File size: 36,781 Bytes
0de20e0 |
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 22:47:35,121 ----------------------------------------------------------------------------------------------------
2023-10-24 22:47:35,122 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 22:47:35,122 ----------------------------------------------------------------------------------------------------
2023-10-24 22:47:35,122 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 22:47:35,122 ----------------------------------------------------------------------------------------------------
2023-10-24 22:47:35,122 Train: 5777 sentences
2023-10-24 22:47:35,122 (train_with_dev=False, train_with_test=False)
2023-10-24 22:47:35,122 ----------------------------------------------------------------------------------------------------
2023-10-24 22:47:35,122 Training Params:
2023-10-24 22:47:35,122 - learning_rate: "5e-05"
2023-10-24 22:47:35,123 - mini_batch_size: "8"
2023-10-24 22:47:35,123 - max_epochs: "10"
2023-10-24 22:47:35,123 - shuffle: "True"
2023-10-24 22:47:35,123 ----------------------------------------------------------------------------------------------------
2023-10-24 22:47:35,123 Plugins:
2023-10-24 22:47:35,123 - TensorboardLogger
2023-10-24 22:47:35,123 - LinearScheduler | warmup_fraction: '0.1'
2023-10-24 22:47:35,123 ----------------------------------------------------------------------------------------------------
2023-10-24 22:47:35,123 Final evaluation on model from best epoch (best-model.pt)
2023-10-24 22:47:35,123 - metric: "('micro avg', 'f1-score')"
2023-10-24 22:47:35,123 ----------------------------------------------------------------------------------------------------
2023-10-24 22:47:35,123 Computation:
2023-10-24 22:47:35,123 - compute on device: cuda:0
2023-10-24 22:47:35,123 - embedding storage: none
2023-10-24 22:47:35,123 ----------------------------------------------------------------------------------------------------
2023-10-24 22:47:35,123 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1"
2023-10-24 22:47:35,123 ----------------------------------------------------------------------------------------------------
2023-10-24 22:47:35,123 ----------------------------------------------------------------------------------------------------
2023-10-24 22:47:35,123 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-24 22:47:43,574 epoch 1 - iter 72/723 - loss 1.90099622 - time (sec): 8.45 - samples/sec: 2093.32 - lr: 0.000005 - momentum: 0.000000
2023-10-24 22:47:52,328 epoch 1 - iter 144/723 - loss 1.09087996 - time (sec): 17.20 - samples/sec: 2047.05 - lr: 0.000010 - momentum: 0.000000
2023-10-24 22:48:01,244 epoch 1 - iter 216/723 - loss 0.77964600 - time (sec): 26.12 - samples/sec: 2072.80 - lr: 0.000015 - momentum: 0.000000
2023-10-24 22:48:09,443 epoch 1 - iter 288/723 - loss 0.63708806 - time (sec): 34.32 - samples/sec: 2055.00 - lr: 0.000020 - momentum: 0.000000
2023-10-24 22:48:17,547 epoch 1 - iter 360/723 - loss 0.54313247 - time (sec): 42.42 - samples/sec: 2054.17 - lr: 0.000025 - momentum: 0.000000
2023-10-24 22:48:25,862 epoch 1 - iter 432/723 - loss 0.48284963 - time (sec): 50.74 - samples/sec: 2053.75 - lr: 0.000030 - momentum: 0.000000
2023-10-24 22:48:34,173 epoch 1 - iter 504/723 - loss 0.43417529 - time (sec): 59.05 - samples/sec: 2046.07 - lr: 0.000035 - momentum: 0.000000
2023-10-24 22:48:43,285 epoch 1 - iter 576/723 - loss 0.39662721 - time (sec): 68.16 - samples/sec: 2037.13 - lr: 0.000040 - momentum: 0.000000
2023-10-24 22:48:51,944 epoch 1 - iter 648/723 - loss 0.36490823 - time (sec): 76.82 - samples/sec: 2044.94 - lr: 0.000045 - momentum: 0.000000
2023-10-24 22:49:01,070 epoch 1 - iter 720/723 - loss 0.33855338 - time (sec): 85.95 - samples/sec: 2044.95 - lr: 0.000050 - momentum: 0.000000
2023-10-24 22:49:01,321 ----------------------------------------------------------------------------------------------------
2023-10-24 22:49:01,321 EPOCH 1 done: loss 0.3381 - lr: 0.000050
2023-10-24 22:49:04,603 DEV : loss 0.13358977437019348 - f1-score (micro avg) 0.5559
2023-10-24 22:49:04,615 saving best model
2023-10-24 22:49:05,173 ----------------------------------------------------------------------------------------------------
2023-10-24 22:49:13,532 epoch 2 - iter 72/723 - loss 0.11563641 - time (sec): 8.36 - samples/sec: 2039.66 - lr: 0.000049 - momentum: 0.000000
2023-10-24 22:49:21,466 epoch 2 - iter 144/723 - loss 0.11274613 - time (sec): 16.29 - samples/sec: 2051.20 - lr: 0.000049 - momentum: 0.000000
2023-10-24 22:49:29,822 epoch 2 - iter 216/723 - loss 0.10902633 - time (sec): 24.65 - samples/sec: 2053.51 - lr: 0.000048 - momentum: 0.000000
2023-10-24 22:49:38,964 epoch 2 - iter 288/723 - loss 0.10367207 - time (sec): 33.79 - samples/sec: 2049.90 - lr: 0.000048 - momentum: 0.000000
2023-10-24 22:49:48,274 epoch 2 - iter 360/723 - loss 0.09939488 - time (sec): 43.10 - samples/sec: 2053.73 - lr: 0.000047 - momentum: 0.000000
2023-10-24 22:49:57,605 epoch 2 - iter 432/723 - loss 0.09748051 - time (sec): 52.43 - samples/sec: 2046.48 - lr: 0.000047 - momentum: 0.000000
2023-10-24 22:50:06,005 epoch 2 - iter 504/723 - loss 0.09514922 - time (sec): 60.83 - samples/sec: 2045.92 - lr: 0.000046 - momentum: 0.000000
2023-10-24 22:50:13,666 epoch 2 - iter 576/723 - loss 0.09875432 - time (sec): 68.49 - samples/sec: 2047.89 - lr: 0.000046 - momentum: 0.000000
2023-10-24 22:50:22,101 epoch 2 - iter 648/723 - loss 0.09818627 - time (sec): 76.93 - samples/sec: 2048.27 - lr: 0.000045 - momentum: 0.000000
2023-10-24 22:50:30,670 epoch 2 - iter 720/723 - loss 0.09775475 - time (sec): 85.50 - samples/sec: 2053.69 - lr: 0.000044 - momentum: 0.000000
2023-10-24 22:50:30,916 ----------------------------------------------------------------------------------------------------
2023-10-24 22:50:30,916 EPOCH 2 done: loss 0.0977 - lr: 0.000044
2023-10-24 22:50:34,642 DEV : loss 0.09923986345529556 - f1-score (micro avg) 0.7434
2023-10-24 22:50:34,654 saving best model
2023-10-24 22:50:35,378 ----------------------------------------------------------------------------------------------------
2023-10-24 22:50:44,044 epoch 3 - iter 72/723 - loss 0.07128608 - time (sec): 8.66 - samples/sec: 2017.83 - lr: 0.000044 - momentum: 0.000000
2023-10-24 22:50:52,525 epoch 3 - iter 144/723 - loss 0.05959143 - time (sec): 17.15 - samples/sec: 2039.09 - lr: 0.000043 - momentum: 0.000000
2023-10-24 22:51:00,812 epoch 3 - iter 216/723 - loss 0.06657607 - time (sec): 25.43 - samples/sec: 2055.01 - lr: 0.000043 - momentum: 0.000000
2023-10-24 22:51:09,580 epoch 3 - iter 288/723 - loss 0.06577637 - time (sec): 34.20 - samples/sec: 2060.35 - lr: 0.000042 - momentum: 0.000000
2023-10-24 22:51:18,393 epoch 3 - iter 360/723 - loss 0.06437789 - time (sec): 43.01 - samples/sec: 2050.83 - lr: 0.000042 - momentum: 0.000000
2023-10-24 22:51:27,526 epoch 3 - iter 432/723 - loss 0.06488597 - time (sec): 52.15 - samples/sec: 2052.47 - lr: 0.000041 - momentum: 0.000000
2023-10-24 22:51:35,847 epoch 3 - iter 504/723 - loss 0.06625285 - time (sec): 60.47 - samples/sec: 2041.55 - lr: 0.000041 - momentum: 0.000000
2023-10-24 22:51:44,188 epoch 3 - iter 576/723 - loss 0.06508266 - time (sec): 68.81 - samples/sec: 2036.26 - lr: 0.000040 - momentum: 0.000000
2023-10-24 22:51:52,876 epoch 3 - iter 648/723 - loss 0.06525563 - time (sec): 77.50 - samples/sec: 2036.55 - lr: 0.000039 - momentum: 0.000000
2023-10-24 22:52:01,622 epoch 3 - iter 720/723 - loss 0.06417277 - time (sec): 86.24 - samples/sec: 2039.48 - lr: 0.000039 - momentum: 0.000000
2023-10-24 22:52:01,826 ----------------------------------------------------------------------------------------------------
2023-10-24 22:52:01,827 EPOCH 3 done: loss 0.0642 - lr: 0.000039
2023-10-24 22:52:05,562 DEV : loss 0.08431313186883926 - f1-score (micro avg) 0.8162
2023-10-24 22:52:05,574 saving best model
2023-10-24 22:52:06,287 ----------------------------------------------------------------------------------------------------
2023-10-24 22:52:14,602 epoch 4 - iter 72/723 - loss 0.04275318 - time (sec): 8.31 - samples/sec: 2104.71 - lr: 0.000038 - momentum: 0.000000
2023-10-24 22:52:23,182 epoch 4 - iter 144/723 - loss 0.04122534 - time (sec): 16.89 - samples/sec: 2056.08 - lr: 0.000038 - momentum: 0.000000
2023-10-24 22:52:30,986 epoch 4 - iter 216/723 - loss 0.04387147 - time (sec): 24.70 - samples/sec: 2053.76 - lr: 0.000037 - momentum: 0.000000
2023-10-24 22:52:39,468 epoch 4 - iter 288/723 - loss 0.04477684 - time (sec): 33.18 - samples/sec: 2026.76 - lr: 0.000037 - momentum: 0.000000
2023-10-24 22:52:48,425 epoch 4 - iter 360/723 - loss 0.04492124 - time (sec): 42.14 - samples/sec: 2037.30 - lr: 0.000036 - momentum: 0.000000
2023-10-24 22:52:57,346 epoch 4 - iter 432/723 - loss 0.04583451 - time (sec): 51.06 - samples/sec: 2038.10 - lr: 0.000036 - momentum: 0.000000
2023-10-24 22:53:06,429 epoch 4 - iter 504/723 - loss 0.04611188 - time (sec): 60.14 - samples/sec: 2037.05 - lr: 0.000035 - momentum: 0.000000
2023-10-24 22:53:15,120 epoch 4 - iter 576/723 - loss 0.04469752 - time (sec): 68.83 - samples/sec: 2040.59 - lr: 0.000034 - momentum: 0.000000
2023-10-24 22:53:23,881 epoch 4 - iter 648/723 - loss 0.04421455 - time (sec): 77.59 - samples/sec: 2036.88 - lr: 0.000034 - momentum: 0.000000
2023-10-24 22:53:32,384 epoch 4 - iter 720/723 - loss 0.04381095 - time (sec): 86.10 - samples/sec: 2041.83 - lr: 0.000033 - momentum: 0.000000
2023-10-24 22:53:32,612 ----------------------------------------------------------------------------------------------------
2023-10-24 22:53:32,612 EPOCH 4 done: loss 0.0440 - lr: 0.000033
2023-10-24 22:53:36,048 DEV : loss 0.0921085774898529 - f1-score (micro avg) 0.8061
2023-10-24 22:53:36,059 ----------------------------------------------------------------------------------------------------
2023-10-24 22:53:45,135 epoch 5 - iter 72/723 - loss 0.02981632 - time (sec): 9.07 - samples/sec: 2016.43 - lr: 0.000033 - momentum: 0.000000
2023-10-24 22:53:54,262 epoch 5 - iter 144/723 - loss 0.03293464 - time (sec): 18.20 - samples/sec: 1965.96 - lr: 0.000032 - momentum: 0.000000
2023-10-24 22:54:02,987 epoch 5 - iter 216/723 - loss 0.02903229 - time (sec): 26.93 - samples/sec: 1981.65 - lr: 0.000032 - momentum: 0.000000
2023-10-24 22:54:12,480 epoch 5 - iter 288/723 - loss 0.02921469 - time (sec): 36.42 - samples/sec: 1984.35 - lr: 0.000031 - momentum: 0.000000
2023-10-24 22:54:20,920 epoch 5 - iter 360/723 - loss 0.03035987 - time (sec): 44.86 - samples/sec: 1994.23 - lr: 0.000031 - momentum: 0.000000
2023-10-24 22:54:29,626 epoch 5 - iter 432/723 - loss 0.03109785 - time (sec): 53.57 - samples/sec: 2009.90 - lr: 0.000030 - momentum: 0.000000
2023-10-24 22:54:37,390 epoch 5 - iter 504/723 - loss 0.03348098 - time (sec): 61.33 - samples/sec: 2014.44 - lr: 0.000029 - momentum: 0.000000
2023-10-24 22:54:46,312 epoch 5 - iter 576/723 - loss 0.03272635 - time (sec): 70.25 - samples/sec: 2013.74 - lr: 0.000029 - momentum: 0.000000
2023-10-24 22:54:54,679 epoch 5 - iter 648/723 - loss 0.03250020 - time (sec): 78.62 - samples/sec: 2009.22 - lr: 0.000028 - momentum: 0.000000
2023-10-24 22:55:03,105 epoch 5 - iter 720/723 - loss 0.03217828 - time (sec): 87.04 - samples/sec: 2015.60 - lr: 0.000028 - momentum: 0.000000
2023-10-24 22:55:03,508 ----------------------------------------------------------------------------------------------------
2023-10-24 22:55:03,508 EPOCH 5 done: loss 0.0323 - lr: 0.000028
2023-10-24 22:55:06,952 DEV : loss 0.12793748080730438 - f1-score (micro avg) 0.8201
2023-10-24 22:55:06,964 saving best model
2023-10-24 22:55:07,671 ----------------------------------------------------------------------------------------------------
2023-10-24 22:55:16,444 epoch 6 - iter 72/723 - loss 0.01771827 - time (sec): 8.77 - samples/sec: 1953.71 - lr: 0.000027 - momentum: 0.000000
2023-10-24 22:55:24,853 epoch 6 - iter 144/723 - loss 0.02179612 - time (sec): 17.18 - samples/sec: 1999.81 - lr: 0.000027 - momentum: 0.000000
2023-10-24 22:55:34,164 epoch 6 - iter 216/723 - loss 0.02091712 - time (sec): 26.49 - samples/sec: 2012.54 - lr: 0.000026 - momentum: 0.000000
2023-10-24 22:55:42,847 epoch 6 - iter 288/723 - loss 0.02115975 - time (sec): 35.18 - samples/sec: 1995.33 - lr: 0.000026 - momentum: 0.000000
2023-10-24 22:55:51,268 epoch 6 - iter 360/723 - loss 0.02279607 - time (sec): 43.60 - samples/sec: 2003.54 - lr: 0.000025 - momentum: 0.000000
2023-10-24 22:55:59,917 epoch 6 - iter 432/723 - loss 0.02299399 - time (sec): 52.25 - samples/sec: 2015.37 - lr: 0.000024 - momentum: 0.000000
2023-10-24 22:56:08,369 epoch 6 - iter 504/723 - loss 0.02253209 - time (sec): 60.70 - samples/sec: 2032.10 - lr: 0.000024 - momentum: 0.000000
2023-10-24 22:56:16,964 epoch 6 - iter 576/723 - loss 0.02318425 - time (sec): 69.29 - samples/sec: 2032.54 - lr: 0.000023 - momentum: 0.000000
2023-10-24 22:56:25,279 epoch 6 - iter 648/723 - loss 0.02365793 - time (sec): 77.61 - samples/sec: 2042.94 - lr: 0.000023 - momentum: 0.000000
2023-10-24 22:56:33,600 epoch 6 - iter 720/723 - loss 0.02442479 - time (sec): 85.93 - samples/sec: 2044.40 - lr: 0.000022 - momentum: 0.000000
2023-10-24 22:56:33,868 ----------------------------------------------------------------------------------------------------
2023-10-24 22:56:33,869 EPOCH 6 done: loss 0.0244 - lr: 0.000022
2023-10-24 22:56:37,590 DEV : loss 0.13028167188167572 - f1-score (micro avg) 0.8206
2023-10-24 22:56:37,602 saving best model
2023-10-24 22:56:38,305 ----------------------------------------------------------------------------------------------------
2023-10-24 22:56:46,715 epoch 7 - iter 72/723 - loss 0.01204270 - time (sec): 8.41 - samples/sec: 2127.69 - lr: 0.000022 - momentum: 0.000000
2023-10-24 22:56:55,800 epoch 7 - iter 144/723 - loss 0.01568493 - time (sec): 17.49 - samples/sec: 2021.54 - lr: 0.000021 - momentum: 0.000000
2023-10-24 22:57:04,172 epoch 7 - iter 216/723 - loss 0.01685436 - time (sec): 25.87 - samples/sec: 2034.94 - lr: 0.000021 - momentum: 0.000000
2023-10-24 22:57:12,899 epoch 7 - iter 288/723 - loss 0.01586004 - time (sec): 34.59 - samples/sec: 2049.11 - lr: 0.000020 - momentum: 0.000000
2023-10-24 22:57:22,002 epoch 7 - iter 360/723 - loss 0.01669915 - time (sec): 43.70 - samples/sec: 2039.42 - lr: 0.000019 - momentum: 0.000000
2023-10-24 22:57:30,281 epoch 7 - iter 432/723 - loss 0.01664053 - time (sec): 51.98 - samples/sec: 2027.06 - lr: 0.000019 - momentum: 0.000000
2023-10-24 22:57:38,651 epoch 7 - iter 504/723 - loss 0.01665777 - time (sec): 60.35 - samples/sec: 2027.21 - lr: 0.000018 - momentum: 0.000000
2023-10-24 22:57:47,215 epoch 7 - iter 576/723 - loss 0.01672193 - time (sec): 68.91 - samples/sec: 2029.42 - lr: 0.000018 - momentum: 0.000000
2023-10-24 22:57:56,065 epoch 7 - iter 648/723 - loss 0.01634593 - time (sec): 77.76 - samples/sec: 2032.35 - lr: 0.000017 - momentum: 0.000000
2023-10-24 22:58:04,677 epoch 7 - iter 720/723 - loss 0.01622942 - time (sec): 86.37 - samples/sec: 2032.46 - lr: 0.000017 - momentum: 0.000000
2023-10-24 22:58:05,043 ----------------------------------------------------------------------------------------------------
2023-10-24 22:58:05,043 EPOCH 7 done: loss 0.0162 - lr: 0.000017
2023-10-24 22:58:08,477 DEV : loss 0.16047385334968567 - f1-score (micro avg) 0.8284
2023-10-24 22:58:08,489 saving best model
2023-10-24 22:58:09,187 ----------------------------------------------------------------------------------------------------
2023-10-24 22:58:18,118 epoch 8 - iter 72/723 - loss 0.00701010 - time (sec): 8.93 - samples/sec: 1976.12 - lr: 0.000016 - momentum: 0.000000
2023-10-24 22:58:27,226 epoch 8 - iter 144/723 - loss 0.00950195 - time (sec): 18.04 - samples/sec: 1966.17 - lr: 0.000016 - momentum: 0.000000
2023-10-24 22:58:35,453 epoch 8 - iter 216/723 - loss 0.01036607 - time (sec): 26.26 - samples/sec: 2020.43 - lr: 0.000015 - momentum: 0.000000
2023-10-24 22:58:44,819 epoch 8 - iter 288/723 - loss 0.01038046 - time (sec): 35.63 - samples/sec: 2054.12 - lr: 0.000014 - momentum: 0.000000
2023-10-24 22:58:53,155 epoch 8 - iter 360/723 - loss 0.01050105 - time (sec): 43.97 - samples/sec: 2051.11 - lr: 0.000014 - momentum: 0.000000
2023-10-24 22:59:01,634 epoch 8 - iter 432/723 - loss 0.01077764 - time (sec): 52.45 - samples/sec: 2053.71 - lr: 0.000013 - momentum: 0.000000
2023-10-24 22:59:10,359 epoch 8 - iter 504/723 - loss 0.01155176 - time (sec): 61.17 - samples/sec: 2044.08 - lr: 0.000013 - momentum: 0.000000
2023-10-24 22:59:18,085 epoch 8 - iter 576/723 - loss 0.01170645 - time (sec): 68.90 - samples/sec: 2035.43 - lr: 0.000012 - momentum: 0.000000
2023-10-24 22:59:26,361 epoch 8 - iter 648/723 - loss 0.01132420 - time (sec): 77.17 - samples/sec: 2036.57 - lr: 0.000012 - momentum: 0.000000
2023-10-24 22:59:35,154 epoch 8 - iter 720/723 - loss 0.01110679 - time (sec): 85.97 - samples/sec: 2041.54 - lr: 0.000011 - momentum: 0.000000
2023-10-24 22:59:35,626 ----------------------------------------------------------------------------------------------------
2023-10-24 22:59:35,626 EPOCH 8 done: loss 0.0111 - lr: 0.000011
2023-10-24 22:59:39,060 DEV : loss 0.17271144688129425 - f1-score (micro avg) 0.8152
2023-10-24 22:59:39,072 ----------------------------------------------------------------------------------------------------
2023-10-24 22:59:48,021 epoch 9 - iter 72/723 - loss 0.00436475 - time (sec): 8.95 - samples/sec: 2093.86 - lr: 0.000011 - momentum: 0.000000
2023-10-24 22:59:55,966 epoch 9 - iter 144/723 - loss 0.00673411 - time (sec): 16.89 - samples/sec: 2075.39 - lr: 0.000010 - momentum: 0.000000
2023-10-24 23:00:05,069 epoch 9 - iter 216/723 - loss 0.00655734 - time (sec): 26.00 - samples/sec: 2059.97 - lr: 0.000009 - momentum: 0.000000
2023-10-24 23:00:13,711 epoch 9 - iter 288/723 - loss 0.00707595 - time (sec): 34.64 - samples/sec: 2050.59 - lr: 0.000009 - momentum: 0.000000
2023-10-24 23:00:22,414 epoch 9 - iter 360/723 - loss 0.00702570 - time (sec): 43.34 - samples/sec: 2037.79 - lr: 0.000008 - momentum: 0.000000
2023-10-24 23:00:30,933 epoch 9 - iter 432/723 - loss 0.00653348 - time (sec): 51.86 - samples/sec: 2047.10 - lr: 0.000008 - momentum: 0.000000
2023-10-24 23:00:39,615 epoch 9 - iter 504/723 - loss 0.00730415 - time (sec): 60.54 - samples/sec: 2047.35 - lr: 0.000007 - momentum: 0.000000
2023-10-24 23:00:47,843 epoch 9 - iter 576/723 - loss 0.00716059 - time (sec): 68.77 - samples/sec: 2051.75 - lr: 0.000007 - momentum: 0.000000
2023-10-24 23:00:56,415 epoch 9 - iter 648/723 - loss 0.00716027 - time (sec): 77.34 - samples/sec: 2049.01 - lr: 0.000006 - momentum: 0.000000
2023-10-24 23:01:05,125 epoch 9 - iter 720/723 - loss 0.00769558 - time (sec): 86.05 - samples/sec: 2043.30 - lr: 0.000006 - momentum: 0.000000
2023-10-24 23:01:05,342 ----------------------------------------------------------------------------------------------------
2023-10-24 23:01:05,342 EPOCH 9 done: loss 0.0077 - lr: 0.000006
2023-10-24 23:01:09,068 DEV : loss 0.18762636184692383 - f1-score (micro avg) 0.8138
2023-10-24 23:01:09,080 ----------------------------------------------------------------------------------------------------
2023-10-24 23:01:17,501 epoch 10 - iter 72/723 - loss 0.00700037 - time (sec): 8.42 - samples/sec: 2072.79 - lr: 0.000005 - momentum: 0.000000
2023-10-24 23:01:25,989 epoch 10 - iter 144/723 - loss 0.00538041 - time (sec): 16.91 - samples/sec: 2100.23 - lr: 0.000004 - momentum: 0.000000
2023-10-24 23:01:34,941 epoch 10 - iter 216/723 - loss 0.00524047 - time (sec): 25.86 - samples/sec: 2105.37 - lr: 0.000004 - momentum: 0.000000
2023-10-24 23:01:44,291 epoch 10 - iter 288/723 - loss 0.00587406 - time (sec): 35.21 - samples/sec: 2067.64 - lr: 0.000003 - momentum: 0.000000
2023-10-24 23:01:52,710 epoch 10 - iter 360/723 - loss 0.00546117 - time (sec): 43.63 - samples/sec: 2052.35 - lr: 0.000003 - momentum: 0.000000
2023-10-24 23:02:01,634 epoch 10 - iter 432/723 - loss 0.00525314 - time (sec): 52.55 - samples/sec: 2031.85 - lr: 0.000002 - momentum: 0.000000
2023-10-24 23:02:10,260 epoch 10 - iter 504/723 - loss 0.00559956 - time (sec): 61.18 - samples/sec: 2028.96 - lr: 0.000002 - momentum: 0.000000
2023-10-24 23:02:18,569 epoch 10 - iter 576/723 - loss 0.00567395 - time (sec): 69.49 - samples/sec: 2036.85 - lr: 0.000001 - momentum: 0.000000
2023-10-24 23:02:27,420 epoch 10 - iter 648/723 - loss 0.00543119 - time (sec): 78.34 - samples/sec: 2025.44 - lr: 0.000001 - momentum: 0.000000
2023-10-24 23:02:35,701 epoch 10 - iter 720/723 - loss 0.00550425 - time (sec): 86.62 - samples/sec: 2029.78 - lr: 0.000000 - momentum: 0.000000
2023-10-24 23:02:35,912 ----------------------------------------------------------------------------------------------------
2023-10-24 23:02:35,913 EPOCH 10 done: loss 0.0055 - lr: 0.000000
2023-10-24 23:02:39,645 DEV : loss 0.19829346239566803 - f1-score (micro avg) 0.8156
2023-10-24 23:02:40,213 ----------------------------------------------------------------------------------------------------
2023-10-24 23:02:40,214 Loading model from best epoch ...
2023-10-24 23:02:42,032 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 23:02:45,283
Results:
- F-score (micro) 0.8006
- F-score (macro) 0.6747
- Accuracy 0.6799
By class:
precision recall f1-score support
PER 0.8527 0.7925 0.8215 482
LOC 0.8801 0.8013 0.8389 458
ORG 0.4231 0.3188 0.3636 69
micro avg 0.8408 0.7641 0.8006 1009
macro avg 0.7186 0.6376 0.6747 1009
weighted avg 0.8357 0.7641 0.7981 1009
2023-10-24 23:02:45,283 ----------------------------------------------------------------------------------------------------
|