File size: 24,073 Bytes
f2d0eeb |
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 |
2023-10-17 13:37:25,646 ----------------------------------------------------------------------------------------------------
2023-10-17 13:37:25,647 Model: "SequenceTagger(
(embeddings): TransformerWordEmbeddings(
(model): ElectraModel(
(embeddings): ElectraEmbeddings(
(word_embeddings): Embedding(32001, 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): ElectraEncoder(
(layer): ModuleList(
(0-11): 12 x ElectraLayer(
(attention): ElectraAttention(
(self): ElectraSelfAttention(
(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): ElectraSelfOutput(
(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): ElectraIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): ElectraOutput(
(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)
)
)
)
)
)
)
(locked_dropout): LockedDropout(p=0.5)
(linear): Linear(in_features=768, out_features=17, bias=True)
(loss_function): CrossEntropyLoss()
)"
2023-10-17 13:37:25,647 ----------------------------------------------------------------------------------------------------
2023-10-17 13:37:25,647 MultiCorpus: 7142 train + 698 dev + 2570 test sentences
- NER_HIPE_2022 Corpus: 7142 train + 698 dev + 2570 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fr/with_doc_seperator
2023-10-17 13:37:25,647 ----------------------------------------------------------------------------------------------------
2023-10-17 13:37:25,648 Train: 7142 sentences
2023-10-17 13:37:25,648 (train_with_dev=False, train_with_test=False)
2023-10-17 13:37:25,648 ----------------------------------------------------------------------------------------------------
2023-10-17 13:37:25,648 Training Params:
2023-10-17 13:37:25,648 - learning_rate: "3e-05"
2023-10-17 13:37:25,648 - mini_batch_size: "4"
2023-10-17 13:37:25,648 - max_epochs: "10"
2023-10-17 13:37:25,648 - shuffle: "True"
2023-10-17 13:37:25,648 ----------------------------------------------------------------------------------------------------
2023-10-17 13:37:25,648 Plugins:
2023-10-17 13:37:25,648 - TensorboardLogger
2023-10-17 13:37:25,648 - LinearScheduler | warmup_fraction: '0.1'
2023-10-17 13:37:25,648 ----------------------------------------------------------------------------------------------------
2023-10-17 13:37:25,648 Final evaluation on model from best epoch (best-model.pt)
2023-10-17 13:37:25,648 - metric: "('micro avg', 'f1-score')"
2023-10-17 13:37:25,648 ----------------------------------------------------------------------------------------------------
2023-10-17 13:37:25,648 Computation:
2023-10-17 13:37:25,648 - compute on device: cuda:0
2023-10-17 13:37:25,648 - embedding storage: none
2023-10-17 13:37:25,648 ----------------------------------------------------------------------------------------------------
2023-10-17 13:37:25,648 Model training base path: "hmbench-newseye/fr-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2"
2023-10-17 13:37:25,648 ----------------------------------------------------------------------------------------------------
2023-10-17 13:37:25,648 ----------------------------------------------------------------------------------------------------
2023-10-17 13:37:25,648 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-17 13:37:34,100 epoch 1 - iter 178/1786 - loss 2.71850894 - time (sec): 8.45 - samples/sec: 2722.97 - lr: 0.000003 - momentum: 0.000000
2023-10-17 13:37:43,271 epoch 1 - iter 356/1786 - loss 1.55627930 - time (sec): 17.62 - samples/sec: 2818.11 - lr: 0.000006 - momentum: 0.000000
2023-10-17 13:37:52,167 epoch 1 - iter 534/1786 - loss 1.17692252 - time (sec): 26.52 - samples/sec: 2810.54 - lr: 0.000009 - momentum: 0.000000
2023-10-17 13:38:01,158 epoch 1 - iter 712/1786 - loss 0.95620126 - time (sec): 35.51 - samples/sec: 2847.03 - lr: 0.000012 - momentum: 0.000000
2023-10-17 13:38:09,947 epoch 1 - iter 890/1786 - loss 0.81990248 - time (sec): 44.30 - samples/sec: 2817.71 - lr: 0.000015 - momentum: 0.000000
2023-10-17 13:38:18,765 epoch 1 - iter 1068/1786 - loss 0.72732692 - time (sec): 53.12 - samples/sec: 2789.30 - lr: 0.000018 - momentum: 0.000000
2023-10-17 13:38:27,632 epoch 1 - iter 1246/1786 - loss 0.64882748 - time (sec): 61.98 - samples/sec: 2788.02 - lr: 0.000021 - momentum: 0.000000
2023-10-17 13:38:36,725 epoch 1 - iter 1424/1786 - loss 0.58363820 - time (sec): 71.08 - samples/sec: 2803.30 - lr: 0.000024 - momentum: 0.000000
2023-10-17 13:38:45,495 epoch 1 - iter 1602/1786 - loss 0.53997715 - time (sec): 79.85 - samples/sec: 2791.05 - lr: 0.000027 - momentum: 0.000000
2023-10-17 13:38:54,216 epoch 1 - iter 1780/1786 - loss 0.49959069 - time (sec): 88.57 - samples/sec: 2802.19 - lr: 0.000030 - momentum: 0.000000
2023-10-17 13:38:54,485 ----------------------------------------------------------------------------------------------------
2023-10-17 13:38:54,486 EPOCH 1 done: loss 0.4990 - lr: 0.000030
2023-10-17 13:38:57,577 DEV : loss 0.11935114115476608 - f1-score (micro avg) 0.7585
2023-10-17 13:38:57,593 saving best model
2023-10-17 13:38:57,942 ----------------------------------------------------------------------------------------------------
2023-10-17 13:39:07,045 epoch 2 - iter 178/1786 - loss 0.14207407 - time (sec): 9.10 - samples/sec: 2671.63 - lr: 0.000030 - momentum: 0.000000
2023-10-17 13:39:15,919 epoch 2 - iter 356/1786 - loss 0.13038487 - time (sec): 17.98 - samples/sec: 2672.23 - lr: 0.000029 - momentum: 0.000000
2023-10-17 13:39:24,577 epoch 2 - iter 534/1786 - loss 0.12627094 - time (sec): 26.63 - samples/sec: 2624.39 - lr: 0.000029 - momentum: 0.000000
2023-10-17 13:39:33,752 epoch 2 - iter 712/1786 - loss 0.12554020 - time (sec): 35.81 - samples/sec: 2655.03 - lr: 0.000029 - momentum: 0.000000
2023-10-17 13:39:42,945 epoch 2 - iter 890/1786 - loss 0.12266313 - time (sec): 45.00 - samples/sec: 2698.72 - lr: 0.000028 - momentum: 0.000000
2023-10-17 13:39:52,042 epoch 2 - iter 1068/1786 - loss 0.12077729 - time (sec): 54.10 - samples/sec: 2723.97 - lr: 0.000028 - momentum: 0.000000
2023-10-17 13:40:01,199 epoch 2 - iter 1246/1786 - loss 0.11775653 - time (sec): 63.26 - samples/sec: 2764.92 - lr: 0.000028 - momentum: 0.000000
2023-10-17 13:40:09,971 epoch 2 - iter 1424/1786 - loss 0.12007377 - time (sec): 72.03 - samples/sec: 2776.20 - lr: 0.000027 - momentum: 0.000000
2023-10-17 13:40:18,872 epoch 2 - iter 1602/1786 - loss 0.11957108 - time (sec): 80.93 - samples/sec: 2773.10 - lr: 0.000027 - momentum: 0.000000
2023-10-17 13:40:27,656 epoch 2 - iter 1780/1786 - loss 0.11971143 - time (sec): 89.71 - samples/sec: 2765.64 - lr: 0.000027 - momentum: 0.000000
2023-10-17 13:40:27,939 ----------------------------------------------------------------------------------------------------
2023-10-17 13:40:27,940 EPOCH 2 done: loss 0.1195 - lr: 0.000027
2023-10-17 13:40:32,630 DEV : loss 0.10125791281461716 - f1-score (micro avg) 0.8144
2023-10-17 13:40:32,647 saving best model
2023-10-17 13:40:33,095 ----------------------------------------------------------------------------------------------------
2023-10-17 13:40:41,901 epoch 3 - iter 178/1786 - loss 0.07418424 - time (sec): 8.80 - samples/sec: 2788.17 - lr: 0.000026 - momentum: 0.000000
2023-10-17 13:40:50,945 epoch 3 - iter 356/1786 - loss 0.07570841 - time (sec): 17.84 - samples/sec: 2768.58 - lr: 0.000026 - momentum: 0.000000
2023-10-17 13:40:59,866 epoch 3 - iter 534/1786 - loss 0.07399212 - time (sec): 26.77 - samples/sec: 2788.93 - lr: 0.000026 - momentum: 0.000000
2023-10-17 13:41:08,915 epoch 3 - iter 712/1786 - loss 0.07427751 - time (sec): 35.82 - samples/sec: 2747.68 - lr: 0.000025 - momentum: 0.000000
2023-10-17 13:41:17,929 epoch 3 - iter 890/1786 - loss 0.07471144 - time (sec): 44.83 - samples/sec: 2766.71 - lr: 0.000025 - momentum: 0.000000
2023-10-17 13:41:26,655 epoch 3 - iter 1068/1786 - loss 0.07885766 - time (sec): 53.56 - samples/sec: 2762.94 - lr: 0.000025 - momentum: 0.000000
2023-10-17 13:41:35,429 epoch 3 - iter 1246/1786 - loss 0.08022104 - time (sec): 62.33 - samples/sec: 2764.62 - lr: 0.000024 - momentum: 0.000000
2023-10-17 13:41:44,429 epoch 3 - iter 1424/1786 - loss 0.07990684 - time (sec): 71.33 - samples/sec: 2780.82 - lr: 0.000024 - momentum: 0.000000
2023-10-17 13:41:53,237 epoch 3 - iter 1602/1786 - loss 0.08236359 - time (sec): 80.14 - samples/sec: 2781.43 - lr: 0.000024 - momentum: 0.000000
2023-10-17 13:42:02,129 epoch 3 - iter 1780/1786 - loss 0.08175112 - time (sec): 89.03 - samples/sec: 2781.51 - lr: 0.000023 - momentum: 0.000000
2023-10-17 13:42:02,455 ----------------------------------------------------------------------------------------------------
2023-10-17 13:42:02,456 EPOCH 3 done: loss 0.0818 - lr: 0.000023
2023-10-17 13:42:06,658 DEV : loss 0.13423609733581543 - f1-score (micro avg) 0.7967
2023-10-17 13:42:06,675 ----------------------------------------------------------------------------------------------------
2023-10-17 13:42:15,751 epoch 4 - iter 178/1786 - loss 0.05237935 - time (sec): 9.07 - samples/sec: 2848.98 - lr: 0.000023 - momentum: 0.000000
2023-10-17 13:42:24,779 epoch 4 - iter 356/1786 - loss 0.05659288 - time (sec): 18.10 - samples/sec: 2788.78 - lr: 0.000023 - momentum: 0.000000
2023-10-17 13:42:33,850 epoch 4 - iter 534/1786 - loss 0.05742020 - time (sec): 27.17 - samples/sec: 2798.72 - lr: 0.000022 - momentum: 0.000000
2023-10-17 13:42:43,043 epoch 4 - iter 712/1786 - loss 0.05968286 - time (sec): 36.37 - samples/sec: 2804.23 - lr: 0.000022 - momentum: 0.000000
2023-10-17 13:42:51,955 epoch 4 - iter 890/1786 - loss 0.06046189 - time (sec): 45.28 - samples/sec: 2778.04 - lr: 0.000022 - momentum: 0.000000
2023-10-17 13:43:01,044 epoch 4 - iter 1068/1786 - loss 0.05955440 - time (sec): 54.37 - samples/sec: 2780.27 - lr: 0.000021 - momentum: 0.000000
2023-10-17 13:43:10,485 epoch 4 - iter 1246/1786 - loss 0.06002902 - time (sec): 63.81 - samples/sec: 2761.07 - lr: 0.000021 - momentum: 0.000000
2023-10-17 13:43:19,111 epoch 4 - iter 1424/1786 - loss 0.05900004 - time (sec): 72.43 - samples/sec: 2757.94 - lr: 0.000021 - momentum: 0.000000
2023-10-17 13:43:27,980 epoch 4 - iter 1602/1786 - loss 0.05891783 - time (sec): 81.30 - samples/sec: 2755.96 - lr: 0.000020 - momentum: 0.000000
2023-10-17 13:43:36,698 epoch 4 - iter 1780/1786 - loss 0.05878177 - time (sec): 90.02 - samples/sec: 2752.92 - lr: 0.000020 - momentum: 0.000000
2023-10-17 13:43:37,002 ----------------------------------------------------------------------------------------------------
2023-10-17 13:43:37,002 EPOCH 4 done: loss 0.0589 - lr: 0.000020
2023-10-17 13:43:41,153 DEV : loss 0.16494163870811462 - f1-score (micro avg) 0.8072
2023-10-17 13:43:41,170 ----------------------------------------------------------------------------------------------------
2023-10-17 13:43:50,053 epoch 5 - iter 178/1786 - loss 0.03097896 - time (sec): 8.88 - samples/sec: 2793.19 - lr: 0.000020 - momentum: 0.000000
2023-10-17 13:43:59,171 epoch 5 - iter 356/1786 - loss 0.04055098 - time (sec): 18.00 - samples/sec: 2843.10 - lr: 0.000019 - momentum: 0.000000
2023-10-17 13:44:08,165 epoch 5 - iter 534/1786 - loss 0.04153062 - time (sec): 26.99 - samples/sec: 2845.05 - lr: 0.000019 - momentum: 0.000000
2023-10-17 13:44:17,143 epoch 5 - iter 712/1786 - loss 0.04112747 - time (sec): 35.97 - samples/sec: 2818.48 - lr: 0.000019 - momentum: 0.000000
2023-10-17 13:44:25,980 epoch 5 - iter 890/1786 - loss 0.04124960 - time (sec): 44.81 - samples/sec: 2781.91 - lr: 0.000018 - momentum: 0.000000
2023-10-17 13:44:34,928 epoch 5 - iter 1068/1786 - loss 0.04175525 - time (sec): 53.76 - samples/sec: 2789.27 - lr: 0.000018 - momentum: 0.000000
2023-10-17 13:44:43,746 epoch 5 - iter 1246/1786 - loss 0.04113562 - time (sec): 62.57 - samples/sec: 2782.73 - lr: 0.000018 - momentum: 0.000000
2023-10-17 13:44:52,508 epoch 5 - iter 1424/1786 - loss 0.04207320 - time (sec): 71.34 - samples/sec: 2788.00 - lr: 0.000017 - momentum: 0.000000
2023-10-17 13:45:01,198 epoch 5 - iter 1602/1786 - loss 0.04173442 - time (sec): 80.03 - samples/sec: 2765.43 - lr: 0.000017 - momentum: 0.000000
2023-10-17 13:45:10,322 epoch 5 - iter 1780/1786 - loss 0.04286181 - time (sec): 89.15 - samples/sec: 2780.12 - lr: 0.000017 - momentum: 0.000000
2023-10-17 13:45:10,633 ----------------------------------------------------------------------------------------------------
2023-10-17 13:45:10,633 EPOCH 5 done: loss 0.0427 - lr: 0.000017
2023-10-17 13:45:15,433 DEV : loss 0.1587299257516861 - f1-score (micro avg) 0.8108
2023-10-17 13:45:15,450 ----------------------------------------------------------------------------------------------------
2023-10-17 13:45:24,076 epoch 6 - iter 178/1786 - loss 0.03579067 - time (sec): 8.62 - samples/sec: 2905.87 - lr: 0.000016 - momentum: 0.000000
2023-10-17 13:45:33,127 epoch 6 - iter 356/1786 - loss 0.02738996 - time (sec): 17.68 - samples/sec: 2901.45 - lr: 0.000016 - momentum: 0.000000
2023-10-17 13:45:42,026 epoch 6 - iter 534/1786 - loss 0.02902765 - time (sec): 26.57 - samples/sec: 2826.40 - lr: 0.000016 - momentum: 0.000000
2023-10-17 13:45:51,006 epoch 6 - iter 712/1786 - loss 0.02908652 - time (sec): 35.56 - samples/sec: 2805.29 - lr: 0.000015 - momentum: 0.000000
2023-10-17 13:45:59,947 epoch 6 - iter 890/1786 - loss 0.02990502 - time (sec): 44.50 - samples/sec: 2780.47 - lr: 0.000015 - momentum: 0.000000
2023-10-17 13:46:08,899 epoch 6 - iter 1068/1786 - loss 0.02956431 - time (sec): 53.45 - samples/sec: 2767.16 - lr: 0.000015 - momentum: 0.000000
2023-10-17 13:46:17,597 epoch 6 - iter 1246/1786 - loss 0.03095708 - time (sec): 62.15 - samples/sec: 2779.68 - lr: 0.000014 - momentum: 0.000000
2023-10-17 13:46:26,112 epoch 6 - iter 1424/1786 - loss 0.03171714 - time (sec): 70.66 - samples/sec: 2810.15 - lr: 0.000014 - momentum: 0.000000
2023-10-17 13:46:34,499 epoch 6 - iter 1602/1786 - loss 0.03200171 - time (sec): 79.05 - samples/sec: 2818.22 - lr: 0.000014 - momentum: 0.000000
2023-10-17 13:46:42,973 epoch 6 - iter 1780/1786 - loss 0.03147805 - time (sec): 87.52 - samples/sec: 2831.86 - lr: 0.000013 - momentum: 0.000000
2023-10-17 13:46:43,268 ----------------------------------------------------------------------------------------------------
2023-10-17 13:46:43,269 EPOCH 6 done: loss 0.0315 - lr: 0.000013
2023-10-17 13:46:47,415 DEV : loss 0.1810143142938614 - f1-score (micro avg) 0.8208
2023-10-17 13:46:47,432 saving best model
2023-10-17 13:46:47,883 ----------------------------------------------------------------------------------------------------
2023-10-17 13:46:57,010 epoch 7 - iter 178/1786 - loss 0.02252211 - time (sec): 9.12 - samples/sec: 2815.32 - lr: 0.000013 - momentum: 0.000000
2023-10-17 13:47:06,046 epoch 7 - iter 356/1786 - loss 0.02178416 - time (sec): 18.16 - samples/sec: 2798.52 - lr: 0.000013 - momentum: 0.000000
2023-10-17 13:47:14,788 epoch 7 - iter 534/1786 - loss 0.02234988 - time (sec): 26.90 - samples/sec: 2783.93 - lr: 0.000012 - momentum: 0.000000
2023-10-17 13:47:23,746 epoch 7 - iter 712/1786 - loss 0.02071319 - time (sec): 35.86 - samples/sec: 2801.18 - lr: 0.000012 - momentum: 0.000000
2023-10-17 13:47:32,861 epoch 7 - iter 890/1786 - loss 0.02322136 - time (sec): 44.97 - samples/sec: 2795.86 - lr: 0.000012 - momentum: 0.000000
2023-10-17 13:47:41,641 epoch 7 - iter 1068/1786 - loss 0.02464583 - time (sec): 53.75 - samples/sec: 2781.24 - lr: 0.000011 - momentum: 0.000000
2023-10-17 13:47:50,621 epoch 7 - iter 1246/1786 - loss 0.02371729 - time (sec): 62.73 - samples/sec: 2796.27 - lr: 0.000011 - momentum: 0.000000
2023-10-17 13:47:59,715 epoch 7 - iter 1424/1786 - loss 0.02337745 - time (sec): 71.83 - samples/sec: 2785.17 - lr: 0.000011 - momentum: 0.000000
2023-10-17 13:48:08,615 epoch 7 - iter 1602/1786 - loss 0.02344878 - time (sec): 80.73 - samples/sec: 2757.92 - lr: 0.000010 - momentum: 0.000000
2023-10-17 13:48:17,579 epoch 7 - iter 1780/1786 - loss 0.02410208 - time (sec): 89.69 - samples/sec: 2758.99 - lr: 0.000010 - momentum: 0.000000
2023-10-17 13:48:17,888 ----------------------------------------------------------------------------------------------------
2023-10-17 13:48:17,889 EPOCH 7 done: loss 0.0240 - lr: 0.000010
2023-10-17 13:48:22,557 DEV : loss 0.1769118458032608 - f1-score (micro avg) 0.8345
2023-10-17 13:48:22,574 saving best model
2023-10-17 13:48:23,039 ----------------------------------------------------------------------------------------------------
2023-10-17 13:48:31,997 epoch 8 - iter 178/1786 - loss 0.01978668 - time (sec): 8.95 - samples/sec: 2710.28 - lr: 0.000010 - momentum: 0.000000
2023-10-17 13:48:40,923 epoch 8 - iter 356/1786 - loss 0.01795202 - time (sec): 17.88 - samples/sec: 2730.53 - lr: 0.000009 - momentum: 0.000000
2023-10-17 13:48:49,838 epoch 8 - iter 534/1786 - loss 0.01682048 - time (sec): 26.80 - samples/sec: 2732.43 - lr: 0.000009 - momentum: 0.000000
2023-10-17 13:48:58,809 epoch 8 - iter 712/1786 - loss 0.01706732 - time (sec): 35.77 - samples/sec: 2726.67 - lr: 0.000009 - momentum: 0.000000
2023-10-17 13:49:07,606 epoch 8 - iter 890/1786 - loss 0.01709347 - time (sec): 44.56 - samples/sec: 2745.57 - lr: 0.000008 - momentum: 0.000000
2023-10-17 13:49:16,512 epoch 8 - iter 1068/1786 - loss 0.01748776 - time (sec): 53.47 - samples/sec: 2735.99 - lr: 0.000008 - momentum: 0.000000
2023-10-17 13:49:25,400 epoch 8 - iter 1246/1786 - loss 0.01730659 - time (sec): 62.36 - samples/sec: 2751.55 - lr: 0.000008 - momentum: 0.000000
2023-10-17 13:49:34,658 epoch 8 - iter 1424/1786 - loss 0.01760562 - time (sec): 71.62 - samples/sec: 2770.82 - lr: 0.000007 - momentum: 0.000000
2023-10-17 13:49:43,429 epoch 8 - iter 1602/1786 - loss 0.01761039 - time (sec): 80.39 - samples/sec: 2768.58 - lr: 0.000007 - momentum: 0.000000
2023-10-17 13:49:52,395 epoch 8 - iter 1780/1786 - loss 0.01754475 - time (sec): 89.35 - samples/sec: 2776.92 - lr: 0.000007 - momentum: 0.000000
2023-10-17 13:49:52,677 ----------------------------------------------------------------------------------------------------
2023-10-17 13:49:52,677 EPOCH 8 done: loss 0.0177 - lr: 0.000007
2023-10-17 13:49:56,898 DEV : loss 0.196747824549675 - f1-score (micro avg) 0.8165
2023-10-17 13:49:56,915 ----------------------------------------------------------------------------------------------------
2023-10-17 13:50:06,356 epoch 9 - iter 178/1786 - loss 0.01085864 - time (sec): 9.44 - samples/sec: 2704.82 - lr: 0.000006 - momentum: 0.000000
2023-10-17 13:50:15,176 epoch 9 - iter 356/1786 - loss 0.01281656 - time (sec): 18.26 - samples/sec: 2792.68 - lr: 0.000006 - momentum: 0.000000
2023-10-17 13:50:24,105 epoch 9 - iter 534/1786 - loss 0.01358075 - time (sec): 27.19 - samples/sec: 2767.01 - lr: 0.000006 - momentum: 0.000000
2023-10-17 13:50:33,067 epoch 9 - iter 712/1786 - loss 0.01366083 - time (sec): 36.15 - samples/sec: 2736.04 - lr: 0.000005 - momentum: 0.000000
2023-10-17 13:50:41,994 epoch 9 - iter 890/1786 - loss 0.01227376 - time (sec): 45.08 - samples/sec: 2741.15 - lr: 0.000005 - momentum: 0.000000
2023-10-17 13:50:50,756 epoch 9 - iter 1068/1786 - loss 0.01291585 - time (sec): 53.84 - samples/sec: 2748.82 - lr: 0.000005 - momentum: 0.000000
2023-10-17 13:51:00,405 epoch 9 - iter 1246/1786 - loss 0.01335982 - time (sec): 63.49 - samples/sec: 2696.12 - lr: 0.000004 - momentum: 0.000000
2023-10-17 13:51:09,305 epoch 9 - iter 1424/1786 - loss 0.01331987 - time (sec): 72.39 - samples/sec: 2709.60 - lr: 0.000004 - momentum: 0.000000
2023-10-17 13:51:18,256 epoch 9 - iter 1602/1786 - loss 0.01337922 - time (sec): 81.34 - samples/sec: 2723.43 - lr: 0.000004 - momentum: 0.000000
2023-10-17 13:51:27,315 epoch 9 - iter 1780/1786 - loss 0.01258706 - time (sec): 90.40 - samples/sec: 2745.83 - lr: 0.000003 - momentum: 0.000000
2023-10-17 13:51:27,587 ----------------------------------------------------------------------------------------------------
2023-10-17 13:51:27,587 EPOCH 9 done: loss 0.0126 - lr: 0.000003
2023-10-17 13:51:31,855 DEV : loss 0.20930074155330658 - f1-score (micro avg) 0.8232
2023-10-17 13:51:31,872 ----------------------------------------------------------------------------------------------------
2023-10-17 13:51:40,887 epoch 10 - iter 178/1786 - loss 0.00609129 - time (sec): 9.01 - samples/sec: 2824.04 - lr: 0.000003 - momentum: 0.000000
2023-10-17 13:51:50,023 epoch 10 - iter 356/1786 - loss 0.00715017 - time (sec): 18.15 - samples/sec: 2816.42 - lr: 0.000003 - momentum: 0.000000
2023-10-17 13:51:58,978 epoch 10 - iter 534/1786 - loss 0.00851154 - time (sec): 27.10 - samples/sec: 2809.81 - lr: 0.000002 - momentum: 0.000000
2023-10-17 13:52:07,808 epoch 10 - iter 712/1786 - loss 0.00825487 - time (sec): 35.93 - samples/sec: 2767.77 - lr: 0.000002 - momentum: 0.000000
2023-10-17 13:52:17,310 epoch 10 - iter 890/1786 - loss 0.00855273 - time (sec): 45.44 - samples/sec: 2755.18 - lr: 0.000002 - momentum: 0.000000
2023-10-17 13:52:26,532 epoch 10 - iter 1068/1786 - loss 0.00838603 - time (sec): 54.66 - samples/sec: 2739.98 - lr: 0.000001 - momentum: 0.000000
2023-10-17 13:52:35,578 epoch 10 - iter 1246/1786 - loss 0.00840932 - time (sec): 63.70 - samples/sec: 2727.41 - lr: 0.000001 - momentum: 0.000000
2023-10-17 13:52:44,438 epoch 10 - iter 1424/1786 - loss 0.00809426 - time (sec): 72.56 - samples/sec: 2745.31 - lr: 0.000001 - momentum: 0.000000
2023-10-17 13:52:53,121 epoch 10 - iter 1602/1786 - loss 0.00816111 - time (sec): 81.25 - samples/sec: 2749.63 - lr: 0.000000 - momentum: 0.000000
2023-10-17 13:53:01,991 epoch 10 - iter 1780/1786 - loss 0.00858460 - time (sec): 90.12 - samples/sec: 2750.90 - lr: 0.000000 - momentum: 0.000000
2023-10-17 13:53:02,263 ----------------------------------------------------------------------------------------------------
2023-10-17 13:53:02,263 EPOCH 10 done: loss 0.0086 - lr: 0.000000
2023-10-17 13:53:06,932 DEV : loss 0.20266954600811005 - f1-score (micro avg) 0.8289
2023-10-17 13:53:07,291 ----------------------------------------------------------------------------------------------------
2023-10-17 13:53:07,292 Loading model from best epoch ...
2023-10-17 13:53:08,632 SequenceTagger predicts: Dictionary with 17 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
2023-10-17 13:53:18,265
Results:
- F-score (micro) 0.705
- F-score (macro) 0.6364
- Accuracy 0.5623
By class:
precision recall f1-score support
LOC 0.7285 0.6959 0.7118 1095
PER 0.7944 0.7787 0.7864 1012
ORG 0.4503 0.5714 0.5037 357
HumanProd 0.4237 0.7576 0.5435 33
micro avg 0.6976 0.7125 0.7050 2497
macro avg 0.5992 0.7009 0.6364 2497
weighted avg 0.7114 0.7125 0.7101 2497
2023-10-17 13:53:18,265 ----------------------------------------------------------------------------------------------------
|