Hugging Face's logo

stefan-it
/
flair-co-funer-german_bert_base-bs8-e10-lr5e-05-2

Token Classification
Flair
PyTorch
TensorBoard
German
sequence-tagger-model
hetzner
hetzner-gex44
hetzner-gpu
Model card Files Metrics Community
2
flair-co-funer-german_bert_base-bs8-e10-lr5e-05-2
File size: 23,737 Bytes 
bfc0b70
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
 
2024-03-26 11:25:08,484 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:08,484 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(30001, 768)
        (position_embeddings): Embedding(512, 768)
        (token_type_embeddings): Embedding(2, 768)
        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (encoder): BertEncoder(
        (layer): ModuleList(
          (0-11): 12 x BertLayer(
            (attention): BertAttention(
              (self): BertSelfAttention(
                (query): Linear(in_features=768, out_features=768, bias=True)
                (key): Linear(in_features=768, out_features=768, bias=True)
                (value): Linear(in_features=768, out_features=768, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): BertSelfOutput(
                (dense): Linear(in_features=768, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): BertIntermediate(
              (dense): Linear(in_features=768, out_features=3072, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): BertOutput(
              (dense): Linear(in_features=3072, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (pooler): BertPooler(
        (dense): Linear(in_features=768, out_features=768, bias=True)
        (activation): Tanh()
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=768, out_features=17, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2024-03-26 11:25:08,484 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:08,484 Corpus: 758 train + 94 dev + 96 test sentences
2024-03-26 11:25:08,484 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:08,484 Train:  758 sentences
2024-03-26 11:25:08,484         (train_with_dev=False, train_with_test=False)
2024-03-26 11:25:08,484 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:08,484 Training Params:
2024-03-26 11:25:08,484  - learning_rate: "5e-05" 
2024-03-26 11:25:08,484  - mini_batch_size: "8"
2024-03-26 11:25:08,484  - max_epochs: "10"
2024-03-26 11:25:08,484  - shuffle: "True"
2024-03-26 11:25:08,484 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:08,484 Plugins:
2024-03-26 11:25:08,484  - TensorboardLogger
2024-03-26 11:25:08,485  - LinearScheduler | warmup_fraction: '0.1'
2024-03-26 11:25:08,485 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:08,485 Final evaluation on model from best epoch (best-model.pt)
2024-03-26 11:25:08,485  - metric: "('micro avg', 'f1-score')"
2024-03-26 11:25:08,485 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:08,485 Computation:
2024-03-26 11:25:08,485  - compute on device: cuda:0
2024-03-26 11:25:08,485  - embedding storage: none
2024-03-26 11:25:08,485 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:08,485 Model training base path: "flair-co-funer-german_bert_base-bs8-e10-lr5e-05-2"
2024-03-26 11:25:08,485 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:08,485 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:08,485 Logging anything other than scalars to TensorBoard is currently not supported.
2024-03-26 11:25:10,385 epoch 1 - iter 9/95 - loss 3.09716797 - time (sec): 1.90 - samples/sec: 1854.36 - lr: 0.000004 - momentum: 0.000000
2024-03-26 11:25:12,609 epoch 1 - iter 18/95 - loss 2.98425342 - time (sec): 4.12 - samples/sec: 1747.66 - lr: 0.000009 - momentum: 0.000000
2024-03-26 11:25:14,206 epoch 1 - iter 27/95 - loss 2.75626977 - time (sec): 5.72 - samples/sec: 1762.22 - lr: 0.000014 - momentum: 0.000000
2024-03-26 11:25:16,182 epoch 1 - iter 36/95 - loss 2.52340752 - time (sec): 7.70 - samples/sec: 1793.07 - lr: 0.000018 - momentum: 0.000000
2024-03-26 11:25:18,306 epoch 1 - iter 45/95 - loss 2.33537982 - time (sec): 9.82 - samples/sec: 1736.01 - lr: 0.000023 - momentum: 0.000000
2024-03-26 11:25:20,363 epoch 1 - iter 54/95 - loss 2.14784687 - time (sec): 11.88 - samples/sec: 1709.56 - lr: 0.000028 - momentum: 0.000000
2024-03-26 11:25:21,947 epoch 1 - iter 63/95 - loss 1.99895001 - time (sec): 13.46 - samples/sec: 1718.46 - lr: 0.000033 - momentum: 0.000000
2024-03-26 11:25:23,260 epoch 1 - iter 72/95 - loss 1.86613909 - time (sec): 14.77 - samples/sec: 1769.97 - lr: 0.000037 - momentum: 0.000000
2024-03-26 11:25:24,845 epoch 1 - iter 81/95 - loss 1.73720955 - time (sec): 16.36 - samples/sec: 1798.08 - lr: 0.000042 - momentum: 0.000000
2024-03-26 11:25:26,876 epoch 1 - iter 90/95 - loss 1.62324711 - time (sec): 18.39 - samples/sec: 1773.05 - lr: 0.000047 - momentum: 0.000000
2024-03-26 11:25:27,991 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:27,991 EPOCH 1 done: loss 1.5628 - lr: 0.000047
2024-03-26 11:25:28,925 DEV : loss 0.42946571111679077 - f1-score (micro avg)  0.6734
2024-03-26 11:25:28,926 saving best model
2024-03-26 11:25:29,186 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:30,531 epoch 2 - iter 9/95 - loss 0.52425744 - time (sec): 1.34 - samples/sec: 2412.74 - lr: 0.000050 - momentum: 0.000000
2024-03-26 11:25:32,418 epoch 2 - iter 18/95 - loss 0.42454204 - time (sec): 3.23 - samples/sec: 2125.97 - lr: 0.000049 - momentum: 0.000000
2024-03-26 11:25:35,299 epoch 2 - iter 27/95 - loss 0.35787075 - time (sec): 6.11 - samples/sec: 1890.91 - lr: 0.000048 - momentum: 0.000000
2024-03-26 11:25:37,460 epoch 2 - iter 36/95 - loss 0.33935478 - time (sec): 8.27 - samples/sec: 1796.90 - lr: 0.000048 - momentum: 0.000000
2024-03-26 11:25:39,264 epoch 2 - iter 45/95 - loss 0.32453877 - time (sec): 10.08 - samples/sec: 1783.53 - lr: 0.000047 - momentum: 0.000000
2024-03-26 11:25:41,421 epoch 2 - iter 54/95 - loss 0.31681048 - time (sec): 12.23 - samples/sec: 1739.10 - lr: 0.000047 - momentum: 0.000000
2024-03-26 11:25:43,050 epoch 2 - iter 63/95 - loss 0.32280342 - time (sec): 13.86 - samples/sec: 1755.49 - lr: 0.000046 - momentum: 0.000000
2024-03-26 11:25:44,582 epoch 2 - iter 72/95 - loss 0.31919664 - time (sec): 15.40 - samples/sec: 1781.82 - lr: 0.000046 - momentum: 0.000000
2024-03-26 11:25:45,762 epoch 2 - iter 81/95 - loss 0.31666683 - time (sec): 16.58 - samples/sec: 1817.58 - lr: 0.000045 - momentum: 0.000000
2024-03-26 11:25:47,062 epoch 2 - iter 90/95 - loss 0.31133068 - time (sec): 17.88 - samples/sec: 1840.38 - lr: 0.000045 - momentum: 0.000000
2024-03-26 11:25:48,058 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:48,058 EPOCH 2 done: loss 0.3026 - lr: 0.000045
2024-03-26 11:25:48,986 DEV : loss 0.2420356720685959 - f1-score (micro avg)  0.8595
2024-03-26 11:25:48,987 saving best model
2024-03-26 11:25:49,407 ----------------------------------------------------------------------------------------------------
2024-03-26 11:25:51,437 epoch 3 - iter 9/95 - loss 0.16715958 - time (sec): 2.03 - samples/sec: 1641.60 - lr: 0.000044 - momentum: 0.000000
2024-03-26 11:25:53,556 epoch 3 - iter 18/95 - loss 0.17381845 - time (sec): 4.15 - samples/sec: 1751.20 - lr: 0.000043 - momentum: 0.000000
2024-03-26 11:25:54,531 epoch 3 - iter 27/95 - loss 0.17679162 - time (sec): 5.12 - samples/sec: 1878.86 - lr: 0.000043 - momentum: 0.000000
2024-03-26 11:25:56,295 epoch 3 - iter 36/95 - loss 0.18025597 - time (sec): 6.89 - samples/sec: 1842.80 - lr: 0.000042 - momentum: 0.000000
2024-03-26 11:25:57,578 epoch 3 - iter 45/95 - loss 0.19307875 - time (sec): 8.17 - samples/sec: 1885.50 - lr: 0.000042 - momentum: 0.000000
2024-03-26 11:25:59,636 epoch 3 - iter 54/95 - loss 0.19055428 - time (sec): 10.23 - samples/sec: 1828.08 - lr: 0.000041 - momentum: 0.000000
2024-03-26 11:26:01,285 epoch 3 - iter 63/95 - loss 0.18895940 - time (sec): 11.88 - samples/sec: 1837.87 - lr: 0.000041 - momentum: 0.000000
2024-03-26 11:26:02,859 epoch 3 - iter 72/95 - loss 0.18580014 - time (sec): 13.45 - samples/sec: 1842.25 - lr: 0.000040 - momentum: 0.000000
2024-03-26 11:26:04,674 epoch 3 - iter 81/95 - loss 0.17923377 - time (sec): 15.27 - samples/sec: 1831.34 - lr: 0.000040 - momentum: 0.000000
2024-03-26 11:26:07,356 epoch 3 - iter 90/95 - loss 0.16261609 - time (sec): 17.95 - samples/sec: 1822.14 - lr: 0.000039 - momentum: 0.000000
2024-03-26 11:26:08,536 ----------------------------------------------------------------------------------------------------
2024-03-26 11:26:08,536 EPOCH 3 done: loss 0.1590 - lr: 0.000039
2024-03-26 11:26:09,461 DEV : loss 0.2054598480463028 - f1-score (micro avg)  0.8766
2024-03-26 11:26:09,462 saving best model
2024-03-26 11:26:09,906 ----------------------------------------------------------------------------------------------------
2024-03-26 11:26:11,637 epoch 4 - iter 9/95 - loss 0.14638681 - time (sec): 1.73 - samples/sec: 1859.05 - lr: 0.000039 - momentum: 0.000000
2024-03-26 11:26:13,727 epoch 4 - iter 18/95 - loss 0.12145680 - time (sec): 3.82 - samples/sec: 1763.69 - lr: 0.000038 - momentum: 0.000000
2024-03-26 11:26:14,982 epoch 4 - iter 27/95 - loss 0.11451776 - time (sec): 5.07 - samples/sec: 1854.74 - lr: 0.000037 - momentum: 0.000000
2024-03-26 11:26:16,682 epoch 4 - iter 36/95 - loss 0.11076776 - time (sec): 6.77 - samples/sec: 1832.49 - lr: 0.000037 - momentum: 0.000000
2024-03-26 11:26:18,901 epoch 4 - iter 45/95 - loss 0.11578584 - time (sec): 8.99 - samples/sec: 1775.37 - lr: 0.000036 - momentum: 0.000000
2024-03-26 11:26:20,465 epoch 4 - iter 54/95 - loss 0.12488811 - time (sec): 10.56 - samples/sec: 1787.73 - lr: 0.000036 - momentum: 0.000000
2024-03-26 11:26:22,981 epoch 4 - iter 63/95 - loss 0.12320034 - time (sec): 13.07 - samples/sec: 1743.98 - lr: 0.000035 - momentum: 0.000000
2024-03-26 11:26:25,529 epoch 4 - iter 72/95 - loss 0.11446488 - time (sec): 15.62 - samples/sec: 1712.38 - lr: 0.000035 - momentum: 0.000000
2024-03-26 11:26:26,993 epoch 4 - iter 81/95 - loss 0.11224908 - time (sec): 17.09 - samples/sec: 1720.04 - lr: 0.000034 - momentum: 0.000000
2024-03-26 11:26:28,824 epoch 4 - iter 90/95 - loss 0.11286006 - time (sec): 18.92 - samples/sec: 1719.75 - lr: 0.000034 - momentum: 0.000000
2024-03-26 11:26:29,991 ----------------------------------------------------------------------------------------------------
2024-03-26 11:26:29,991 EPOCH 4 done: loss 0.1095 - lr: 0.000034
2024-03-26 11:26:30,941 DEV : loss 0.17261318862438202 - f1-score (micro avg)  0.9079
2024-03-26 11:26:30,942 saving best model
2024-03-26 11:26:31,383 ----------------------------------------------------------------------------------------------------
2024-03-26 11:26:32,354 epoch 5 - iter 9/95 - loss 0.06453080 - time (sec): 0.97 - samples/sec: 2125.49 - lr: 0.000033 - momentum: 0.000000
2024-03-26 11:26:33,984 epoch 5 - iter 18/95 - loss 0.08104053 - time (sec): 2.60 - samples/sec: 2047.72 - lr: 0.000032 - momentum: 0.000000
2024-03-26 11:26:36,556 epoch 5 - iter 27/95 - loss 0.08099319 - time (sec): 5.17 - samples/sec: 1762.87 - lr: 0.000032 - momentum: 0.000000
2024-03-26 11:26:38,458 epoch 5 - iter 36/95 - loss 0.07354078 - time (sec): 7.07 - samples/sec: 1753.97 - lr: 0.000031 - momentum: 0.000000
2024-03-26 11:26:40,466 epoch 5 - iter 45/95 - loss 0.07068799 - time (sec): 9.08 - samples/sec: 1721.12 - lr: 0.000031 - momentum: 0.000000
2024-03-26 11:26:42,119 epoch 5 - iter 54/95 - loss 0.07369301 - time (sec): 10.73 - samples/sec: 1755.40 - lr: 0.000030 - momentum: 0.000000
2024-03-26 11:26:44,549 epoch 5 - iter 63/95 - loss 0.07522678 - time (sec): 13.16 - samples/sec: 1739.81 - lr: 0.000030 - momentum: 0.000000
2024-03-26 11:26:45,980 epoch 5 - iter 72/95 - loss 0.08100158 - time (sec): 14.60 - samples/sec: 1759.93 - lr: 0.000029 - momentum: 0.000000
2024-03-26 11:26:47,908 epoch 5 - iter 81/95 - loss 0.07798969 - time (sec): 16.52 - samples/sec: 1737.95 - lr: 0.000029 - momentum: 0.000000
2024-03-26 11:26:49,814 epoch 5 - iter 90/95 - loss 0.07834740 - time (sec): 18.43 - samples/sec: 1739.35 - lr: 0.000028 - momentum: 0.000000
2024-03-26 11:26:51,195 ----------------------------------------------------------------------------------------------------
2024-03-26 11:26:51,195 EPOCH 5 done: loss 0.0785 - lr: 0.000028
2024-03-26 11:26:52,138 DEV : loss 0.18624915182590485 - f1-score (micro avg)  0.9194
2024-03-26 11:26:52,141 saving best model
2024-03-26 11:26:52,566 ----------------------------------------------------------------------------------------------------
2024-03-26 11:26:54,098 epoch 6 - iter 9/95 - loss 0.05414098 - time (sec): 1.53 - samples/sec: 1882.26 - lr: 0.000027 - momentum: 0.000000
2024-03-26 11:26:56,308 epoch 6 - iter 18/95 - loss 0.05167176 - time (sec): 3.74 - samples/sec: 1917.26 - lr: 0.000027 - momentum: 0.000000
2024-03-26 11:26:57,907 epoch 6 - iter 27/95 - loss 0.05120337 - time (sec): 5.34 - samples/sec: 1881.23 - lr: 0.000026 - momentum: 0.000000
2024-03-26 11:26:59,948 epoch 6 - iter 36/95 - loss 0.05928124 - time (sec): 7.38 - samples/sec: 1829.62 - lr: 0.000026 - momentum: 0.000000
2024-03-26 11:27:02,159 epoch 6 - iter 45/95 - loss 0.06953449 - time (sec): 9.59 - samples/sec: 1852.31 - lr: 0.000025 - momentum: 0.000000
2024-03-26 11:27:03,364 epoch 6 - iter 54/95 - loss 0.06636505 - time (sec): 10.79 - samples/sec: 1872.73 - lr: 0.000025 - momentum: 0.000000
2024-03-26 11:27:04,452 epoch 6 - iter 63/95 - loss 0.06537325 - time (sec): 11.88 - samples/sec: 1894.58 - lr: 0.000024 - momentum: 0.000000
2024-03-26 11:27:06,016 epoch 6 - iter 72/95 - loss 0.06000050 - time (sec): 13.45 - samples/sec: 1898.69 - lr: 0.000024 - momentum: 0.000000
2024-03-26 11:27:08,069 epoch 6 - iter 81/95 - loss 0.05966242 - time (sec): 15.50 - samples/sec: 1886.10 - lr: 0.000023 - momentum: 0.000000
2024-03-26 11:27:10,124 epoch 6 - iter 90/95 - loss 0.05915014 - time (sec): 17.56 - samples/sec: 1873.42 - lr: 0.000023 - momentum: 0.000000
2024-03-26 11:27:11,088 ----------------------------------------------------------------------------------------------------
2024-03-26 11:27:11,088 EPOCH 6 done: loss 0.0573 - lr: 0.000023
2024-03-26 11:27:12,028 DEV : loss 0.19410739839076996 - f1-score (micro avg)  0.9256
2024-03-26 11:27:12,031 saving best model
2024-03-26 11:27:12,463 ----------------------------------------------------------------------------------------------------
2024-03-26 11:27:13,906 epoch 7 - iter 9/95 - loss 0.01965996 - time (sec): 1.44 - samples/sec: 1844.63 - lr: 0.000022 - momentum: 0.000000
2024-03-26 11:27:15,772 epoch 7 - iter 18/95 - loss 0.03318242 - time (sec): 3.31 - samples/sec: 1752.16 - lr: 0.000021 - momentum: 0.000000
2024-03-26 11:27:17,412 epoch 7 - iter 27/95 - loss 0.03102681 - time (sec): 4.95 - samples/sec: 1843.86 - lr: 0.000021 - momentum: 0.000000
2024-03-26 11:27:19,183 epoch 7 - iter 36/95 - loss 0.03313823 - time (sec): 6.72 - samples/sec: 1789.96 - lr: 0.000020 - momentum: 0.000000
2024-03-26 11:27:20,563 epoch 7 - iter 45/95 - loss 0.03466366 - time (sec): 8.10 - samples/sec: 1810.58 - lr: 0.000020 - momentum: 0.000000
2024-03-26 11:27:22,675 epoch 7 - iter 54/95 - loss 0.03377152 - time (sec): 10.21 - samples/sec: 1755.65 - lr: 0.000019 - momentum: 0.000000
2024-03-26 11:27:24,978 epoch 7 - iter 63/95 - loss 0.03490540 - time (sec): 12.51 - samples/sec: 1706.02 - lr: 0.000019 - momentum: 0.000000
2024-03-26 11:27:27,678 epoch 7 - iter 72/95 - loss 0.04113555 - time (sec): 15.21 - samples/sec: 1694.55 - lr: 0.000018 - momentum: 0.000000
2024-03-26 11:27:29,683 epoch 7 - iter 81/95 - loss 0.04662207 - time (sec): 17.22 - samples/sec: 1702.18 - lr: 0.000018 - momentum: 0.000000
2024-03-26 11:27:31,707 epoch 7 - iter 90/95 - loss 0.04632564 - time (sec): 19.24 - samples/sec: 1703.41 - lr: 0.000017 - momentum: 0.000000
2024-03-26 11:27:32,660 ----------------------------------------------------------------------------------------------------
2024-03-26 11:27:32,660 EPOCH 7 done: loss 0.0458 - lr: 0.000017
2024-03-26 11:27:33,604 DEV : loss 0.19183886051177979 - f1-score (micro avg)  0.9122
2024-03-26 11:27:33,607 ----------------------------------------------------------------------------------------------------
2024-03-26 11:27:35,920 epoch 8 - iter 9/95 - loss 0.03236922 - time (sec): 2.31 - samples/sec: 1638.73 - lr: 0.000016 - momentum: 0.000000
2024-03-26 11:27:37,498 epoch 8 - iter 18/95 - loss 0.03529936 - time (sec): 3.89 - samples/sec: 1771.62 - lr: 0.000016 - momentum: 0.000000
2024-03-26 11:27:39,688 epoch 8 - iter 27/95 - loss 0.04103026 - time (sec): 6.08 - samples/sec: 1738.97 - lr: 0.000015 - momentum: 0.000000
2024-03-26 11:27:41,266 epoch 8 - iter 36/95 - loss 0.03739362 - time (sec): 7.66 - samples/sec: 1761.75 - lr: 0.000015 - momentum: 0.000000
2024-03-26 11:27:43,191 epoch 8 - iter 45/95 - loss 0.03747045 - time (sec): 9.58 - samples/sec: 1737.32 - lr: 0.000014 - momentum: 0.000000
2024-03-26 11:27:44,956 epoch 8 - iter 54/95 - loss 0.03977666 - time (sec): 11.35 - samples/sec: 1740.83 - lr: 0.000014 - momentum: 0.000000
2024-03-26 11:27:46,766 epoch 8 - iter 63/95 - loss 0.03873128 - time (sec): 13.16 - samples/sec: 1745.22 - lr: 0.000013 - momentum: 0.000000
2024-03-26 11:27:48,092 epoch 8 - iter 72/95 - loss 0.03720393 - time (sec): 14.48 - samples/sec: 1766.25 - lr: 0.000013 - momentum: 0.000000
2024-03-26 11:27:49,977 epoch 8 - iter 81/95 - loss 0.03735308 - time (sec): 16.37 - samples/sec: 1788.75 - lr: 0.000012 - momentum: 0.000000
2024-03-26 11:27:52,509 epoch 8 - iter 90/95 - loss 0.03457049 - time (sec): 18.90 - samples/sec: 1745.14 - lr: 0.000012 - momentum: 0.000000
2024-03-26 11:27:53,363 ----------------------------------------------------------------------------------------------------
2024-03-26 11:27:53,363 EPOCH 8 done: loss 0.0350 - lr: 0.000012
2024-03-26 11:27:54,304 DEV : loss 0.19405929744243622 - f1-score (micro avg)  0.9388
2024-03-26 11:27:54,305 saving best model
2024-03-26 11:27:54,714 ----------------------------------------------------------------------------------------------------
2024-03-26 11:27:56,495 epoch 9 - iter 9/95 - loss 0.03300844 - time (sec): 1.78 - samples/sec: 1907.81 - lr: 0.000011 - momentum: 0.000000
2024-03-26 11:27:58,733 epoch 9 - iter 18/95 - loss 0.02341987 - time (sec): 4.02 - samples/sec: 1725.89 - lr: 0.000010 - momentum: 0.000000
2024-03-26 11:28:00,673 epoch 9 - iter 27/95 - loss 0.03209448 - time (sec): 5.96 - samples/sec: 1751.40 - lr: 0.000010 - momentum: 0.000000
2024-03-26 11:28:02,260 epoch 9 - iter 36/95 - loss 0.03363631 - time (sec): 7.55 - samples/sec: 1759.39 - lr: 0.000009 - momentum: 0.000000
2024-03-26 11:28:03,712 epoch 9 - iter 45/95 - loss 0.02843998 - time (sec): 9.00 - samples/sec: 1792.89 - lr: 0.000009 - momentum: 0.000000
2024-03-26 11:28:05,141 epoch 9 - iter 54/95 - loss 0.02593440 - time (sec): 10.43 - samples/sec: 1844.58 - lr: 0.000008 - momentum: 0.000000
2024-03-26 11:28:07,051 epoch 9 - iter 63/95 - loss 0.03115415 - time (sec): 12.34 - samples/sec: 1842.43 - lr: 0.000008 - momentum: 0.000000
2024-03-26 11:28:09,104 epoch 9 - iter 72/95 - loss 0.03264374 - time (sec): 14.39 - samples/sec: 1815.38 - lr: 0.000007 - momentum: 0.000000
2024-03-26 11:28:11,445 epoch 9 - iter 81/95 - loss 0.03396355 - time (sec): 16.73 - samples/sec: 1774.04 - lr: 0.000007 - momentum: 0.000000
2024-03-26 11:28:13,232 epoch 9 - iter 90/95 - loss 0.03292421 - time (sec): 18.52 - samples/sec: 1787.49 - lr: 0.000006 - momentum: 0.000000
2024-03-26 11:28:13,832 ----------------------------------------------------------------------------------------------------
2024-03-26 11:28:13,832 EPOCH 9 done: loss 0.0325 - lr: 0.000006
2024-03-26 11:28:14,774 DEV : loss 0.18781226873397827 - f1-score (micro avg)  0.9312
2024-03-26 11:28:14,776 ----------------------------------------------------------------------------------------------------
2024-03-26 11:28:16,894 epoch 10 - iter 9/95 - loss 0.00384695 - time (sec): 2.12 - samples/sec: 1823.81 - lr: 0.000005 - momentum: 0.000000
2024-03-26 11:28:18,697 epoch 10 - iter 18/95 - loss 0.01394885 - time (sec): 3.92 - samples/sec: 1812.19 - lr: 0.000005 - momentum: 0.000000
2024-03-26 11:28:19,808 epoch 10 - iter 27/95 - loss 0.01175287 - time (sec): 5.03 - samples/sec: 1893.10 - lr: 0.000004 - momentum: 0.000000
2024-03-26 11:28:21,357 epoch 10 - iter 36/95 - loss 0.01921167 - time (sec): 6.58 - samples/sec: 1902.68 - lr: 0.000004 - momentum: 0.000000
2024-03-26 11:28:23,413 epoch 10 - iter 45/95 - loss 0.02733494 - time (sec): 8.64 - samples/sec: 1827.85 - lr: 0.000003 - momentum: 0.000000
2024-03-26 11:28:24,524 epoch 10 - iter 54/95 - loss 0.03012670 - time (sec): 9.75 - samples/sec: 1878.06 - lr: 0.000003 - momentum: 0.000000
2024-03-26 11:28:25,797 epoch 10 - iter 63/95 - loss 0.02746056 - time (sec): 11.02 - samples/sec: 1904.01 - lr: 0.000002 - momentum: 0.000000
2024-03-26 11:28:27,748 epoch 10 - iter 72/95 - loss 0.02769668 - time (sec): 12.97 - samples/sec: 1903.69 - lr: 0.000002 - momentum: 0.000000
2024-03-26 11:28:30,400 epoch 10 - iter 81/95 - loss 0.02547260 - time (sec): 15.62 - samples/sec: 1874.86 - lr: 0.000001 - momentum: 0.000000
2024-03-26 11:28:32,496 epoch 10 - iter 90/95 - loss 0.02619711 - time (sec): 17.72 - samples/sec: 1851.69 - lr: 0.000001 - momentum: 0.000000
2024-03-26 11:28:33,443 ----------------------------------------------------------------------------------------------------
2024-03-26 11:28:33,443 EPOCH 10 done: loss 0.0255 - lr: 0.000001
2024-03-26 11:28:34,380 DEV : loss 0.2008616179227829 - f1-score (micro avg)  0.9344
2024-03-26 11:28:34,663 ----------------------------------------------------------------------------------------------------
2024-03-26 11:28:34,663 Loading model from best epoch ...
2024-03-26 11:28:35,501 SequenceTagger predicts: Dictionary with 17 tags: O, S-Unternehmen, B-Unternehmen, E-Unternehmen, I-Unternehmen, S-Auslagerung, B-Auslagerung, E-Auslagerung, I-Auslagerung, S-Ort, B-Ort, E-Ort, I-Ort, S-Software, B-Software, E-Software, I-Software
2024-03-26 11:28:36,262 
Results:
- F-score (micro) 0.9159
- F-score (macro) 0.6952
- Accuracy 0.846

By class:
              precision    recall  f1-score   support

 Unternehmen     0.9157    0.8985    0.9070       266
 Auslagerung     0.8769    0.9157    0.8959       249
         Ort     0.9706    0.9851    0.9778       134
    Software     0.0000    0.0000    0.0000         0

   micro avg     0.9090    0.9230    0.9159       649
   macro avg     0.6908    0.6998    0.6952       649
weighted avg     0.9122    0.9230    0.9174       649

2024-03-26 11:28:36,263 ----------------------------------------------------------------------------------------------------