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pretrained-m-bert-400
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: nan
- Validation Loss: nan
- Epoch: 399
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
Train Loss | Validation Loss | Epoch |
---|---|---|
10.2737 | 10.9284 | 0 |
7.7909 | 10.9299 | 1 |
6.9555 | 11.4698 | 2 |
6.4835 | 11.5760 | 3 |
6.3613 | 11.4740 | 4 |
6.3206 | 12.1134 | 5 |
6.5777 | 11.8247 | 6 |
6.0680 | 12.1764 | 7 |
5.9353 | 12.1038 | 8 |
6.0701 | 12.0687 | 9 |
6.0047 | 12.4985 | 10 |
5.9272 | 12.8107 | 11 |
5.9446 | 12.5006 | 12 |
5.9716 | 12.8384 | 13 |
6.0548 | 12.3585 | 14 |
5.8666 | 12.5273 | 15 |
5.8951 | 12.8032 | 16 |
5.9528 | 13.0296 | 17 |
5.8313 | 12.9472 | 18 |
5.8324 | 13.0546 | 19 |
5.8763 | 13.1180 | 20 |
5.7623 | 13.2588 | 21 |
5.8636 | 13.2705 | 22 |
5.8864 | 13.1756 | 23 |
5.7413 | 13.0413 | 24 |
5.6825 | 13.2796 | 25 |
5.8705 | 13.5748 | 26 |
5.7772 | 14.0853 | 27 |
5.8029 | 13.5957 | 28 |
5.9590 | 13.0809 | 29 |
5.8240 | 13.0912 | 30 |
5.7687 | 13.5828 | 31 |
5.9836 | 13.5432 | 32 |
5.6915 | 13.7082 | 33 |
5.7853 | 13.6080 | 34 |
5.7760 | 13.3992 | 35 |
5.7134 | 13.6552 | 36 |
5.8415 | 13.5479 | 37 |
5.8087 | 13.6847 | 38 |
5.8611 | 13.6231 | 39 |
5.6492 | 13.8722 | 40 |
5.7846 | 13.7056 | 41 |
5.7455 | 14.1208 | 42 |
5.7129 | 13.7774 | 43 |
5.7185 | 14.0725 | 44 |
5.6655 | 14.0980 | 45 |
5.6734 | 14.0126 | 46 |
5.6658 | 14.1982 | 47 |
5.6720 | 14.0881 | 48 |
5.8220 | 14.0841 | 49 |
5.8567 | 14.0512 | 50 |
5.6811 | 14.4002 | 51 |
5.7271 | 14.1651 | 52 |
5.7329 | 13.8948 | 53 |
5.9306 | 14.1744 | 54 |
5.6757 | 13.6761 | 55 |
5.8785 | 14.1815 | 56 |
5.7267 | 14.4592 | 57 |
5.7313 | 14.1220 | 58 |
5.7537 | 14.3197 | 59 |
5.7907 | 13.9449 | 60 |
5.6611 | 14.0992 | 61 |
5.7009 | 14.4630 | 62 |
5.6760 | 14.3946 | 63 |
5.7096 | 14.2776 | 64 |
5.7778 | 14.3569 | 65 |
5.7223 | 14.5246 | 66 |
5.7954 | 14.2003 | 67 |
5.9369 | 14.3692 | 68 |
5.8792 | 14.5358 | 69 |
5.7172 | 14.3076 | 70 |
5.6608 | 14.1833 | 71 |
5.8710 | 14.1070 | 72 |
5.6896 | 13.9720 | 73 |
5.7404 | 14.8816 | 74 |
5.8649 | 14.1338 | 75 |
5.8223 | 14.4951 | 76 |
5.7701 | 14.4468 | 77 |
5.7852 | 14.3300 | 78 |
5.7038 | 14.0439 | 79 |
5.7386 | 14.3995 | 80 |
5.7594 | 14.1541 | 81 |
5.7688 | 14.3452 | 82 |
5.6329 | 14.9077 | 83 |
5.8224 | 14.5802 | 84 |
5.7969 | 14.8311 | 85 |
5.8297 | 14.5400 | 86 |
5.6488 | 15.0369 | 87 |
5.8020 | 14.7833 | 88 |
5.7345 | 14.3675 | 89 |
5.7530 | 14.1602 | 90 |
5.7599 | 15.2734 | 91 |
5.7256 | 14.9461 | 92 |
5.6803 | 14.8461 | 93 |
5.8602 | 14.5303 | 94 |
5.6319 | 14.8087 | 95 |
5.7581 | 14.7901 | 96 |
5.6319 | 14.8252 | 97 |
5.6869 | 14.8271 | 98 |
5.7664 | 15.2408 | 99 |
5.7731 | 15.0161 | 100 |
5.7304 | 15.4610 | 101 |
5.8412 | 14.8069 | 102 |
5.7723 | 14.1676 | 103 |
5.6118 | 15.4048 | 104 |
5.7734 | 15.1165 | 105 |
5.8253 | 14.9264 | 106 |
5.8027 | 14.7596 | 107 |
5.7457 | 15.1671 | 108 |
5.7381 | 15.2209 | 109 |
5.8087 | 14.7408 | 110 |
5.8030 | 15.2581 | 111 |
5.8320 | 14.9718 | 112 |
5.6863 | 15.2960 | 113 |
5.8710 | 14.7119 | 114 |
5.7232 | 15.0268 | 115 |
5.8191 | 14.8410 | 116 |
5.6752 | 15.1243 | 117 |
5.8462 | 15.0258 | 118 |
5.6872 | 15.1223 | 119 |
5.7252 | 14.9306 | 120 |
5.8288 | 15.0880 | 121 |
5.7154 | 15.1128 | 122 |
5.7731 | 15.4266 | 123 |
5.7265 | 14.9659 | 124 |
5.6335 | 15.1716 | 125 |
5.7175 | 15.6793 | 126 |
5.7104 | 15.4801 | 127 |
5.6567 | 15.0955 | 128 |
5.9180 | 15.0188 | 129 |
5.5288 | 15.3623 | 130 |
5.7597 | 15.4404 | 131 |
5.8730 | 15.1977 | 132 |
5.7699 | 15.5542 | 133 |
5.6693 | 15.1629 | 134 |
5.7615 | 15.1227 | 135 |
5.7240 | 15.4303 | 136 |
5.7303 | 15.1579 | 137 |
5.7373 | 15.2233 | 138 |
5.8327 | 14.7246 | 139 |
5.6575 | 15.5657 | 140 |
5.6499 | 15.1918 | 141 |
5.7592 | 14.9380 | 142 |
5.7694 | 15.3017 | 143 |
5.7230 | 15.3452 | 144 |
5.8646 | 14.7439 | 145 |
5.7644 | 15.3110 | 146 |
5.7203 | 15.8015 | 147 |
5.6472 | 15.6682 | 148 |
5.8608 | 15.1659 | 149 |
5.7988 | 15.8322 | 150 |
5.8471 | 15.3636 | 151 |
5.7727 | 15.7957 | 152 |
5.6508 | 15.1050 | 153 |
5.7816 | 15.3157 | 154 |
5.8210 | 15.6138 | 155 |
5.7539 | 15.1308 | 156 |
5.7941 | 15.3775 | 157 |
5.6000 | 15.4838 | 158 |
5.7631 | 15.5708 | 159 |
5.7086 | 15.5326 | 160 |
5.7873 | 15.4520 | 161 |
5.7033 | 15.1639 | 162 |
5.8096 | 14.9752 | 163 |
5.7741 | 15.1489 | 164 |
5.6837 | 15.8508 | 165 |
5.6194 | 15.6911 | 166 |
5.8802 | 15.8204 | 167 |
5.7230 | 15.3510 | 168 |
5.7498 | 15.5641 | 169 |
5.6593 | 16.0866 | 170 |
5.7106 | 15.6393 | 171 |
5.7150 | 15.7347 | 172 |
5.8900 | 15.1096 | 173 |
5.6876 | 16.0257 | 174 |
5.7460 | 15.7940 | 175 |
5.6921 | 15.4479 | 176 |
5.7380 | 15.8687 | 177 |
5.6652 | 15.7234 | 178 |
5.7337 | 15.9722 | 179 |
5.7107 | 15.6004 | 180 |
5.6688 | 15.9564 | 181 |
5.7698 | 15.4953 | 182 |
5.6752 | 15.4695 | 183 |
5.7951 | 15.3870 | 184 |
5.6820 | 15.3892 | 185 |
5.7012 | 15.3069 | 186 |
5.8445 | 15.4246 | 187 |
5.7400 | 15.7290 | 188 |
5.7490 | 15.6652 | 189 |
5.7101 | 15.9525 | 190 |
5.8126 | 15.2409 | 191 |
5.7394 | 15.6082 | 192 |
5.6442 | 15.7932 | 193 |
5.7402 | 15.1385 | 194 |
5.7884 | 15.4226 | 195 |
5.5444 | 16.1383 | 196 |
5.7094 | 15.8144 | 197 |
5.6645 | 16.0524 | 198 |
5.6880 | 15.7864 | 199 |
5.7136 | 15.5176 | 200 |
5.6637 | 15.2914 | 201 |
5.6428 | 16.0657 | 202 |
5.7652 | 16.0983 | 203 |
5.7069 | 16.2021 | 204 |
5.7251 | 15.6593 | 205 |
5.7546 | 15.5634 | 206 |
5.6854 | 16.0332 | 207 |
5.7590 | 15.9248 | 208 |
5.7916 | 15.4682 | 209 |
5.7794 | 16.1797 | 210 |
5.6640 | 15.8133 | 211 |
5.5957 | 15.9253 | 212 |
5.8607 | 16.3813 | 213 |
5.7572 | 16.1253 | 214 |
5.7549 | 16.0874 | 215 |
5.6915 | 16.3464 | 216 |
5.6458 | 16.7042 | 217 |
5.6706 | 15.7191 | 218 |
5.7297 | 16.0799 | 219 |
5.6525 | 15.7766 | 220 |
5.6576 | 16.2920 | 221 |
5.6630 | 15.7460 | 222 |
5.7052 | 16.1921 | 223 |
5.7096 | 15.9475 | 224 |
5.8168 | 15.6872 | 225 |
5.7823 | 16.1230 | 226 |
5.7059 | 15.8028 | 227 |
5.7665 | 16.0063 | 228 |
5.7908 | 16.2456 | 229 |
5.7071 | 16.0749 | 230 |
5.7506 | 16.0597 | 231 |
5.6364 | 16.1518 | 232 |
5.7050 | 16.2710 | 233 |
5.7266 | 16.2172 | 234 |
5.7804 | 15.8192 | 235 |
5.7076 | 16.1186 | 236 |
5.6965 | 16.1123 | 237 |
5.7385 | 15.7495 | 238 |
5.7877 | 16.0528 | 239 |
5.5933 | 16.1774 | 240 |
5.6745 | 16.5711 | 241 |
5.6913 | 16.3114 | 242 |
5.7292 | 16.3525 | 243 |
5.7804 | 15.9284 | 244 |
5.6428 | 16.1581 | 245 |
5.6294 | 16.0005 | 246 |
5.7076 | 16.2986 | 247 |
5.7254 | 16.1134 | 248 |
5.7657 | 16.3133 | 249 |
5.7027 | 15.9364 | 250 |
5.6698 | 16.6521 | 251 |
5.6628 | 15.7597 | 252 |
5.6293 | 16.2164 | 253 |
5.6199 | 15.8001 | 254 |
5.6581 | 16.0590 | 255 |
5.6714 | 16.0894 | 256 |
5.7542 | 16.1121 | 257 |
5.7222 | 16.1358 | 258 |
5.8194 | 15.9213 | 259 |
5.6567 | 16.0372 | 260 |
5.6751 | 16.3755 | 261 |
5.6469 | 16.0893 | 262 |
5.6717 | 16.1122 | 263 |
5.7958 | 15.8767 | 264 |
5.8171 | 16.2429 | 265 |
5.7119 | 16.2250 | 266 |
5.7855 | 16.4065 | 267 |
5.7949 | 15.4456 | 268 |
5.7782 | 16.4003 | 269 |
5.7497 | 15.8927 | 270 |
5.6609 | 16.3852 | 271 |
5.8071 | 16.3739 | 272 |
5.7726 | 15.9497 | 273 |
5.6874 | 16.0825 | 274 |
5.7265 | 15.9471 | 275 |
5.8261 | 15.9685 | 276 |
5.6508 | 16.3370 | 277 |
5.6734 | 16.3040 | 278 |
5.6986 | 16.2803 | 279 |
5.7025 | 16.6162 | 280 |
5.7346 | 16.1483 | 281 |
5.6689 | 16.1718 | 282 |
5.6913 | 16.0822 | 283 |
5.7541 | 15.5025 | 284 |
5.7325 | 16.2702 | 285 |
5.8124 | 15.7343 | 286 |
5.6972 | 16.3263 | 287 |
5.7388 | 16.2631 | 288 |
5.7337 | 16.1185 | 289 |
5.5873 | 16.2938 | 290 |
5.6859 | 16.2026 | 291 |
5.8711 | 15.2906 | 292 |
5.6716 | 15.5945 | 293 |
5.7098 | 16.1930 | 294 |
5.6214 | 16.2915 | 295 |
5.7025 | 16.1317 | 296 |
5.6574 | 16.6203 | 297 |
5.7909 | 16.1098 | 298 |
5.8298 | 15.5068 | 299 |
5.7457 | 16.4390 | 300 |
5.6674 | 16.1994 | 301 |
5.6473 | 16.4981 | 302 |
5.7486 | 16.0311 | 303 |
5.5914 | 16.8805 | 304 |
5.7065 | 15.6899 | 305 |
5.7049 | 16.4121 | 306 |
5.7791 | 16.5573 | 307 |
5.7422 | 16.4121 | 308 |
5.6493 | 15.8135 | 309 |
5.7504 | 16.1579 | 310 |
5.7983 | 15.7995 | 311 |
5.6670 | 15.7541 | 312 |
5.6519 | 15.8321 | 313 |
5.7987 | 16.1384 | 314 |
5.8223 | 16.2025 | 315 |
5.7494 | 16.2737 | 316 |
5.7390 | 16.2242 | 317 |
5.7607 | 16.4318 | 318 |
5.6872 | 16.4799 | 319 |
5.7474 | 16.3387 | 320 |
5.8631 | 16.0204 | 321 |
5.7391 | 15.9252 | 322 |
5.6785 | 17.0023 | 323 |
5.6791 | 16.0897 | 324 |
5.7644 | 16.0529 | 325 |
5.7013 | 16.6206 | 326 |
5.8280 | 16.4234 | 327 |
5.6553 | 16.4436 | 328 |
5.6920 | nan | 329 |
5.7356 | 16.2001 | 330 |
5.7204 | 16.1120 | 331 |
5.5542 | 16.2315 | 332 |
5.7303 | 16.2902 | 333 |
5.6750 | 16.3221 | 334 |
5.6405 | 16.5904 | 335 |
5.8191 | 16.0871 | 336 |
5.7464 | 15.8915 | 337 |
5.7566 | 16.6865 | 338 |
5.7168 | 16.0537 | 339 |
5.6113 | 16.5037 | 340 |
5.6066 | 15.8096 | 341 |
5.6343 | 16.5955 | 342 |
nan | nan | 343 |
nan | nan | 344 |
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nan | nan | 362 |
nan | nan | 363 |
nan | nan | 364 |
nan | nan | 365 |
nan | nan | 366 |
nan | nan | 367 |
nan | nan | 368 |
nan | nan | 369 |
nan | nan | 370 |
nan | nan | 371 |
nan | nan | 372 |
nan | nan | 373 |
nan | nan | 374 |
nan | nan | 375 |
nan | nan | 376 |
nan | nan | 377 |
nan | nan | 378 |
nan | nan | 379 |
nan | nan | 380 |
nan | nan | 381 |
nan | nan | 382 |
nan | nan | 383 |
nan | nan | 384 |
nan | nan | 385 |
nan | nan | 386 |
nan | nan | 387 |
nan | nan | 388 |
nan | nan | 389 |
nan | nan | 390 |
nan | nan | 391 |
nan | nan | 392 |
nan | nan | 393 |
nan | nan | 394 |
nan | nan | 395 |
nan | nan | 396 |
nan | nan | 397 |
nan | nan | 398 |
nan | nan | 399 |
Framework versions
- Transformers 4.27.0.dev0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2