File size: 25,558 Bytes
55e02dd |
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 |
2023-10-10 10:17:25,583 ----------------------------------------------------------------------------------------------------
2023-10-10 10:17:25,586 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): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
(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): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
(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): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
(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): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(final_layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
(locked_dropout): LockedDropout(p=0.5)
(linear): Linear(in_features=1472, out_features=17, bias=True)
(loss_function): CrossEntropyLoss()
)"
2023-10-10 10:17:25,586 ----------------------------------------------------------------------------------------------------
2023-10-10 10:17:25,587 MultiCorpus: 20847 train + 1123 dev + 3350 test sentences
- NER_HIPE_2022 Corpus: 20847 train + 1123 dev + 3350 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/de/with_doc_seperator
2023-10-10 10:17:25,587 ----------------------------------------------------------------------------------------------------
2023-10-10 10:17:25,587 Train: 20847 sentences
2023-10-10 10:17:25,587 (train_with_dev=False, train_with_test=False)
2023-10-10 10:17:25,587 ----------------------------------------------------------------------------------------------------
2023-10-10 10:17:25,587 Training Params:
2023-10-10 10:17:25,587 - learning_rate: "0.00015"
2023-10-10 10:17:25,587 - mini_batch_size: "8"
2023-10-10 10:17:25,587 - max_epochs: "10"
2023-10-10 10:17:25,587 - shuffle: "True"
2023-10-10 10:17:25,587 ----------------------------------------------------------------------------------------------------
2023-10-10 10:17:25,587 Plugins:
2023-10-10 10:17:25,588 - TensorboardLogger
2023-10-10 10:17:25,588 - LinearScheduler | warmup_fraction: '0.1'
2023-10-10 10:17:25,588 ----------------------------------------------------------------------------------------------------
2023-10-10 10:17:25,588 Final evaluation on model from best epoch (best-model.pt)
2023-10-10 10:17:25,588 - metric: "('micro avg', 'f1-score')"
2023-10-10 10:17:25,588 ----------------------------------------------------------------------------------------------------
2023-10-10 10:17:25,588 Computation:
2023-10-10 10:17:25,588 - compute on device: cuda:0
2023-10-10 10:17:25,588 - embedding storage: none
2023-10-10 10:17:25,588 ----------------------------------------------------------------------------------------------------
2023-10-10 10:17:25,588 Model training base path: "hmbench-newseye/de-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2"
2023-10-10 10:17:25,588 ----------------------------------------------------------------------------------------------------
2023-10-10 10:17:25,588 ----------------------------------------------------------------------------------------------------
2023-10-10 10:17:25,589 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-10 10:19:50,358 epoch 1 - iter 260/2606 - loss 2.82858377 - time (sec): 144.77 - samples/sec: 246.69 - lr: 0.000015 - momentum: 0.000000
2023-10-10 10:22:07,314 epoch 1 - iter 520/2606 - loss 2.59379089 - time (sec): 281.72 - samples/sec: 255.43 - lr: 0.000030 - momentum: 0.000000
2023-10-10 10:24:28,436 epoch 1 - iter 780/2606 - loss 2.18946733 - time (sec): 422.84 - samples/sec: 258.37 - lr: 0.000045 - momentum: 0.000000
2023-10-10 10:26:51,376 epoch 1 - iter 1040/2606 - loss 1.78347214 - time (sec): 565.78 - samples/sec: 262.46 - lr: 0.000060 - momentum: 0.000000
2023-10-10 10:29:09,559 epoch 1 - iter 1300/2606 - loss 1.51733285 - time (sec): 703.97 - samples/sec: 263.06 - lr: 0.000075 - momentum: 0.000000
2023-10-10 10:31:37,873 epoch 1 - iter 1560/2606 - loss 1.33965070 - time (sec): 852.28 - samples/sec: 261.90 - lr: 0.000090 - momentum: 0.000000
2023-10-10 10:33:58,911 epoch 1 - iter 1820/2606 - loss 1.20517898 - time (sec): 993.32 - samples/sec: 261.43 - lr: 0.000105 - momentum: 0.000000
2023-10-10 10:36:19,665 epoch 1 - iter 2080/2606 - loss 1.09396343 - time (sec): 1134.07 - samples/sec: 260.27 - lr: 0.000120 - momentum: 0.000000
2023-10-10 10:38:40,561 epoch 1 - iter 2340/2606 - loss 1.00927714 - time (sec): 1274.97 - samples/sec: 258.05 - lr: 0.000135 - momentum: 0.000000
2023-10-10 10:41:03,325 epoch 1 - iter 2600/2606 - loss 0.92825533 - time (sec): 1417.73 - samples/sec: 258.37 - lr: 0.000150 - momentum: 0.000000
2023-10-10 10:41:06,639 ----------------------------------------------------------------------------------------------------
2023-10-10 10:41:06,639 EPOCH 1 done: loss 0.9266 - lr: 0.000150
2023-10-10 10:41:45,319 DEV : loss 0.13984432816505432 - f1-score (micro avg) 0.2605
2023-10-10 10:41:45,373 saving best model
2023-10-10 10:41:46,388 ----------------------------------------------------------------------------------------------------
2023-10-10 10:44:03,782 epoch 2 - iter 260/2606 - loss 0.20048946 - time (sec): 137.39 - samples/sec: 254.87 - lr: 0.000148 - momentum: 0.000000
2023-10-10 10:46:21,256 epoch 2 - iter 520/2606 - loss 0.19474593 - time (sec): 274.86 - samples/sec: 257.90 - lr: 0.000147 - momentum: 0.000000
2023-10-10 10:48:42,738 epoch 2 - iter 780/2606 - loss 0.18435066 - time (sec): 416.35 - samples/sec: 258.36 - lr: 0.000145 - momentum: 0.000000
2023-10-10 10:51:06,768 epoch 2 - iter 1040/2606 - loss 0.17821735 - time (sec): 560.38 - samples/sec: 254.46 - lr: 0.000143 - momentum: 0.000000
2023-10-10 10:53:29,090 epoch 2 - iter 1300/2606 - loss 0.17222389 - time (sec): 702.70 - samples/sec: 255.82 - lr: 0.000142 - momentum: 0.000000
2023-10-10 10:55:53,869 epoch 2 - iter 1560/2606 - loss 0.16461177 - time (sec): 847.48 - samples/sec: 256.54 - lr: 0.000140 - momentum: 0.000000
2023-10-10 10:58:21,709 epoch 2 - iter 1820/2606 - loss 0.16129296 - time (sec): 995.32 - samples/sec: 256.21 - lr: 0.000138 - momentum: 0.000000
2023-10-10 11:00:47,042 epoch 2 - iter 2080/2606 - loss 0.15850392 - time (sec): 1140.65 - samples/sec: 254.82 - lr: 0.000137 - momentum: 0.000000
2023-10-10 11:03:09,654 epoch 2 - iter 2340/2606 - loss 0.15546215 - time (sec): 1283.26 - samples/sec: 254.16 - lr: 0.000135 - momentum: 0.000000
2023-10-10 11:05:34,501 epoch 2 - iter 2600/2606 - loss 0.15151244 - time (sec): 1428.11 - samples/sec: 256.78 - lr: 0.000133 - momentum: 0.000000
2023-10-10 11:05:37,581 ----------------------------------------------------------------------------------------------------
2023-10-10 11:05:37,582 EPOCH 2 done: loss 0.1514 - lr: 0.000133
2023-10-10 11:06:18,193 DEV : loss 0.12324459105730057 - f1-score (micro avg) 0.3194
2023-10-10 11:06:18,251 saving best model
2023-10-10 11:06:20,967 ----------------------------------------------------------------------------------------------------
2023-10-10 11:08:38,954 epoch 3 - iter 260/2606 - loss 0.08453196 - time (sec): 137.98 - samples/sec: 257.85 - lr: 0.000132 - momentum: 0.000000
2023-10-10 11:11:00,136 epoch 3 - iter 520/2606 - loss 0.08884281 - time (sec): 279.16 - samples/sec: 259.92 - lr: 0.000130 - momentum: 0.000000
2023-10-10 11:13:22,749 epoch 3 - iter 780/2606 - loss 0.09171291 - time (sec): 421.78 - samples/sec: 259.93 - lr: 0.000128 - momentum: 0.000000
2023-10-10 11:15:41,613 epoch 3 - iter 1040/2606 - loss 0.09501175 - time (sec): 560.64 - samples/sec: 259.52 - lr: 0.000127 - momentum: 0.000000
2023-10-10 11:18:01,854 epoch 3 - iter 1300/2606 - loss 0.09753731 - time (sec): 700.88 - samples/sec: 264.60 - lr: 0.000125 - momentum: 0.000000
2023-10-10 11:20:24,416 epoch 3 - iter 1560/2606 - loss 0.09540397 - time (sec): 843.45 - samples/sec: 264.50 - lr: 0.000123 - momentum: 0.000000
2023-10-10 11:22:44,671 epoch 3 - iter 1820/2606 - loss 0.09431741 - time (sec): 983.70 - samples/sec: 264.06 - lr: 0.000122 - momentum: 0.000000
2023-10-10 11:25:05,740 epoch 3 - iter 2080/2606 - loss 0.09273873 - time (sec): 1124.77 - samples/sec: 263.24 - lr: 0.000120 - momentum: 0.000000
2023-10-10 11:27:19,392 epoch 3 - iter 2340/2606 - loss 0.09140459 - time (sec): 1258.42 - samples/sec: 261.51 - lr: 0.000118 - momentum: 0.000000
2023-10-10 11:29:46,096 epoch 3 - iter 2600/2606 - loss 0.09148228 - time (sec): 1405.13 - samples/sec: 260.87 - lr: 0.000117 - momentum: 0.000000
2023-10-10 11:29:49,569 ----------------------------------------------------------------------------------------------------
2023-10-10 11:29:49,569 EPOCH 3 done: loss 0.0914 - lr: 0.000117
2023-10-10 11:30:33,329 DEV : loss 0.16454216837882996 - f1-score (micro avg) 0.375
2023-10-10 11:30:33,407 saving best model
2023-10-10 11:30:36,290 ----------------------------------------------------------------------------------------------------
2023-10-10 11:33:00,647 epoch 4 - iter 260/2606 - loss 0.05238673 - time (sec): 144.35 - samples/sec: 255.20 - lr: 0.000115 - momentum: 0.000000
2023-10-10 11:35:25,933 epoch 4 - iter 520/2606 - loss 0.06195508 - time (sec): 289.64 - samples/sec: 263.40 - lr: 0.000113 - momentum: 0.000000
2023-10-10 11:37:44,551 epoch 4 - iter 780/2606 - loss 0.06048133 - time (sec): 428.26 - samples/sec: 263.11 - lr: 0.000112 - momentum: 0.000000
2023-10-10 11:40:01,800 epoch 4 - iter 1040/2606 - loss 0.06150890 - time (sec): 565.51 - samples/sec: 262.28 - lr: 0.000110 - momentum: 0.000000
2023-10-10 11:42:24,722 epoch 4 - iter 1300/2606 - loss 0.05978791 - time (sec): 708.43 - samples/sec: 261.76 - lr: 0.000108 - momentum: 0.000000
2023-10-10 11:44:44,516 epoch 4 - iter 1560/2606 - loss 0.06398599 - time (sec): 848.22 - samples/sec: 261.95 - lr: 0.000107 - momentum: 0.000000
2023-10-10 11:47:03,146 epoch 4 - iter 1820/2606 - loss 0.06393966 - time (sec): 986.85 - samples/sec: 263.46 - lr: 0.000105 - momentum: 0.000000
2023-10-10 11:49:18,081 epoch 4 - iter 2080/2606 - loss 0.06373753 - time (sec): 1121.79 - samples/sec: 262.88 - lr: 0.000103 - momentum: 0.000000
2023-10-10 11:51:36,877 epoch 4 - iter 2340/2606 - loss 0.06554623 - time (sec): 1260.58 - samples/sec: 262.80 - lr: 0.000102 - momentum: 0.000000
2023-10-10 11:53:53,323 epoch 4 - iter 2600/2606 - loss 0.06560053 - time (sec): 1397.03 - samples/sec: 262.60 - lr: 0.000100 - momentum: 0.000000
2023-10-10 11:53:56,216 ----------------------------------------------------------------------------------------------------
2023-10-10 11:53:56,216 EPOCH 4 done: loss 0.0656 - lr: 0.000100
2023-10-10 11:54:36,789 DEV : loss 0.26484549045562744 - f1-score (micro avg) 0.3304
2023-10-10 11:54:36,848 ----------------------------------------------------------------------------------------------------
2023-10-10 11:56:53,975 epoch 5 - iter 260/2606 - loss 0.04025488 - time (sec): 137.12 - samples/sec: 250.50 - lr: 0.000098 - momentum: 0.000000
2023-10-10 11:59:13,175 epoch 5 - iter 520/2606 - loss 0.04564273 - time (sec): 276.32 - samples/sec: 254.62 - lr: 0.000097 - momentum: 0.000000
2023-10-10 12:01:31,458 epoch 5 - iter 780/2606 - loss 0.04191303 - time (sec): 414.61 - samples/sec: 261.94 - lr: 0.000095 - momentum: 0.000000
2023-10-10 12:03:47,948 epoch 5 - iter 1040/2606 - loss 0.04370480 - time (sec): 551.10 - samples/sec: 263.07 - lr: 0.000093 - momentum: 0.000000
2023-10-10 12:06:09,643 epoch 5 - iter 1300/2606 - loss 0.04555479 - time (sec): 692.79 - samples/sec: 263.14 - lr: 0.000092 - momentum: 0.000000
2023-10-10 12:08:27,869 epoch 5 - iter 1560/2606 - loss 0.04638323 - time (sec): 831.02 - samples/sec: 263.60 - lr: 0.000090 - momentum: 0.000000
2023-10-10 12:10:44,931 epoch 5 - iter 1820/2606 - loss 0.04605700 - time (sec): 968.08 - samples/sec: 262.91 - lr: 0.000088 - momentum: 0.000000
2023-10-10 12:13:06,008 epoch 5 - iter 2080/2606 - loss 0.04618736 - time (sec): 1109.16 - samples/sec: 264.32 - lr: 0.000087 - momentum: 0.000000
2023-10-10 12:15:28,125 epoch 5 - iter 2340/2606 - loss 0.04664304 - time (sec): 1251.27 - samples/sec: 264.95 - lr: 0.000085 - momentum: 0.000000
2023-10-10 12:17:46,232 epoch 5 - iter 2600/2606 - loss 0.04668505 - time (sec): 1389.38 - samples/sec: 263.95 - lr: 0.000083 - momentum: 0.000000
2023-10-10 12:17:49,292 ----------------------------------------------------------------------------------------------------
2023-10-10 12:17:49,292 EPOCH 5 done: loss 0.0467 - lr: 0.000083
2023-10-10 12:18:32,521 DEV : loss 0.29650354385375977 - f1-score (micro avg) 0.3622
2023-10-10 12:18:32,581 ----------------------------------------------------------------------------------------------------
2023-10-10 12:20:49,821 epoch 6 - iter 260/2606 - loss 0.03241520 - time (sec): 137.24 - samples/sec: 254.95 - lr: 0.000082 - momentum: 0.000000
2023-10-10 12:23:05,715 epoch 6 - iter 520/2606 - loss 0.02994548 - time (sec): 273.13 - samples/sec: 256.33 - lr: 0.000080 - momentum: 0.000000
2023-10-10 12:25:25,839 epoch 6 - iter 780/2606 - loss 0.03207625 - time (sec): 413.25 - samples/sec: 261.76 - lr: 0.000078 - momentum: 0.000000
2023-10-10 12:27:48,917 epoch 6 - iter 1040/2606 - loss 0.03228089 - time (sec): 556.33 - samples/sec: 263.64 - lr: 0.000077 - momentum: 0.000000
2023-10-10 12:30:11,973 epoch 6 - iter 1300/2606 - loss 0.03163962 - time (sec): 699.39 - samples/sec: 265.05 - lr: 0.000075 - momentum: 0.000000
2023-10-10 12:32:30,584 epoch 6 - iter 1560/2606 - loss 0.03281868 - time (sec): 838.00 - samples/sec: 263.89 - lr: 0.000073 - momentum: 0.000000
2023-10-10 12:34:50,290 epoch 6 - iter 1820/2606 - loss 0.03179360 - time (sec): 977.71 - samples/sec: 263.53 - lr: 0.000072 - momentum: 0.000000
2023-10-10 12:37:07,793 epoch 6 - iter 2080/2606 - loss 0.03208471 - time (sec): 1115.21 - samples/sec: 264.32 - lr: 0.000070 - momentum: 0.000000
2023-10-10 12:39:24,116 epoch 6 - iter 2340/2606 - loss 0.03214342 - time (sec): 1251.53 - samples/sec: 263.15 - lr: 0.000068 - momentum: 0.000000
2023-10-10 12:41:43,053 epoch 6 - iter 2600/2606 - loss 0.03446797 - time (sec): 1390.47 - samples/sec: 263.73 - lr: 0.000067 - momentum: 0.000000
2023-10-10 12:41:45,990 ----------------------------------------------------------------------------------------------------
2023-10-10 12:41:45,991 EPOCH 6 done: loss 0.0344 - lr: 0.000067
2023-10-10 12:42:28,894 DEV : loss 0.33373507857322693 - f1-score (micro avg) 0.378
2023-10-10 12:42:28,972 saving best model
2023-10-10 12:42:31,708 ----------------------------------------------------------------------------------------------------
2023-10-10 12:44:49,513 epoch 7 - iter 260/2606 - loss 0.02101641 - time (sec): 137.80 - samples/sec: 257.43 - lr: 0.000065 - momentum: 0.000000
2023-10-10 12:47:08,074 epoch 7 - iter 520/2606 - loss 0.01944662 - time (sec): 276.36 - samples/sec: 258.39 - lr: 0.000063 - momentum: 0.000000
2023-10-10 12:49:26,954 epoch 7 - iter 780/2606 - loss 0.02172094 - time (sec): 415.24 - samples/sec: 259.97 - lr: 0.000062 - momentum: 0.000000
2023-10-10 12:51:45,464 epoch 7 - iter 1040/2606 - loss 0.02238459 - time (sec): 553.75 - samples/sec: 260.69 - lr: 0.000060 - momentum: 0.000000
2023-10-10 12:54:07,890 epoch 7 - iter 1300/2606 - loss 0.02535700 - time (sec): 696.18 - samples/sec: 262.29 - lr: 0.000058 - momentum: 0.000000
2023-10-10 12:56:26,291 epoch 7 - iter 1560/2606 - loss 0.02596897 - time (sec): 834.58 - samples/sec: 262.39 - lr: 0.000057 - momentum: 0.000000
2023-10-10 12:58:45,212 epoch 7 - iter 1820/2606 - loss 0.02683384 - time (sec): 973.50 - samples/sec: 262.42 - lr: 0.000055 - momentum: 0.000000
2023-10-10 13:01:01,249 epoch 7 - iter 2080/2606 - loss 0.02612235 - time (sec): 1109.54 - samples/sec: 260.72 - lr: 0.000053 - momentum: 0.000000
2023-10-10 13:03:21,969 epoch 7 - iter 2340/2606 - loss 0.02627648 - time (sec): 1250.26 - samples/sec: 261.53 - lr: 0.000052 - momentum: 0.000000
2023-10-10 13:05:45,810 epoch 7 - iter 2600/2606 - loss 0.02563448 - time (sec): 1394.10 - samples/sec: 262.94 - lr: 0.000050 - momentum: 0.000000
2023-10-10 13:05:48,990 ----------------------------------------------------------------------------------------------------
2023-10-10 13:05:48,990 EPOCH 7 done: loss 0.0256 - lr: 0.000050
2023-10-10 13:06:31,953 DEV : loss 0.3978530466556549 - f1-score (micro avg) 0.3722
2023-10-10 13:06:32,018 ----------------------------------------------------------------------------------------------------
2023-10-10 13:08:55,328 epoch 8 - iter 260/2606 - loss 0.01412281 - time (sec): 143.31 - samples/sec: 277.93 - lr: 0.000048 - momentum: 0.000000
2023-10-10 13:11:15,605 epoch 8 - iter 520/2606 - loss 0.01664019 - time (sec): 283.58 - samples/sec: 270.88 - lr: 0.000047 - momentum: 0.000000
2023-10-10 13:13:32,923 epoch 8 - iter 780/2606 - loss 0.01697467 - time (sec): 420.90 - samples/sec: 266.21 - lr: 0.000045 - momentum: 0.000000
2023-10-10 13:15:54,238 epoch 8 - iter 1040/2606 - loss 0.01697407 - time (sec): 562.22 - samples/sec: 265.97 - lr: 0.000043 - momentum: 0.000000
2023-10-10 13:18:13,799 epoch 8 - iter 1300/2606 - loss 0.01628044 - time (sec): 701.78 - samples/sec: 263.85 - lr: 0.000042 - momentum: 0.000000
2023-10-10 13:20:35,746 epoch 8 - iter 1560/2606 - loss 0.01692923 - time (sec): 843.73 - samples/sec: 263.69 - lr: 0.000040 - momentum: 0.000000
2023-10-10 13:22:55,645 epoch 8 - iter 1820/2606 - loss 0.01698632 - time (sec): 983.62 - samples/sec: 263.30 - lr: 0.000038 - momentum: 0.000000
2023-10-10 13:25:13,795 epoch 8 - iter 2080/2606 - loss 0.01667188 - time (sec): 1121.77 - samples/sec: 261.61 - lr: 0.000037 - momentum: 0.000000
2023-10-10 13:27:29,647 epoch 8 - iter 2340/2606 - loss 0.01736529 - time (sec): 1257.63 - samples/sec: 260.13 - lr: 0.000035 - momentum: 0.000000
2023-10-10 13:29:50,730 epoch 8 - iter 2600/2606 - loss 0.01789555 - time (sec): 1398.71 - samples/sec: 262.23 - lr: 0.000033 - momentum: 0.000000
2023-10-10 13:29:53,639 ----------------------------------------------------------------------------------------------------
2023-10-10 13:29:53,640 EPOCH 8 done: loss 0.0179 - lr: 0.000033
2023-10-10 13:30:36,471 DEV : loss 0.40461236238479614 - f1-score (micro avg) 0.3935
2023-10-10 13:30:36,533 saving best model
2023-10-10 13:30:39,231 ----------------------------------------------------------------------------------------------------
2023-10-10 13:32:59,878 epoch 9 - iter 260/2606 - loss 0.01216931 - time (sec): 140.64 - samples/sec: 263.28 - lr: 0.000032 - momentum: 0.000000
2023-10-10 13:35:25,145 epoch 9 - iter 520/2606 - loss 0.01085860 - time (sec): 285.91 - samples/sec: 270.57 - lr: 0.000030 - momentum: 0.000000
2023-10-10 13:37:44,303 epoch 9 - iter 780/2606 - loss 0.01126911 - time (sec): 425.07 - samples/sec: 266.36 - lr: 0.000028 - momentum: 0.000000
2023-10-10 13:39:59,037 epoch 9 - iter 1040/2606 - loss 0.01140504 - time (sec): 559.80 - samples/sec: 261.97 - lr: 0.000027 - momentum: 0.000000
2023-10-10 13:42:20,737 epoch 9 - iter 1300/2606 - loss 0.01208397 - time (sec): 701.50 - samples/sec: 264.42 - lr: 0.000025 - momentum: 0.000000
2023-10-10 13:44:42,081 epoch 9 - iter 1560/2606 - loss 0.01245932 - time (sec): 842.85 - samples/sec: 261.13 - lr: 0.000023 - momentum: 0.000000
2023-10-10 13:46:58,383 epoch 9 - iter 1820/2606 - loss 0.01254583 - time (sec): 979.15 - samples/sec: 260.67 - lr: 0.000022 - momentum: 0.000000
2023-10-10 13:49:17,990 epoch 9 - iter 2080/2606 - loss 0.01291335 - time (sec): 1118.75 - samples/sec: 260.52 - lr: 0.000020 - momentum: 0.000000
2023-10-10 13:51:38,722 epoch 9 - iter 2340/2606 - loss 0.01272261 - time (sec): 1259.49 - samples/sec: 260.05 - lr: 0.000018 - momentum: 0.000000
2023-10-10 13:53:58,619 epoch 9 - iter 2600/2606 - loss 0.01295044 - time (sec): 1399.38 - samples/sec: 262.18 - lr: 0.000017 - momentum: 0.000000
2023-10-10 13:54:01,480 ----------------------------------------------------------------------------------------------------
2023-10-10 13:54:01,480 EPOCH 9 done: loss 0.0129 - lr: 0.000017
2023-10-10 13:54:42,752 DEV : loss 0.4415739178657532 - f1-score (micro avg) 0.3754
2023-10-10 13:54:42,814 ----------------------------------------------------------------------------------------------------
2023-10-10 13:57:04,272 epoch 10 - iter 260/2606 - loss 0.00790204 - time (sec): 141.46 - samples/sec: 270.22 - lr: 0.000015 - momentum: 0.000000
2023-10-10 13:59:26,749 epoch 10 - iter 520/2606 - loss 0.00758597 - time (sec): 283.93 - samples/sec: 270.20 - lr: 0.000013 - momentum: 0.000000
2023-10-10 14:01:45,507 epoch 10 - iter 780/2606 - loss 0.00856811 - time (sec): 422.69 - samples/sec: 266.23 - lr: 0.000012 - momentum: 0.000000
2023-10-10 14:04:03,408 epoch 10 - iter 1040/2606 - loss 0.00841758 - time (sec): 560.59 - samples/sec: 259.01 - lr: 0.000010 - momentum: 0.000000
2023-10-10 14:06:23,118 epoch 10 - iter 1300/2606 - loss 0.00819062 - time (sec): 700.30 - samples/sec: 260.67 - lr: 0.000008 - momentum: 0.000000
2023-10-10 14:08:41,954 epoch 10 - iter 1560/2606 - loss 0.00817874 - time (sec): 839.14 - samples/sec: 260.44 - lr: 0.000007 - momentum: 0.000000
2023-10-10 14:11:00,012 epoch 10 - iter 1820/2606 - loss 0.00826103 - time (sec): 977.20 - samples/sec: 261.61 - lr: 0.000005 - momentum: 0.000000
2023-10-10 14:13:22,211 epoch 10 - iter 2080/2606 - loss 0.00858032 - time (sec): 1119.40 - samples/sec: 263.35 - lr: 0.000003 - momentum: 0.000000
2023-10-10 14:15:40,101 epoch 10 - iter 2340/2606 - loss 0.00841120 - time (sec): 1257.28 - samples/sec: 262.99 - lr: 0.000002 - momentum: 0.000000
2023-10-10 14:17:58,169 epoch 10 - iter 2600/2606 - loss 0.00852789 - time (sec): 1395.35 - samples/sec: 262.65 - lr: 0.000000 - momentum: 0.000000
2023-10-10 14:18:01,398 ----------------------------------------------------------------------------------------------------
2023-10-10 14:18:01,398 EPOCH 10 done: loss 0.0085 - lr: 0.000000
2023-10-10 14:18:43,420 DEV : loss 0.46130311489105225 - f1-score (micro avg) 0.3804
2023-10-10 14:18:44,392 ----------------------------------------------------------------------------------------------------
2023-10-10 14:18:44,394 Loading model from best epoch ...
2023-10-10 14:18:48,948 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
2023-10-10 14:20:38,998
Results:
- F-score (micro) 0.4462
- F-score (macro) 0.3145
- Accuracy 0.2916
By class:
precision recall f1-score support
LOC 0.4555 0.5231 0.4870 1214
PER 0.4229 0.4517 0.4369 808
ORG 0.3343 0.3343 0.3343 353
HumanProd 0.0000 0.0000 0.0000 15
micro avg 0.4266 0.4678 0.4462 2390
macro avg 0.3032 0.3273 0.3145 2390
weighted avg 0.4237 0.4678 0.4444 2390
2023-10-10 14:20:38,999 ----------------------------------------------------------------------------------------------------
|