File size: 25,462 Bytes
64919c1 |
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 |
2023-10-19 02:07:58,307 ----------------------------------------------------------------------------------------------------
2023-10-19 02:07:58,308 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=81, bias=True)
(loss_function): CrossEntropyLoss()
)"
2023-10-19 02:07:58,308 ----------------------------------------------------------------------------------------------------
2023-10-19 02:07:58,309 Corpus: 6900 train + 1576 dev + 1833 test sentences
2023-10-19 02:07:58,309 ----------------------------------------------------------------------------------------------------
2023-10-19 02:07:58,309 Train: 6900 sentences
2023-10-19 02:07:58,309 (train_with_dev=False, train_with_test=False)
2023-10-19 02:07:58,309 ----------------------------------------------------------------------------------------------------
2023-10-19 02:07:58,309 Training Params:
2023-10-19 02:07:58,309 - learning_rate: "3e-05"
2023-10-19 02:07:58,309 - mini_batch_size: "16"
2023-10-19 02:07:58,309 - max_epochs: "10"
2023-10-19 02:07:58,309 - shuffle: "True"
2023-10-19 02:07:58,309 ----------------------------------------------------------------------------------------------------
2023-10-19 02:07:58,309 Plugins:
2023-10-19 02:07:58,309 - TensorboardLogger
2023-10-19 02:07:58,309 - LinearScheduler | warmup_fraction: '0.1'
2023-10-19 02:07:58,309 ----------------------------------------------------------------------------------------------------
2023-10-19 02:07:58,309 Final evaluation on model from best epoch (best-model.pt)
2023-10-19 02:07:58,309 - metric: "('micro avg', 'f1-score')"
2023-10-19 02:07:58,309 ----------------------------------------------------------------------------------------------------
2023-10-19 02:07:58,309 Computation:
2023-10-19 02:07:58,310 - compute on device: cuda:0
2023-10-19 02:07:58,310 - embedding storage: none
2023-10-19 02:07:58,310 ----------------------------------------------------------------------------------------------------
2023-10-19 02:07:58,310 Model training base path: "autotrain-flair-mobie-gbert_base-bs16-e10-lr3e-05-4"
2023-10-19 02:07:58,310 ----------------------------------------------------------------------------------------------------
2023-10-19 02:07:58,310 ----------------------------------------------------------------------------------------------------
2023-10-19 02:07:58,310 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-19 02:08:13,517 epoch 1 - iter 43/432 - loss 4.93250462 - time (sec): 15.21 - samples/sec: 429.82 - lr: 0.000003 - momentum: 0.000000
2023-10-19 02:08:28,839 epoch 1 - iter 86/432 - loss 3.97734066 - time (sec): 30.53 - samples/sec: 411.99 - lr: 0.000006 - momentum: 0.000000
2023-10-19 02:08:43,873 epoch 1 - iter 129/432 - loss 3.26708172 - time (sec): 45.56 - samples/sec: 408.49 - lr: 0.000009 - momentum: 0.000000
2023-10-19 02:08:59,143 epoch 1 - iter 172/432 - loss 2.86543349 - time (sec): 60.83 - samples/sec: 410.11 - lr: 0.000012 - momentum: 0.000000
2023-10-19 02:09:15,576 epoch 1 - iter 215/432 - loss 2.59028422 - time (sec): 77.26 - samples/sec: 401.88 - lr: 0.000015 - momentum: 0.000000
2023-10-19 02:09:30,082 epoch 1 - iter 258/432 - loss 2.35703794 - time (sec): 91.77 - samples/sec: 406.04 - lr: 0.000018 - momentum: 0.000000
2023-10-19 02:09:44,936 epoch 1 - iter 301/432 - loss 2.15806965 - time (sec): 106.63 - samples/sec: 406.83 - lr: 0.000021 - momentum: 0.000000
2023-10-19 02:09:59,763 epoch 1 - iter 344/432 - loss 2.00613180 - time (sec): 121.45 - samples/sec: 408.73 - lr: 0.000024 - momentum: 0.000000
2023-10-19 02:10:13,958 epoch 1 - iter 387/432 - loss 1.88001586 - time (sec): 135.65 - samples/sec: 409.08 - lr: 0.000027 - momentum: 0.000000
2023-10-19 02:10:29,196 epoch 1 - iter 430/432 - loss 1.76470516 - time (sec): 150.89 - samples/sec: 408.67 - lr: 0.000030 - momentum: 0.000000
2023-10-19 02:10:29,817 ----------------------------------------------------------------------------------------------------
2023-10-19 02:10:29,817 EPOCH 1 done: loss 1.7614 - lr: 0.000030
2023-10-19 02:10:43,326 DEV : loss 0.5575976967811584 - f1-score (micro avg) 0.6297
2023-10-19 02:10:43,351 saving best model
2023-10-19 02:10:43,792 ----------------------------------------------------------------------------------------------------
2023-10-19 02:10:58,128 epoch 2 - iter 43/432 - loss 0.62560817 - time (sec): 14.33 - samples/sec: 414.75 - lr: 0.000030 - momentum: 0.000000
2023-10-19 02:11:12,293 epoch 2 - iter 86/432 - loss 0.61003222 - time (sec): 28.50 - samples/sec: 441.84 - lr: 0.000029 - momentum: 0.000000
2023-10-19 02:11:27,392 epoch 2 - iter 129/432 - loss 0.58076243 - time (sec): 43.60 - samples/sec: 420.07 - lr: 0.000029 - momentum: 0.000000
2023-10-19 02:11:41,758 epoch 2 - iter 172/432 - loss 0.56106454 - time (sec): 57.96 - samples/sec: 421.61 - lr: 0.000029 - momentum: 0.000000
2023-10-19 02:11:56,333 epoch 2 - iter 215/432 - loss 0.54986490 - time (sec): 72.54 - samples/sec: 419.66 - lr: 0.000028 - momentum: 0.000000
2023-10-19 02:12:11,136 epoch 2 - iter 258/432 - loss 0.53748417 - time (sec): 87.34 - samples/sec: 421.39 - lr: 0.000028 - momentum: 0.000000
2023-10-19 02:12:26,691 epoch 2 - iter 301/432 - loss 0.52065632 - time (sec): 102.90 - samples/sec: 417.10 - lr: 0.000028 - momentum: 0.000000
2023-10-19 02:12:42,153 epoch 2 - iter 344/432 - loss 0.50823824 - time (sec): 118.36 - samples/sec: 411.50 - lr: 0.000027 - momentum: 0.000000
2023-10-19 02:12:58,398 epoch 2 - iter 387/432 - loss 0.49509518 - time (sec): 134.60 - samples/sec: 409.60 - lr: 0.000027 - momentum: 0.000000
2023-10-19 02:13:13,413 epoch 2 - iter 430/432 - loss 0.48286405 - time (sec): 149.62 - samples/sec: 412.11 - lr: 0.000027 - momentum: 0.000000
2023-10-19 02:13:13,985 ----------------------------------------------------------------------------------------------------
2023-10-19 02:13:13,986 EPOCH 2 done: loss 0.4828 - lr: 0.000027
2023-10-19 02:13:27,322 DEV : loss 0.3526449203491211 - f1-score (micro avg) 0.7754
2023-10-19 02:13:27,346 saving best model
2023-10-19 02:13:28,590 ----------------------------------------------------------------------------------------------------
2023-10-19 02:13:43,341 epoch 3 - iter 43/432 - loss 0.31230210 - time (sec): 14.75 - samples/sec: 421.77 - lr: 0.000026 - momentum: 0.000000
2023-10-19 02:13:57,500 epoch 3 - iter 86/432 - loss 0.30333100 - time (sec): 28.91 - samples/sec: 427.74 - lr: 0.000026 - momentum: 0.000000
2023-10-19 02:14:12,357 epoch 3 - iter 129/432 - loss 0.29986924 - time (sec): 43.76 - samples/sec: 420.79 - lr: 0.000026 - momentum: 0.000000
2023-10-19 02:14:27,258 epoch 3 - iter 172/432 - loss 0.30292860 - time (sec): 58.67 - samples/sec: 422.53 - lr: 0.000025 - momentum: 0.000000
2023-10-19 02:14:42,390 epoch 3 - iter 215/432 - loss 0.30311275 - time (sec): 73.80 - samples/sec: 417.71 - lr: 0.000025 - momentum: 0.000000
2023-10-19 02:14:57,121 epoch 3 - iter 258/432 - loss 0.30056148 - time (sec): 88.53 - samples/sec: 417.74 - lr: 0.000025 - momentum: 0.000000
2023-10-19 02:15:12,777 epoch 3 - iter 301/432 - loss 0.30074375 - time (sec): 104.19 - samples/sec: 416.07 - lr: 0.000024 - momentum: 0.000000
2023-10-19 02:15:27,941 epoch 3 - iter 344/432 - loss 0.30006914 - time (sec): 119.35 - samples/sec: 414.44 - lr: 0.000024 - momentum: 0.000000
2023-10-19 02:15:43,189 epoch 3 - iter 387/432 - loss 0.29793746 - time (sec): 134.60 - samples/sec: 414.38 - lr: 0.000024 - momentum: 0.000000
2023-10-19 02:15:57,165 epoch 3 - iter 430/432 - loss 0.29556403 - time (sec): 148.57 - samples/sec: 414.85 - lr: 0.000023 - momentum: 0.000000
2023-10-19 02:15:57,703 ----------------------------------------------------------------------------------------------------
2023-10-19 02:15:57,703 EPOCH 3 done: loss 0.2954 - lr: 0.000023
2023-10-19 02:16:11,087 DEV : loss 0.30149412155151367 - f1-score (micro avg) 0.8069
2023-10-19 02:16:11,111 saving best model
2023-10-19 02:16:12,352 ----------------------------------------------------------------------------------------------------
2023-10-19 02:16:27,084 epoch 4 - iter 43/432 - loss 0.21264161 - time (sec): 14.73 - samples/sec: 411.11 - lr: 0.000023 - momentum: 0.000000
2023-10-19 02:16:43,109 epoch 4 - iter 86/432 - loss 0.22163872 - time (sec): 30.76 - samples/sec: 394.07 - lr: 0.000023 - momentum: 0.000000
2023-10-19 02:16:58,411 epoch 4 - iter 129/432 - loss 0.22101676 - time (sec): 46.06 - samples/sec: 396.91 - lr: 0.000022 - momentum: 0.000000
2023-10-19 02:17:13,941 epoch 4 - iter 172/432 - loss 0.22361025 - time (sec): 61.59 - samples/sec: 395.37 - lr: 0.000022 - momentum: 0.000000
2023-10-19 02:17:27,914 epoch 4 - iter 215/432 - loss 0.22111072 - time (sec): 75.56 - samples/sec: 402.30 - lr: 0.000022 - momentum: 0.000000
2023-10-19 02:17:43,302 epoch 4 - iter 258/432 - loss 0.21935857 - time (sec): 90.95 - samples/sec: 397.99 - lr: 0.000021 - momentum: 0.000000
2023-10-19 02:17:57,934 epoch 4 - iter 301/432 - loss 0.21595980 - time (sec): 105.58 - samples/sec: 403.99 - lr: 0.000021 - momentum: 0.000000
2023-10-19 02:18:13,452 epoch 4 - iter 344/432 - loss 0.21581270 - time (sec): 121.10 - samples/sec: 408.20 - lr: 0.000021 - momentum: 0.000000
2023-10-19 02:18:28,710 epoch 4 - iter 387/432 - loss 0.21528790 - time (sec): 136.36 - samples/sec: 406.51 - lr: 0.000020 - momentum: 0.000000
2023-10-19 02:18:43,163 epoch 4 - iter 430/432 - loss 0.21420583 - time (sec): 150.81 - samples/sec: 408.75 - lr: 0.000020 - momentum: 0.000000
2023-10-19 02:18:43,749 ----------------------------------------------------------------------------------------------------
2023-10-19 02:18:43,749 EPOCH 4 done: loss 0.2144 - lr: 0.000020
2023-10-19 02:18:57,091 DEV : loss 0.3102978467941284 - f1-score (micro avg) 0.8163
2023-10-19 02:18:57,116 saving best model
2023-10-19 02:18:58,362 ----------------------------------------------------------------------------------------------------
2023-10-19 02:19:12,811 epoch 5 - iter 43/432 - loss 0.15083744 - time (sec): 14.45 - samples/sec: 412.38 - lr: 0.000020 - momentum: 0.000000
2023-10-19 02:19:27,463 epoch 5 - iter 86/432 - loss 0.15320825 - time (sec): 29.10 - samples/sec: 418.60 - lr: 0.000019 - momentum: 0.000000
2023-10-19 02:19:42,164 epoch 5 - iter 129/432 - loss 0.15857775 - time (sec): 43.80 - samples/sec: 428.40 - lr: 0.000019 - momentum: 0.000000
2023-10-19 02:19:57,171 epoch 5 - iter 172/432 - loss 0.15560054 - time (sec): 58.81 - samples/sec: 426.51 - lr: 0.000019 - momentum: 0.000000
2023-10-19 02:20:12,653 epoch 5 - iter 215/432 - loss 0.15299635 - time (sec): 74.29 - samples/sec: 412.79 - lr: 0.000018 - momentum: 0.000000
2023-10-19 02:20:26,815 epoch 5 - iter 258/432 - loss 0.15415565 - time (sec): 88.45 - samples/sec: 413.63 - lr: 0.000018 - momentum: 0.000000
2023-10-19 02:20:41,252 epoch 5 - iter 301/432 - loss 0.15470623 - time (sec): 102.89 - samples/sec: 415.99 - lr: 0.000018 - momentum: 0.000000
2023-10-19 02:20:57,180 epoch 5 - iter 344/432 - loss 0.15738806 - time (sec): 118.82 - samples/sec: 413.47 - lr: 0.000017 - momentum: 0.000000
2023-10-19 02:21:12,778 epoch 5 - iter 387/432 - loss 0.15861707 - time (sec): 134.41 - samples/sec: 411.86 - lr: 0.000017 - momentum: 0.000000
2023-10-19 02:21:28,825 epoch 5 - iter 430/432 - loss 0.15871889 - time (sec): 150.46 - samples/sec: 409.68 - lr: 0.000017 - momentum: 0.000000
2023-10-19 02:21:29,386 ----------------------------------------------------------------------------------------------------
2023-10-19 02:21:29,387 EPOCH 5 done: loss 0.1591 - lr: 0.000017
2023-10-19 02:21:42,711 DEV : loss 0.3180293142795563 - f1-score (micro avg) 0.8294
2023-10-19 02:21:42,736 saving best model
2023-10-19 02:21:43,978 ----------------------------------------------------------------------------------------------------
2023-10-19 02:21:58,649 epoch 6 - iter 43/432 - loss 0.11722725 - time (sec): 14.67 - samples/sec: 429.78 - lr: 0.000016 - momentum: 0.000000
2023-10-19 02:22:13,269 epoch 6 - iter 86/432 - loss 0.11922248 - time (sec): 29.29 - samples/sec: 428.61 - lr: 0.000016 - momentum: 0.000000
2023-10-19 02:22:29,215 epoch 6 - iter 129/432 - loss 0.11571691 - time (sec): 45.24 - samples/sec: 417.24 - lr: 0.000016 - momentum: 0.000000
2023-10-19 02:22:44,481 epoch 6 - iter 172/432 - loss 0.11512543 - time (sec): 60.50 - samples/sec: 415.94 - lr: 0.000015 - momentum: 0.000000
2023-10-19 02:22:59,365 epoch 6 - iter 215/432 - loss 0.11922747 - time (sec): 75.39 - samples/sec: 415.99 - lr: 0.000015 - momentum: 0.000000
2023-10-19 02:23:13,706 epoch 6 - iter 258/432 - loss 0.12213480 - time (sec): 89.73 - samples/sec: 412.85 - lr: 0.000015 - momentum: 0.000000
2023-10-19 02:23:28,113 epoch 6 - iter 301/432 - loss 0.12324684 - time (sec): 104.13 - samples/sec: 413.96 - lr: 0.000014 - momentum: 0.000000
2023-10-19 02:23:42,639 epoch 6 - iter 344/432 - loss 0.12430268 - time (sec): 118.66 - samples/sec: 415.94 - lr: 0.000014 - momentum: 0.000000
2023-10-19 02:23:57,051 epoch 6 - iter 387/432 - loss 0.12584428 - time (sec): 133.07 - samples/sec: 416.41 - lr: 0.000014 - momentum: 0.000000
2023-10-19 02:24:11,447 epoch 6 - iter 430/432 - loss 0.12693815 - time (sec): 147.47 - samples/sec: 418.17 - lr: 0.000013 - momentum: 0.000000
2023-10-19 02:24:12,214 ----------------------------------------------------------------------------------------------------
2023-10-19 02:24:12,215 EPOCH 6 done: loss 0.1269 - lr: 0.000013
2023-10-19 02:24:25,523 DEV : loss 0.32838910818099976 - f1-score (micro avg) 0.8206
2023-10-19 02:24:25,547 ----------------------------------------------------------------------------------------------------
2023-10-19 02:24:39,692 epoch 7 - iter 43/432 - loss 0.08912504 - time (sec): 14.14 - samples/sec: 441.62 - lr: 0.000013 - momentum: 0.000000
2023-10-19 02:24:54,333 epoch 7 - iter 86/432 - loss 0.09693013 - time (sec): 28.78 - samples/sec: 423.29 - lr: 0.000013 - momentum: 0.000000
2023-10-19 02:25:09,997 epoch 7 - iter 129/432 - loss 0.09674099 - time (sec): 44.45 - samples/sec: 416.87 - lr: 0.000012 - momentum: 0.000000
2023-10-19 02:25:24,036 epoch 7 - iter 172/432 - loss 0.09757105 - time (sec): 58.49 - samples/sec: 418.36 - lr: 0.000012 - momentum: 0.000000
2023-10-19 02:25:38,207 epoch 7 - iter 215/432 - loss 0.09807207 - time (sec): 72.66 - samples/sec: 416.52 - lr: 0.000012 - momentum: 0.000000
2023-10-19 02:25:53,354 epoch 7 - iter 258/432 - loss 0.09815033 - time (sec): 87.81 - samples/sec: 414.87 - lr: 0.000011 - momentum: 0.000000
2023-10-19 02:26:08,690 epoch 7 - iter 301/432 - loss 0.09658140 - time (sec): 103.14 - samples/sec: 415.61 - lr: 0.000011 - momentum: 0.000000
2023-10-19 02:26:23,350 epoch 7 - iter 344/432 - loss 0.09822029 - time (sec): 117.80 - samples/sec: 414.25 - lr: 0.000011 - momentum: 0.000000
2023-10-19 02:26:38,076 epoch 7 - iter 387/432 - loss 0.09934983 - time (sec): 132.53 - samples/sec: 417.40 - lr: 0.000010 - momentum: 0.000000
2023-10-19 02:26:53,511 epoch 7 - iter 430/432 - loss 0.10039982 - time (sec): 147.96 - samples/sec: 416.66 - lr: 0.000010 - momentum: 0.000000
2023-10-19 02:26:53,991 ----------------------------------------------------------------------------------------------------
2023-10-19 02:26:53,991 EPOCH 7 done: loss 0.1008 - lr: 0.000010
2023-10-19 02:27:07,624 DEV : loss 0.34814995527267456 - f1-score (micro avg) 0.832
2023-10-19 02:27:07,647 saving best model
2023-10-19 02:27:08,914 ----------------------------------------------------------------------------------------------------
2023-10-19 02:27:22,666 epoch 8 - iter 43/432 - loss 0.10350894 - time (sec): 13.75 - samples/sec: 470.25 - lr: 0.000010 - momentum: 0.000000
2023-10-19 02:27:36,004 epoch 8 - iter 86/432 - loss 0.09852409 - time (sec): 27.09 - samples/sec: 476.91 - lr: 0.000009 - momentum: 0.000000
2023-10-19 02:27:50,049 epoch 8 - iter 129/432 - loss 0.09286219 - time (sec): 41.13 - samples/sec: 465.85 - lr: 0.000009 - momentum: 0.000000
2023-10-19 02:28:03,617 epoch 8 - iter 172/432 - loss 0.08743720 - time (sec): 54.70 - samples/sec: 455.60 - lr: 0.000009 - momentum: 0.000000
2023-10-19 02:28:17,277 epoch 8 - iter 215/432 - loss 0.08573511 - time (sec): 68.36 - samples/sec: 459.17 - lr: 0.000008 - momentum: 0.000000
2023-10-19 02:28:30,969 epoch 8 - iter 258/432 - loss 0.08331380 - time (sec): 82.05 - samples/sec: 462.63 - lr: 0.000008 - momentum: 0.000000
2023-10-19 02:28:44,354 epoch 8 - iter 301/432 - loss 0.08249316 - time (sec): 95.44 - samples/sec: 457.26 - lr: 0.000008 - momentum: 0.000000
2023-10-19 02:28:58,899 epoch 8 - iter 344/432 - loss 0.08196643 - time (sec): 109.98 - samples/sec: 448.87 - lr: 0.000007 - momentum: 0.000000
2023-10-19 02:29:12,883 epoch 8 - iter 387/432 - loss 0.08297009 - time (sec): 123.97 - samples/sec: 447.78 - lr: 0.000007 - momentum: 0.000000
2023-10-19 02:29:26,795 epoch 8 - iter 430/432 - loss 0.08266864 - time (sec): 137.88 - samples/sec: 447.49 - lr: 0.000007 - momentum: 0.000000
2023-10-19 02:29:27,277 ----------------------------------------------------------------------------------------------------
2023-10-19 02:29:27,277 EPOCH 8 done: loss 0.0826 - lr: 0.000007
2023-10-19 02:29:39,277 DEV : loss 0.34838762879371643 - f1-score (micro avg) 0.8366
2023-10-19 02:29:39,301 saving best model
2023-10-19 02:29:40,574 ----------------------------------------------------------------------------------------------------
2023-10-19 02:29:53,319 epoch 9 - iter 43/432 - loss 0.06186272 - time (sec): 12.74 - samples/sec: 474.43 - lr: 0.000006 - momentum: 0.000000
2023-10-19 02:30:08,597 epoch 9 - iter 86/432 - loss 0.06634905 - time (sec): 28.02 - samples/sec: 422.46 - lr: 0.000006 - momentum: 0.000000
2023-10-19 02:30:22,346 epoch 9 - iter 129/432 - loss 0.07167375 - time (sec): 41.77 - samples/sec: 424.58 - lr: 0.000006 - momentum: 0.000000
2023-10-19 02:30:36,275 epoch 9 - iter 172/432 - loss 0.06969364 - time (sec): 55.70 - samples/sec: 426.04 - lr: 0.000005 - momentum: 0.000000
2023-10-19 02:30:50,140 epoch 9 - iter 215/432 - loss 0.06782716 - time (sec): 69.56 - samples/sec: 430.13 - lr: 0.000005 - momentum: 0.000000
2023-10-19 02:31:04,623 epoch 9 - iter 258/432 - loss 0.06830674 - time (sec): 84.05 - samples/sec: 428.69 - lr: 0.000005 - momentum: 0.000000
2023-10-19 02:31:18,536 epoch 9 - iter 301/432 - loss 0.06808899 - time (sec): 97.96 - samples/sec: 431.85 - lr: 0.000004 - momentum: 0.000000
2023-10-19 02:31:31,833 epoch 9 - iter 344/432 - loss 0.06544761 - time (sec): 111.26 - samples/sec: 438.17 - lr: 0.000004 - momentum: 0.000000
2023-10-19 02:31:45,008 epoch 9 - iter 387/432 - loss 0.06628105 - time (sec): 124.43 - samples/sec: 443.94 - lr: 0.000004 - momentum: 0.000000
2023-10-19 02:31:58,464 epoch 9 - iter 430/432 - loss 0.06698673 - time (sec): 137.89 - samples/sec: 446.90 - lr: 0.000003 - momentum: 0.000000
2023-10-19 02:31:58,887 ----------------------------------------------------------------------------------------------------
2023-10-19 02:31:58,887 EPOCH 9 done: loss 0.0669 - lr: 0.000003
2023-10-19 02:32:10,770 DEV : loss 0.37735414505004883 - f1-score (micro avg) 0.8355
2023-10-19 02:32:10,795 ----------------------------------------------------------------------------------------------------
2023-10-19 02:32:24,493 epoch 10 - iter 43/432 - loss 0.06688660 - time (sec): 13.70 - samples/sec: 479.67 - lr: 0.000003 - momentum: 0.000000
2023-10-19 02:32:38,648 epoch 10 - iter 86/432 - loss 0.06022775 - time (sec): 27.85 - samples/sec: 445.00 - lr: 0.000003 - momentum: 0.000000
2023-10-19 02:32:52,077 epoch 10 - iter 129/432 - loss 0.05771265 - time (sec): 41.28 - samples/sec: 452.85 - lr: 0.000002 - momentum: 0.000000
2023-10-19 02:33:05,697 epoch 10 - iter 172/432 - loss 0.05574235 - time (sec): 54.90 - samples/sec: 453.57 - lr: 0.000002 - momentum: 0.000000
2023-10-19 02:33:19,731 epoch 10 - iter 215/432 - loss 0.05793529 - time (sec): 68.93 - samples/sec: 450.88 - lr: 0.000002 - momentum: 0.000000
2023-10-19 02:33:32,567 epoch 10 - iter 258/432 - loss 0.05732065 - time (sec): 81.77 - samples/sec: 451.59 - lr: 0.000001 - momentum: 0.000000
2023-10-19 02:33:45,896 epoch 10 - iter 301/432 - loss 0.05663405 - time (sec): 95.10 - samples/sec: 448.98 - lr: 0.000001 - momentum: 0.000000
2023-10-19 02:33:59,944 epoch 10 - iter 344/432 - loss 0.05723962 - time (sec): 109.15 - samples/sec: 448.98 - lr: 0.000001 - momentum: 0.000000
2023-10-19 02:34:13,929 epoch 10 - iter 387/432 - loss 0.05878775 - time (sec): 123.13 - samples/sec: 447.55 - lr: 0.000000 - momentum: 0.000000
2023-10-19 02:34:27,981 epoch 10 - iter 430/432 - loss 0.05901642 - time (sec): 137.18 - samples/sec: 449.90 - lr: 0.000000 - momentum: 0.000000
2023-10-19 02:34:28,415 ----------------------------------------------------------------------------------------------------
2023-10-19 02:34:28,415 EPOCH 10 done: loss 0.0589 - lr: 0.000000
2023-10-19 02:34:40,654 DEV : loss 0.38429826498031616 - f1-score (micro avg) 0.8381
2023-10-19 02:34:40,679 saving best model
2023-10-19 02:34:42,621 ----------------------------------------------------------------------------------------------------
2023-10-19 02:34:42,623 Loading model from best epoch ...
2023-10-19 02:34:44,813 SequenceTagger predicts: Dictionary with 81 tags: O, S-location-route, B-location-route, E-location-route, I-location-route, S-location-stop, B-location-stop, E-location-stop, I-location-stop, S-trigger, B-trigger, E-trigger, I-trigger, S-organization-company, B-organization-company, E-organization-company, I-organization-company, S-location-city, B-location-city, E-location-city, I-location-city, S-location, B-location, E-location, I-location, S-event-cause, B-event-cause, E-event-cause, I-event-cause, S-location-street, B-location-street, E-location-street, I-location-street, S-time, B-time, E-time, I-time, S-date, B-date, E-date, I-date, S-number, B-number, E-number, I-number, S-duration, B-duration, E-duration, I-duration, S-organization
2023-10-19 02:35:01,412
Results:
- F-score (micro) 0.7634
- F-score (macro) 0.5759
- Accuracy 0.6618
By class:
precision recall f1-score support
trigger 0.7056 0.6158 0.6577 833
location-stop 0.8486 0.8353 0.8419 765
location 0.7905 0.8286 0.8091 665
location-city 0.8088 0.8746 0.8404 566
date 0.8836 0.8477 0.8653 394
location-street 0.9315 0.8808 0.9055 386
time 0.7889 0.8906 0.8367 256
location-route 0.7976 0.6937 0.7420 284
organization-company 0.7946 0.7063 0.7479 252
distance 0.9940 1.0000 0.9970 167
number 0.6721 0.8255 0.7410 149
duration 0.3455 0.3497 0.3476 163
event-cause 0.0000 0.0000 0.0000 0
disaster-type 0.9375 0.4348 0.5941 69
organization 0.4706 0.5714 0.5161 28
person 0.3636 0.8000 0.5000 10
set 0.0000 0.0000 0.0000 0
org-position 0.0000 0.0000 0.0000 1
money 0.0000 0.0000 0.0000 0
micro avg 0.7503 0.7771 0.7634 4988
macro avg 0.5860 0.5871 0.5759 4988
weighted avg 0.7941 0.7771 0.7826 4988
2023-10-19 02:35:01,413 ----------------------------------------------------------------------------------------------------
|