File size: 26,659 Bytes
3f0e079 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 |
2024-03-26 10:32:23,127 ----------------------------------------------------------------------------------------------------
2024-03-26 10:32:23,128 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 10:32:23,128 ----------------------------------------------------------------------------------------------------
2024-03-26 10:32:23,128 Corpus: 758 train + 94 dev + 96 test sentences
2024-03-26 10:32:23,128 ----------------------------------------------------------------------------------------------------
2024-03-26 10:32:23,128 Train: 758 sentences
2024-03-26 10:32:23,128 (train_with_dev=False, train_with_test=False)
2024-03-26 10:32:23,128 ----------------------------------------------------------------------------------------------------
2024-03-26 10:32:23,128 Training Params:
2024-03-26 10:32:23,128 - learning_rate: "3e-05"
2024-03-26 10:32:23,128 - mini_batch_size: "16"
2024-03-26 10:32:23,128 - max_epochs: "10"
2024-03-26 10:32:23,128 - shuffle: "True"
2024-03-26 10:32:23,128 ----------------------------------------------------------------------------------------------------
2024-03-26 10:32:23,128 Plugins:
2024-03-26 10:32:23,128 - TensorboardLogger
2024-03-26 10:32:23,128 - LinearScheduler | warmup_fraction: '0.1'
2024-03-26 10:32:23,128 ----------------------------------------------------------------------------------------------------
2024-03-26 10:32:23,128 Final evaluation on model from best epoch (best-model.pt)
2024-03-26 10:32:23,128 - metric: "('micro avg', 'f1-score')"
2024-03-26 10:32:23,128 ----------------------------------------------------------------------------------------------------
2024-03-26 10:32:23,128 Computation:
2024-03-26 10:32:23,128 - compute on device: cuda:0
2024-03-26 10:32:23,128 - embedding storage: none
2024-03-26 10:32:23,128 ----------------------------------------------------------------------------------------------------
2024-03-26 10:32:23,128 Model training base path: "flair-co-funer-gbert_base-bs16-e10-lr3e-05-5"
2024-03-26 10:32:23,128 ----------------------------------------------------------------------------------------------------
2024-03-26 10:32:23,128 ----------------------------------------------------------------------------------------------------
2024-03-26 10:32:23,128 Logging anything other than scalars to TensorBoard is currently not supported.
2024-03-26 10:32:24,598 epoch 1 - iter 4/48 - loss 3.42174338 - time (sec): 1.47 - samples/sec: 1783.63 - lr: 0.000002 - momentum: 0.000000
2024-03-26 10:32:27,269 epoch 1 - iter 8/48 - loss 3.34969237 - time (sec): 4.14 - samples/sec: 1469.88 - lr: 0.000004 - momentum: 0.000000
2024-03-26 10:32:29,096 epoch 1 - iter 12/48 - loss 3.21022744 - time (sec): 5.97 - samples/sec: 1492.57 - lr: 0.000007 - momentum: 0.000000
2024-03-26 10:32:30,643 epoch 1 - iter 16/48 - loss 3.08575155 - time (sec): 7.52 - samples/sec: 1600.92 - lr: 0.000009 - momentum: 0.000000
2024-03-26 10:32:32,765 epoch 1 - iter 20/48 - loss 2.96305669 - time (sec): 9.64 - samples/sec: 1564.66 - lr: 0.000012 - momentum: 0.000000
2024-03-26 10:32:35,464 epoch 1 - iter 24/48 - loss 2.80357407 - time (sec): 12.34 - samples/sec: 1495.63 - lr: 0.000014 - momentum: 0.000000
2024-03-26 10:32:37,074 epoch 1 - iter 28/48 - loss 2.70398010 - time (sec): 13.95 - samples/sec: 1504.87 - lr: 0.000017 - momentum: 0.000000
2024-03-26 10:32:39,150 epoch 1 - iter 32/48 - loss 2.59920726 - time (sec): 16.02 - samples/sec: 1500.36 - lr: 0.000019 - momentum: 0.000000
2024-03-26 10:32:40,684 epoch 1 - iter 36/48 - loss 2.51975944 - time (sec): 17.56 - samples/sec: 1520.78 - lr: 0.000022 - momentum: 0.000000
2024-03-26 10:32:43,449 epoch 1 - iter 40/48 - loss 2.42029873 - time (sec): 20.32 - samples/sec: 1471.62 - lr: 0.000024 - momentum: 0.000000
2024-03-26 10:32:44,635 epoch 1 - iter 44/48 - loss 2.34834067 - time (sec): 21.51 - samples/sec: 1494.53 - lr: 0.000027 - momentum: 0.000000
2024-03-26 10:32:46,460 epoch 1 - iter 48/48 - loss 2.28449819 - time (sec): 23.33 - samples/sec: 1477.49 - lr: 0.000029 - momentum: 0.000000
2024-03-26 10:32:46,460 ----------------------------------------------------------------------------------------------------
2024-03-26 10:32:46,460 EPOCH 1 done: loss 2.2845 - lr: 0.000029
2024-03-26 10:32:47,268 DEV : loss 0.9604241847991943 - f1-score (micro avg) 0.3618
2024-03-26 10:32:47,270 saving best model
2024-03-26 10:32:47,541 ----------------------------------------------------------------------------------------------------
2024-03-26 10:32:50,227 epoch 2 - iter 4/48 - loss 1.28816124 - time (sec): 2.69 - samples/sec: 1285.61 - lr: 0.000030 - momentum: 0.000000
2024-03-26 10:32:52,063 epoch 2 - iter 8/48 - loss 1.17022404 - time (sec): 4.52 - samples/sec: 1355.15 - lr: 0.000030 - momentum: 0.000000
2024-03-26 10:32:53,954 epoch 2 - iter 12/48 - loss 1.08807460 - time (sec): 6.41 - samples/sec: 1391.37 - lr: 0.000029 - momentum: 0.000000
2024-03-26 10:32:56,597 epoch 2 - iter 16/48 - loss 1.00135461 - time (sec): 9.06 - samples/sec: 1397.48 - lr: 0.000029 - momentum: 0.000000
2024-03-26 10:32:57,967 epoch 2 - iter 20/48 - loss 0.95421133 - time (sec): 10.43 - samples/sec: 1436.97 - lr: 0.000029 - momentum: 0.000000
2024-03-26 10:33:00,756 epoch 2 - iter 24/48 - loss 0.89570225 - time (sec): 13.22 - samples/sec: 1354.36 - lr: 0.000028 - momentum: 0.000000
2024-03-26 10:33:02,346 epoch 2 - iter 28/48 - loss 0.87277024 - time (sec): 14.80 - samples/sec: 1388.82 - lr: 0.000028 - momentum: 0.000000
2024-03-26 10:33:04,346 epoch 2 - iter 32/48 - loss 0.83120939 - time (sec): 16.80 - samples/sec: 1381.65 - lr: 0.000028 - momentum: 0.000000
2024-03-26 10:33:06,098 epoch 2 - iter 36/48 - loss 0.80333747 - time (sec): 18.56 - samples/sec: 1412.40 - lr: 0.000028 - momentum: 0.000000
2024-03-26 10:33:08,454 epoch 2 - iter 40/48 - loss 0.78653155 - time (sec): 20.91 - samples/sec: 1399.75 - lr: 0.000027 - momentum: 0.000000
2024-03-26 10:33:10,633 epoch 2 - iter 44/48 - loss 0.75591748 - time (sec): 23.09 - samples/sec: 1404.58 - lr: 0.000027 - momentum: 0.000000
2024-03-26 10:33:11,882 epoch 2 - iter 48/48 - loss 0.74272218 - time (sec): 24.34 - samples/sec: 1416.25 - lr: 0.000027 - momentum: 0.000000
2024-03-26 10:33:11,882 ----------------------------------------------------------------------------------------------------
2024-03-26 10:33:11,882 EPOCH 2 done: loss 0.7427 - lr: 0.000027
2024-03-26 10:33:12,826 DEV : loss 0.40468811988830566 - f1-score (micro avg) 0.7292
2024-03-26 10:33:12,829 saving best model
2024-03-26 10:33:13,266 ----------------------------------------------------------------------------------------------------
2024-03-26 10:33:14,352 epoch 3 - iter 4/48 - loss 0.49596632 - time (sec): 1.08 - samples/sec: 2058.76 - lr: 0.000026 - momentum: 0.000000
2024-03-26 10:33:16,238 epoch 3 - iter 8/48 - loss 0.46225213 - time (sec): 2.97 - samples/sec: 1659.01 - lr: 0.000026 - momentum: 0.000000
2024-03-26 10:33:18,449 epoch 3 - iter 12/48 - loss 0.42796127 - time (sec): 5.18 - samples/sec: 1655.05 - lr: 0.000026 - momentum: 0.000000
2024-03-26 10:33:20,399 epoch 3 - iter 16/48 - loss 0.42776954 - time (sec): 7.13 - samples/sec: 1595.06 - lr: 0.000026 - momentum: 0.000000
2024-03-26 10:33:22,249 epoch 3 - iter 20/48 - loss 0.41446283 - time (sec): 8.98 - samples/sec: 1578.55 - lr: 0.000025 - momentum: 0.000000
2024-03-26 10:33:24,188 epoch 3 - iter 24/48 - loss 0.39349862 - time (sec): 10.92 - samples/sec: 1535.14 - lr: 0.000025 - momentum: 0.000000
2024-03-26 10:33:27,365 epoch 3 - iter 28/48 - loss 0.38449578 - time (sec): 14.10 - samples/sec: 1420.00 - lr: 0.000025 - momentum: 0.000000
2024-03-26 10:33:28,862 epoch 3 - iter 32/48 - loss 0.38744582 - time (sec): 15.59 - samples/sec: 1444.44 - lr: 0.000025 - momentum: 0.000000
2024-03-26 10:33:32,145 epoch 3 - iter 36/48 - loss 0.37499873 - time (sec): 18.88 - samples/sec: 1374.10 - lr: 0.000024 - momentum: 0.000000
2024-03-26 10:33:34,511 epoch 3 - iter 40/48 - loss 0.37551102 - time (sec): 21.24 - samples/sec: 1377.47 - lr: 0.000024 - momentum: 0.000000
2024-03-26 10:33:36,637 epoch 3 - iter 44/48 - loss 0.36375984 - time (sec): 23.37 - samples/sec: 1373.08 - lr: 0.000024 - momentum: 0.000000
2024-03-26 10:33:38,217 epoch 3 - iter 48/48 - loss 0.35890800 - time (sec): 24.95 - samples/sec: 1381.71 - lr: 0.000023 - momentum: 0.000000
2024-03-26 10:33:38,217 ----------------------------------------------------------------------------------------------------
2024-03-26 10:33:38,217 EPOCH 3 done: loss 0.3589 - lr: 0.000023
2024-03-26 10:33:39,112 DEV : loss 0.27302658557891846 - f1-score (micro avg) 0.8268
2024-03-26 10:33:39,113 saving best model
2024-03-26 10:33:39,570 ----------------------------------------------------------------------------------------------------
2024-03-26 10:33:42,514 epoch 4 - iter 4/48 - loss 0.19657748 - time (sec): 2.94 - samples/sec: 1268.10 - lr: 0.000023 - momentum: 0.000000
2024-03-26 10:33:44,032 epoch 4 - iter 8/48 - loss 0.26531707 - time (sec): 4.46 - samples/sec: 1394.75 - lr: 0.000023 - momentum: 0.000000
2024-03-26 10:33:46,535 epoch 4 - iter 12/48 - loss 0.23600165 - time (sec): 6.96 - samples/sec: 1335.78 - lr: 0.000023 - momentum: 0.000000
2024-03-26 10:33:49,184 epoch 4 - iter 16/48 - loss 0.22424944 - time (sec): 9.61 - samples/sec: 1320.94 - lr: 0.000022 - momentum: 0.000000
2024-03-26 10:33:51,440 epoch 4 - iter 20/48 - loss 0.22450465 - time (sec): 11.87 - samples/sec: 1329.79 - lr: 0.000022 - momentum: 0.000000
2024-03-26 10:33:52,949 epoch 4 - iter 24/48 - loss 0.21775032 - time (sec): 13.38 - samples/sec: 1362.99 - lr: 0.000022 - momentum: 0.000000
2024-03-26 10:33:55,323 epoch 4 - iter 28/48 - loss 0.21925009 - time (sec): 15.75 - samples/sec: 1348.67 - lr: 0.000022 - momentum: 0.000000
2024-03-26 10:33:58,301 epoch 4 - iter 32/48 - loss 0.21788102 - time (sec): 18.73 - samples/sec: 1338.14 - lr: 0.000021 - momentum: 0.000000
2024-03-26 10:33:59,940 epoch 4 - iter 36/48 - loss 0.22148320 - time (sec): 20.37 - samples/sec: 1362.82 - lr: 0.000021 - momentum: 0.000000
2024-03-26 10:34:00,924 epoch 4 - iter 40/48 - loss 0.22402948 - time (sec): 21.35 - samples/sec: 1406.42 - lr: 0.000021 - momentum: 0.000000
2024-03-26 10:34:02,373 epoch 4 - iter 44/48 - loss 0.22269543 - time (sec): 22.80 - samples/sec: 1426.91 - lr: 0.000020 - momentum: 0.000000
2024-03-26 10:34:03,248 epoch 4 - iter 48/48 - loss 0.22546892 - time (sec): 23.68 - samples/sec: 1456.04 - lr: 0.000020 - momentum: 0.000000
2024-03-26 10:34:03,249 ----------------------------------------------------------------------------------------------------
2024-03-26 10:34:03,249 EPOCH 4 done: loss 0.2255 - lr: 0.000020
2024-03-26 10:34:04,178 DEV : loss 0.21694348752498627 - f1-score (micro avg) 0.8668
2024-03-26 10:34:04,179 saving best model
2024-03-26 10:34:04,626 ----------------------------------------------------------------------------------------------------
2024-03-26 10:34:06,458 epoch 5 - iter 4/48 - loss 0.19428206 - time (sec): 1.83 - samples/sec: 1568.42 - lr: 0.000020 - momentum: 0.000000
2024-03-26 10:34:08,320 epoch 5 - iter 8/48 - loss 0.17767071 - time (sec): 3.69 - samples/sec: 1680.03 - lr: 0.000020 - momentum: 0.000000
2024-03-26 10:34:11,423 epoch 5 - iter 12/48 - loss 0.16683946 - time (sec): 6.80 - samples/sec: 1415.75 - lr: 0.000019 - momentum: 0.000000
2024-03-26 10:34:12,740 epoch 5 - iter 16/48 - loss 0.16232137 - time (sec): 8.11 - samples/sec: 1465.64 - lr: 0.000019 - momentum: 0.000000
2024-03-26 10:34:15,012 epoch 5 - iter 20/48 - loss 0.17440101 - time (sec): 10.39 - samples/sec: 1450.97 - lr: 0.000019 - momentum: 0.000000
2024-03-26 10:34:17,156 epoch 5 - iter 24/48 - loss 0.17192285 - time (sec): 12.53 - samples/sec: 1418.79 - lr: 0.000018 - momentum: 0.000000
2024-03-26 10:34:18,518 epoch 5 - iter 28/48 - loss 0.17925284 - time (sec): 13.89 - samples/sec: 1460.83 - lr: 0.000018 - momentum: 0.000000
2024-03-26 10:34:19,893 epoch 5 - iter 32/48 - loss 0.17792134 - time (sec): 15.27 - samples/sec: 1493.34 - lr: 0.000018 - momentum: 0.000000
2024-03-26 10:34:22,020 epoch 5 - iter 36/48 - loss 0.17567673 - time (sec): 17.39 - samples/sec: 1484.72 - lr: 0.000018 - momentum: 0.000000
2024-03-26 10:34:23,851 epoch 5 - iter 40/48 - loss 0.17203264 - time (sec): 19.22 - samples/sec: 1482.94 - lr: 0.000017 - momentum: 0.000000
2024-03-26 10:34:25,850 epoch 5 - iter 44/48 - loss 0.16894302 - time (sec): 21.22 - samples/sec: 1495.78 - lr: 0.000017 - momentum: 0.000000
2024-03-26 10:34:27,967 epoch 5 - iter 48/48 - loss 0.16401284 - time (sec): 23.34 - samples/sec: 1476.91 - lr: 0.000017 - momentum: 0.000000
2024-03-26 10:34:27,968 ----------------------------------------------------------------------------------------------------
2024-03-26 10:34:27,968 EPOCH 5 done: loss 0.1640 - lr: 0.000017
2024-03-26 10:34:28,903 DEV : loss 0.18316593766212463 - f1-score (micro avg) 0.8989
2024-03-26 10:34:28,904 saving best model
2024-03-26 10:34:29,357 ----------------------------------------------------------------------------------------------------
2024-03-26 10:34:31,248 epoch 6 - iter 4/48 - loss 0.11402572 - time (sec): 1.89 - samples/sec: 1452.03 - lr: 0.000017 - momentum: 0.000000
2024-03-26 10:34:33,993 epoch 6 - iter 8/48 - loss 0.13108460 - time (sec): 4.64 - samples/sec: 1371.26 - lr: 0.000016 - momentum: 0.000000
2024-03-26 10:34:35,896 epoch 6 - iter 12/48 - loss 0.13466569 - time (sec): 6.54 - samples/sec: 1381.02 - lr: 0.000016 - momentum: 0.000000
2024-03-26 10:34:37,391 epoch 6 - iter 16/48 - loss 0.14181831 - time (sec): 8.03 - samples/sec: 1440.26 - lr: 0.000016 - momentum: 0.000000
2024-03-26 10:34:40,086 epoch 6 - iter 20/48 - loss 0.13391803 - time (sec): 10.73 - samples/sec: 1359.11 - lr: 0.000015 - momentum: 0.000000
2024-03-26 10:34:42,753 epoch 6 - iter 24/48 - loss 0.12515977 - time (sec): 13.40 - samples/sec: 1333.77 - lr: 0.000015 - momentum: 0.000000
2024-03-26 10:34:45,203 epoch 6 - iter 28/48 - loss 0.12156669 - time (sec): 15.84 - samples/sec: 1309.57 - lr: 0.000015 - momentum: 0.000000
2024-03-26 10:34:46,575 epoch 6 - iter 32/48 - loss 0.12956941 - time (sec): 17.22 - samples/sec: 1354.12 - lr: 0.000015 - momentum: 0.000000
2024-03-26 10:34:48,427 epoch 6 - iter 36/48 - loss 0.12512582 - time (sec): 19.07 - samples/sec: 1365.67 - lr: 0.000014 - momentum: 0.000000
2024-03-26 10:34:49,401 epoch 6 - iter 40/48 - loss 0.12510773 - time (sec): 20.04 - samples/sec: 1407.42 - lr: 0.000014 - momentum: 0.000000
2024-03-26 10:34:51,921 epoch 6 - iter 44/48 - loss 0.12302160 - time (sec): 22.56 - samples/sec: 1378.98 - lr: 0.000014 - momentum: 0.000000
2024-03-26 10:34:54,677 epoch 6 - iter 48/48 - loss 0.11948777 - time (sec): 25.32 - samples/sec: 1361.49 - lr: 0.000014 - momentum: 0.000000
2024-03-26 10:34:54,677 ----------------------------------------------------------------------------------------------------
2024-03-26 10:34:54,677 EPOCH 6 done: loss 0.1195 - lr: 0.000014
2024-03-26 10:34:55,602 DEV : loss 0.17593741416931152 - f1-score (micro avg) 0.9047
2024-03-26 10:34:55,603 saving best model
2024-03-26 10:34:56,043 ----------------------------------------------------------------------------------------------------
2024-03-26 10:34:58,190 epoch 7 - iter 4/48 - loss 0.07193929 - time (sec): 2.15 - samples/sec: 1355.94 - lr: 0.000013 - momentum: 0.000000
2024-03-26 10:34:59,884 epoch 7 - iter 8/48 - loss 0.07829483 - time (sec): 3.84 - samples/sec: 1388.19 - lr: 0.000013 - momentum: 0.000000
2024-03-26 10:35:01,302 epoch 7 - iter 12/48 - loss 0.09926755 - time (sec): 5.26 - samples/sec: 1443.79 - lr: 0.000013 - momentum: 0.000000
2024-03-26 10:35:03,166 epoch 7 - iter 16/48 - loss 0.09217692 - time (sec): 7.12 - samples/sec: 1490.81 - lr: 0.000012 - momentum: 0.000000
2024-03-26 10:35:05,426 epoch 7 - iter 20/48 - loss 0.10826327 - time (sec): 9.38 - samples/sec: 1544.71 - lr: 0.000012 - momentum: 0.000000
2024-03-26 10:35:06,765 epoch 7 - iter 24/48 - loss 0.10534646 - time (sec): 10.72 - samples/sec: 1589.25 - lr: 0.000012 - momentum: 0.000000
2024-03-26 10:35:08,993 epoch 7 - iter 28/48 - loss 0.10590492 - time (sec): 12.95 - samples/sec: 1540.41 - lr: 0.000012 - momentum: 0.000000
2024-03-26 10:35:10,852 epoch 7 - iter 32/48 - loss 0.10727827 - time (sec): 14.81 - samples/sec: 1537.67 - lr: 0.000011 - momentum: 0.000000
2024-03-26 10:35:12,826 epoch 7 - iter 36/48 - loss 0.10332516 - time (sec): 16.78 - samples/sec: 1506.23 - lr: 0.000011 - momentum: 0.000000
2024-03-26 10:35:15,604 epoch 7 - iter 40/48 - loss 0.09911907 - time (sec): 19.56 - samples/sec: 1489.04 - lr: 0.000011 - momentum: 0.000000
2024-03-26 10:35:17,089 epoch 7 - iter 44/48 - loss 0.10167987 - time (sec): 21.04 - samples/sec: 1504.90 - lr: 0.000010 - momentum: 0.000000
2024-03-26 10:35:19,230 epoch 7 - iter 48/48 - loss 0.09882403 - time (sec): 23.19 - samples/sec: 1486.78 - lr: 0.000010 - momentum: 0.000000
2024-03-26 10:35:19,231 ----------------------------------------------------------------------------------------------------
2024-03-26 10:35:19,231 EPOCH 7 done: loss 0.0988 - lr: 0.000010
2024-03-26 10:35:20,253 DEV : loss 0.1646314412355423 - f1-score (micro avg) 0.9061
2024-03-26 10:35:20,255 saving best model
2024-03-26 10:35:20,700 ----------------------------------------------------------------------------------------------------
2024-03-26 10:35:22,898 epoch 8 - iter 4/48 - loss 0.11098392 - time (sec): 2.20 - samples/sec: 1268.70 - lr: 0.000010 - momentum: 0.000000
2024-03-26 10:35:24,438 epoch 8 - iter 8/48 - loss 0.07876314 - time (sec): 3.74 - samples/sec: 1454.16 - lr: 0.000010 - momentum: 0.000000
2024-03-26 10:35:27,315 epoch 8 - iter 12/48 - loss 0.07330689 - time (sec): 6.61 - samples/sec: 1360.55 - lr: 0.000009 - momentum: 0.000000
2024-03-26 10:35:29,741 epoch 8 - iter 16/48 - loss 0.07476028 - time (sec): 9.04 - samples/sec: 1358.86 - lr: 0.000009 - momentum: 0.000000
2024-03-26 10:35:31,169 epoch 8 - iter 20/48 - loss 0.07344860 - time (sec): 10.47 - samples/sec: 1418.97 - lr: 0.000009 - momentum: 0.000000
2024-03-26 10:35:32,574 epoch 8 - iter 24/48 - loss 0.07668558 - time (sec): 11.87 - samples/sec: 1490.26 - lr: 0.000009 - momentum: 0.000000
2024-03-26 10:35:33,889 epoch 8 - iter 28/48 - loss 0.07782502 - time (sec): 13.19 - samples/sec: 1550.34 - lr: 0.000008 - momentum: 0.000000
2024-03-26 10:35:36,042 epoch 8 - iter 32/48 - loss 0.07998551 - time (sec): 15.34 - samples/sec: 1510.12 - lr: 0.000008 - momentum: 0.000000
2024-03-26 10:35:38,563 epoch 8 - iter 36/48 - loss 0.07829167 - time (sec): 17.86 - samples/sec: 1465.75 - lr: 0.000008 - momentum: 0.000000
2024-03-26 10:35:40,493 epoch 8 - iter 40/48 - loss 0.07846917 - time (sec): 19.79 - samples/sec: 1474.58 - lr: 0.000007 - momentum: 0.000000
2024-03-26 10:35:42,623 epoch 8 - iter 44/48 - loss 0.07975602 - time (sec): 21.92 - samples/sec: 1457.06 - lr: 0.000007 - momentum: 0.000000
2024-03-26 10:35:44,229 epoch 8 - iter 48/48 - loss 0.07998176 - time (sec): 23.53 - samples/sec: 1465.11 - lr: 0.000007 - momentum: 0.000000
2024-03-26 10:35:44,229 ----------------------------------------------------------------------------------------------------
2024-03-26 10:35:44,229 EPOCH 8 done: loss 0.0800 - lr: 0.000007
2024-03-26 10:35:45,173 DEV : loss 0.15970855951309204 - f1-score (micro avg) 0.9157
2024-03-26 10:35:45,174 saving best model
2024-03-26 10:35:45,649 ----------------------------------------------------------------------------------------------------
2024-03-26 10:35:48,341 epoch 9 - iter 4/48 - loss 0.08143347 - time (sec): 2.69 - samples/sec: 1300.71 - lr: 0.000007 - momentum: 0.000000
2024-03-26 10:35:50,382 epoch 9 - iter 8/48 - loss 0.06854402 - time (sec): 4.73 - samples/sec: 1349.75 - lr: 0.000006 - momentum: 0.000000
2024-03-26 10:35:53,210 epoch 9 - iter 12/48 - loss 0.06763631 - time (sec): 7.56 - samples/sec: 1286.71 - lr: 0.000006 - momentum: 0.000000
2024-03-26 10:35:56,316 epoch 9 - iter 16/48 - loss 0.07652995 - time (sec): 10.67 - samples/sec: 1260.55 - lr: 0.000006 - momentum: 0.000000
2024-03-26 10:35:57,193 epoch 9 - iter 20/48 - loss 0.07456477 - time (sec): 11.54 - samples/sec: 1349.45 - lr: 0.000006 - momentum: 0.000000
2024-03-26 10:35:59,066 epoch 9 - iter 24/48 - loss 0.07180523 - time (sec): 13.42 - samples/sec: 1343.61 - lr: 0.000005 - momentum: 0.000000
2024-03-26 10:36:01,066 epoch 9 - iter 28/48 - loss 0.07078084 - time (sec): 15.42 - samples/sec: 1358.35 - lr: 0.000005 - momentum: 0.000000
2024-03-26 10:36:02,055 epoch 9 - iter 32/48 - loss 0.07277106 - time (sec): 16.41 - samples/sec: 1424.08 - lr: 0.000005 - momentum: 0.000000
2024-03-26 10:36:03,174 epoch 9 - iter 36/48 - loss 0.07325922 - time (sec): 17.52 - samples/sec: 1478.45 - lr: 0.000004 - momentum: 0.000000
2024-03-26 10:36:04,484 epoch 9 - iter 40/48 - loss 0.07069114 - time (sec): 18.83 - samples/sec: 1505.97 - lr: 0.000004 - momentum: 0.000000
2024-03-26 10:36:07,539 epoch 9 - iter 44/48 - loss 0.07071166 - time (sec): 21.89 - samples/sec: 1473.69 - lr: 0.000004 - momentum: 0.000000
2024-03-26 10:36:09,018 epoch 9 - iter 48/48 - loss 0.06860300 - time (sec): 23.37 - samples/sec: 1475.18 - lr: 0.000004 - momentum: 0.000000
2024-03-26 10:36:09,018 ----------------------------------------------------------------------------------------------------
2024-03-26 10:36:09,018 EPOCH 9 done: loss 0.0686 - lr: 0.000004
2024-03-26 10:36:09,944 DEV : loss 0.15753228962421417 - f1-score (micro avg) 0.9145
2024-03-26 10:36:09,946 ----------------------------------------------------------------------------------------------------
2024-03-26 10:36:12,787 epoch 10 - iter 4/48 - loss 0.07280782 - time (sec): 2.84 - samples/sec: 1307.60 - lr: 0.000003 - momentum: 0.000000
2024-03-26 10:36:14,756 epoch 10 - iter 8/48 - loss 0.06728858 - time (sec): 4.81 - samples/sec: 1343.72 - lr: 0.000003 - momentum: 0.000000
2024-03-26 10:36:16,943 epoch 10 - iter 12/48 - loss 0.06391531 - time (sec): 7.00 - samples/sec: 1299.82 - lr: 0.000003 - momentum: 0.000000
2024-03-26 10:36:19,395 epoch 10 - iter 16/48 - loss 0.06018551 - time (sec): 9.45 - samples/sec: 1263.95 - lr: 0.000002 - momentum: 0.000000
2024-03-26 10:36:21,959 epoch 10 - iter 20/48 - loss 0.05973564 - time (sec): 12.01 - samples/sec: 1267.40 - lr: 0.000002 - momentum: 0.000000
2024-03-26 10:36:23,375 epoch 10 - iter 24/48 - loss 0.05860970 - time (sec): 13.43 - samples/sec: 1328.32 - lr: 0.000002 - momentum: 0.000000
2024-03-26 10:36:24,259 epoch 10 - iter 28/48 - loss 0.05937806 - time (sec): 14.31 - samples/sec: 1399.56 - lr: 0.000002 - momentum: 0.000000
2024-03-26 10:36:26,197 epoch 10 - iter 32/48 - loss 0.06338573 - time (sec): 16.25 - samples/sec: 1419.38 - lr: 0.000001 - momentum: 0.000000
2024-03-26 10:36:28,468 epoch 10 - iter 36/48 - loss 0.06419294 - time (sec): 18.52 - samples/sec: 1396.82 - lr: 0.000001 - momentum: 0.000000
2024-03-26 10:36:30,126 epoch 10 - iter 40/48 - loss 0.06609780 - time (sec): 20.18 - samples/sec: 1423.24 - lr: 0.000001 - momentum: 0.000000
2024-03-26 10:36:33,296 epoch 10 - iter 44/48 - loss 0.06545318 - time (sec): 23.35 - samples/sec: 1403.65 - lr: 0.000001 - momentum: 0.000000
2024-03-26 10:36:34,015 epoch 10 - iter 48/48 - loss 0.06560482 - time (sec): 24.07 - samples/sec: 1432.26 - lr: 0.000000 - momentum: 0.000000
2024-03-26 10:36:34,015 ----------------------------------------------------------------------------------------------------
2024-03-26 10:36:34,016 EPOCH 10 done: loss 0.0656 - lr: 0.000000
2024-03-26 10:36:34,922 DEV : loss 0.15896496176719666 - f1-score (micro avg) 0.9196
2024-03-26 10:36:34,924 saving best model
2024-03-26 10:36:35,661 ----------------------------------------------------------------------------------------------------
2024-03-26 10:36:35,661 Loading model from best epoch ...
2024-03-26 10:36:36,521 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 10:36:37,385
Results:
- F-score (micro) 0.8921
- F-score (macro) 0.6787
- Accuracy 0.8086
By class:
precision recall f1-score support
Unternehmen 0.8996 0.8759 0.8876 266
Auslagerung 0.8385 0.8755 0.8566 249
Ort 0.9565 0.9851 0.9706 134
Software 0.0000 0.0000 0.0000 0
micro avg 0.8860 0.8983 0.8921 649
macro avg 0.6736 0.6841 0.6787 649
weighted avg 0.8879 0.8983 0.8928 649
2024-03-26 10:36:37,385 ----------------------------------------------------------------------------------------------------
|