Hugging Face's logo

stefan-it
/
flair-co-funer-gbert_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
3
flair-co-funer-gbert_base-bs8-e10-lr5e-05-2
File size: 23,640 Bytes 
5d2c939
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
2024-03-26 09:55:54,516 ----------------------------------------------------------------------------------------------------
2024-03-26 09:55:54,517 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 09:55:54,517 ----------------------------------------------------------------------------------------------------
2024-03-26 09:55:54,517 Corpus: 758 train + 94 dev + 96 test sentences
2024-03-26 09:55:54,517 ----------------------------------------------------------------------------------------------------
2024-03-26 09:55:54,517 Train:  758 sentences
2024-03-26 09:55:54,517         (train_with_dev=False, train_with_test=False)
2024-03-26 09:55:54,517 ----------------------------------------------------------------------------------------------------
2024-03-26 09:55:54,517 Training Params:
2024-03-26 09:55:54,517  - learning_rate: "5e-05" 
2024-03-26 09:55:54,517  - mini_batch_size: "8"
2024-03-26 09:55:54,517  - max_epochs: "10"
2024-03-26 09:55:54,517  - shuffle: "True"
2024-03-26 09:55:54,517 ----------------------------------------------------------------------------------------------------
2024-03-26 09:55:54,517 Plugins:
2024-03-26 09:55:54,517  - TensorboardLogger
2024-03-26 09:55:54,517  - LinearScheduler | warmup_fraction: '0.1'
2024-03-26 09:55:54,517 ----------------------------------------------------------------------------------------------------
2024-03-26 09:55:54,517 Final evaluation on model from best epoch (best-model.pt)
2024-03-26 09:55:54,517  - metric: "('micro avg', 'f1-score')"
2024-03-26 09:55:54,517 ----------------------------------------------------------------------------------------------------
2024-03-26 09:55:54,517 Computation:
2024-03-26 09:55:54,517  - compute on device: cuda:0
2024-03-26 09:55:54,517  - embedding storage: none
2024-03-26 09:55:54,517 ----------------------------------------------------------------------------------------------------
2024-03-26 09:55:54,517 Model training base path: "flair-co-funer-gbert_base-bs8-e10-lr5e-05-2"
2024-03-26 09:55:54,517 ----------------------------------------------------------------------------------------------------
2024-03-26 09:55:54,517 ----------------------------------------------------------------------------------------------------
2024-03-26 09:55:54,517 Logging anything other than scalars to TensorBoard is currently not supported.
2024-03-26 09:55:56,359 epoch 1 - iter 9/95 - loss 3.51295648 - time (sec): 1.84 - samples/sec: 1913.27 - lr: 0.000004 - momentum: 0.000000
2024-03-26 09:55:58,471 epoch 1 - iter 18/95 - loss 3.24148185 - time (sec): 3.95 - samples/sec: 1822.75 - lr: 0.000009 - momentum: 0.000000
2024-03-26 09:56:00,044 epoch 1 - iter 27/95 - loss 2.94359502 - time (sec): 5.53 - samples/sec: 1824.22 - lr: 0.000014 - momentum: 0.000000
2024-03-26 09:56:01,995 epoch 1 - iter 36/95 - loss 2.72268801 - time (sec): 7.48 - samples/sec: 1845.85 - lr: 0.000018 - momentum: 0.000000
2024-03-26 09:56:04,076 epoch 1 - iter 45/95 - loss 2.52912232 - time (sec): 9.56 - samples/sec: 1783.63 - lr: 0.000023 - momentum: 0.000000
2024-03-26 09:56:06,050 epoch 1 - iter 54/95 - loss 2.35533618 - time (sec): 11.53 - samples/sec: 1760.66 - lr: 0.000028 - momentum: 0.000000
2024-03-26 09:56:07,584 epoch 1 - iter 63/95 - loss 2.22092888 - time (sec): 13.07 - samples/sec: 1770.48 - lr: 0.000033 - momentum: 0.000000
2024-03-26 09:56:08,848 epoch 1 - iter 72/95 - loss 2.07343945 - time (sec): 14.33 - samples/sec: 1824.85 - lr: 0.000037 - momentum: 0.000000
2024-03-26 09:56:10,406 epoch 1 - iter 81/95 - loss 1.94077724 - time (sec): 15.89 - samples/sec: 1851.36 - lr: 0.000042 - momentum: 0.000000
2024-03-26 09:56:12,341 epoch 1 - iter 90/95 - loss 1.81672289 - time (sec): 17.82 - samples/sec: 1829.55 - lr: 0.000047 - momentum: 0.000000
2024-03-26 09:56:13,406 ----------------------------------------------------------------------------------------------------
2024-03-26 09:56:13,406 EPOCH 1 done: loss 1.7469 - lr: 0.000047
2024-03-26 09:56:14,309 DEV : loss 0.4293653666973114 - f1-score (micro avg)  0.7027
2024-03-26 09:56:14,310 saving best model
2024-03-26 09:56:14,570 ----------------------------------------------------------------------------------------------------
2024-03-26 09:56:15,883 epoch 2 - iter 9/95 - loss 0.60874536 - time (sec): 1.31 - samples/sec: 2473.32 - lr: 0.000050 - momentum: 0.000000
2024-03-26 09:56:17,748 epoch 2 - iter 18/95 - loss 0.49896854 - time (sec): 3.18 - samples/sec: 2162.35 - lr: 0.000049 - momentum: 0.000000
2024-03-26 09:56:20,547 epoch 2 - iter 27/95 - loss 0.41484753 - time (sec): 5.98 - samples/sec: 1934.16 - lr: 0.000048 - momentum: 0.000000
2024-03-26 09:56:22,603 epoch 2 - iter 36/95 - loss 0.39641865 - time (sec): 8.03 - samples/sec: 1850.91 - lr: 0.000048 - momentum: 0.000000
2024-03-26 09:56:24,354 epoch 2 - iter 45/95 - loss 0.37116751 - time (sec): 9.78 - samples/sec: 1837.44 - lr: 0.000047 - momentum: 0.000000
2024-03-26 09:56:26,443 epoch 2 - iter 54/95 - loss 0.35768210 - time (sec): 11.87 - samples/sec: 1792.21 - lr: 0.000047 - momentum: 0.000000
2024-03-26 09:56:27,969 epoch 2 - iter 63/95 - loss 0.36146991 - time (sec): 13.40 - samples/sec: 1816.49 - lr: 0.000046 - momentum: 0.000000
2024-03-26 09:56:29,446 epoch 2 - iter 72/95 - loss 0.35422116 - time (sec): 14.87 - samples/sec: 1844.21 - lr: 0.000046 - momentum: 0.000000
2024-03-26 09:56:30,605 epoch 2 - iter 81/95 - loss 0.34979111 - time (sec): 16.03 - samples/sec: 1878.96 - lr: 0.000045 - momentum: 0.000000
2024-03-26 09:56:31,880 epoch 2 - iter 90/95 - loss 0.34698102 - time (sec): 17.31 - samples/sec: 1900.71 - lr: 0.000045 - momentum: 0.000000
2024-03-26 09:56:32,837 ----------------------------------------------------------------------------------------------------
2024-03-26 09:56:32,837 EPOCH 2 done: loss 0.3378 - lr: 0.000045
2024-03-26 09:56:33,723 DEV : loss 0.25087055563926697 - f1-score (micro avg)  0.8296
2024-03-26 09:56:33,724 saving best model
2024-03-26 09:56:34,168 ----------------------------------------------------------------------------------------------------
2024-03-26 09:56:36,165 epoch 3 - iter 9/95 - loss 0.18016583 - time (sec): 2.00 - samples/sec: 1668.07 - lr: 0.000044 - momentum: 0.000000
2024-03-26 09:56:38,211 epoch 3 - iter 18/95 - loss 0.20468785 - time (sec): 4.04 - samples/sec: 1796.92 - lr: 0.000043 - momentum: 0.000000
2024-03-26 09:56:39,167 epoch 3 - iter 27/95 - loss 0.22616043 - time (sec): 5.00 - samples/sec: 1925.55 - lr: 0.000043 - momentum: 0.000000
2024-03-26 09:56:40,892 epoch 3 - iter 36/95 - loss 0.22034828 - time (sec): 6.72 - samples/sec: 1887.55 - lr: 0.000042 - momentum: 0.000000
2024-03-26 09:56:42,136 epoch 3 - iter 45/95 - loss 0.22037517 - time (sec): 7.97 - samples/sec: 1933.42 - lr: 0.000042 - momentum: 0.000000
2024-03-26 09:56:44,155 epoch 3 - iter 54/95 - loss 0.21422754 - time (sec): 9.99 - samples/sec: 1872.38 - lr: 0.000041 - momentum: 0.000000
2024-03-26 09:56:45,763 epoch 3 - iter 63/95 - loss 0.20959317 - time (sec): 11.59 - samples/sec: 1882.81 - lr: 0.000041 - momentum: 0.000000
2024-03-26 09:56:47,260 epoch 3 - iter 72/95 - loss 0.20513440 - time (sec): 13.09 - samples/sec: 1892.83 - lr: 0.000040 - momentum: 0.000000
2024-03-26 09:56:49,036 epoch 3 - iter 81/95 - loss 0.19871669 - time (sec): 14.87 - samples/sec: 1880.58 - lr: 0.000040 - momentum: 0.000000
2024-03-26 09:56:51,620 epoch 3 - iter 90/95 - loss 0.18183164 - time (sec): 17.45 - samples/sec: 1874.07 - lr: 0.000039 - momentum: 0.000000
2024-03-26 09:56:52,702 ----------------------------------------------------------------------------------------------------
2024-03-26 09:56:52,702 EPOCH 3 done: loss 0.1792 - lr: 0.000039
2024-03-26 09:56:53,591 DEV : loss 0.22550131380558014 - f1-score (micro avg)  0.8869
2024-03-26 09:56:53,591 saving best model
2024-03-26 09:56:54,016 ----------------------------------------------------------------------------------------------------
2024-03-26 09:56:55,692 epoch 4 - iter 9/95 - loss 0.16564534 - time (sec): 1.67 - samples/sec: 1918.92 - lr: 0.000039 - momentum: 0.000000
2024-03-26 09:56:57,658 epoch 4 - iter 18/95 - loss 0.14460325 - time (sec): 3.64 - samples/sec: 1850.14 - lr: 0.000038 - momentum: 0.000000
2024-03-26 09:56:58,873 epoch 4 - iter 27/95 - loss 0.13705328 - time (sec): 4.86 - samples/sec: 1938.49 - lr: 0.000037 - momentum: 0.000000
2024-03-26 09:57:00,516 epoch 4 - iter 36/95 - loss 0.13381213 - time (sec): 6.50 - samples/sec: 1910.14 - lr: 0.000037 - momentum: 0.000000
2024-03-26 09:57:02,650 epoch 4 - iter 45/95 - loss 0.13001690 - time (sec): 8.63 - samples/sec: 1849.74 - lr: 0.000036 - momentum: 0.000000
2024-03-26 09:57:04,167 epoch 4 - iter 54/95 - loss 0.13595740 - time (sec): 10.15 - samples/sec: 1859.49 - lr: 0.000036 - momentum: 0.000000
2024-03-26 09:57:06,602 epoch 4 - iter 63/95 - loss 0.13274558 - time (sec): 12.58 - samples/sec: 1811.67 - lr: 0.000035 - momentum: 0.000000
2024-03-26 09:57:09,090 epoch 4 - iter 72/95 - loss 0.12565390 - time (sec): 15.07 - samples/sec: 1774.73 - lr: 0.000035 - momentum: 0.000000
2024-03-26 09:57:10,512 epoch 4 - iter 81/95 - loss 0.12269682 - time (sec): 16.49 - samples/sec: 1781.62 - lr: 0.000034 - momentum: 0.000000
2024-03-26 09:57:12,279 epoch 4 - iter 90/95 - loss 0.12294753 - time (sec): 18.26 - samples/sec: 1781.38 - lr: 0.000034 - momentum: 0.000000
2024-03-26 09:57:13,394 ----------------------------------------------------------------------------------------------------
2024-03-26 09:57:13,394 EPOCH 4 done: loss 0.1198 - lr: 0.000034
2024-03-26 09:57:14,288 DEV : loss 0.19063404202461243 - f1-score (micro avg)  0.877
2024-03-26 09:57:14,289 ----------------------------------------------------------------------------------------------------
2024-03-26 09:57:15,247 epoch 5 - iter 9/95 - loss 0.07671720 - time (sec): 0.96 - samples/sec: 2150.56 - lr: 0.000033 - momentum: 0.000000
2024-03-26 09:57:16,837 epoch 5 - iter 18/95 - loss 0.08732704 - time (sec): 2.55 - samples/sec: 2089.09 - lr: 0.000032 - momentum: 0.000000
2024-03-26 09:57:19,294 epoch 5 - iter 27/95 - loss 0.08678902 - time (sec): 5.00 - samples/sec: 1821.53 - lr: 0.000032 - momentum: 0.000000
2024-03-26 09:57:21,118 epoch 5 - iter 36/95 - loss 0.08229238 - time (sec): 6.83 - samples/sec: 1816.91 - lr: 0.000031 - momentum: 0.000000
2024-03-26 09:57:23,067 epoch 5 - iter 45/95 - loss 0.08039814 - time (sec): 8.78 - samples/sec: 1780.80 - lr: 0.000031 - momentum: 0.000000
2024-03-26 09:57:24,661 epoch 5 - iter 54/95 - loss 0.08120875 - time (sec): 10.37 - samples/sec: 1816.90 - lr: 0.000030 - momentum: 0.000000
2024-03-26 09:57:26,989 epoch 5 - iter 63/95 - loss 0.07961933 - time (sec): 12.70 - samples/sec: 1803.60 - lr: 0.000030 - momentum: 0.000000
2024-03-26 09:57:28,389 epoch 5 - iter 72/95 - loss 0.08356768 - time (sec): 14.10 - samples/sec: 1821.80 - lr: 0.000029 - momentum: 0.000000
2024-03-26 09:57:30,275 epoch 5 - iter 81/95 - loss 0.08023690 - time (sec): 15.99 - samples/sec: 1796.39 - lr: 0.000029 - momentum: 0.000000
2024-03-26 09:57:32,125 epoch 5 - iter 90/95 - loss 0.08106053 - time (sec): 17.84 - samples/sec: 1797.19 - lr: 0.000028 - momentum: 0.000000
2024-03-26 09:57:33,476 ----------------------------------------------------------------------------------------------------
2024-03-26 09:57:33,476 EPOCH 5 done: loss 0.0808 - lr: 0.000028
2024-03-26 09:57:34,464 DEV : loss 0.16919878125190735 - f1-score (micro avg)  0.9171
2024-03-26 09:57:34,465 saving best model
2024-03-26 09:57:34,895 ----------------------------------------------------------------------------------------------------
2024-03-26 09:57:36,259 epoch 6 - iter 9/95 - loss 0.05513905 - time (sec): 1.36 - samples/sec: 2113.60 - lr: 0.000027 - momentum: 0.000000
2024-03-26 09:57:38,408 epoch 6 - iter 18/95 - loss 0.05798422 - time (sec): 3.51 - samples/sec: 2042.14 - lr: 0.000027 - momentum: 0.000000
2024-03-26 09:57:39,966 epoch 6 - iter 27/95 - loss 0.05351134 - time (sec): 5.07 - samples/sec: 1980.67 - lr: 0.000026 - momentum: 0.000000
2024-03-26 09:57:41,933 epoch 6 - iter 36/95 - loss 0.05595926 - time (sec): 7.04 - samples/sec: 1918.68 - lr: 0.000026 - momentum: 0.000000
2024-03-26 09:57:44,066 epoch 6 - iter 45/95 - loss 0.06826327 - time (sec): 9.17 - samples/sec: 1937.30 - lr: 0.000025 - momentum: 0.000000
2024-03-26 09:57:45,252 epoch 6 - iter 54/95 - loss 0.06442393 - time (sec): 10.36 - samples/sec: 1952.25 - lr: 0.000025 - momentum: 0.000000
2024-03-26 09:57:46,310 epoch 6 - iter 63/95 - loss 0.06557445 - time (sec): 11.41 - samples/sec: 1972.50 - lr: 0.000024 - momentum: 0.000000
2024-03-26 09:57:47,834 epoch 6 - iter 72/95 - loss 0.06074062 - time (sec): 12.94 - samples/sec: 1973.50 - lr: 0.000024 - momentum: 0.000000
2024-03-26 09:57:49,828 epoch 6 - iter 81/95 - loss 0.05887365 - time (sec): 14.93 - samples/sec: 1957.97 - lr: 0.000023 - momentum: 0.000000
2024-03-26 09:57:51,808 epoch 6 - iter 90/95 - loss 0.05836864 - time (sec): 16.91 - samples/sec: 1944.79 - lr: 0.000023 - momentum: 0.000000
2024-03-26 09:57:52,732 ----------------------------------------------------------------------------------------------------
2024-03-26 09:57:52,732 EPOCH 6 done: loss 0.0565 - lr: 0.000023
2024-03-26 09:57:53,630 DEV : loss 0.1597478985786438 - f1-score (micro avg)  0.935
2024-03-26 09:57:53,631 saving best model
2024-03-26 09:57:54,057 ----------------------------------------------------------------------------------------------------
2024-03-26 09:57:55,485 epoch 7 - iter 9/95 - loss 0.03244858 - time (sec): 1.43 - samples/sec: 1864.42 - lr: 0.000022 - momentum: 0.000000
2024-03-26 09:57:57,279 epoch 7 - iter 18/95 - loss 0.03958068 - time (sec): 3.22 - samples/sec: 1800.01 - lr: 0.000021 - momentum: 0.000000
2024-03-26 09:57:58,885 epoch 7 - iter 27/95 - loss 0.03643340 - time (sec): 4.82 - samples/sec: 1890.63 - lr: 0.000021 - momentum: 0.000000
2024-03-26 09:58:00,601 epoch 7 - iter 36/95 - loss 0.03750744 - time (sec): 6.54 - samples/sec: 1838.53 - lr: 0.000020 - momentum: 0.000000
2024-03-26 09:58:01,955 epoch 7 - iter 45/95 - loss 0.03833106 - time (sec): 7.90 - samples/sec: 1856.99 - lr: 0.000020 - momentum: 0.000000
2024-03-26 09:58:04,002 epoch 7 - iter 54/95 - loss 0.03880059 - time (sec): 9.94 - samples/sec: 1803.00 - lr: 0.000019 - momentum: 0.000000
2024-03-26 09:58:06,229 epoch 7 - iter 63/95 - loss 0.03928334 - time (sec): 12.17 - samples/sec: 1754.30 - lr: 0.000019 - momentum: 0.000000
2024-03-26 09:58:08,782 epoch 7 - iter 72/95 - loss 0.04446132 - time (sec): 14.72 - samples/sec: 1751.07 - lr: 0.000018 - momentum: 0.000000
2024-03-26 09:58:10,730 epoch 7 - iter 81/95 - loss 0.04659624 - time (sec): 16.67 - samples/sec: 1758.17 - lr: 0.000018 - momentum: 0.000000
2024-03-26 09:58:12,692 epoch 7 - iter 90/95 - loss 0.04764396 - time (sec): 18.63 - samples/sec: 1759.23 - lr: 0.000017 - momentum: 0.000000
2024-03-26 09:58:13,582 ----------------------------------------------------------------------------------------------------
2024-03-26 09:58:13,582 EPOCH 7 done: loss 0.0462 - lr: 0.000017
2024-03-26 09:58:14,481 DEV : loss 0.1805838942527771 - f1-score (micro avg)  0.9222
2024-03-26 09:58:14,482 ----------------------------------------------------------------------------------------------------
2024-03-26 09:58:16,739 epoch 8 - iter 9/95 - loss 0.03112277 - time (sec): 2.26 - samples/sec: 1678.52 - lr: 0.000016 - momentum: 0.000000
2024-03-26 09:58:18,290 epoch 8 - iter 18/95 - loss 0.03749051 - time (sec): 3.81 - samples/sec: 1809.73 - lr: 0.000016 - momentum: 0.000000
2024-03-26 09:58:20,455 epoch 8 - iter 27/95 - loss 0.04423972 - time (sec): 5.97 - samples/sec: 1770.15 - lr: 0.000015 - momentum: 0.000000
2024-03-26 09:58:21,995 epoch 8 - iter 36/95 - loss 0.03953226 - time (sec): 7.51 - samples/sec: 1795.95 - lr: 0.000015 - momentum: 0.000000
2024-03-26 09:58:23,855 epoch 8 - iter 45/95 - loss 0.03510558 - time (sec): 9.37 - samples/sec: 1776.30 - lr: 0.000014 - momentum: 0.000000
2024-03-26 09:58:25,542 epoch 8 - iter 54/95 - loss 0.04168451 - time (sec): 11.06 - samples/sec: 1786.17 - lr: 0.000014 - momentum: 0.000000
2024-03-26 09:58:27,341 epoch 8 - iter 63/95 - loss 0.04091545 - time (sec): 12.86 - samples/sec: 1785.80 - lr: 0.000013 - momentum: 0.000000
2024-03-26 09:58:28,638 epoch 8 - iter 72/95 - loss 0.03864673 - time (sec): 14.16 - samples/sec: 1807.18 - lr: 0.000013 - momentum: 0.000000
2024-03-26 09:58:30,461 epoch 8 - iter 81/95 - loss 0.03694964 - time (sec): 15.98 - samples/sec: 1832.46 - lr: 0.000012 - momentum: 0.000000
2024-03-26 09:58:32,860 epoch 8 - iter 90/95 - loss 0.03440669 - time (sec): 18.38 - samples/sec: 1794.90 - lr: 0.000012 - momentum: 0.000000
2024-03-26 09:58:33,664 ----------------------------------------------------------------------------------------------------
2024-03-26 09:58:33,664 EPOCH 8 done: loss 0.0361 - lr: 0.000012
2024-03-26 09:58:34,570 DEV : loss 0.20250095427036285 - f1-score (micro avg)  0.9227
2024-03-26 09:58:34,571 ----------------------------------------------------------------------------------------------------
2024-03-26 09:58:36,320 epoch 9 - iter 9/95 - loss 0.04686787 - time (sec): 1.75 - samples/sec: 1942.96 - lr: 0.000011 - momentum: 0.000000
2024-03-26 09:58:38,509 epoch 9 - iter 18/95 - loss 0.02950774 - time (sec): 3.94 - samples/sec: 1761.42 - lr: 0.000010 - momentum: 0.000000
2024-03-26 09:58:40,380 epoch 9 - iter 27/95 - loss 0.03881406 - time (sec): 5.81 - samples/sec: 1796.36 - lr: 0.000010 - momentum: 0.000000
2024-03-26 09:58:41,918 epoch 9 - iter 36/95 - loss 0.03710701 - time (sec): 7.35 - samples/sec: 1807.00 - lr: 0.000009 - momentum: 0.000000
2024-03-26 09:58:43,318 epoch 9 - iter 45/95 - loss 0.03227612 - time (sec): 8.75 - samples/sec: 1844.24 - lr: 0.000009 - momentum: 0.000000
2024-03-26 09:58:44,720 epoch 9 - iter 54/95 - loss 0.02974616 - time (sec): 10.15 - samples/sec: 1895.14 - lr: 0.000008 - momentum: 0.000000
2024-03-26 09:58:46,529 epoch 9 - iter 63/95 - loss 0.03186545 - time (sec): 11.96 - samples/sec: 1900.89 - lr: 0.000008 - momentum: 0.000000
2024-03-26 09:58:48,529 epoch 9 - iter 72/95 - loss 0.03279052 - time (sec): 13.96 - samples/sec: 1871.55 - lr: 0.000007 - momentum: 0.000000
2024-03-26 09:58:50,796 epoch 9 - iter 81/95 - loss 0.03338961 - time (sec): 16.22 - samples/sec: 1829.34 - lr: 0.000007 - momentum: 0.000000
2024-03-26 09:58:52,521 epoch 9 - iter 90/95 - loss 0.03218886 - time (sec): 17.95 - samples/sec: 1844.02 - lr: 0.000006 - momentum: 0.000000
2024-03-26 09:58:53,105 ----------------------------------------------------------------------------------------------------
2024-03-26 09:58:53,105 EPOCH 9 done: loss 0.0313 - lr: 0.000006
2024-03-26 09:58:54,013 DEV : loss 0.194478839635849 - f1-score (micro avg)  0.9333
2024-03-26 09:58:54,016 ----------------------------------------------------------------------------------------------------
2024-03-26 09:58:56,147 epoch 10 - iter 9/95 - loss 0.00307710 - time (sec): 2.13 - samples/sec: 1812.59 - lr: 0.000005 - momentum: 0.000000
2024-03-26 09:58:57,890 epoch 10 - iter 18/95 - loss 0.01367119 - time (sec): 3.87 - samples/sec: 1833.86 - lr: 0.000005 - momentum: 0.000000
2024-03-26 09:58:58,993 epoch 10 - iter 27/95 - loss 0.01197108 - time (sec): 4.98 - samples/sec: 1913.69 - lr: 0.000004 - momentum: 0.000000
2024-03-26 09:59:00,434 epoch 10 - iter 36/95 - loss 0.01892848 - time (sec): 6.42 - samples/sec: 1950.84 - lr: 0.000004 - momentum: 0.000000
2024-03-26 09:59:02,371 epoch 10 - iter 45/95 - loss 0.02347308 - time (sec): 8.35 - samples/sec: 1889.33 - lr: 0.000003 - momentum: 0.000000
2024-03-26 09:59:03,458 epoch 10 - iter 54/95 - loss 0.02477599 - time (sec): 9.44 - samples/sec: 1938.85 - lr: 0.000003 - momentum: 0.000000
2024-03-26 09:59:04,679 epoch 10 - iter 63/95 - loss 0.02232245 - time (sec): 10.66 - samples/sec: 1967.89 - lr: 0.000002 - momentum: 0.000000
2024-03-26 09:59:06,585 epoch 10 - iter 72/95 - loss 0.02358988 - time (sec): 12.57 - samples/sec: 1964.69 - lr: 0.000002 - momentum: 0.000000
2024-03-26 09:59:09,224 epoch 10 - iter 81/95 - loss 0.02309473 - time (sec): 15.21 - samples/sec: 1926.11 - lr: 0.000001 - momentum: 0.000000
2024-03-26 09:59:11,224 epoch 10 - iter 90/95 - loss 0.02402159 - time (sec): 17.21 - samples/sec: 1906.77 - lr: 0.000001 - momentum: 0.000000
2024-03-26 09:59:12,141 ----------------------------------------------------------------------------------------------------
2024-03-26 09:59:12,141 EPOCH 10 done: loss 0.0238 - lr: 0.000001
2024-03-26 09:59:13,096 DEV : loss 0.20281948149204254 - f1-score (micro avg)  0.9285
2024-03-26 09:59:13,379 ----------------------------------------------------------------------------------------------------
2024-03-26 09:59:13,380 Loading model from best epoch ...
2024-03-26 09:59:14,264 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 09:59:15,009 
Results:
- F-score (micro) 0.9117
- F-score (macro) 0.692
- Accuracy 0.8402

By class:
              precision    recall  f1-score   support

 Unternehmen     0.9328    0.8872    0.9094       266
 Auslagerung     0.8626    0.9076    0.8845       249
         Ort     0.9635    0.9851    0.9742       134
    Software     0.0000    0.0000    0.0000         0

   micro avg     0.9083    0.9153    0.9117       649
   macro avg     0.6897    0.6950    0.6920       649
weighted avg     0.9122    0.9153    0.9133       649

2024-03-26 09:59:15,009 ----------------------------------------------------------------------------------------------------