Hugging Face's logo

stefan-it
/
flair-co-funer-german_dbmdz_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_dbmdz_bert_base-bs8-e10-lr5e-05-2
File size: 23,819 Bytes 
3e0f742
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
246
247
 
2024-03-26 15:43:55,078 ----------------------------------------------------------------------------------------------------
2024-03-26 15:43:55,078 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 15:43:55,078 ----------------------------------------------------------------------------------------------------
2024-03-26 15:43:55,079 Corpus: 758 train + 94 dev + 96 test sentences
2024-03-26 15:43:55,079 ----------------------------------------------------------------------------------------------------
2024-03-26 15:43:55,079 Train:  758 sentences
2024-03-26 15:43:55,079         (train_with_dev=False, train_with_test=False)
2024-03-26 15:43:55,079 ----------------------------------------------------------------------------------------------------
2024-03-26 15:43:55,079 Training Params:
2024-03-26 15:43:55,079  - learning_rate: "5e-05" 
2024-03-26 15:43:55,079  - mini_batch_size: "8"
2024-03-26 15:43:55,079  - max_epochs: "10"
2024-03-26 15:43:55,079  - shuffle: "True"
2024-03-26 15:43:55,079 ----------------------------------------------------------------------------------------------------
2024-03-26 15:43:55,079 Plugins:
2024-03-26 15:43:55,079  - TensorboardLogger
2024-03-26 15:43:55,079  - LinearScheduler | warmup_fraction: '0.1'
2024-03-26 15:43:55,079 ----------------------------------------------------------------------------------------------------
2024-03-26 15:43:55,079 Final evaluation on model from best epoch (best-model.pt)
2024-03-26 15:43:55,079  - metric: "('micro avg', 'f1-score')"
2024-03-26 15:43:55,079 ----------------------------------------------------------------------------------------------------
2024-03-26 15:43:55,079 Computation:
2024-03-26 15:43:55,079  - compute on device: cuda:0
2024-03-26 15:43:55,079  - embedding storage: none
2024-03-26 15:43:55,079 ----------------------------------------------------------------------------------------------------
2024-03-26 15:43:55,079 Model training base path: "flair-co-funer-german_dbmdz_bert_base-bs8-e10-lr5e-05-2"
2024-03-26 15:43:55,079 ----------------------------------------------------------------------------------------------------
2024-03-26 15:43:55,079 ----------------------------------------------------------------------------------------------------
2024-03-26 15:43:55,079 Logging anything other than scalars to TensorBoard is currently not supported.
2024-03-26 15:43:56,891 epoch 1 - iter 9/95 - loss 3.04906218 - time (sec): 1.81 - samples/sec: 1945.01 - lr: 0.000004 - momentum: 0.000000
2024-03-26 15:43:58,966 epoch 1 - iter 18/95 - loss 2.90354229 - time (sec): 3.89 - samples/sec: 1854.23 - lr: 0.000009 - momentum: 0.000000
2024-03-26 15:44:00,517 epoch 1 - iter 27/95 - loss 2.67761977 - time (sec): 5.44 - samples/sec: 1854.06 - lr: 0.000014 - momentum: 0.000000
2024-03-26 15:44:02,437 epoch 1 - iter 36/95 - loss 2.47492641 - time (sec): 7.36 - samples/sec: 1875.88 - lr: 0.000018 - momentum: 0.000000
2024-03-26 15:44:04,489 epoch 1 - iter 45/95 - loss 2.30291695 - time (sec): 9.41 - samples/sec: 1811.90 - lr: 0.000023 - momentum: 0.000000
2024-03-26 15:44:06,443 epoch 1 - iter 54/95 - loss 2.13831895 - time (sec): 11.36 - samples/sec: 1786.97 - lr: 0.000028 - momentum: 0.000000
2024-03-26 15:44:07,963 epoch 1 - iter 63/95 - loss 2.00131862 - time (sec): 12.88 - samples/sec: 1795.65 - lr: 0.000033 - momentum: 0.000000
2024-03-26 15:44:09,208 epoch 1 - iter 72/95 - loss 1.87302257 - time (sec): 14.13 - samples/sec: 1850.86 - lr: 0.000037 - momentum: 0.000000
2024-03-26 15:44:10,744 epoch 1 - iter 81/95 - loss 1.75332523 - time (sec): 15.67 - samples/sec: 1877.79 - lr: 0.000042 - momentum: 0.000000
2024-03-26 15:44:12,668 epoch 1 - iter 90/95 - loss 1.64074065 - time (sec): 17.59 - samples/sec: 1853.99 - lr: 0.000047 - momentum: 0.000000
2024-03-26 15:44:13,717 ----------------------------------------------------------------------------------------------------
2024-03-26 15:44:13,717 EPOCH 1 done: loss 1.5782 - lr: 0.000047
2024-03-26 15:44:14,599 DEV : loss 0.429016649723053 - f1-score (micro avg)  0.6995
2024-03-26 15:44:14,600 saving best model
2024-03-26 15:44:14,862 ----------------------------------------------------------------------------------------------------
2024-03-26 15:44:16,175 epoch 2 - iter 9/95 - loss 0.55171150 - time (sec): 1.31 - samples/sec: 2472.16 - lr: 0.000050 - momentum: 0.000000
2024-03-26 15:44:18,041 epoch 2 - iter 18/95 - loss 0.44710038 - time (sec): 3.18 - samples/sec: 2161.54 - lr: 0.000049 - momentum: 0.000000
2024-03-26 15:44:20,841 epoch 2 - iter 27/95 - loss 0.37050551 - time (sec): 5.98 - samples/sec: 1933.54 - lr: 0.000048 - momentum: 0.000000
2024-03-26 15:44:22,897 epoch 2 - iter 36/95 - loss 0.35311636 - time (sec): 8.03 - samples/sec: 1850.45 - lr: 0.000048 - momentum: 0.000000
2024-03-26 15:44:24,654 epoch 2 - iter 45/95 - loss 0.33135861 - time (sec): 9.79 - samples/sec: 1835.82 - lr: 0.000047 - momentum: 0.000000
2024-03-26 15:44:26,731 epoch 2 - iter 54/95 - loss 0.32511028 - time (sec): 11.87 - samples/sec: 1792.70 - lr: 0.000047 - momentum: 0.000000
2024-03-26 15:44:28,256 epoch 2 - iter 63/95 - loss 0.33001788 - time (sec): 13.39 - samples/sec: 1817.17 - lr: 0.000046 - momentum: 0.000000
2024-03-26 15:44:29,742 epoch 2 - iter 72/95 - loss 0.32705052 - time (sec): 14.88 - samples/sec: 1843.65 - lr: 0.000046 - momentum: 0.000000
2024-03-26 15:44:30,906 epoch 2 - iter 81/95 - loss 0.32519771 - time (sec): 16.04 - samples/sec: 1877.87 - lr: 0.000045 - momentum: 0.000000
2024-03-26 15:44:32,175 epoch 2 - iter 90/95 - loss 0.31988236 - time (sec): 17.31 - samples/sec: 1900.31 - lr: 0.000045 - momentum: 0.000000
2024-03-26 15:44:33,124 ----------------------------------------------------------------------------------------------------
2024-03-26 15:44:33,124 EPOCH 2 done: loss 0.3123 - lr: 0.000045
2024-03-26 15:44:34,014 DEV : loss 0.25234273076057434 - f1-score (micro avg)  0.8359
2024-03-26 15:44:34,015 saving best model
2024-03-26 15:44:34,442 ----------------------------------------------------------------------------------------------------
2024-03-26 15:44:36,441 epoch 3 - iter 9/95 - loss 0.15396058 - time (sec): 2.00 - samples/sec: 1667.13 - lr: 0.000044 - momentum: 0.000000
2024-03-26 15:44:38,484 epoch 3 - iter 18/95 - loss 0.18281328 - time (sec): 4.04 - samples/sec: 1798.03 - lr: 0.000043 - momentum: 0.000000
2024-03-26 15:44:39,437 epoch 3 - iter 27/95 - loss 0.20249771 - time (sec): 4.99 - samples/sec: 1927.75 - lr: 0.000043 - momentum: 0.000000
2024-03-26 15:44:41,157 epoch 3 - iter 36/95 - loss 0.20428948 - time (sec): 6.71 - samples/sec: 1890.40 - lr: 0.000042 - momentum: 0.000000
2024-03-26 15:44:42,399 epoch 3 - iter 45/95 - loss 0.21368920 - time (sec): 7.95 - samples/sec: 1936.39 - lr: 0.000042 - momentum: 0.000000
2024-03-26 15:44:44,409 epoch 3 - iter 54/95 - loss 0.20866549 - time (sec): 9.96 - samples/sec: 1876.21 - lr: 0.000041 - momentum: 0.000000
2024-03-26 15:44:46,019 epoch 3 - iter 63/95 - loss 0.20420607 - time (sec): 11.57 - samples/sec: 1885.92 - lr: 0.000041 - momentum: 0.000000
2024-03-26 15:44:47,516 epoch 3 - iter 72/95 - loss 0.19797482 - time (sec): 13.07 - samples/sec: 1895.69 - lr: 0.000040 - momentum: 0.000000
2024-03-26 15:44:49,294 epoch 3 - iter 81/95 - loss 0.19325066 - time (sec): 14.85 - samples/sec: 1882.62 - lr: 0.000040 - momentum: 0.000000
2024-03-26 15:44:51,874 epoch 3 - iter 90/95 - loss 0.17774909 - time (sec): 17.43 - samples/sec: 1876.25 - lr: 0.000039 - momentum: 0.000000
2024-03-26 15:44:52,958 ----------------------------------------------------------------------------------------------------
2024-03-26 15:44:52,959 EPOCH 3 done: loss 0.1745 - lr: 0.000039
2024-03-26 15:44:53,855 DEV : loss 0.2372324913740158 - f1-score (micro avg)  0.8683
2024-03-26 15:44:53,858 saving best model
2024-03-26 15:44:54,299 ----------------------------------------------------------------------------------------------------
2024-03-26 15:44:55,969 epoch 4 - iter 9/95 - loss 0.14538574 - time (sec): 1.67 - samples/sec: 1926.45 - lr: 0.000039 - momentum: 0.000000
2024-03-26 15:44:57,933 epoch 4 - iter 18/95 - loss 0.12829142 - time (sec): 3.63 - samples/sec: 1854.32 - lr: 0.000038 - momentum: 0.000000
2024-03-26 15:44:59,144 epoch 4 - iter 27/95 - loss 0.13259479 - time (sec): 4.84 - samples/sec: 1943.48 - lr: 0.000037 - momentum: 0.000000
2024-03-26 15:45:00,782 epoch 4 - iter 36/95 - loss 0.13132510 - time (sec): 6.48 - samples/sec: 1915.32 - lr: 0.000037 - momentum: 0.000000
2024-03-26 15:45:02,907 epoch 4 - iter 45/95 - loss 0.12758950 - time (sec): 8.61 - samples/sec: 1855.40 - lr: 0.000036 - momentum: 0.000000
2024-03-26 15:45:04,418 epoch 4 - iter 54/95 - loss 0.13166202 - time (sec): 10.12 - samples/sec: 1865.49 - lr: 0.000036 - momentum: 0.000000
2024-03-26 15:45:06,845 epoch 4 - iter 63/95 - loss 0.12800293 - time (sec): 12.54 - samples/sec: 1817.50 - lr: 0.000035 - momentum: 0.000000
2024-03-26 15:45:09,327 epoch 4 - iter 72/95 - loss 0.11981137 - time (sec): 15.03 - samples/sec: 1780.27 - lr: 0.000035 - momentum: 0.000000
2024-03-26 15:45:10,754 epoch 4 - iter 81/95 - loss 0.11873174 - time (sec): 16.45 - samples/sec: 1786.17 - lr: 0.000034 - momentum: 0.000000
2024-03-26 15:45:12,517 epoch 4 - iter 90/95 - loss 0.11781756 - time (sec): 18.22 - samples/sec: 1785.85 - lr: 0.000034 - momentum: 0.000000
2024-03-26 15:45:13,628 ----------------------------------------------------------------------------------------------------
2024-03-26 15:45:13,628 EPOCH 4 done: loss 0.1153 - lr: 0.000034
2024-03-26 15:45:14,535 DEV : loss 0.2066950798034668 - f1-score (micro avg)  0.8863
2024-03-26 15:45:14,536 saving best model
2024-03-26 15:45:14,976 ----------------------------------------------------------------------------------------------------
2024-03-26 15:45:15,943 epoch 5 - iter 9/95 - loss 0.06807319 - time (sec): 0.97 - samples/sec: 2132.15 - lr: 0.000033 - momentum: 0.000000
2024-03-26 15:45:17,532 epoch 5 - iter 18/95 - loss 0.08290938 - time (sec): 2.55 - samples/sec: 2083.78 - lr: 0.000032 - momentum: 0.000000
2024-03-26 15:45:19,983 epoch 5 - iter 27/95 - loss 0.08255992 - time (sec): 5.01 - samples/sec: 1821.36 - lr: 0.000032 - momentum: 0.000000
2024-03-26 15:45:21,822 epoch 5 - iter 36/95 - loss 0.08473432 - time (sec): 6.84 - samples/sec: 1812.71 - lr: 0.000031 - momentum: 0.000000
2024-03-26 15:45:23,776 epoch 5 - iter 45/95 - loss 0.08269002 - time (sec): 8.80 - samples/sec: 1776.58 - lr: 0.000031 - momentum: 0.000000
2024-03-26 15:45:25,367 epoch 5 - iter 54/95 - loss 0.08277269 - time (sec): 10.39 - samples/sec: 1813.62 - lr: 0.000030 - momentum: 0.000000
2024-03-26 15:45:27,697 epoch 5 - iter 63/95 - loss 0.08331892 - time (sec): 12.72 - samples/sec: 1800.68 - lr: 0.000030 - momentum: 0.000000
2024-03-26 15:45:29,103 epoch 5 - iter 72/95 - loss 0.08802009 - time (sec): 14.13 - samples/sec: 1818.52 - lr: 0.000029 - momentum: 0.000000
2024-03-26 15:45:30,994 epoch 5 - iter 81/95 - loss 0.08437929 - time (sec): 16.02 - samples/sec: 1792.95 - lr: 0.000029 - momentum: 0.000000
2024-03-26 15:45:32,837 epoch 5 - iter 90/95 - loss 0.08550405 - time (sec): 17.86 - samples/sec: 1794.86 - lr: 0.000028 - momentum: 0.000000
2024-03-26 15:45:34,185 ----------------------------------------------------------------------------------------------------
2024-03-26 15:45:34,185 EPOCH 5 done: loss 0.0855 - lr: 0.000028
2024-03-26 15:45:35,085 DEV : loss 0.19790305197238922 - f1-score (micro avg)  0.8901
2024-03-26 15:45:35,087 saving best model
2024-03-26 15:45:35,506 ----------------------------------------------------------------------------------------------------
2024-03-26 15:45:36,879 epoch 6 - iter 9/95 - loss 0.05356144 - time (sec): 1.37 - samples/sec: 2100.97 - lr: 0.000027 - momentum: 0.000000
2024-03-26 15:45:39,111 epoch 6 - iter 18/95 - loss 0.06201362 - time (sec): 3.60 - samples/sec: 1990.64 - lr: 0.000027 - momentum: 0.000000
2024-03-26 15:45:40,667 epoch 6 - iter 27/95 - loss 0.06021193 - time (sec): 5.16 - samples/sec: 1946.92 - lr: 0.000026 - momentum: 0.000000
2024-03-26 15:45:42,632 epoch 6 - iter 36/95 - loss 0.06452764 - time (sec): 7.12 - samples/sec: 1895.25 - lr: 0.000026 - momentum: 0.000000
2024-03-26 15:45:44,758 epoch 6 - iter 45/95 - loss 0.07905695 - time (sec): 9.25 - samples/sec: 1920.75 - lr: 0.000025 - momentum: 0.000000
2024-03-26 15:45:45,947 epoch 6 - iter 54/95 - loss 0.07603800 - time (sec): 10.44 - samples/sec: 1936.71 - lr: 0.000025 - momentum: 0.000000
2024-03-26 15:45:47,009 epoch 6 - iter 63/95 - loss 0.07510932 - time (sec): 11.50 - samples/sec: 1957.68 - lr: 0.000024 - momentum: 0.000000
2024-03-26 15:45:48,541 epoch 6 - iter 72/95 - loss 0.06975631 - time (sec): 13.03 - samples/sec: 1959.14 - lr: 0.000024 - momentum: 0.000000
2024-03-26 15:45:50,541 epoch 6 - iter 81/95 - loss 0.06716849 - time (sec): 15.03 - samples/sec: 1944.87 - lr: 0.000023 - momentum: 0.000000
2024-03-26 15:45:52,516 epoch 6 - iter 90/95 - loss 0.06603697 - time (sec): 17.01 - samples/sec: 1933.77 - lr: 0.000023 - momentum: 0.000000
2024-03-26 15:45:53,440 ----------------------------------------------------------------------------------------------------
2024-03-26 15:45:53,440 EPOCH 6 done: loss 0.0653 - lr: 0.000023
2024-03-26 15:45:54,335 DEV : loss 0.19000057876110077 - f1-score (micro avg)  0.9076
2024-03-26 15:45:54,336 saving best model
2024-03-26 15:45:54,769 ----------------------------------------------------------------------------------------------------
2024-03-26 15:45:56,183 epoch 7 - iter 9/95 - loss 0.04441026 - time (sec): 1.41 - samples/sec: 1881.32 - lr: 0.000022 - momentum: 0.000000
2024-03-26 15:45:57,977 epoch 7 - iter 18/95 - loss 0.04434418 - time (sec): 3.21 - samples/sec: 1807.29 - lr: 0.000021 - momentum: 0.000000
2024-03-26 15:45:59,583 epoch 7 - iter 27/95 - loss 0.04390939 - time (sec): 4.81 - samples/sec: 1895.17 - lr: 0.000021 - momentum: 0.000000
2024-03-26 15:46:01,296 epoch 7 - iter 36/95 - loss 0.04576864 - time (sec): 6.53 - samples/sec: 1842.86 - lr: 0.000020 - momentum: 0.000000
2024-03-26 15:46:02,652 epoch 7 - iter 45/95 - loss 0.04494806 - time (sec): 7.88 - samples/sec: 1860.17 - lr: 0.000020 - momentum: 0.000000
2024-03-26 15:46:04,712 epoch 7 - iter 54/95 - loss 0.04435586 - time (sec): 9.94 - samples/sec: 1802.99 - lr: 0.000019 - momentum: 0.000000
2024-03-26 15:46:06,946 epoch 7 - iter 63/95 - loss 0.04482951 - time (sec): 12.18 - samples/sec: 1753.31 - lr: 0.000019 - momentum: 0.000000
2024-03-26 15:46:09,500 epoch 7 - iter 72/95 - loss 0.05400091 - time (sec): 14.73 - samples/sec: 1750.13 - lr: 0.000018 - momentum: 0.000000
2024-03-26 15:46:11,443 epoch 7 - iter 81/95 - loss 0.05662332 - time (sec): 16.67 - samples/sec: 1757.87 - lr: 0.000018 - momentum: 0.000000
2024-03-26 15:46:13,410 epoch 7 - iter 90/95 - loss 0.05734886 - time (sec): 18.64 - samples/sec: 1758.49 - lr: 0.000017 - momentum: 0.000000
2024-03-26 15:46:14,303 ----------------------------------------------------------------------------------------------------
2024-03-26 15:46:14,304 EPOCH 7 done: loss 0.0558 - lr: 0.000017
2024-03-26 15:46:15,199 DEV : loss 0.1933394968509674 - f1-score (micro avg)  0.92
2024-03-26 15:46:15,200 saving best model
2024-03-26 15:46:15,649 ----------------------------------------------------------------------------------------------------
2024-03-26 15:46:17,905 epoch 8 - iter 9/95 - loss 0.03916721 - time (sec): 2.25 - samples/sec: 1681.22 - lr: 0.000016 - momentum: 0.000000
2024-03-26 15:46:19,452 epoch 8 - iter 18/95 - loss 0.03789649 - time (sec): 3.80 - samples/sec: 1813.49 - lr: 0.000016 - momentum: 0.000000
2024-03-26 15:46:21,612 epoch 8 - iter 27/95 - loss 0.05354041 - time (sec): 5.96 - samples/sec: 1774.02 - lr: 0.000015 - momentum: 0.000000
2024-03-26 15:46:23,155 epoch 8 - iter 36/95 - loss 0.04724161 - time (sec): 7.50 - samples/sec: 1798.27 - lr: 0.000015 - momentum: 0.000000
2024-03-26 15:46:25,015 epoch 8 - iter 45/95 - loss 0.04108329 - time (sec): 9.36 - samples/sec: 1778.12 - lr: 0.000014 - momentum: 0.000000
2024-03-26 15:46:26,701 epoch 8 - iter 54/95 - loss 0.04522755 - time (sec): 11.05 - samples/sec: 1787.98 - lr: 0.000014 - momentum: 0.000000
2024-03-26 15:46:28,507 epoch 8 - iter 63/95 - loss 0.04539544 - time (sec): 12.86 - samples/sec: 1786.30 - lr: 0.000013 - momentum: 0.000000
2024-03-26 15:46:29,812 epoch 8 - iter 72/95 - loss 0.04420003 - time (sec): 14.16 - samples/sec: 1806.70 - lr: 0.000013 - momentum: 0.000000
2024-03-26 15:46:31,633 epoch 8 - iter 81/95 - loss 0.04348041 - time (sec): 15.98 - samples/sec: 1832.19 - lr: 0.000012 - momentum: 0.000000
2024-03-26 15:46:34,035 epoch 8 - iter 90/95 - loss 0.04015730 - time (sec): 18.38 - samples/sec: 1794.31 - lr: 0.000012 - momentum: 0.000000
2024-03-26 15:46:34,842 ----------------------------------------------------------------------------------------------------
2024-03-26 15:46:34,842 EPOCH 8 done: loss 0.0410 - lr: 0.000012
2024-03-26 15:46:35,746 DEV : loss 0.22626325488090515 - f1-score (micro avg)  0.9277
2024-03-26 15:46:35,747 saving best model
2024-03-26 15:46:36,180 ----------------------------------------------------------------------------------------------------
2024-03-26 15:46:37,921 epoch 9 - iter 9/95 - loss 0.06402781 - time (sec): 1.74 - samples/sec: 1953.34 - lr: 0.000011 - momentum: 0.000000
2024-03-26 15:46:40,109 epoch 9 - iter 18/95 - loss 0.04075891 - time (sec): 3.93 - samples/sec: 1766.04 - lr: 0.000010 - momentum: 0.000000
2024-03-26 15:46:41,981 epoch 9 - iter 27/95 - loss 0.04754879 - time (sec): 5.80 - samples/sec: 1799.43 - lr: 0.000010 - momentum: 0.000000
2024-03-26 15:46:43,517 epoch 9 - iter 36/95 - loss 0.04592789 - time (sec): 7.33 - samples/sec: 1809.98 - lr: 0.000009 - momentum: 0.000000
2024-03-26 15:46:44,918 epoch 9 - iter 45/95 - loss 0.03894026 - time (sec): 8.74 - samples/sec: 1846.68 - lr: 0.000009 - momentum: 0.000000
2024-03-26 15:46:46,322 epoch 9 - iter 54/95 - loss 0.03544114 - time (sec): 10.14 - samples/sec: 1896.79 - lr: 0.000008 - momentum: 0.000000
2024-03-26 15:46:48,132 epoch 9 - iter 63/95 - loss 0.04097507 - time (sec): 11.95 - samples/sec: 1902.10 - lr: 0.000008 - momentum: 0.000000
2024-03-26 15:46:50,128 epoch 9 - iter 72/95 - loss 0.04044040 - time (sec): 13.95 - samples/sec: 1873.16 - lr: 0.000007 - momentum: 0.000000
2024-03-26 15:46:52,395 epoch 9 - iter 81/95 - loss 0.04054164 - time (sec): 16.21 - samples/sec: 1830.64 - lr: 0.000007 - momentum: 0.000000
2024-03-26 15:46:54,122 epoch 9 - iter 90/95 - loss 0.03879098 - time (sec): 17.94 - samples/sec: 1845.06 - lr: 0.000006 - momentum: 0.000000
2024-03-26 15:46:54,706 ----------------------------------------------------------------------------------------------------
2024-03-26 15:46:54,706 EPOCH 9 done: loss 0.0376 - lr: 0.000006
2024-03-26 15:46:55,606 DEV : loss 0.2059858739376068 - f1-score (micro avg)  0.9262
2024-03-26 15:46:55,607 ----------------------------------------------------------------------------------------------------
2024-03-26 15:46:57,654 epoch 10 - iter 9/95 - loss 0.00549981 - time (sec): 2.05 - samples/sec: 1887.20 - lr: 0.000005 - momentum: 0.000000
2024-03-26 15:46:59,396 epoch 10 - iter 18/95 - loss 0.01199100 - time (sec): 3.79 - samples/sec: 1875.24 - lr: 0.000005 - momentum: 0.000000
2024-03-26 15:47:00,503 epoch 10 - iter 27/95 - loss 0.01029262 - time (sec): 4.90 - samples/sec: 1945.52 - lr: 0.000004 - momentum: 0.000000
2024-03-26 15:47:02,011 epoch 10 - iter 36/95 - loss 0.01999514 - time (sec): 6.40 - samples/sec: 1955.23 - lr: 0.000004 - momentum: 0.000000
2024-03-26 15:47:03,952 epoch 10 - iter 45/95 - loss 0.02876719 - time (sec): 8.34 - samples/sec: 1891.83 - lr: 0.000003 - momentum: 0.000000
2024-03-26 15:47:05,038 epoch 10 - iter 54/95 - loss 0.03284923 - time (sec): 9.43 - samples/sec: 1941.08 - lr: 0.000003 - momentum: 0.000000
2024-03-26 15:47:06,261 epoch 10 - iter 63/95 - loss 0.02918205 - time (sec): 10.65 - samples/sec: 1969.63 - lr: 0.000002 - momentum: 0.000000
2024-03-26 15:47:08,166 epoch 10 - iter 72/95 - loss 0.02945329 - time (sec): 12.56 - samples/sec: 1966.32 - lr: 0.000002 - momentum: 0.000000
2024-03-26 15:47:10,805 epoch 10 - iter 81/95 - loss 0.02814755 - time (sec): 15.20 - samples/sec: 1927.41 - lr: 0.000001 - momentum: 0.000000
2024-03-26 15:47:12,807 epoch 10 - iter 90/95 - loss 0.02870849 - time (sec): 17.20 - samples/sec: 1907.74 - lr: 0.000001 - momentum: 0.000000
2024-03-26 15:47:13,723 ----------------------------------------------------------------------------------------------------
2024-03-26 15:47:13,723 EPOCH 10 done: loss 0.0284 - lr: 0.000001
2024-03-26 15:47:14,622 DEV : loss 0.22495582699775696 - f1-score (micro avg)  0.928
2024-03-26 15:47:14,623 saving best model
2024-03-26 15:47:15,360 ----------------------------------------------------------------------------------------------------
2024-03-26 15:47:15,360 Loading model from best epoch ...
2024-03-26 15:47:16,209 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 15:47:16,955 
Results:
- F-score (micro) 0.9076
- F-score (macro) 0.6885
- Accuracy 0.8331

By class:
              precision    recall  f1-score   support

 Unternehmen     0.9046    0.8910    0.8977       266
 Auslagerung     0.8755    0.9036    0.8893       249
         Ort     0.9496    0.9851    0.9670       134
    Software     0.0000    0.0000    0.0000         0

   micro avg     0.9000    0.9153    0.9076       649
   macro avg     0.6824    0.6949    0.6885       649
weighted avg     0.9027    0.9153    0.9088       649

2024-03-26 15:47:16,955 ----------------------------------------------------------------------------------------------------