Hugging Face's logo

stefan-it
/
flair-co-funer-german_dbmdz_bert_base-bs8-e10-lr5e-05-4

Token Classification
Flair
PyTorch
TensorBoard
German
sequence-tagger-model
hetzner
hetzner-gex44
hetzner-gpu
Model card Files Metrics Community
2
flair-co-funer-german_dbmdz_bert_base-bs8-e10-lr5e-05-4
File size: 23,700 Bytes 
7927357
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
2024-03-26 16:16:46,479 ----------------------------------------------------------------------------------------------------
2024-03-26 16:16:46,479 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(31103, 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 16:16:46,479 ----------------------------------------------------------------------------------------------------
2024-03-26 16:16:46,479 Corpus: 758 train + 94 dev + 96 test sentences
2024-03-26 16:16:46,479 ----------------------------------------------------------------------------------------------------
2024-03-26 16:16:46,479 Train:  758 sentences
2024-03-26 16:16:46,480         (train_with_dev=False, train_with_test=False)
2024-03-26 16:16:46,480 ----------------------------------------------------------------------------------------------------
2024-03-26 16:16:46,480 Training Params:
2024-03-26 16:16:46,480  - learning_rate: "5e-05" 
2024-03-26 16:16:46,480  - mini_batch_size: "8"
2024-03-26 16:16:46,480  - max_epochs: "10"
2024-03-26 16:16:46,480  - shuffle: "True"
2024-03-26 16:16:46,480 ----------------------------------------------------------------------------------------------------
2024-03-26 16:16:46,480 Plugins:
2024-03-26 16:16:46,480  - TensorboardLogger
2024-03-26 16:16:46,480  - LinearScheduler | warmup_fraction: '0.1'
2024-03-26 16:16:46,480 ----------------------------------------------------------------------------------------------------
2024-03-26 16:16:46,480 Final evaluation on model from best epoch (best-model.pt)
2024-03-26 16:16:46,480  - metric: "('micro avg', 'f1-score')"
2024-03-26 16:16:46,480 ----------------------------------------------------------------------------------------------------
2024-03-26 16:16:46,480 Computation:
2024-03-26 16:16:46,480  - compute on device: cuda:0
2024-03-26 16:16:46,480  - embedding storage: none
2024-03-26 16:16:46,480 ----------------------------------------------------------------------------------------------------
2024-03-26 16:16:46,480 Model training base path: "flair-co-funer-german_dbmdz_bert_base-bs8-e10-lr5e-05-4"
2024-03-26 16:16:46,480 ----------------------------------------------------------------------------------------------------
2024-03-26 16:16:46,480 ----------------------------------------------------------------------------------------------------
2024-03-26 16:16:46,480 Logging anything other than scalars to TensorBoard is currently not supported.
2024-03-26 16:16:47,838 epoch 1 - iter 9/95 - loss 3.00262350 - time (sec): 1.36 - samples/sec: 2136.27 - lr: 0.000004 - momentum: 0.000000
2024-03-26 16:16:49,226 epoch 1 - iter 18/95 - loss 2.86865129 - time (sec): 2.75 - samples/sec: 2008.21 - lr: 0.000009 - momentum: 0.000000
2024-03-26 16:16:50,851 epoch 1 - iter 27/95 - loss 2.63140926 - time (sec): 4.37 - samples/sec: 1962.15 - lr: 0.000014 - momentum: 0.000000
2024-03-26 16:16:52,827 epoch 1 - iter 36/95 - loss 2.40956991 - time (sec): 6.35 - samples/sec: 1880.94 - lr: 0.000018 - momentum: 0.000000
2024-03-26 16:16:54,688 epoch 1 - iter 45/95 - loss 2.22256453 - time (sec): 8.21 - samples/sec: 1899.81 - lr: 0.000023 - momentum: 0.000000
2024-03-26 16:16:56,915 epoch 1 - iter 54/95 - loss 2.05453490 - time (sec): 10.44 - samples/sec: 1834.76 - lr: 0.000028 - momentum: 0.000000
2024-03-26 16:16:58,921 epoch 1 - iter 63/95 - loss 1.90050388 - time (sec): 12.44 - samples/sec: 1816.73 - lr: 0.000033 - momentum: 0.000000
2024-03-26 16:16:59,900 epoch 1 - iter 72/95 - loss 1.81010731 - time (sec): 13.42 - samples/sec: 1861.47 - lr: 0.000037 - momentum: 0.000000
2024-03-26 16:17:02,184 epoch 1 - iter 81/95 - loss 1.66282756 - time (sec): 15.70 - samples/sec: 1808.76 - lr: 0.000042 - momentum: 0.000000
2024-03-26 16:17:03,533 epoch 1 - iter 90/95 - loss 1.52988728 - time (sec): 17.05 - samples/sec: 1874.19 - lr: 0.000047 - momentum: 0.000000
2024-03-26 16:17:04,805 ----------------------------------------------------------------------------------------------------
2024-03-26 16:17:04,805 EPOCH 1 done: loss 1.4651 - lr: 0.000047
2024-03-26 16:17:05,646 DEV : loss 0.43743523955345154 - f1-score (micro avg)  0.6884
2024-03-26 16:17:05,647 saving best model
2024-03-26 16:17:05,931 ----------------------------------------------------------------------------------------------------
2024-03-26 16:17:07,529 epoch 2 - iter 9/95 - loss 0.51277770 - time (sec): 1.60 - samples/sec: 1804.41 - lr: 0.000050 - momentum: 0.000000
2024-03-26 16:17:09,176 epoch 2 - iter 18/95 - loss 0.42088332 - time (sec): 3.24 - samples/sec: 1905.21 - lr: 0.000049 - momentum: 0.000000
2024-03-26 16:17:10,963 epoch 2 - iter 27/95 - loss 0.37975632 - time (sec): 5.03 - samples/sec: 1879.49 - lr: 0.000048 - momentum: 0.000000
2024-03-26 16:17:13,348 epoch 2 - iter 36/95 - loss 0.32956882 - time (sec): 7.42 - samples/sec: 1759.32 - lr: 0.000048 - momentum: 0.000000
2024-03-26 16:17:15,313 epoch 2 - iter 45/95 - loss 0.31872249 - time (sec): 9.38 - samples/sec: 1758.76 - lr: 0.000047 - momentum: 0.000000
2024-03-26 16:17:17,066 epoch 2 - iter 54/95 - loss 0.33330241 - time (sec): 11.13 - samples/sec: 1781.26 - lr: 0.000047 - momentum: 0.000000
2024-03-26 16:17:19,485 epoch 2 - iter 63/95 - loss 0.31620779 - time (sec): 13.55 - samples/sec: 1764.22 - lr: 0.000046 - momentum: 0.000000
2024-03-26 16:17:21,313 epoch 2 - iter 72/95 - loss 0.31691850 - time (sec): 15.38 - samples/sec: 1758.20 - lr: 0.000046 - momentum: 0.000000
2024-03-26 16:17:23,486 epoch 2 - iter 81/95 - loss 0.30645467 - time (sec): 17.55 - samples/sec: 1742.75 - lr: 0.000045 - momentum: 0.000000
2024-03-26 16:17:24,779 epoch 2 - iter 90/95 - loss 0.30584570 - time (sec): 18.85 - samples/sec: 1767.74 - lr: 0.000045 - momentum: 0.000000
2024-03-26 16:17:25,228 ----------------------------------------------------------------------------------------------------
2024-03-26 16:17:25,229 EPOCH 2 done: loss 0.3034 - lr: 0.000045
2024-03-26 16:17:26,171 DEV : loss 0.2551613748073578 - f1-score (micro avg)  0.858
2024-03-26 16:17:26,172 saving best model
2024-03-26 16:17:26,631 ----------------------------------------------------------------------------------------------------
2024-03-26 16:17:28,219 epoch 3 - iter 9/95 - loss 0.19929203 - time (sec): 1.59 - samples/sec: 1670.30 - lr: 0.000044 - momentum: 0.000000
2024-03-26 16:17:29,946 epoch 3 - iter 18/95 - loss 0.16484069 - time (sec): 3.31 - samples/sec: 1686.74 - lr: 0.000043 - momentum: 0.000000
2024-03-26 16:17:31,786 epoch 3 - iter 27/95 - loss 0.17282286 - time (sec): 5.15 - samples/sec: 1727.88 - lr: 0.000043 - momentum: 0.000000
2024-03-26 16:17:33,566 epoch 3 - iter 36/95 - loss 0.17904644 - time (sec): 6.93 - samples/sec: 1740.98 - lr: 0.000042 - momentum: 0.000000
2024-03-26 16:17:35,568 epoch 3 - iter 45/95 - loss 0.18041617 - time (sec): 8.94 - samples/sec: 1766.89 - lr: 0.000042 - momentum: 0.000000
2024-03-26 16:17:37,791 epoch 3 - iter 54/95 - loss 0.17709416 - time (sec): 11.16 - samples/sec: 1735.74 - lr: 0.000041 - momentum: 0.000000
2024-03-26 16:17:39,504 epoch 3 - iter 63/95 - loss 0.17142452 - time (sec): 12.87 - samples/sec: 1738.04 - lr: 0.000041 - momentum: 0.000000
2024-03-26 16:17:41,461 epoch 3 - iter 72/95 - loss 0.16783064 - time (sec): 14.83 - samples/sec: 1745.68 - lr: 0.000040 - momentum: 0.000000
2024-03-26 16:17:43,425 epoch 3 - iter 81/95 - loss 0.17363582 - time (sec): 16.79 - samples/sec: 1763.70 - lr: 0.000040 - momentum: 0.000000
2024-03-26 16:17:45,653 epoch 3 - iter 90/95 - loss 0.16703518 - time (sec): 19.02 - samples/sec: 1743.54 - lr: 0.000039 - momentum: 0.000000
2024-03-26 16:17:46,270 ----------------------------------------------------------------------------------------------------
2024-03-26 16:17:46,270 EPOCH 3 done: loss 0.1695 - lr: 0.000039
2024-03-26 16:17:47,172 DEV : loss 0.1839575171470642 - f1-score (micro avg)  0.8961
2024-03-26 16:17:47,173 saving best model
2024-03-26 16:17:47,627 ----------------------------------------------------------------------------------------------------
2024-03-26 16:17:50,006 epoch 4 - iter 9/95 - loss 0.05728205 - time (sec): 2.38 - samples/sec: 1663.06 - lr: 0.000039 - momentum: 0.000000
2024-03-26 16:17:51,172 epoch 4 - iter 18/95 - loss 0.08157196 - time (sec): 3.54 - samples/sec: 1805.96 - lr: 0.000038 - momentum: 0.000000
2024-03-26 16:17:53,286 epoch 4 - iter 27/95 - loss 0.09339161 - time (sec): 5.66 - samples/sec: 1837.92 - lr: 0.000037 - momentum: 0.000000
2024-03-26 16:17:54,770 epoch 4 - iter 36/95 - loss 0.10074164 - time (sec): 7.14 - samples/sec: 1867.92 - lr: 0.000037 - momentum: 0.000000
2024-03-26 16:17:56,073 epoch 4 - iter 45/95 - loss 0.09879610 - time (sec): 8.45 - samples/sec: 1900.17 - lr: 0.000036 - momentum: 0.000000
2024-03-26 16:17:58,115 epoch 4 - iter 54/95 - loss 0.09811905 - time (sec): 10.49 - samples/sec: 1845.66 - lr: 0.000036 - momentum: 0.000000
2024-03-26 16:18:00,366 epoch 4 - iter 63/95 - loss 0.11314807 - time (sec): 12.74 - samples/sec: 1817.60 - lr: 0.000035 - momentum: 0.000000
2024-03-26 16:18:01,812 epoch 4 - iter 72/95 - loss 0.11201398 - time (sec): 14.18 - samples/sec: 1852.70 - lr: 0.000035 - momentum: 0.000000
2024-03-26 16:18:03,404 epoch 4 - iter 81/95 - loss 0.10950966 - time (sec): 15.78 - samples/sec: 1882.73 - lr: 0.000034 - momentum: 0.000000
2024-03-26 16:18:05,006 epoch 4 - iter 90/95 - loss 0.10868240 - time (sec): 17.38 - samples/sec: 1910.85 - lr: 0.000034 - momentum: 0.000000
2024-03-26 16:18:05,629 ----------------------------------------------------------------------------------------------------
2024-03-26 16:18:05,629 EPOCH 4 done: loss 0.1093 - lr: 0.000034
2024-03-26 16:18:06,542 DEV : loss 0.1929311454296112 - f1-score (micro avg)  0.9018
2024-03-26 16:18:06,544 saving best model
2024-03-26 16:18:07,019 ----------------------------------------------------------------------------------------------------
2024-03-26 16:18:08,211 epoch 5 - iter 9/95 - loss 0.13206801 - time (sec): 1.19 - samples/sec: 2481.33 - lr: 0.000033 - momentum: 0.000000
2024-03-26 16:18:09,650 epoch 5 - iter 18/95 - loss 0.10335413 - time (sec): 2.63 - samples/sec: 2215.56 - lr: 0.000032 - momentum: 0.000000
2024-03-26 16:18:11,630 epoch 5 - iter 27/95 - loss 0.09796425 - time (sec): 4.61 - samples/sec: 1994.76 - lr: 0.000032 - momentum: 0.000000
2024-03-26 16:18:14,031 epoch 5 - iter 36/95 - loss 0.09235968 - time (sec): 7.01 - samples/sec: 1811.48 - lr: 0.000031 - momentum: 0.000000
2024-03-26 16:18:15,260 epoch 5 - iter 45/95 - loss 0.09846890 - time (sec): 8.24 - samples/sec: 1853.46 - lr: 0.000031 - momentum: 0.000000
2024-03-26 16:18:17,112 epoch 5 - iter 54/95 - loss 0.09331254 - time (sec): 10.09 - samples/sec: 1896.66 - lr: 0.000030 - momentum: 0.000000
2024-03-26 16:18:19,174 epoch 5 - iter 63/95 - loss 0.08754826 - time (sec): 12.15 - samples/sec: 1880.16 - lr: 0.000030 - momentum: 0.000000
2024-03-26 16:18:20,430 epoch 5 - iter 72/95 - loss 0.08687357 - time (sec): 13.41 - samples/sec: 1908.90 - lr: 0.000029 - momentum: 0.000000
2024-03-26 16:18:22,971 epoch 5 - iter 81/95 - loss 0.08065071 - time (sec): 15.95 - samples/sec: 1841.25 - lr: 0.000029 - momentum: 0.000000
2024-03-26 16:18:25,001 epoch 5 - iter 90/95 - loss 0.07883050 - time (sec): 17.98 - samples/sec: 1820.88 - lr: 0.000028 - momentum: 0.000000
2024-03-26 16:18:25,867 ----------------------------------------------------------------------------------------------------
2024-03-26 16:18:25,868 EPOCH 5 done: loss 0.0800 - lr: 0.000028
2024-03-26 16:18:26,761 DEV : loss 0.15941660106182098 - f1-score (micro avg)  0.9261
2024-03-26 16:18:26,762 saving best model
2024-03-26 16:18:27,217 ----------------------------------------------------------------------------------------------------
2024-03-26 16:18:28,866 epoch 6 - iter 9/95 - loss 0.11886282 - time (sec): 1.65 - samples/sec: 2013.01 - lr: 0.000027 - momentum: 0.000000
2024-03-26 16:18:30,914 epoch 6 - iter 18/95 - loss 0.07533932 - time (sec): 3.70 - samples/sec: 1834.10 - lr: 0.000027 - momentum: 0.000000
2024-03-26 16:18:32,340 epoch 6 - iter 27/95 - loss 0.07074169 - time (sec): 5.12 - samples/sec: 1865.99 - lr: 0.000026 - momentum: 0.000000
2024-03-26 16:18:34,658 epoch 6 - iter 36/95 - loss 0.05744451 - time (sec): 7.44 - samples/sec: 1726.36 - lr: 0.000026 - momentum: 0.000000
2024-03-26 16:18:36,422 epoch 6 - iter 45/95 - loss 0.05462378 - time (sec): 9.20 - samples/sec: 1748.10 - lr: 0.000025 - momentum: 0.000000
2024-03-26 16:18:38,887 epoch 6 - iter 54/95 - loss 0.06498712 - time (sec): 11.67 - samples/sec: 1725.51 - lr: 0.000025 - momentum: 0.000000
2024-03-26 16:18:40,389 epoch 6 - iter 63/95 - loss 0.06417750 - time (sec): 13.17 - samples/sec: 1743.00 - lr: 0.000024 - momentum: 0.000000
2024-03-26 16:18:41,922 epoch 6 - iter 72/95 - loss 0.06288103 - time (sec): 14.70 - samples/sec: 1764.28 - lr: 0.000024 - momentum: 0.000000
2024-03-26 16:18:43,996 epoch 6 - iter 81/95 - loss 0.06385600 - time (sec): 16.78 - samples/sec: 1757.25 - lr: 0.000023 - momentum: 0.000000
2024-03-26 16:18:45,182 epoch 6 - iter 90/95 - loss 0.06585962 - time (sec): 17.96 - samples/sec: 1800.68 - lr: 0.000023 - momentum: 0.000000
2024-03-26 16:18:46,511 ----------------------------------------------------------------------------------------------------
2024-03-26 16:18:46,511 EPOCH 6 done: loss 0.0643 - lr: 0.000023
2024-03-26 16:18:47,424 DEV : loss 0.17195448279380798 - f1-score (micro avg)  0.9238
2024-03-26 16:18:47,426 ----------------------------------------------------------------------------------------------------
2024-03-26 16:18:48,790 epoch 7 - iter 9/95 - loss 0.03530178 - time (sec): 1.36 - samples/sec: 2325.10 - lr: 0.000022 - momentum: 0.000000
2024-03-26 16:18:50,925 epoch 7 - iter 18/95 - loss 0.03302929 - time (sec): 3.50 - samples/sec: 1926.67 - lr: 0.000021 - momentum: 0.000000
2024-03-26 16:18:52,890 epoch 7 - iter 27/95 - loss 0.04450097 - time (sec): 5.46 - samples/sec: 1788.82 - lr: 0.000021 - momentum: 0.000000
2024-03-26 16:18:54,176 epoch 7 - iter 36/95 - loss 0.04363758 - time (sec): 6.75 - samples/sec: 1853.12 - lr: 0.000020 - momentum: 0.000000
2024-03-26 16:18:55,871 epoch 7 - iter 45/95 - loss 0.04190499 - time (sec): 8.44 - samples/sec: 1863.18 - lr: 0.000020 - momentum: 0.000000
2024-03-26 16:18:58,083 epoch 7 - iter 54/95 - loss 0.04193570 - time (sec): 10.66 - samples/sec: 1838.49 - lr: 0.000019 - momentum: 0.000000
2024-03-26 16:19:00,128 epoch 7 - iter 63/95 - loss 0.04441541 - time (sec): 12.70 - samples/sec: 1792.46 - lr: 0.000019 - momentum: 0.000000
2024-03-26 16:19:02,249 epoch 7 - iter 72/95 - loss 0.04244818 - time (sec): 14.82 - samples/sec: 1770.31 - lr: 0.000018 - momentum: 0.000000
2024-03-26 16:19:03,772 epoch 7 - iter 81/95 - loss 0.04586669 - time (sec): 16.35 - samples/sec: 1776.56 - lr: 0.000018 - momentum: 0.000000
2024-03-26 16:19:05,650 epoch 7 - iter 90/95 - loss 0.05078658 - time (sec): 18.22 - samples/sec: 1804.80 - lr: 0.000017 - momentum: 0.000000
2024-03-26 16:19:06,349 ----------------------------------------------------------------------------------------------------
2024-03-26 16:19:06,349 EPOCH 7 done: loss 0.0496 - lr: 0.000017
2024-03-26 16:19:07,285 DEV : loss 0.18096165359020233 - f1-score (micro avg)  0.9374
2024-03-26 16:19:07,286 saving best model
2024-03-26 16:19:07,772 ----------------------------------------------------------------------------------------------------
2024-03-26 16:19:09,421 epoch 8 - iter 9/95 - loss 0.02220779 - time (sec): 1.65 - samples/sec: 1784.88 - lr: 0.000016 - momentum: 0.000000
2024-03-26 16:19:11,555 epoch 8 - iter 18/95 - loss 0.02181199 - time (sec): 3.78 - samples/sec: 1752.93 - lr: 0.000016 - momentum: 0.000000
2024-03-26 16:19:13,408 epoch 8 - iter 27/95 - loss 0.03194019 - time (sec): 5.63 - samples/sec: 1722.39 - lr: 0.000015 - momentum: 0.000000
2024-03-26 16:19:15,387 epoch 8 - iter 36/95 - loss 0.03072831 - time (sec): 7.61 - samples/sec: 1729.84 - lr: 0.000015 - momentum: 0.000000
2024-03-26 16:19:16,420 epoch 8 - iter 45/95 - loss 0.04177717 - time (sec): 8.65 - samples/sec: 1814.20 - lr: 0.000014 - momentum: 0.000000
2024-03-26 16:19:18,363 epoch 8 - iter 54/95 - loss 0.04283646 - time (sec): 10.59 - samples/sec: 1803.72 - lr: 0.000014 - momentum: 0.000000
2024-03-26 16:19:20,599 epoch 8 - iter 63/95 - loss 0.04245969 - time (sec): 12.83 - samples/sec: 1786.91 - lr: 0.000013 - momentum: 0.000000
2024-03-26 16:19:22,802 epoch 8 - iter 72/95 - loss 0.04307749 - time (sec): 15.03 - samples/sec: 1778.69 - lr: 0.000013 - momentum: 0.000000
2024-03-26 16:19:24,523 epoch 8 - iter 81/95 - loss 0.04167099 - time (sec): 16.75 - samples/sec: 1781.78 - lr: 0.000012 - momentum: 0.000000
2024-03-26 16:19:26,456 epoch 8 - iter 90/95 - loss 0.03893558 - time (sec): 18.68 - samples/sec: 1776.98 - lr: 0.000012 - momentum: 0.000000
2024-03-26 16:19:27,059 ----------------------------------------------------------------------------------------------------
2024-03-26 16:19:27,060 EPOCH 8 done: loss 0.0395 - lr: 0.000012
2024-03-26 16:19:27,991 DEV : loss 0.19180938601493835 - f1-score (micro avg)  0.9328
2024-03-26 16:19:27,992 ----------------------------------------------------------------------------------------------------
2024-03-26 16:19:29,525 epoch 9 - iter 9/95 - loss 0.02551063 - time (sec): 1.53 - samples/sec: 2074.81 - lr: 0.000011 - momentum: 0.000000
2024-03-26 16:19:31,827 epoch 9 - iter 18/95 - loss 0.02041207 - time (sec): 3.83 - samples/sec: 1776.05 - lr: 0.000010 - momentum: 0.000000
2024-03-26 16:19:33,434 epoch 9 - iter 27/95 - loss 0.01720301 - time (sec): 5.44 - samples/sec: 1791.13 - lr: 0.000010 - momentum: 0.000000
2024-03-26 16:19:35,752 epoch 9 - iter 36/95 - loss 0.02365288 - time (sec): 7.76 - samples/sec: 1748.34 - lr: 0.000009 - momentum: 0.000000
2024-03-26 16:19:37,655 epoch 9 - iter 45/95 - loss 0.02464645 - time (sec): 9.66 - samples/sec: 1721.92 - lr: 0.000009 - momentum: 0.000000
2024-03-26 16:19:39,039 epoch 9 - iter 54/95 - loss 0.02829044 - time (sec): 11.05 - samples/sec: 1766.46 - lr: 0.000008 - momentum: 0.000000
2024-03-26 16:19:41,123 epoch 9 - iter 63/95 - loss 0.02602905 - time (sec): 13.13 - samples/sec: 1746.05 - lr: 0.000008 - momentum: 0.000000
2024-03-26 16:19:42,354 epoch 9 - iter 72/95 - loss 0.03105579 - time (sec): 14.36 - samples/sec: 1779.91 - lr: 0.000007 - momentum: 0.000000
2024-03-26 16:19:45,131 epoch 9 - iter 81/95 - loss 0.02971196 - time (sec): 17.14 - samples/sec: 1732.69 - lr: 0.000007 - momentum: 0.000000
2024-03-26 16:19:46,775 epoch 9 - iter 90/95 - loss 0.02721575 - time (sec): 18.78 - samples/sec: 1753.95 - lr: 0.000006 - momentum: 0.000000
2024-03-26 16:19:47,436 ----------------------------------------------------------------------------------------------------
2024-03-26 16:19:47,436 EPOCH 9 done: loss 0.0300 - lr: 0.000006
2024-03-26 16:19:48,347 DEV : loss 0.20131999254226685 - f1-score (micro avg)  0.9279
2024-03-26 16:19:48,348 ----------------------------------------------------------------------------------------------------
2024-03-26 16:19:50,202 epoch 10 - iter 9/95 - loss 0.03091050 - time (sec): 1.85 - samples/sec: 1672.95 - lr: 0.000005 - momentum: 0.000000
2024-03-26 16:19:52,390 epoch 10 - iter 18/95 - loss 0.03060304 - time (sec): 4.04 - samples/sec: 1649.22 - lr: 0.000005 - momentum: 0.000000
2024-03-26 16:19:53,812 epoch 10 - iter 27/95 - loss 0.02620574 - time (sec): 5.46 - samples/sec: 1795.39 - lr: 0.000004 - momentum: 0.000000
2024-03-26 16:19:55,530 epoch 10 - iter 36/95 - loss 0.02158266 - time (sec): 7.18 - samples/sec: 1837.63 - lr: 0.000004 - momentum: 0.000000
2024-03-26 16:19:56,956 epoch 10 - iter 45/95 - loss 0.02055081 - time (sec): 8.61 - samples/sec: 1865.79 - lr: 0.000003 - momentum: 0.000000
2024-03-26 16:19:57,985 epoch 10 - iter 54/95 - loss 0.01888960 - time (sec): 9.64 - samples/sec: 1936.51 - lr: 0.000003 - momentum: 0.000000
2024-03-26 16:19:59,789 epoch 10 - iter 63/95 - loss 0.01754753 - time (sec): 11.44 - samples/sec: 1910.67 - lr: 0.000002 - momentum: 0.000000
2024-03-26 16:20:02,059 epoch 10 - iter 72/95 - loss 0.02462830 - time (sec): 13.71 - samples/sec: 1859.55 - lr: 0.000002 - momentum: 0.000000
2024-03-26 16:20:03,695 epoch 10 - iter 81/95 - loss 0.02696906 - time (sec): 15.35 - samples/sec: 1851.50 - lr: 0.000001 - momentum: 0.000000
2024-03-26 16:20:06,017 epoch 10 - iter 90/95 - loss 0.02524721 - time (sec): 17.67 - samples/sec: 1839.45 - lr: 0.000001 - momentum: 0.000000
2024-03-26 16:20:07,264 ----------------------------------------------------------------------------------------------------
2024-03-26 16:20:07,264 EPOCH 10 done: loss 0.0269 - lr: 0.000001
2024-03-26 16:20:08,197 DEV : loss 0.21133708953857422 - f1-score (micro avg)  0.9359
2024-03-26 16:20:08,505 ----------------------------------------------------------------------------------------------------
2024-03-26 16:20:08,505 Loading model from best epoch ...
2024-03-26 16:20:09,531 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 16:20:10,310 
Results:
- F-score (micro) 0.8821
- F-score (macro) 0.6728
- Accuracy 0.7912

By class:
              precision    recall  f1-score   support

 Unternehmen     0.9055    0.8647    0.8846       266
 Auslagerung     0.8137    0.8594    0.8359       249
         Ort     0.9565    0.9851    0.9706       134
    Software     0.0000    0.0000    0.0000         0

   micro avg     0.8767    0.8875    0.8821       649
   macro avg     0.6689    0.6773    0.6728       649
weighted avg     0.8808    0.8875    0.8837       649

2024-03-26 16:20:10,310 ----------------------------------------------------------------------------------------------------