019008 - samples/sec: 8.30 - lr: 0.000195
389
+ 2022-03-30 13:23:50,768 epoch 152 - iter 240/300 - loss 0.03007176 - samples/sec: 8.33 - lr: 0.000195
390
+ 2022-03-30 13:24:53,673 epoch 152 - iter 270/300 - loss 0.03025321 - samples/sec: 7.81 - lr: 0.000195
391
+ 2022-03-30 13:25:54,892 epoch 152 - iter 300/300 - loss 0.03032258 - samples/sec: 7.99 - lr: 0.000195
392
+ 2022-03-30 13:25:56,061 ----------------------------------------------------------------------------------------------------
393
+ 2022-03-30 13:25:56,072 EPOCH 152 done: loss 0.0303 - lr 0.0001953
394
+ 2022-03-30 13:26:34,122 DEV : loss 0.10201038420200348 - f1-score (micro avg) 0.98
395
+ 2022-03-30 13:26:34,143 BAD EPOCHS (no improvement): 1
396
+ 2022-03-30 13:26:36,389 ----------------------------------------------------------------------------------------------------
397
+ 2022-03-30 13:27:36,309 epoch 153 - iter 30/300 - loss 0.02570798 - samples/sec: 8.01 - lr: 0.000195
398
+ 2022-03-30 13:28:42,666 epoch 153 - iter 60/300 - loss 0.02826468 - samples/sec: 7.36 - lr: 0.000195
399
+ 2022-03-30 13:29:47,512 epoch 153 - iter 90/300 - loss 0.02966814 - samples/sec: 7.52 - lr: 0.000195
400
+ 2022-03-30 13:30:51,568 epoch 153 - iter 120/300 - loss 0.02962908 - samples/sec: 7.60 - lr: 0.000195
401
+ 2022-03-30 13:31:50,204 epoch 153 - iter 150/300 - loss 0.02963920 - samples/sec: 8.33 - lr: 0.000195
402
+ 2022-03-30 13:32:46,591 epoch 153 - iter 180/300 - loss 0.03019015 - samples/sec: 8.67 - lr: 0.000195
403
+ 2022-03-30 13:33:41,403 epoch 153 - iter 210/300 - loss 0.03069690 - samples/sec: 8.90 - lr: 0.000195
404
+ 2022-03-30 13:34:41,987 epoch 153 - iter 240/300 - loss 0.03112855 - samples/sec: 8.06 - lr: 0.000195
405
+ 2022-03-30 13:35:42,286 epoch 153 - iter 270/300 - loss 0.03128193 - samples/sec: 8.09 - lr: 0.000195
406
+ 2022-03-30 13:36:42,717 epoch 153 - iter 300/300 - loss 0.03096604 - samples/sec: 8.07 - lr: 0.000195
407
+ 2022-03-30 13:36:43,706 ----------------------------------------------------------------------------------------------------
408
+ 2022-03-30 13:36:43,716 EPOCH 153 done: loss 0.0310 - lr 0.0001953
409
+ 2022-03-30 13:37:19,205 DEV : loss 0.10202408581972122 - f1-score (micro avg) 0.98
410
+ 2022-03-30 13:37:19,219 BAD EPOCHS (no improvement): 2
411
+ 2022-03-30 13:37:21,203 ----------------------------------------------------------------------------------------------------
412
+ 2022-03-30 13:38:20,787 epoch 154 - iter 30/300 - loss 0.03084118 - samples/sec: 8.06 - lr: 0.000195
413
+ 2022-03-30 13:39:23,198 epoch 154 - iter 60/300 - loss 0.03093184 - samples/sec: 7.82 - lr: 0.000195
414
+ 2022-03-30 13:40:23,736 epoch 154 - iter 90/300 - loss 0.03080735 - samples/sec: 8.05 - lr: 0.000195
415
+ 2022-03-30 13:41:23,844 epoch 154 - iter 120/300 - loss 0.03091830 - samples/sec: 8.12 - lr: 0.000195
416
+ 2022-03-30 13:42:24,937 epoch 154 - iter 150/300 - loss 0.03055376 - samples/sec: 7.99 - lr: 0.000195
417
+ 2022-03-30 13:43:28,630 epoch 154 - iter 180/300 - loss 0.03022854 - samples/sec: 7.65 - lr: 0.000195
418
+ 2022-03-30 13:44:24,721 epoch 154 - iter 210/300 - loss 0.03042921 - samples/sec: 8.70 - lr: 0.000195
419
+ 2022-03-30 13:45:22,613 epoch 154 - iter 240/300 - loss 0.03014891 - samples/sec: 8.44 - lr: 0.000195
420
+ 2022-03-30 13:46:21,702 epoch 154 - iter 270/300 - loss 0.03032649 - samples/sec: 8.26 - lr: 0.000195
421
+ 2022-03-30 13:47:19,740 epoch 154 - iter 300/300 - loss 0.03013623 - samples/sec: 8.41 - lr: 0.000195
422
+ 2022-03-30 13:47:20,775 ----------------------------------------------------------------------------------------------------
423
+ 2022-03-30 13:47:20,785 EPOCH 154 done: loss 0.0301 - lr 0.0001953
424
+ 2022-03-30 13:47:54,972 DEV : loss 0.10201508551836014 - f1-score (micro avg) 0.98
425
+ 2022-03-30 13:47:54,985 BAD EPOCHS (no improvement): 3
426
+ 2022-03-30 13:47:57,280 ----------------------------------------------------------------------------------------------------
427
+ 2022-03-30 13:48:53,744 epoch 155 - iter 30/300 - loss 0.02969199 - samples/sec: 8.50 - lr: 0.000195
428
+ 2022-03-30 13:50:00,140 epoch 155 - iter 60/300 - loss 0.02952413 - samples/sec: 7.34 - lr: 0.000195
429
+ 2022-03-30 13:50:57,335 epoch 155 - iter 90/300 - loss 0.02895664 - samples/sec: 8.55 - lr: 0.000195
430
+ 2022-03-30 13:52:00,770 epoch 155 - iter 120/300 - loss 0.02939865 - samples/sec: 7.70 - lr: 0.000195
431
+ 2022-03-30 13:52:55,754 epoch 155 - iter 150/300 - loss 0.02914908 - samples/sec: 8.89 - lr: 0.000195
432
+ 2022-03-30 13:53:58,653 epoch 155 - iter 180/300 - loss 0.02964743 - samples/sec: 7.75 - lr: 0.000195
433
+ 2022-03-30 13:54:58,348 epoch 155 - iter 210/300 - loss 0.02989400 - samples/sec: 8.17 - lr: 0.000195
434
+ 2022-03-30 13:55:57,923 epoch 155 - iter 240/300 - loss 0.03024802 - samples/sec: 8.19 - lr: 0.000195
435
+ 2022-03-30 13:56:54,633 epoch 155 - iter 270/300 - loss 0.03030596 - samples/sec: 8.61 - lr: 0.000195
436
+ 2022-03-30 13:57:51,732 epoch 155 - iter 300/300 - loss 0.03018545 - samples/sec: 8.56 - lr: 0.000195
437
+ 2022-03-30 13:57:52,773 ----------------------------------------------------------------------------------------------------
438
+ 2022-03-30 13:57:52,781 EPOCH 155 done: loss 0.0302 - lr 0.0001953
439
+ 2022-03-30 13:58:26,906 DEV : loss 0.10200126469135284 - f1-score (micro avg) 0.98
440
+ 2022-03-30 13:58:26,923 BAD EPOCHS (no improvement): 4
441
+ 2022-03-30 13:58:29,111 ----------------------------------------------------------------------------------------------------
442
+ 2022-03-30 13:58:29,114 ----------------------------------------------------------------------------------------------------
443
+ 2022-03-30 13:58:29,118 learning rate too small - quitting training!
444
+ 2022-03-30 13:58:29,132 ----------------------------------------------------------------------------------------------------
445
+ 2022-03-30 13:58:40,931 ----------------------------------------------------------------------------------------------------
446
+ 2022-03-30 13:58:40,950 loading file /content/drive/MyDrive/project/data/upos/model/best-model.pt
447
+ 2022-03-30 14:07:05,835 0.977 0.977 0.977 0.977
448
+ 2022-03-30 14:07:05,843
449
  Results:
450
+ - F-score (micro) 0.977
451
+ - F-score (macro) 0.9456
452
+ - Accuracy 0.977
453
 
454
  By class:
455
  precision recall f1-score support
456
 
457
+ NOUN 0.9768 0.9850 0.9809 6420
458
+ ADP 0.9947 0.9916 0.9932 1909
459
+ ADJ 0.9336 0.9128 0.9231 1525
460
  PUNCT 1.0000 1.0000 1.0000 1365
461
+ VERB 0.9831 0.9693 0.9762 1141
462
+ CCONJ 0.9912 0.9924 0.9918 794
463
+ AUX 0.9604 0.9780 0.9691 546
464
+ PRON 0.9751 0.9845 0.9798 517
465
+ SCONJ 0.9777 0.9757 0.9767 494
466
+ NUM 0.9948 1.0000 0.9974 385
467
+ ADV 0.9368 0.9006 0.9183 362
468
+ DET 0.9742 0.9711 0.9726 311
469
  PART 0.9916 1.0000 0.9958 237
470
+ INTJ 0.8889 0.8000 0.8421 10
471
+ X 0.7143 0.6250 0.6667 8
472
 
473
+ micro avg 0.9770 0.9770 0.9770 16024
474
+ macro avg 0.9529 0.9391 0.9456 16024
475
+ weighted avg 0.9769 0.9770 0.9769 16024
476
+ samples avg 0.9770 0.9770 0.9770 16024
477
 
478
+ 2022-03-30 14:07:05,846 ----------------------------------------------------------------------------------------------------