File size: 25,106 Bytes
1e5ec8d |
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 |
2023-10-06 21:01:36,944 ----------------------------------------------------------------------------------------------------
2023-10-06 21:01:36,946 Model: "SequenceTagger(
(embeddings): ByT5Embeddings(
(model): T5EncoderModel(
(shared): Embedding(384, 1472)
(encoder): T5Stack(
(embed_tokens): Embedding(384, 1472)
(block): ModuleList(
(0): T5Block(
(layer): ModuleList(
(0): T5LayerSelfAttention(
(SelfAttention): T5Attention(
(q): Linear(in_features=1472, out_features=384, bias=False)
(k): Linear(in_features=1472, out_features=384, bias=False)
(v): Linear(in_features=1472, out_features=384, bias=False)
(o): Linear(in_features=384, out_features=1472, bias=False)
(relative_attention_bias): Embedding(32, 6)
)
(layer_norm): T5LayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
(1): T5LayerFF(
(DenseReluDense): T5DenseGatedActDense(
(wi_0): Linear(in_features=1472, out_features=3584, bias=False)
(wi_1): Linear(in_features=1472, out_features=3584, bias=False)
(wo): Linear(in_features=3584, out_features=1472, bias=False)
(dropout): Dropout(p=0.1, inplace=False)
(act): NewGELUActivation()
)
(layer_norm): T5LayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
(1-11): 11 x T5Block(
(layer): ModuleList(
(0): T5LayerSelfAttention(
(SelfAttention): T5Attention(
(q): Linear(in_features=1472, out_features=384, bias=False)
(k): Linear(in_features=1472, out_features=384, bias=False)
(v): Linear(in_features=1472, out_features=384, bias=False)
(o): Linear(in_features=384, out_features=1472, bias=False)
)
(layer_norm): T5LayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
(1): T5LayerFF(
(DenseReluDense): T5DenseGatedActDense(
(wi_0): Linear(in_features=1472, out_features=3584, bias=False)
(wi_1): Linear(in_features=1472, out_features=3584, bias=False)
(wo): Linear(in_features=3584, out_features=1472, bias=False)
(dropout): Dropout(p=0.1, inplace=False)
(act): NewGELUActivation()
)
(layer_norm): T5LayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(final_layer_norm): T5LayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
(locked_dropout): LockedDropout(p=0.5)
(linear): Linear(in_features=1472, out_features=25, bias=True)
(loss_function): CrossEntropyLoss()
)"
2023-10-06 21:01:36,946 ----------------------------------------------------------------------------------------------------
2023-10-06 21:01:36,946 MultiCorpus: 1100 train + 206 dev + 240 test sentences
- NER_HIPE_2022 Corpus: 1100 train + 206 dev + 240 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/ajmc/de/with_doc_seperator
2023-10-06 21:01:36,946 ----------------------------------------------------------------------------------------------------
2023-10-06 21:01:36,946 Train: 1100 sentences
2023-10-06 21:01:36,946 (train_with_dev=False, train_with_test=False)
2023-10-06 21:01:36,946 ----------------------------------------------------------------------------------------------------
2023-10-06 21:01:36,946 Training Params:
2023-10-06 21:01:36,946 - learning_rate: "0.00016"
2023-10-06 21:01:36,946 - mini_batch_size: "4"
2023-10-06 21:01:36,946 - max_epochs: "10"
2023-10-06 21:01:36,946 - shuffle: "True"
2023-10-06 21:01:36,946 ----------------------------------------------------------------------------------------------------
2023-10-06 21:01:36,946 Plugins:
2023-10-06 21:01:36,946 - TensorboardLogger
2023-10-06 21:01:36,946 - LinearScheduler | warmup_fraction: '0.1'
2023-10-06 21:01:36,946 ----------------------------------------------------------------------------------------------------
2023-10-06 21:01:36,947 Final evaluation on model from best epoch (best-model.pt)
2023-10-06 21:01:36,947 - metric: "('micro avg', 'f1-score')"
2023-10-06 21:01:36,947 ----------------------------------------------------------------------------------------------------
2023-10-06 21:01:36,947 Computation:
2023-10-06 21:01:36,947 - compute on device: cuda:0
2023-10-06 21:01:36,947 - embedding storage: none
2023-10-06 21:01:36,947 ----------------------------------------------------------------------------------------------------
2023-10-06 21:01:36,947 Model training base path: "hmbench-ajmc/de-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1"
2023-10-06 21:01:36,947 ----------------------------------------------------------------------------------------------------
2023-10-06 21:01:36,947 ----------------------------------------------------------------------------------------------------
2023-10-06 21:01:36,947 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-06 21:01:47,402 epoch 1 - iter 27/275 - loss 3.22833207 - time (sec): 10.45 - samples/sec: 211.40 - lr: 0.000015 - momentum: 0.000000
2023-10-06 21:01:58,525 epoch 1 - iter 54/275 - loss 3.21740774 - time (sec): 21.58 - samples/sec: 203.64 - lr: 0.000031 - momentum: 0.000000
2023-10-06 21:02:09,265 epoch 1 - iter 81/275 - loss 3.19662009 - time (sec): 32.32 - samples/sec: 202.99 - lr: 0.000047 - momentum: 0.000000
2023-10-06 21:02:19,769 epoch 1 - iter 108/275 - loss 3.14572703 - time (sec): 42.82 - samples/sec: 202.17 - lr: 0.000062 - momentum: 0.000000
2023-10-06 21:02:31,381 epoch 1 - iter 135/275 - loss 3.04591352 - time (sec): 54.43 - samples/sec: 204.64 - lr: 0.000078 - momentum: 0.000000
2023-10-06 21:02:42,609 epoch 1 - iter 162/275 - loss 2.93792612 - time (sec): 65.66 - samples/sec: 205.18 - lr: 0.000094 - momentum: 0.000000
2023-10-06 21:02:53,822 epoch 1 - iter 189/275 - loss 2.82739079 - time (sec): 76.87 - samples/sec: 205.84 - lr: 0.000109 - momentum: 0.000000
2023-10-06 21:03:04,383 epoch 1 - iter 216/275 - loss 2.71282571 - time (sec): 87.44 - samples/sec: 206.16 - lr: 0.000125 - momentum: 0.000000
2023-10-06 21:03:15,131 epoch 1 - iter 243/275 - loss 2.59134021 - time (sec): 98.18 - samples/sec: 206.34 - lr: 0.000141 - momentum: 0.000000
2023-10-06 21:03:25,168 epoch 1 - iter 270/275 - loss 2.48706332 - time (sec): 108.22 - samples/sec: 205.82 - lr: 0.000157 - momentum: 0.000000
2023-10-06 21:03:27,515 ----------------------------------------------------------------------------------------------------
2023-10-06 21:03:27,515 EPOCH 1 done: loss 2.4613 - lr: 0.000157
2023-10-06 21:03:34,060 DEV : loss 1.1231565475463867 - f1-score (micro avg) 0.0
2023-10-06 21:03:34,066 ----------------------------------------------------------------------------------------------------
2023-10-06 21:03:44,652 epoch 2 - iter 27/275 - loss 1.02744112 - time (sec): 10.59 - samples/sec: 207.93 - lr: 0.000158 - momentum: 0.000000
2023-10-06 21:03:55,710 epoch 2 - iter 54/275 - loss 0.91058745 - time (sec): 21.64 - samples/sec: 206.49 - lr: 0.000157 - momentum: 0.000000
2023-10-06 21:04:06,143 epoch 2 - iter 81/275 - loss 0.91519657 - time (sec): 32.08 - samples/sec: 205.36 - lr: 0.000155 - momentum: 0.000000
2023-10-06 21:04:16,151 epoch 2 - iter 108/275 - loss 0.86737118 - time (sec): 42.08 - samples/sec: 202.45 - lr: 0.000153 - momentum: 0.000000
2023-10-06 21:04:26,497 epoch 2 - iter 135/275 - loss 0.84523607 - time (sec): 52.43 - samples/sec: 201.60 - lr: 0.000151 - momentum: 0.000000
2023-10-06 21:04:37,903 epoch 2 - iter 162/275 - loss 0.80302043 - time (sec): 63.84 - samples/sec: 203.27 - lr: 0.000150 - momentum: 0.000000
2023-10-06 21:04:49,320 epoch 2 - iter 189/275 - loss 0.76259034 - time (sec): 75.25 - samples/sec: 204.92 - lr: 0.000148 - momentum: 0.000000
2023-10-06 21:05:00,257 epoch 2 - iter 216/275 - loss 0.72720466 - time (sec): 86.19 - samples/sec: 205.44 - lr: 0.000146 - momentum: 0.000000
2023-10-06 21:05:11,177 epoch 2 - iter 243/275 - loss 0.69492358 - time (sec): 97.11 - samples/sec: 206.05 - lr: 0.000144 - momentum: 0.000000
2023-10-06 21:05:22,007 epoch 2 - iter 270/275 - loss 0.67160554 - time (sec): 107.94 - samples/sec: 206.52 - lr: 0.000143 - momentum: 0.000000
2023-10-06 21:05:24,008 ----------------------------------------------------------------------------------------------------
2023-10-06 21:05:24,008 EPOCH 2 done: loss 0.6684 - lr: 0.000143
2023-10-06 21:05:30,641 DEV : loss 0.39727556705474854 - f1-score (micro avg) 0.3715
2023-10-06 21:05:30,646 saving best model
2023-10-06 21:05:31,694 ----------------------------------------------------------------------------------------------------
2023-10-06 21:05:42,313 epoch 3 - iter 27/275 - loss 0.37832761 - time (sec): 10.62 - samples/sec: 208.72 - lr: 0.000141 - momentum: 0.000000
2023-10-06 21:05:53,709 epoch 3 - iter 54/275 - loss 0.35655984 - time (sec): 22.01 - samples/sec: 212.06 - lr: 0.000139 - momentum: 0.000000
2023-10-06 21:06:04,671 epoch 3 - iter 81/275 - loss 0.35093005 - time (sec): 32.98 - samples/sec: 212.83 - lr: 0.000137 - momentum: 0.000000
2023-10-06 21:06:15,850 epoch 3 - iter 108/275 - loss 0.34679625 - time (sec): 44.15 - samples/sec: 212.59 - lr: 0.000135 - momentum: 0.000000
2023-10-06 21:06:26,372 epoch 3 - iter 135/275 - loss 0.33706333 - time (sec): 54.68 - samples/sec: 211.41 - lr: 0.000134 - momentum: 0.000000
2023-10-06 21:06:37,264 epoch 3 - iter 162/275 - loss 0.33137490 - time (sec): 65.57 - samples/sec: 210.90 - lr: 0.000132 - momentum: 0.000000
2023-10-06 21:06:47,375 epoch 3 - iter 189/275 - loss 0.31978246 - time (sec): 75.68 - samples/sec: 208.92 - lr: 0.000130 - momentum: 0.000000
2023-10-06 21:06:57,740 epoch 3 - iter 216/275 - loss 0.30780358 - time (sec): 86.04 - samples/sec: 207.17 - lr: 0.000128 - momentum: 0.000000
2023-10-06 21:07:09,451 epoch 3 - iter 243/275 - loss 0.29879871 - time (sec): 97.75 - samples/sec: 207.83 - lr: 0.000127 - momentum: 0.000000
2023-10-06 21:07:19,646 epoch 3 - iter 270/275 - loss 0.28697615 - time (sec): 107.95 - samples/sec: 206.64 - lr: 0.000125 - momentum: 0.000000
2023-10-06 21:07:21,888 ----------------------------------------------------------------------------------------------------
2023-10-06 21:07:21,888 EPOCH 3 done: loss 0.2850 - lr: 0.000125
2023-10-06 21:07:28,577 DEV : loss 0.2045798897743225 - f1-score (micro avg) 0.7546
2023-10-06 21:07:28,583 saving best model
2023-10-06 21:07:32,937 ----------------------------------------------------------------------------------------------------
2023-10-06 21:07:44,862 epoch 4 - iter 27/275 - loss 0.16488031 - time (sec): 11.92 - samples/sec: 218.64 - lr: 0.000123 - momentum: 0.000000
2023-10-06 21:07:55,513 epoch 4 - iter 54/275 - loss 0.14918728 - time (sec): 22.58 - samples/sec: 211.07 - lr: 0.000121 - momentum: 0.000000
2023-10-06 21:08:05,888 epoch 4 - iter 81/275 - loss 0.15702343 - time (sec): 32.95 - samples/sec: 205.64 - lr: 0.000119 - momentum: 0.000000
2023-10-06 21:08:16,549 epoch 4 - iter 108/275 - loss 0.16021996 - time (sec): 43.61 - samples/sec: 206.87 - lr: 0.000118 - momentum: 0.000000
2023-10-06 21:08:27,748 epoch 4 - iter 135/275 - loss 0.16143446 - time (sec): 54.81 - samples/sec: 208.91 - lr: 0.000116 - momentum: 0.000000
2023-10-06 21:08:38,232 epoch 4 - iter 162/275 - loss 0.15574263 - time (sec): 65.29 - samples/sec: 207.57 - lr: 0.000114 - momentum: 0.000000
2023-10-06 21:08:49,169 epoch 4 - iter 189/275 - loss 0.15197121 - time (sec): 76.23 - samples/sec: 207.38 - lr: 0.000112 - momentum: 0.000000
2023-10-06 21:08:59,538 epoch 4 - iter 216/275 - loss 0.15516941 - time (sec): 86.60 - samples/sec: 207.22 - lr: 0.000111 - momentum: 0.000000
2023-10-06 21:09:10,047 epoch 4 - iter 243/275 - loss 0.15034681 - time (sec): 97.11 - samples/sec: 207.33 - lr: 0.000109 - momentum: 0.000000
2023-10-06 21:09:20,913 epoch 4 - iter 270/275 - loss 0.14712355 - time (sec): 107.98 - samples/sec: 206.53 - lr: 0.000107 - momentum: 0.000000
2023-10-06 21:09:23,020 ----------------------------------------------------------------------------------------------------
2023-10-06 21:09:23,020 EPOCH 4 done: loss 0.1461 - lr: 0.000107
2023-10-06 21:09:29,701 DEV : loss 0.14199717342853546 - f1-score (micro avg) 0.8412
2023-10-06 21:09:29,706 saving best model
2023-10-06 21:09:34,058 ----------------------------------------------------------------------------------------------------
2023-10-06 21:09:44,862 epoch 5 - iter 27/275 - loss 0.11122000 - time (sec): 10.80 - samples/sec: 202.36 - lr: 0.000105 - momentum: 0.000000
2023-10-06 21:09:55,555 epoch 5 - iter 54/275 - loss 0.09992236 - time (sec): 21.50 - samples/sec: 203.11 - lr: 0.000103 - momentum: 0.000000
2023-10-06 21:10:05,993 epoch 5 - iter 81/275 - loss 0.10665216 - time (sec): 31.93 - samples/sec: 204.89 - lr: 0.000102 - momentum: 0.000000
2023-10-06 21:10:17,851 epoch 5 - iter 108/275 - loss 0.09964466 - time (sec): 43.79 - samples/sec: 208.40 - lr: 0.000100 - momentum: 0.000000
2023-10-06 21:10:29,819 epoch 5 - iter 135/275 - loss 0.09494942 - time (sec): 55.76 - samples/sec: 209.35 - lr: 0.000098 - momentum: 0.000000
2023-10-06 21:10:40,729 epoch 5 - iter 162/275 - loss 0.09497234 - time (sec): 66.67 - samples/sec: 207.68 - lr: 0.000096 - momentum: 0.000000
2023-10-06 21:10:51,626 epoch 5 - iter 189/275 - loss 0.09115680 - time (sec): 77.57 - samples/sec: 207.60 - lr: 0.000095 - momentum: 0.000000
2023-10-06 21:11:02,172 epoch 5 - iter 216/275 - loss 0.09154943 - time (sec): 88.11 - samples/sec: 208.87 - lr: 0.000093 - momentum: 0.000000
2023-10-06 21:11:12,251 epoch 5 - iter 243/275 - loss 0.09193531 - time (sec): 98.19 - samples/sec: 207.31 - lr: 0.000091 - momentum: 0.000000
2023-10-06 21:11:22,446 epoch 5 - iter 270/275 - loss 0.08842455 - time (sec): 108.39 - samples/sec: 206.77 - lr: 0.000089 - momentum: 0.000000
2023-10-06 21:11:24,284 ----------------------------------------------------------------------------------------------------
2023-10-06 21:11:24,284 EPOCH 5 done: loss 0.0891 - lr: 0.000089
2023-10-06 21:11:30,964 DEV : loss 0.12431611120700836 - f1-score (micro avg) 0.8558
2023-10-06 21:11:30,970 saving best model
2023-10-06 21:11:35,373 ----------------------------------------------------------------------------------------------------
2023-10-06 21:11:46,300 epoch 6 - iter 27/275 - loss 0.07057993 - time (sec): 10.93 - samples/sec: 214.82 - lr: 0.000087 - momentum: 0.000000
2023-10-06 21:11:56,693 epoch 6 - iter 54/275 - loss 0.06558269 - time (sec): 21.32 - samples/sec: 210.43 - lr: 0.000086 - momentum: 0.000000
2023-10-06 21:12:07,445 epoch 6 - iter 81/275 - loss 0.06122069 - time (sec): 32.07 - samples/sec: 209.82 - lr: 0.000084 - momentum: 0.000000
2023-10-06 21:12:18,621 epoch 6 - iter 108/275 - loss 0.05940627 - time (sec): 43.25 - samples/sec: 208.48 - lr: 0.000082 - momentum: 0.000000
2023-10-06 21:12:29,634 epoch 6 - iter 135/275 - loss 0.05905171 - time (sec): 54.26 - samples/sec: 207.76 - lr: 0.000080 - momentum: 0.000000
2023-10-06 21:12:40,370 epoch 6 - iter 162/275 - loss 0.05632511 - time (sec): 65.00 - samples/sec: 207.26 - lr: 0.000079 - momentum: 0.000000
2023-10-06 21:12:51,563 epoch 6 - iter 189/275 - loss 0.06501524 - time (sec): 76.19 - samples/sec: 209.02 - lr: 0.000077 - momentum: 0.000000
2023-10-06 21:13:02,225 epoch 6 - iter 216/275 - loss 0.06236298 - time (sec): 86.85 - samples/sec: 208.33 - lr: 0.000075 - momentum: 0.000000
2023-10-06 21:13:13,005 epoch 6 - iter 243/275 - loss 0.06493312 - time (sec): 97.63 - samples/sec: 207.90 - lr: 0.000073 - momentum: 0.000000
2023-10-06 21:13:23,126 epoch 6 - iter 270/275 - loss 0.06505844 - time (sec): 107.75 - samples/sec: 207.01 - lr: 0.000072 - momentum: 0.000000
2023-10-06 21:13:25,396 ----------------------------------------------------------------------------------------------------
2023-10-06 21:13:25,396 EPOCH 6 done: loss 0.0648 - lr: 0.000072
2023-10-06 21:13:32,058 DEV : loss 0.12224514782428741 - f1-score (micro avg) 0.8709
2023-10-06 21:13:32,064 saving best model
2023-10-06 21:13:36,415 ----------------------------------------------------------------------------------------------------
2023-10-06 21:13:46,779 epoch 7 - iter 27/275 - loss 0.04187397 - time (sec): 10.36 - samples/sec: 197.64 - lr: 0.000070 - momentum: 0.000000
2023-10-06 21:13:57,052 epoch 7 - iter 54/275 - loss 0.04070792 - time (sec): 20.63 - samples/sec: 198.02 - lr: 0.000068 - momentum: 0.000000
2023-10-06 21:14:08,314 epoch 7 - iter 81/275 - loss 0.04907858 - time (sec): 31.90 - samples/sec: 202.68 - lr: 0.000066 - momentum: 0.000000
2023-10-06 21:14:19,438 epoch 7 - iter 108/275 - loss 0.03973019 - time (sec): 43.02 - samples/sec: 207.16 - lr: 0.000064 - momentum: 0.000000
2023-10-06 21:14:30,716 epoch 7 - iter 135/275 - loss 0.04264876 - time (sec): 54.30 - samples/sec: 207.55 - lr: 0.000063 - momentum: 0.000000
2023-10-06 21:14:41,116 epoch 7 - iter 162/275 - loss 0.04839100 - time (sec): 64.70 - samples/sec: 205.43 - lr: 0.000061 - momentum: 0.000000
2023-10-06 21:14:52,120 epoch 7 - iter 189/275 - loss 0.04500606 - time (sec): 75.70 - samples/sec: 205.72 - lr: 0.000059 - momentum: 0.000000
2023-10-06 21:15:03,203 epoch 7 - iter 216/275 - loss 0.04635685 - time (sec): 86.79 - samples/sec: 206.21 - lr: 0.000058 - momentum: 0.000000
2023-10-06 21:15:14,451 epoch 7 - iter 243/275 - loss 0.04828398 - time (sec): 98.03 - samples/sec: 207.62 - lr: 0.000056 - momentum: 0.000000
2023-10-06 21:15:25,007 epoch 7 - iter 270/275 - loss 0.05190075 - time (sec): 108.59 - samples/sec: 206.93 - lr: 0.000054 - momentum: 0.000000
2023-10-06 21:15:26,743 ----------------------------------------------------------------------------------------------------
2023-10-06 21:15:26,743 EPOCH 7 done: loss 0.0518 - lr: 0.000054
2023-10-06 21:15:33,413 DEV : loss 0.12933534383773804 - f1-score (micro avg) 0.8627
2023-10-06 21:15:33,419 ----------------------------------------------------------------------------------------------------
2023-10-06 21:15:43,432 epoch 8 - iter 27/275 - loss 0.04380187 - time (sec): 10.01 - samples/sec: 193.98 - lr: 0.000052 - momentum: 0.000000
2023-10-06 21:15:53,820 epoch 8 - iter 54/275 - loss 0.06057894 - time (sec): 20.40 - samples/sec: 203.09 - lr: 0.000050 - momentum: 0.000000
2023-10-06 21:16:04,746 epoch 8 - iter 81/275 - loss 0.05231526 - time (sec): 31.33 - samples/sec: 206.54 - lr: 0.000048 - momentum: 0.000000
2023-10-06 21:16:15,647 epoch 8 - iter 108/275 - loss 0.04889467 - time (sec): 42.23 - samples/sec: 207.48 - lr: 0.000047 - momentum: 0.000000
2023-10-06 21:16:25,985 epoch 8 - iter 135/275 - loss 0.04684457 - time (sec): 52.56 - samples/sec: 204.32 - lr: 0.000045 - momentum: 0.000000
2023-10-06 21:16:37,470 epoch 8 - iter 162/275 - loss 0.04855515 - time (sec): 64.05 - samples/sec: 206.68 - lr: 0.000043 - momentum: 0.000000
2023-10-06 21:16:47,560 epoch 8 - iter 189/275 - loss 0.04673321 - time (sec): 74.14 - samples/sec: 206.10 - lr: 0.000042 - momentum: 0.000000
2023-10-06 21:16:58,987 epoch 8 - iter 216/275 - loss 0.04384603 - time (sec): 85.57 - samples/sec: 206.97 - lr: 0.000040 - momentum: 0.000000
2023-10-06 21:17:10,417 epoch 8 - iter 243/275 - loss 0.04459432 - time (sec): 97.00 - samples/sec: 207.64 - lr: 0.000038 - momentum: 0.000000
2023-10-06 21:17:21,170 epoch 8 - iter 270/275 - loss 0.04257561 - time (sec): 107.75 - samples/sec: 207.30 - lr: 0.000036 - momentum: 0.000000
2023-10-06 21:17:23,241 ----------------------------------------------------------------------------------------------------
2023-10-06 21:17:23,241 EPOCH 8 done: loss 0.0418 - lr: 0.000036
2023-10-06 21:17:29,906 DEV : loss 0.13218103349208832 - f1-score (micro avg) 0.8892
2023-10-06 21:17:29,912 saving best model
2023-10-06 21:17:34,235 ----------------------------------------------------------------------------------------------------
2023-10-06 21:17:44,834 epoch 9 - iter 27/275 - loss 0.02652253 - time (sec): 10.60 - samples/sec: 202.68 - lr: 0.000034 - momentum: 0.000000
2023-10-06 21:17:55,396 epoch 9 - iter 54/275 - loss 0.04854387 - time (sec): 21.16 - samples/sec: 204.49 - lr: 0.000032 - momentum: 0.000000
2023-10-06 21:18:06,066 epoch 9 - iter 81/275 - loss 0.05078626 - time (sec): 31.83 - samples/sec: 204.74 - lr: 0.000031 - momentum: 0.000000
2023-10-06 21:18:17,352 epoch 9 - iter 108/275 - loss 0.04987359 - time (sec): 43.12 - samples/sec: 205.19 - lr: 0.000029 - momentum: 0.000000
2023-10-06 21:18:28,170 epoch 9 - iter 135/275 - loss 0.05203896 - time (sec): 53.93 - samples/sec: 205.62 - lr: 0.000027 - momentum: 0.000000
2023-10-06 21:18:38,499 epoch 9 - iter 162/275 - loss 0.05289938 - time (sec): 64.26 - samples/sec: 206.11 - lr: 0.000026 - momentum: 0.000000
2023-10-06 21:18:50,354 epoch 9 - iter 189/275 - loss 0.04529210 - time (sec): 76.12 - samples/sec: 207.59 - lr: 0.000024 - momentum: 0.000000
2023-10-06 21:19:01,010 epoch 9 - iter 216/275 - loss 0.04172254 - time (sec): 86.77 - samples/sec: 207.63 - lr: 0.000022 - momentum: 0.000000
2023-10-06 21:19:11,223 epoch 9 - iter 243/275 - loss 0.03840025 - time (sec): 96.99 - samples/sec: 206.42 - lr: 0.000020 - momentum: 0.000000
2023-10-06 21:19:22,087 epoch 9 - iter 270/275 - loss 0.03840849 - time (sec): 107.85 - samples/sec: 207.09 - lr: 0.000019 - momentum: 0.000000
2023-10-06 21:19:24,089 ----------------------------------------------------------------------------------------------------
2023-10-06 21:19:24,089 EPOCH 9 done: loss 0.0379 - lr: 0.000019
2023-10-06 21:19:30,702 DEV : loss 0.1376478224992752 - f1-score (micro avg) 0.8664
2023-10-06 21:19:30,708 ----------------------------------------------------------------------------------------------------
2023-10-06 21:19:41,630 epoch 10 - iter 27/275 - loss 0.05058081 - time (sec): 10.92 - samples/sec: 208.06 - lr: 0.000017 - momentum: 0.000000
2023-10-06 21:19:52,250 epoch 10 - iter 54/275 - loss 0.04581674 - time (sec): 21.54 - samples/sec: 205.80 - lr: 0.000015 - momentum: 0.000000
2023-10-06 21:20:02,820 epoch 10 - iter 81/275 - loss 0.03678298 - time (sec): 32.11 - samples/sec: 206.76 - lr: 0.000013 - momentum: 0.000000
2023-10-06 21:20:13,857 epoch 10 - iter 108/275 - loss 0.03737114 - time (sec): 43.15 - samples/sec: 207.80 - lr: 0.000011 - momentum: 0.000000
2023-10-06 21:20:24,327 epoch 10 - iter 135/275 - loss 0.03215051 - time (sec): 53.62 - samples/sec: 205.81 - lr: 0.000010 - momentum: 0.000000
2023-10-06 21:20:34,938 epoch 10 - iter 162/275 - loss 0.03481797 - time (sec): 64.23 - samples/sec: 205.31 - lr: 0.000008 - momentum: 0.000000
2023-10-06 21:20:45,933 epoch 10 - iter 189/275 - loss 0.03404409 - time (sec): 75.22 - samples/sec: 205.40 - lr: 0.000006 - momentum: 0.000000
2023-10-06 21:20:56,901 epoch 10 - iter 216/275 - loss 0.03300710 - time (sec): 86.19 - samples/sec: 206.14 - lr: 0.000004 - momentum: 0.000000
2023-10-06 21:21:07,747 epoch 10 - iter 243/275 - loss 0.03216155 - time (sec): 97.04 - samples/sec: 206.83 - lr: 0.000003 - momentum: 0.000000
2023-10-06 21:21:18,408 epoch 10 - iter 270/275 - loss 0.03352601 - time (sec): 107.70 - samples/sec: 207.18 - lr: 0.000001 - momentum: 0.000000
2023-10-06 21:21:20,592 ----------------------------------------------------------------------------------------------------
2023-10-06 21:21:20,592 EPOCH 10 done: loss 0.0334 - lr: 0.000001
2023-10-06 21:21:27,269 DEV : loss 0.1404709368944168 - f1-score (micro avg) 0.8685
2023-10-06 21:21:28,181 ----------------------------------------------------------------------------------------------------
2023-10-06 21:21:28,182 Loading model from best epoch ...
2023-10-06 21:21:31,522 SequenceTagger predicts: Dictionary with 25 tags: O, S-scope, B-scope, E-scope, I-scope, S-pers, B-pers, E-pers, I-pers, S-work, B-work, E-work, I-work, S-loc, B-loc, E-loc, I-loc, S-object, B-object, E-object, I-object, S-date, B-date, E-date, I-date
2023-10-06 21:21:38,605
Results:
- F-score (micro) 0.8845
- F-score (macro) 0.5336
- Accuracy 0.8101
By class:
precision recall f1-score support
scope 0.8857 0.8807 0.8832 176
pers 0.9370 0.9297 0.9333 128
work 0.8514 0.8514 0.8514 74
object 0.0000 0.0000 0.0000 2
loc 0.0000 0.0000 0.0000 2
micro avg 0.8868 0.8822 0.8845 382
macro avg 0.5348 0.5323 0.5336 382
weighted avg 0.8870 0.8822 0.8846 382
2023-10-06 21:21:38,605 ----------------------------------------------------------------------------------------------------
|