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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
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