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bedus-creation/mbart-small-dataset-ii-eng-to-lim-005
This model is a fine-tuned version of mbart-50 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 4.7245
- Validation Loss: 6.1589
- Epoch: 349
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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 8.4366 | 7.8649 | 0 |
| 7.8684 | 7.6440 | 1 |
| 7.7002 | 7.5328 | 2 |
| 7.5948 | 7.4486 | 3 |
| 7.5176 | 7.3868 | 4 |
| 7.4560 | 7.3324 | 5 |
| 7.4044 | 7.2855 | 6 |
| 7.3559 | 7.2365 | 7 |
| 7.3105 | 7.1809 | 8 |
| 7.2556 | 7.1305 | 9 |
| 7.2074 | 7.0882 | 10 |
| 7.1645 | 7.0523 | 11 |
| 7.1267 | 7.0236 | 12 |
| 7.0951 | 6.9883 | 13 |
| 7.0593 | 6.9593 | 14 |
| 7.0349 | 6.9400 | 15 |
| 7.0110 | 6.9160 | 16 |
| 6.9824 | 6.8902 | 17 |
| 6.9607 | 6.8716 | 18 |
| 6.9412 | 6.8525 | 19 |
| 6.9182 | 6.8337 | 20 |
| 6.8982 | 6.8178 | 21 |
| 6.8824 | 6.7984 | 22 |
| 6.8617 | 6.7825 | 23 |
| 6.8442 | 6.7660 | 24 |
| 6.8259 | 6.7494 | 25 |
| 6.8097 | 6.7386 | 26 |
| 6.7982 | 6.7210 | 27 |
| 6.7809 | 6.7095 | 28 |
| 6.7623 | 6.7007 | 29 |
| 6.7463 | 6.6821 | 30 |
| 6.7365 | 6.6703 | 31 |
| 6.7197 | 6.6623 | 32 |
| 6.7048 | 6.6462 | 33 |
| 6.6967 | 6.6421 | 34 |
| 6.6796 | 6.6343 | 35 |
| 6.6644 | 6.6172 | 36 |
| 6.6519 | 6.6143 | 37 |
| 6.6419 | 6.5981 | 38 |
| 6.6274 | 6.5878 | 39 |
| 6.6165 | 6.5824 | 40 |
| 6.6036 | 6.5701 | 41 |
| 6.5878 | 6.5622 | 42 |
| 6.5831 | 6.5504 | 43 |
| 6.5689 | 6.5434 | 44 |
| 6.5584 | 6.5383 | 45 |
| 6.5399 | 6.5246 | 46 |
| 6.5335 | 6.5189 | 47 |
| 6.5220 | 6.5079 | 48 |
| 6.5128 | 6.4998 | 49 |
| 6.5000 | 6.4904 | 50 |
| 6.4916 | 6.4851 | 51 |
| 6.4780 | 6.4783 | 52 |
| 6.4646 | 6.4720 | 53 |
| 6.4613 | 6.4552 | 54 |
| 6.4490 | 6.4510 | 55 |
| 6.4343 | 6.4442 | 56 |
| 6.4277 | 6.4371 | 57 |
| 6.4194 | 6.4313 | 58 |
| 6.4047 | 6.4199 | 59 |
| 6.3960 | 6.4106 | 60 |
| 6.3860 | 6.4075 | 61 |
| 6.3724 | 6.4045 | 62 |
| 6.3687 | 6.4019 | 63 |
| 6.3549 | 6.3878 | 64 |
| 6.3448 | 6.3807 | 65 |
| 6.3413 | 6.3781 | 66 |
| 6.3290 | 6.3738 | 67 |
| 6.3190 | 6.3642 | 68 |
| 6.3131 | 6.3598 | 69 |
| 6.2984 | 6.3536 | 70 |
| 6.2902 | 6.3422 | 71 |
| 6.2861 | 6.3377 | 72 |
| 6.2722 | 6.3377 | 73 |
| 6.2680 | 6.3278 | 74 |
| 6.2566 | 6.3217 | 75 |
| 6.2483 | 6.3172 | 76 |
| 6.2423 | 6.3098 | 77 |
| 6.2298 | 6.3081 | 78 |
| 6.2227 | 6.3011 | 79 |
| 6.2144 | 6.2932 | 80 |
| 6.2101 | 6.2905 | 81 |
| 6.1995 | 6.2877 | 82 |
| 6.1914 | 6.2838 | 83 |
| 6.1854 | 6.2800 | 84 |
| 6.1717 | 6.2722 | 85 |
| 6.1653 | 6.2689 | 86 |
| 6.1523 | 6.2678 | 87 |
| 6.1478 | 6.2577 | 88 |
| 6.1426 | 6.2567 | 89 |
| 6.1373 | 6.2535 | 90 |
| 6.1280 | 6.2511 | 91 |
| 6.1219 | 6.2371 | 92 |
| 6.1153 | 6.2373 | 93 |
| 6.1040 | 6.2347 | 94 |
| 6.0969 | 6.2340 | 95 |
| 6.0923 | 6.2320 | 96 |
| 6.0803 | 6.2222 | 97 |
| 6.0725 | 6.2178 | 98 |
| 6.0729 | 6.2144 | 99 |
| 6.0577 | 6.2236 | 100 |
| 6.0550 | 6.2041 | 101 |
| 6.0484 | 6.2030 | 102 |
| 6.0361 | 6.2051 | 103 |
| 6.0302 | 6.1977 | 104 |
| 6.0218 | 6.1937 | 105 |
| 6.0174 | 6.1935 | 106 |
| 6.0073 | 6.1899 | 107 |
| 6.0060 | 6.1883 | 108 |
| 5.9978 | 6.1783 | 109 |
| 5.9896 | 6.1827 | 110 |
| 5.9777 | 6.1770 | 111 |
| 5.9778 | 6.1693 | 112 |
| 5.9708 | 6.1707 | 113 |
| 5.9673 | 6.1590 | 114 |
| 5.9527 | 6.1713 | 115 |
| 5.9481 | 6.1604 | 116 |
| 5.9424 | 6.1603 | 117 |
| 5.9370 | 6.1547 | 118 |
| 5.9304 | 6.1574 | 119 |
| 5.9178 | 6.1506 | 120 |
| 5.9134 | 6.1478 | 121 |
| 5.9063 | 6.1440 | 122 |
| 5.8979 | 6.1406 | 123 |
| 5.8954 | 6.1384 | 124 |
| 5.8916 | 6.1418 | 125 |
| 5.8832 | 6.1362 | 126 |
| 5.8768 | 6.1319 | 127 |
| 5.8658 | 6.1348 | 128 |
| 5.8624 | 6.1318 | 129 |
| 5.8533 | 6.1196 | 130 |
| 5.8543 | 6.1273 | 131 |
| 5.8467 | 6.1118 | 132 |
| 5.8442 | 6.1191 | 133 |
| 5.8304 | 6.1320 | 134 |
| 5.8203 | 6.1158 | 135 |
| 5.8213 | 6.1142 | 136 |
| 5.8104 | 6.1116 | 137 |
| 5.8094 | 6.1126 | 138 |
| 5.7985 | 6.1105 | 139 |
| 5.7935 | 6.1018 | 140 |
| 5.7890 | 6.0984 | 141 |
| 5.7830 | 6.1016 | 142 |
| 5.7746 | 6.0977 | 143 |
| 5.7674 | 6.0997 | 144 |
| 5.7672 | 6.1080 | 145 |
| 5.7610 | 6.1039 | 146 |
| 5.7481 | 6.0915 | 147 |
| 5.7424 | 6.0873 | 148 |
| 5.7376 | 6.1008 | 149 |
| 5.7373 | 6.0831 | 150 |
| 5.7297 | 6.0911 | 151 |
| 5.7246 | 6.0920 | 152 |
| 5.7212 | 6.0897 | 153 |
| 5.7130 | 6.0784 | 154 |
| 5.7075 | 6.0794 | 155 |
| 5.6996 | 6.0880 | 156 |
| 5.6904 | 6.0793 | 157 |
| 5.6885 | 6.0713 | 158 |
| 5.6852 | 6.0854 | 159 |
| 5.6778 | 6.0719 | 160 |
| 5.6744 | 6.0712 | 161 |
| 5.6658 | 6.0784 | 162 |
| 5.6502 | 6.0747 | 163 |
| 5.6529 | 6.0715 | 164 |
| 5.6495 | 6.0735 | 165 |
| 5.6423 | 6.0722 | 166 |
| 5.6295 | 6.0707 | 167 |
| 5.6348 | 6.0691 | 168 |
| 5.6265 | 6.0762 | 169 |
| 5.6196 | 6.0679 | 170 |
| 5.6145 | 6.0675 | 171 |
| 5.6079 | 6.0622 | 172 |
| 5.6054 | 6.0676 | 173 |
| 5.5981 | 6.0658 | 174 |
| 5.5913 | 6.0607 | 175 |
| 5.5825 | 6.0546 | 176 |
| 5.5814 | 6.0588 | 177 |
| 5.5798 | 6.0482 | 178 |
| 5.5649 | 6.0603 | 179 |
| 5.5668 | 6.0510 | 180 |
| 5.5597 | 6.0643 | 181 |
| 5.5475 | 6.0641 | 182 |
| 5.5528 | 6.0585 | 183 |
| 5.5409 | 6.0620 | 184 |
| 5.5352 | 6.0466 | 185 |
| 5.5403 | 6.0507 | 186 |
| 5.5293 | 6.0510 | 187 |
| 5.5201 | 6.0662 | 188 |
| 5.5154 | 6.0554 | 189 |
| 5.5134 | 6.0430 | 190 |
| 5.5063 | 6.0596 | 191 |
| 5.4987 | 6.0458 | 192 |
| 5.4974 | 6.0416 | 193 |
| 5.4857 | 6.0499 | 194 |
| 5.4817 | 6.0659 | 195 |
| 5.4750 | 6.0540 | 196 |
| 5.4719 | 6.0493 | 197 |
| 5.4618 | 6.0423 | 198 |
| 5.4644 | 6.0460 | 199 |
| 5.4526 | 6.0523 | 200 |
| 5.4507 | 6.0451 | 201 |
| 5.4504 | 6.0430 | 202 |
| 5.4412 | 6.0421 | 203 |
| 5.4377 | 6.0492 | 204 |
| 5.4367 | 6.0482 | 205 |
| 5.4190 | 6.0259 | 206 |
| 5.4210 | 6.0281 | 207 |
| 5.4191 | 6.0418 | 208 |
| 5.4090 | 6.0383 | 209 |
| 5.4051 | 6.0445 | 210 |
| 5.3975 | 6.0565 | 211 |
| 5.3942 | 6.0581 | 212 |
| 5.3930 | 6.0509 | 213 |
| 5.3825 | 6.0506 | 214 |
| 5.3811 | 6.0428 | 215 |
| 5.3722 | 6.0368 | 216 |
| 5.3676 | 6.0392 | 217 |
| 5.3655 | 6.0460 | 218 |
| 5.3577 | 6.0488 | 219 |
| 5.3539 | 6.0431 | 220 |
| 5.3497 | 6.0410 | 221 |
| 5.3433 | 6.0381 | 222 |
| 5.3437 | 6.0376 | 223 |
| 5.3369 | 6.0409 | 224 |
| 5.3283 | 6.0320 | 225 |
| 5.3231 | 6.0516 | 226 |
| 5.3160 | 6.0432 | 227 |
| 5.3075 | 6.0544 | 228 |
| 5.3095 | 6.0537 | 229 |
| 5.3025 | 6.0458 | 230 |
| 5.2969 | 6.0451 | 231 |
| 5.2807 | 6.0449 | 232 |
| 5.2925 | 6.0455 | 233 |
| 5.2767 | 6.0551 | 234 |
| 5.2778 | 6.0392 | 235 |
| 5.2713 | 6.0419 | 236 |
| 5.2691 | 6.0435 | 237 |
| 5.2570 | 6.0495 | 238 |
| 5.2574 | 6.0301 | 239 |
| 5.2521 | 6.0362 | 240 |
| 5.2458 | 6.0449 | 241 |
| 5.2352 | 6.0462 | 242 |
| 5.2389 | 6.0425 | 243 |
| 5.2265 | 6.0372 | 244 |
| 5.2297 | 6.0372 | 245 |
| 5.2244 | 6.0580 | 246 |
| 5.2181 | 6.0523 | 247 |
| 5.2061 | 6.0487 | 248 |
| 5.2100 | 6.0475 | 249 |
| 5.1985 | 6.0405 | 250 |
| 5.1945 | 6.0451 | 251 |
| 5.1911 | 6.0552 | 252 |
| 5.1839 | 6.0503 | 253 |
| 5.1829 | 6.0510 | 254 |
| 5.1797 | 6.0456 | 255 |
| 5.1747 | 6.0627 | 256 |
| 5.1652 | 6.0384 | 257 |
| 5.1659 | 6.0546 | 258 |
| 5.1449 | 6.0503 | 259 |
| 5.1592 | 6.0514 | 260 |
| 5.1448 | 6.0491 | 261 |
| 5.1405 | 6.0556 | 262 |
| 5.1391 | 6.0594 | 263 |
| 5.1346 | 6.0362 | 264 |
| 5.1275 | 6.0367 | 265 |
| 5.1218 | 6.0447 | 266 |
| 5.1144 | 6.0636 | 267 |
| 5.1152 | 6.0556 | 268 |
| 5.1083 | 6.0503 | 269 |
| 5.1046 | 6.0597 | 270 |
| 5.0923 | 6.0726 | 271 |
| 5.0988 | 6.0692 | 272 |
| 5.0926 | 6.0654 | 273 |
| 5.0892 | 6.0757 | 274 |
| 5.0772 | 6.0547 | 275 |
| 5.0774 | 6.0703 | 276 |
| 5.0696 | 6.0715 | 277 |
| 5.0645 | 6.0838 | 278 |
| 5.0599 | 6.0687 | 279 |
| 5.0565 | 6.0621 | 280 |
| 5.0535 | 6.0846 | 281 |
| 5.0409 | 6.0779 | 282 |
| 5.0413 | 6.0753 | 283 |
| 5.0380 | 6.0609 | 284 |
| 5.0336 | 6.0889 | 285 |
| 5.0248 | 6.0762 | 286 |
| 5.0230 | 6.0876 | 287 |
| 5.0155 | 6.0588 | 288 |
| 5.0121 | 6.0788 | 289 |
| 5.0035 | 6.0777 | 290 |
| 5.0067 | 6.0848 | 291 |
| 5.0016 | 6.0831 | 292 |
| 4.9929 | 6.0991 | 293 |
| 4.9889 | 6.1011 | 294 |
| 4.9837 | 6.0805 | 295 |
| 4.9777 | 6.0858 | 296 |
| 4.9738 | 6.0803 | 297 |
| 4.9708 | 6.0757 | 298 |
| 4.9677 | 6.0886 | 299 |
| 4.9630 | 6.0828 | 300 |
| 4.9541 | 6.0883 | 301 |
| 4.9541 | 6.1026 | 302 |
| 4.9453 | 6.0925 | 303 |
| 4.9385 | 6.0854 | 304 |
| 4.9337 | 6.1038 | 305 |
| 4.9290 | 6.0854 | 306 |
| 4.9287 | 6.1008 | 307 |
| 4.9214 | 6.1174 | 308 |
| 4.9151 | 6.1056 | 309 |
| 4.9118 | 6.0934 | 310 |
| 4.9087 | 6.0919 | 311 |
| 4.8985 | 6.1064 | 312 |
| 4.9003 | 6.1010 | 313 |
| 4.8951 | 6.1118 | 314 |
| 4.8824 | 6.1020 | 315 |
| 4.8834 | 6.1020 | 316 |
| 4.8764 | 6.1173 | 317 |
| 4.8704 | 6.1189 | 318 |
| 4.8690 | 6.0976 | 319 |
| 4.8662 | 6.1058 | 320 |
| 4.8586 | 6.1060 | 321 |
| 4.8571 | 6.1026 | 322 |
| 4.8514 | 6.1102 | 323 |
| 4.8426 | 6.1298 | 324 |
| 4.8375 | 6.1047 | 325 |
| 4.8341 | 6.1111 | 326 |
| 4.8303 | 6.1144 | 327 |
| 4.8320 | 6.1271 | 328 |
| 4.8190 | 6.1221 | 329 |
| 4.8214 | 6.1342 | 330 |
| 4.8055 | 6.1497 | 331 |
| 4.8082 | 6.1288 | 332 |
| 4.7967 | 6.1218 | 333 |
| 4.7966 | 6.1433 | 334 |
| 4.7859 | 6.1117 | 335 |
| 4.7841 | 6.1447 | 336 |
| 4.7871 | 6.1406 | 337 |
| 4.7743 | 6.1606 | 338 |
| 4.7696 | 6.1391 | 339 |
| 4.7652 | 6.1216 | 340 |
| 4.7684 | 6.1420 | 341 |
| 4.7607 | 6.1365 | 342 |
| 4.7596 | 6.1462 | 343 |
| 4.7539 | 6.1352 | 344 |
| 4.7382 | 6.1507 | 345 |
| 4.7425 | 6.1461 | 346 |
| 4.7299 | 6.1556 | 347 |
| 4.7268 | 6.1298 | 348 |
| 4.7245 | 6.1589 | 349 |
Framework versions
- Transformers 4.33.3
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
