File size: 36,945 Bytes
aa00f66 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 |
2023-10-24 22:13:21,924 ----------------------------------------------------------------------------------------------------
2023-10-24 22:13:21,925 Model: "SequenceTagger(
(embeddings): TransformerWordEmbeddings(
(model): BertModel(
(embeddings): BertEmbeddings(
(word_embeddings): Embedding(64001, 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): 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)
)
)
(1): 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)
)
)
(2): 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)
)
)
(3): 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)
)
)
(4): 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)
)
)
(5): 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)
)
)
(6): 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)
)
)
(7): 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)
)
)
(8): 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)
)
)
(9): 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)
)
)
(10): 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)
)
)
(11): 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=13, bias=True)
(loss_function): CrossEntropyLoss()
)"
2023-10-24 22:13:21,925 ----------------------------------------------------------------------------------------------------
2023-10-24 22:13:21,926 MultiCorpus: 5777 train + 722 dev + 723 test sentences
- NER_ICDAR_EUROPEANA Corpus: 5777 train + 722 dev + 723 test sentences - /home/ubuntu/.flair/datasets/ner_icdar_europeana/nl
2023-10-24 22:13:21,926 ----------------------------------------------------------------------------------------------------
2023-10-24 22:13:21,926 Train: 5777 sentences
2023-10-24 22:13:21,926 (train_with_dev=False, train_with_test=False)
2023-10-24 22:13:21,926 ----------------------------------------------------------------------------------------------------
2023-10-24 22:13:21,926 Training Params:
2023-10-24 22:13:21,926 - learning_rate: "5e-05"
2023-10-24 22:13:21,926 - mini_batch_size: "4"
2023-10-24 22:13:21,926 - max_epochs: "10"
2023-10-24 22:13:21,926 - shuffle: "True"
2023-10-24 22:13:21,926 ----------------------------------------------------------------------------------------------------
2023-10-24 22:13:21,926 Plugins:
2023-10-24 22:13:21,926 - TensorboardLogger
2023-10-24 22:13:21,926 - LinearScheduler | warmup_fraction: '0.1'
2023-10-24 22:13:21,926 ----------------------------------------------------------------------------------------------------
2023-10-24 22:13:21,926 Final evaluation on model from best epoch (best-model.pt)
2023-10-24 22:13:21,926 - metric: "('micro avg', 'f1-score')"
2023-10-24 22:13:21,926 ----------------------------------------------------------------------------------------------------
2023-10-24 22:13:21,926 Computation:
2023-10-24 22:13:21,926 - compute on device: cuda:0
2023-10-24 22:13:21,926 - embedding storage: none
2023-10-24 22:13:21,926 ----------------------------------------------------------------------------------------------------
2023-10-24 22:13:21,926 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1"
2023-10-24 22:13:21,926 ----------------------------------------------------------------------------------------------------
2023-10-24 22:13:21,926 ----------------------------------------------------------------------------------------------------
2023-10-24 22:13:21,926 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-24 22:13:32,380 epoch 1 - iter 144/1445 - loss 1.49559085 - time (sec): 10.45 - samples/sec: 1692.34 - lr: 0.000005 - momentum: 0.000000
2023-10-24 22:13:42,853 epoch 1 - iter 288/1445 - loss 0.87195492 - time (sec): 20.93 - samples/sec: 1683.05 - lr: 0.000010 - momentum: 0.000000
2023-10-24 22:13:53,683 epoch 1 - iter 432/1445 - loss 0.64108177 - time (sec): 31.76 - samples/sec: 1704.94 - lr: 0.000015 - momentum: 0.000000
2023-10-24 22:14:03,881 epoch 1 - iter 576/1445 - loss 0.53043413 - time (sec): 41.95 - samples/sec: 1681.07 - lr: 0.000020 - momentum: 0.000000
2023-10-24 22:14:14,069 epoch 1 - iter 720/1445 - loss 0.45645493 - time (sec): 52.14 - samples/sec: 1671.29 - lr: 0.000025 - momentum: 0.000000
2023-10-24 22:14:24,447 epoch 1 - iter 864/1445 - loss 0.40865665 - time (sec): 62.52 - samples/sec: 1666.71 - lr: 0.000030 - momentum: 0.000000
2023-10-24 22:14:34,689 epoch 1 - iter 1008/1445 - loss 0.37243246 - time (sec): 72.76 - samples/sec: 1660.47 - lr: 0.000035 - momentum: 0.000000
2023-10-24 22:14:45,375 epoch 1 - iter 1152/1445 - loss 0.34345336 - time (sec): 83.45 - samples/sec: 1663.95 - lr: 0.000040 - momentum: 0.000000
2023-10-24 22:14:55,909 epoch 1 - iter 1296/1445 - loss 0.31896611 - time (sec): 93.98 - samples/sec: 1671.51 - lr: 0.000045 - momentum: 0.000000
2023-10-24 22:15:06,686 epoch 1 - iter 1440/1445 - loss 0.29904032 - time (sec): 104.76 - samples/sec: 1677.73 - lr: 0.000050 - momentum: 0.000000
2023-10-24 22:15:07,000 ----------------------------------------------------------------------------------------------------
2023-10-24 22:15:07,001 EPOCH 1 done: loss 0.2986 - lr: 0.000050
2023-10-24 22:15:10,276 DEV : loss 0.1465490758419037 - f1-score (micro avg) 0.4443
2023-10-24 22:15:10,288 saving best model
2023-10-24 22:15:10,842 ----------------------------------------------------------------------------------------------------
2023-10-24 22:15:21,246 epoch 2 - iter 144/1445 - loss 0.11682404 - time (sec): 10.40 - samples/sec: 1638.60 - lr: 0.000049 - momentum: 0.000000
2023-10-24 22:15:31,373 epoch 2 - iter 288/1445 - loss 0.11667509 - time (sec): 20.53 - samples/sec: 1627.84 - lr: 0.000049 - momentum: 0.000000
2023-10-24 22:15:41,772 epoch 2 - iter 432/1445 - loss 0.11315670 - time (sec): 30.93 - samples/sec: 1636.53 - lr: 0.000048 - momentum: 0.000000
2023-10-24 22:15:52,605 epoch 2 - iter 576/1445 - loss 0.11090746 - time (sec): 41.76 - samples/sec: 1658.63 - lr: 0.000048 - momentum: 0.000000
2023-10-24 22:16:03,567 epoch 2 - iter 720/1445 - loss 0.10511821 - time (sec): 52.72 - samples/sec: 1678.85 - lr: 0.000047 - momentum: 0.000000
2023-10-24 22:16:14,590 epoch 2 - iter 864/1445 - loss 0.10350836 - time (sec): 63.75 - samples/sec: 1683.22 - lr: 0.000047 - momentum: 0.000000
2023-10-24 22:16:24,933 epoch 2 - iter 1008/1445 - loss 0.10362581 - time (sec): 74.09 - samples/sec: 1679.79 - lr: 0.000046 - momentum: 0.000000
2023-10-24 22:16:34,883 epoch 2 - iter 1152/1445 - loss 0.10658382 - time (sec): 84.04 - samples/sec: 1669.01 - lr: 0.000046 - momentum: 0.000000
2023-10-24 22:16:45,346 epoch 2 - iter 1296/1445 - loss 0.10667648 - time (sec): 94.50 - samples/sec: 1667.31 - lr: 0.000045 - momentum: 0.000000
2023-10-24 22:16:55,925 epoch 2 - iter 1440/1445 - loss 0.10680059 - time (sec): 105.08 - samples/sec: 1670.92 - lr: 0.000044 - momentum: 0.000000
2023-10-24 22:16:56,251 ----------------------------------------------------------------------------------------------------
2023-10-24 22:16:56,251 EPOCH 2 done: loss 0.1070 - lr: 0.000044
2023-10-24 22:16:59,958 DEV : loss 0.10742148011922836 - f1-score (micro avg) 0.7828
2023-10-24 22:16:59,970 saving best model
2023-10-24 22:17:00,625 ----------------------------------------------------------------------------------------------------
2023-10-24 22:17:11,142 epoch 3 - iter 144/1445 - loss 0.07888928 - time (sec): 10.52 - samples/sec: 1662.49 - lr: 0.000044 - momentum: 0.000000
2023-10-24 22:17:21,593 epoch 3 - iter 288/1445 - loss 0.06951416 - time (sec): 20.97 - samples/sec: 1667.45 - lr: 0.000043 - momentum: 0.000000
2023-10-24 22:17:31,937 epoch 3 - iter 432/1445 - loss 0.07610488 - time (sec): 31.31 - samples/sec: 1669.25 - lr: 0.000043 - momentum: 0.000000
2023-10-24 22:17:42,638 epoch 3 - iter 576/1445 - loss 0.07378191 - time (sec): 42.01 - samples/sec: 1677.25 - lr: 0.000042 - momentum: 0.000000
2023-10-24 22:17:53,220 epoch 3 - iter 720/1445 - loss 0.07592950 - time (sec): 52.59 - samples/sec: 1677.29 - lr: 0.000042 - momentum: 0.000000
2023-10-24 22:18:04,012 epoch 3 - iter 864/1445 - loss 0.08537831 - time (sec): 63.39 - samples/sec: 1688.53 - lr: 0.000041 - momentum: 0.000000
2023-10-24 22:18:14,355 epoch 3 - iter 1008/1445 - loss 0.09120584 - time (sec): 73.73 - samples/sec: 1674.36 - lr: 0.000041 - momentum: 0.000000
2023-10-24 22:18:24,684 epoch 3 - iter 1152/1445 - loss 0.08969195 - time (sec): 84.06 - samples/sec: 1666.85 - lr: 0.000040 - momentum: 0.000000
2023-10-24 22:18:35,249 epoch 3 - iter 1296/1445 - loss 0.08985953 - time (sec): 94.62 - samples/sec: 1667.96 - lr: 0.000039 - momentum: 0.000000
2023-10-24 22:18:45,949 epoch 3 - iter 1440/1445 - loss 0.09136075 - time (sec): 105.32 - samples/sec: 1670.01 - lr: 0.000039 - momentum: 0.000000
2023-10-24 22:18:46,238 ----------------------------------------------------------------------------------------------------
2023-10-24 22:18:46,239 EPOCH 3 done: loss 0.0915 - lr: 0.000039
2023-10-24 22:18:49,660 DEV : loss 0.11891528218984604 - f1-score (micro avg) 0.796
2023-10-24 22:18:49,672 saving best model
2023-10-24 22:18:50,385 ----------------------------------------------------------------------------------------------------
2023-10-24 22:19:00,748 epoch 4 - iter 144/1445 - loss 0.05647820 - time (sec): 10.36 - samples/sec: 1688.59 - lr: 0.000038 - momentum: 0.000000
2023-10-24 22:19:11,515 epoch 4 - iter 288/1445 - loss 0.05815810 - time (sec): 21.13 - samples/sec: 1643.99 - lr: 0.000038 - momentum: 0.000000
2023-10-24 22:19:21,630 epoch 4 - iter 432/1445 - loss 0.06297138 - time (sec): 31.24 - samples/sec: 1623.46 - lr: 0.000037 - momentum: 0.000000
2023-10-24 22:19:31,956 epoch 4 - iter 576/1445 - loss 0.06251057 - time (sec): 41.57 - samples/sec: 1617.67 - lr: 0.000037 - momentum: 0.000000
2023-10-24 22:19:42,685 epoch 4 - iter 720/1445 - loss 0.06294971 - time (sec): 52.30 - samples/sec: 1641.43 - lr: 0.000036 - momentum: 0.000000
2023-10-24 22:19:53,347 epoch 4 - iter 864/1445 - loss 0.06501619 - time (sec): 62.96 - samples/sec: 1652.80 - lr: 0.000036 - momentum: 0.000000
2023-10-24 22:20:04,252 epoch 4 - iter 1008/1445 - loss 0.06499533 - time (sec): 73.87 - samples/sec: 1658.53 - lr: 0.000035 - momentum: 0.000000
2023-10-24 22:20:14,785 epoch 4 - iter 1152/1445 - loss 0.06307111 - time (sec): 84.40 - samples/sec: 1664.21 - lr: 0.000034 - momentum: 0.000000
2023-10-24 22:20:25,350 epoch 4 - iter 1296/1445 - loss 0.06234630 - time (sec): 94.96 - samples/sec: 1664.27 - lr: 0.000034 - momentum: 0.000000
2023-10-24 22:20:35,838 epoch 4 - iter 1440/1445 - loss 0.06175381 - time (sec): 105.45 - samples/sec: 1667.05 - lr: 0.000033 - momentum: 0.000000
2023-10-24 22:20:36,143 ----------------------------------------------------------------------------------------------------
2023-10-24 22:20:36,144 EPOCH 4 done: loss 0.0619 - lr: 0.000033
2023-10-24 22:20:39,556 DEV : loss 0.1823125034570694 - f1-score (micro avg) 0.756
2023-10-24 22:20:39,567 ----------------------------------------------------------------------------------------------------
2023-10-24 22:20:50,308 epoch 5 - iter 144/1445 - loss 0.05559863 - time (sec): 10.74 - samples/sec: 1703.77 - lr: 0.000033 - momentum: 0.000000
2023-10-24 22:21:01,046 epoch 5 - iter 288/1445 - loss 0.05287999 - time (sec): 21.48 - samples/sec: 1666.13 - lr: 0.000032 - momentum: 0.000000
2023-10-24 22:21:11,592 epoch 5 - iter 432/1445 - loss 0.04559996 - time (sec): 32.02 - samples/sec: 1666.25 - lr: 0.000032 - momentum: 0.000000
2023-10-24 22:21:22,613 epoch 5 - iter 576/1445 - loss 0.04653938 - time (sec): 43.04 - samples/sec: 1678.93 - lr: 0.000031 - momentum: 0.000000
2023-10-24 22:21:32,932 epoch 5 - iter 720/1445 - loss 0.04780450 - time (sec): 53.36 - samples/sec: 1676.43 - lr: 0.000031 - momentum: 0.000000
2023-10-24 22:21:43,617 epoch 5 - iter 864/1445 - loss 0.04662656 - time (sec): 64.05 - samples/sec: 1680.93 - lr: 0.000030 - momentum: 0.000000
2023-10-24 22:21:53,610 epoch 5 - iter 1008/1445 - loss 0.04653849 - time (sec): 74.04 - samples/sec: 1668.59 - lr: 0.000029 - momentum: 0.000000
2023-10-24 22:22:04,090 epoch 5 - iter 1152/1445 - loss 0.04554055 - time (sec): 84.52 - samples/sec: 1673.76 - lr: 0.000029 - momentum: 0.000000
2023-10-24 22:22:14,414 epoch 5 - iter 1296/1445 - loss 0.04549864 - time (sec): 94.85 - samples/sec: 1665.47 - lr: 0.000028 - momentum: 0.000000
2023-10-24 22:22:24,915 epoch 5 - iter 1440/1445 - loss 0.04622108 - time (sec): 105.35 - samples/sec: 1665.43 - lr: 0.000028 - momentum: 0.000000
2023-10-24 22:22:25,341 ----------------------------------------------------------------------------------------------------
2023-10-24 22:22:25,342 EPOCH 5 done: loss 0.0462 - lr: 0.000028
2023-10-24 22:22:29,053 DEV : loss 0.14015598595142365 - f1-score (micro avg) 0.8063
2023-10-24 22:22:29,065 saving best model
2023-10-24 22:22:29,718 ----------------------------------------------------------------------------------------------------
2023-10-24 22:22:40,293 epoch 6 - iter 144/1445 - loss 0.02737257 - time (sec): 10.57 - samples/sec: 1620.84 - lr: 0.000027 - momentum: 0.000000
2023-10-24 22:22:50,766 epoch 6 - iter 288/1445 - loss 0.02987116 - time (sec): 21.05 - samples/sec: 1632.47 - lr: 0.000027 - momentum: 0.000000
2023-10-24 22:23:01,736 epoch 6 - iter 432/1445 - loss 0.03340606 - time (sec): 32.02 - samples/sec: 1665.29 - lr: 0.000026 - momentum: 0.000000
2023-10-24 22:23:12,193 epoch 6 - iter 576/1445 - loss 0.03514036 - time (sec): 42.47 - samples/sec: 1652.48 - lr: 0.000026 - momentum: 0.000000
2023-10-24 22:23:22,643 epoch 6 - iter 720/1445 - loss 0.03531426 - time (sec): 52.92 - samples/sec: 1650.42 - lr: 0.000025 - momentum: 0.000000
2023-10-24 22:23:33,304 epoch 6 - iter 864/1445 - loss 0.03610013 - time (sec): 63.58 - samples/sec: 1655.96 - lr: 0.000024 - momentum: 0.000000
2023-10-24 22:23:43,755 epoch 6 - iter 1008/1445 - loss 0.03512300 - time (sec): 74.04 - samples/sec: 1666.00 - lr: 0.000024 - momentum: 0.000000
2023-10-24 22:23:54,257 epoch 6 - iter 1152/1445 - loss 0.03710725 - time (sec): 84.54 - samples/sec: 1666.00 - lr: 0.000023 - momentum: 0.000000
2023-10-24 22:24:04,699 epoch 6 - iter 1296/1445 - loss 0.03585885 - time (sec): 94.98 - samples/sec: 1669.28 - lr: 0.000023 - momentum: 0.000000
2023-10-24 22:24:15,046 epoch 6 - iter 1440/1445 - loss 0.03557740 - time (sec): 105.33 - samples/sec: 1667.87 - lr: 0.000022 - momentum: 0.000000
2023-10-24 22:24:15,381 ----------------------------------------------------------------------------------------------------
2023-10-24 22:24:15,382 EPOCH 6 done: loss 0.0355 - lr: 0.000022
2023-10-24 22:24:18,806 DEV : loss 0.18115007877349854 - f1-score (micro avg) 0.786
2023-10-24 22:24:18,817 ----------------------------------------------------------------------------------------------------
2023-10-24 22:24:29,308 epoch 7 - iter 144/1445 - loss 0.02078286 - time (sec): 10.49 - samples/sec: 1705.63 - lr: 0.000022 - momentum: 0.000000
2023-10-24 22:24:39,999 epoch 7 - iter 288/1445 - loss 0.02962769 - time (sec): 21.18 - samples/sec: 1669.68 - lr: 0.000021 - momentum: 0.000000
2023-10-24 22:24:50,656 epoch 7 - iter 432/1445 - loss 0.02907881 - time (sec): 31.84 - samples/sec: 1653.22 - lr: 0.000021 - momentum: 0.000000
2023-10-24 22:25:01,260 epoch 7 - iter 576/1445 - loss 0.03114169 - time (sec): 42.44 - samples/sec: 1670.16 - lr: 0.000020 - momentum: 0.000000
2023-10-24 22:25:12,090 epoch 7 - iter 720/1445 - loss 0.02943001 - time (sec): 53.27 - samples/sec: 1672.86 - lr: 0.000019 - momentum: 0.000000
2023-10-24 22:25:22,358 epoch 7 - iter 864/1445 - loss 0.02860415 - time (sec): 63.54 - samples/sec: 1658.11 - lr: 0.000019 - momentum: 0.000000
2023-10-24 22:25:32,771 epoch 7 - iter 1008/1445 - loss 0.02721034 - time (sec): 73.95 - samples/sec: 1654.20 - lr: 0.000018 - momentum: 0.000000
2023-10-24 22:25:43,289 epoch 7 - iter 1152/1445 - loss 0.02659125 - time (sec): 84.47 - samples/sec: 1655.55 - lr: 0.000018 - momentum: 0.000000
2023-10-24 22:25:53,971 epoch 7 - iter 1296/1445 - loss 0.02604572 - time (sec): 95.15 - samples/sec: 1660.84 - lr: 0.000017 - momentum: 0.000000
2023-10-24 22:26:04,502 epoch 7 - iter 1440/1445 - loss 0.02528759 - time (sec): 105.68 - samples/sec: 1661.04 - lr: 0.000017 - momentum: 0.000000
2023-10-24 22:26:04,906 ----------------------------------------------------------------------------------------------------
2023-10-24 22:26:04,906 EPOCH 7 done: loss 0.0252 - lr: 0.000017
2023-10-24 22:26:08,329 DEV : loss 0.19167011976242065 - f1-score (micro avg) 0.811
2023-10-24 22:26:08,341 saving best model
2023-10-24 22:26:08,996 ----------------------------------------------------------------------------------------------------
2023-10-24 22:26:19,544 epoch 8 - iter 144/1445 - loss 0.01368515 - time (sec): 10.55 - samples/sec: 1673.27 - lr: 0.000016 - momentum: 0.000000
2023-10-24 22:26:30,355 epoch 8 - iter 288/1445 - loss 0.01538066 - time (sec): 21.36 - samples/sec: 1660.55 - lr: 0.000016 - momentum: 0.000000
2023-10-24 22:26:40,676 epoch 8 - iter 432/1445 - loss 0.01436584 - time (sec): 31.68 - samples/sec: 1675.14 - lr: 0.000015 - momentum: 0.000000
2023-10-24 22:26:51,893 epoch 8 - iter 576/1445 - loss 0.01432006 - time (sec): 42.90 - samples/sec: 1706.24 - lr: 0.000014 - momentum: 0.000000
2023-10-24 22:27:02,324 epoch 8 - iter 720/1445 - loss 0.01409563 - time (sec): 53.33 - samples/sec: 1691.08 - lr: 0.000014 - momentum: 0.000000
2023-10-24 22:27:12,778 epoch 8 - iter 864/1445 - loss 0.01487126 - time (sec): 63.78 - samples/sec: 1688.73 - lr: 0.000013 - momentum: 0.000000
2023-10-24 22:27:23,350 epoch 8 - iter 1008/1445 - loss 0.01619878 - time (sec): 74.35 - samples/sec: 1681.67 - lr: 0.000013 - momentum: 0.000000
2023-10-24 22:27:33,298 epoch 8 - iter 1152/1445 - loss 0.01597473 - time (sec): 84.30 - samples/sec: 1663.50 - lr: 0.000012 - momentum: 0.000000
2023-10-24 22:27:43,579 epoch 8 - iter 1296/1445 - loss 0.01520411 - time (sec): 94.58 - samples/sec: 1661.71 - lr: 0.000012 - momentum: 0.000000
2023-10-24 22:27:54,314 epoch 8 - iter 1440/1445 - loss 0.01673962 - time (sec): 105.32 - samples/sec: 1666.43 - lr: 0.000011 - momentum: 0.000000
2023-10-24 22:27:54,743 ----------------------------------------------------------------------------------------------------
2023-10-24 22:27:54,744 EPOCH 8 done: loss 0.0167 - lr: 0.000011
2023-10-24 22:27:58,460 DEV : loss 0.20966801047325134 - f1-score (micro avg) 0.8068
2023-10-24 22:27:58,472 ----------------------------------------------------------------------------------------------------
2023-10-24 22:28:09,302 epoch 9 - iter 144/1445 - loss 0.00335298 - time (sec): 10.83 - samples/sec: 1730.28 - lr: 0.000011 - momentum: 0.000000
2023-10-24 22:28:19,408 epoch 9 - iter 288/1445 - loss 0.00713944 - time (sec): 20.93 - samples/sec: 1674.71 - lr: 0.000010 - momentum: 0.000000
2023-10-24 22:28:30,389 epoch 9 - iter 432/1445 - loss 0.00831560 - time (sec): 31.92 - samples/sec: 1677.91 - lr: 0.000009 - momentum: 0.000000
2023-10-24 22:28:40,925 epoch 9 - iter 576/1445 - loss 0.01125306 - time (sec): 42.45 - samples/sec: 1673.19 - lr: 0.000009 - momentum: 0.000000
2023-10-24 22:28:51,398 epoch 9 - iter 720/1445 - loss 0.01066392 - time (sec): 52.92 - samples/sec: 1668.82 - lr: 0.000008 - momentum: 0.000000
2023-10-24 22:29:01,925 epoch 9 - iter 864/1445 - loss 0.00979328 - time (sec): 63.45 - samples/sec: 1673.13 - lr: 0.000008 - momentum: 0.000000
2023-10-24 22:29:12,556 epoch 9 - iter 1008/1445 - loss 0.01050402 - time (sec): 74.08 - samples/sec: 1673.14 - lr: 0.000007 - momentum: 0.000000
2023-10-24 22:29:22,908 epoch 9 - iter 1152/1445 - loss 0.01017532 - time (sec): 84.43 - samples/sec: 1671.11 - lr: 0.000007 - momentum: 0.000000
2023-10-24 22:29:33,357 epoch 9 - iter 1296/1445 - loss 0.00941237 - time (sec): 94.88 - samples/sec: 1670.19 - lr: 0.000006 - momentum: 0.000000
2023-10-24 22:29:43,936 epoch 9 - iter 1440/1445 - loss 0.00966527 - time (sec): 105.46 - samples/sec: 1667.23 - lr: 0.000006 - momentum: 0.000000
2023-10-24 22:29:44,236 ----------------------------------------------------------------------------------------------------
2023-10-24 22:29:44,236 EPOCH 9 done: loss 0.0096 - lr: 0.000006
2023-10-24 22:29:47,661 DEV : loss 0.22105184197425842 - f1-score (micro avg) 0.8086
2023-10-24 22:29:47,672 ----------------------------------------------------------------------------------------------------
2023-10-24 22:29:58,237 epoch 10 - iter 144/1445 - loss 0.00621614 - time (sec): 10.56 - samples/sec: 1652.08 - lr: 0.000005 - momentum: 0.000000
2023-10-24 22:30:08,967 epoch 10 - iter 288/1445 - loss 0.01088022 - time (sec): 21.29 - samples/sec: 1667.64 - lr: 0.000004 - momentum: 0.000000
2023-10-24 22:30:19,753 epoch 10 - iter 432/1445 - loss 0.00891142 - time (sec): 32.08 - samples/sec: 1697.21 - lr: 0.000004 - momentum: 0.000000
2023-10-24 22:30:30,666 epoch 10 - iter 576/1445 - loss 0.00890582 - time (sec): 42.99 - samples/sec: 1693.35 - lr: 0.000003 - momentum: 0.000000
2023-10-24 22:30:40,999 epoch 10 - iter 720/1445 - loss 0.00818322 - time (sec): 53.33 - samples/sec: 1679.18 - lr: 0.000003 - momentum: 0.000000
2023-10-24 22:30:51,571 epoch 10 - iter 864/1445 - loss 0.00748506 - time (sec): 63.90 - samples/sec: 1671.13 - lr: 0.000002 - momentum: 0.000000
2023-10-24 22:31:02,171 epoch 10 - iter 1008/1445 - loss 0.00750558 - time (sec): 74.50 - samples/sec: 1666.22 - lr: 0.000002 - momentum: 0.000000
2023-10-24 22:31:12,576 epoch 10 - iter 1152/1445 - loss 0.00743769 - time (sec): 84.90 - samples/sec: 1667.05 - lr: 0.000001 - momentum: 0.000000
2023-10-24 22:31:23,189 epoch 10 - iter 1296/1445 - loss 0.00721825 - time (sec): 95.52 - samples/sec: 1661.21 - lr: 0.000001 - momentum: 0.000000
2023-10-24 22:31:33,509 epoch 10 - iter 1440/1445 - loss 0.00720286 - time (sec): 105.84 - samples/sec: 1661.25 - lr: 0.000000 - momentum: 0.000000
2023-10-24 22:31:33,805 ----------------------------------------------------------------------------------------------------
2023-10-24 22:31:33,805 EPOCH 10 done: loss 0.0072 - lr: 0.000000
2023-10-24 22:31:37,236 DEV : loss 0.22644661366939545 - f1-score (micro avg) 0.8158
2023-10-24 22:31:37,249 saving best model
2023-10-24 22:31:38,458 ----------------------------------------------------------------------------------------------------
2023-10-24 22:31:38,459 Loading model from best epoch ...
2023-10-24 22:31:40,317 SequenceTagger predicts: Dictionary with 13 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
2023-10-24 22:31:43,856
Results:
- F-score (micro) 0.7971
- F-score (macro) 0.6618
- Accuracy 0.678
By class:
precision recall f1-score support
PER 0.8545 0.7676 0.8087 482
LOC 0.8913 0.8057 0.8463 458
ORG 0.4130 0.2754 0.3304 69
micro avg 0.8488 0.7512 0.7971 1009
macro avg 0.7196 0.6162 0.6618 1009
weighted avg 0.8410 0.7512 0.7931 1009
2023-10-24 22:31:43,856 ----------------------------------------------------------------------------------------------------
|