NMTIndoBaliBART / README.md
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metadata
license: apache-2.0
base_model: facebook/bart-base
tags:
  - generated_from_keras_callback
model-index:
  - name: pijarcandra22/NMTIndoBaliBART
    results: []

pijarcandra22/NMTIndoBaliBART

This model is a fine-tuned version of facebook/bart-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 5.5376
  • Validation Loss: 5.5549
  • Epoch: 287

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': 0.02, '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
9.7885 5.6003 0
5.5737 5.5523 1
5.5346 5.5361 2
5.5189 5.5283 3
5.5149 5.5252 4
5.5123 5.5233 5
5.5116 5.5485 6
5.5095 5.5314 7
5.5120 5.5569 8
5.5137 5.5239 9
5.5170 5.5289 10
5.5180 5.5298 11
5.5217 5.5513 12
5.5219 5.5344 13
5.5248 5.5366 14
5.5268 5.5493 15
5.5260 5.5313 16
5.5290 5.5462 17
5.5299 5.5570 18
5.5293 5.5480 19
5.5378 5.5524 20
5.5317 5.5740 21
5.5328 5.5543 22
5.5327 5.5537 23
5.5330 5.5356 24
5.5304 5.5492 25
5.5355 5.5388 26
5.5337 5.5812 27
5.5355 5.5598 28
5.5348 5.5489 29
5.5373 5.5526 30
5.5357 5.5575 31
5.5377 5.5439 32
5.5404 5.5367 33
5.5383 5.5819 34
5.5359 5.5815 35
5.5370 5.5499 36
5.5340 5.5622 37
5.5373 5.5667 38
5.5360 5.5548 39
5.5327 5.5555 40
5.5365 5.5642 41
5.5375 5.5496 42
5.5336 5.5424 43
5.5359 5.5761 44
5.5360 5.5821 45
5.5362 5.5742 46
5.5352 5.5635 47
5.5335 5.5507 48
5.5340 5.5613 49
5.5368 5.5599 50
5.5375 5.5541 51
5.5368 5.5536 52
5.5366 5.5438 53
5.5363 5.5497 54
5.5364 5.5721 55
5.5388 5.5493 56
5.5361 5.5719 57
5.5372 5.5920 58
5.5346 5.5534 59
5.5354 5.5526 60
5.5357 5.5788 61
5.5370 5.5531 62
5.5374 5.5613 63
5.5366 5.5585 64
5.5370 5.5652 65
5.5354 5.5463 66
5.5354 5.5689 67
5.5355 5.5508 68
5.5350 5.5522 69
5.5334 5.5574 70
5.5354 5.5555 71
5.5354 5.5503 72
5.5368 5.5562 73
5.5373 5.5524 74
5.5356 5.5544 75
5.5365 5.5508 76
5.5357 5.5650 77
5.5355 5.5665 78
5.5365 5.5471 79
5.5356 5.5535 80
5.5357 5.5801 81
5.5354 5.5570 82
5.5361 5.5596 83
5.5377 5.5584 84
5.5333 5.5570 85
5.5348 5.5513 86
5.5367 5.5508 87
5.5354 5.5333 88
5.5375 5.5530 89
5.5353 5.5386 90
5.5372 5.5966 91
5.5365 5.5582 92
5.5349 5.5776 93
5.5348 5.5700 94
5.5356 5.5518 95
5.5371 5.5692 96
5.5374 5.5572 97
5.5369 5.5621 98
5.5343 5.5593 99
5.5372 5.5698 100
5.5367 5.5422 101
5.5366 5.5846 102
5.5387 5.5687 103
5.5377 5.5590 104
5.5307 5.5640 105
5.5360 5.5421 106
5.5355 5.5542 107
5.5346 5.5460 108
5.5375 5.5610 109
5.5332 5.5676 110
5.5355 5.5364 111
5.5332 5.5630 112
5.5363 5.5600 113
5.5362 5.5705 114
5.5358 5.5700 115
5.5368 5.5578 116
5.5364 5.5531 117
5.5345 5.5688 118
5.5350 5.5620 119
5.5336 5.5764 120
5.5364 5.5476 121
5.5358 5.5623 122
5.5364 5.5569 123
5.5337 5.5713 124
5.5346 5.5936 125
5.5357 5.5645 126
5.5358 5.5566 127
5.5399 5.5494 128
5.5362 5.5586 129
5.5394 5.5611 130
5.5355 5.5691 131
5.5361 5.5471 132
5.5343 5.5549 133
5.5379 5.5587 134
5.5380 5.5728 135
5.5366 5.5780 136
5.5363 5.5567 137
5.5395 5.5515 138
5.5337 5.5563 139
5.5341 5.5561 140
5.5336 5.5520 141
5.5340 5.5689 142
5.5363 5.5472 143
5.5356 5.5503 144
5.5338 5.5630 145
5.5357 5.5385 146
5.5349 5.5545 147
5.5363 5.5514 148
5.5361 5.5591 149
5.5379 5.5567 150
5.5340 5.5434 151
5.5362 5.5677 152
5.5350 5.5574 153
5.5356 5.5782 154
5.5371 5.5484 155
5.5330 5.5557 156
5.5340 5.5583 157
5.5350 5.5641 158
5.5350 5.5595 159
5.5356 5.5622 160
5.5386 5.5545 161
5.5347 5.5667 162
5.5318 5.5692 163
5.5370 5.5704 164
5.5361 5.5654 165
5.5358 5.5645 166
5.5337 5.5518 167
5.5356 5.5574 168
5.5364 5.5621 169
5.5360 5.5573 170
5.5388 5.5438 171
5.5343 5.5623 172
5.5368 5.5505 173
5.5345 5.5798 174
5.5369 5.5449 175
5.5364 5.5664 176
5.5365 5.5530 177
5.5369 5.5533 178
5.5348 5.5432 179
5.5379 5.5875 180
5.5370 5.5531 181
5.5340 5.5695 182
5.5372 5.5529 183
5.5356 5.5778 184
5.5371 5.5465 185
5.5370 5.5459 186
5.5356 5.5457 187
5.5375 5.5761 188
5.5338 5.5409 189
5.5369 5.5698 190
5.5373 5.5665 191
5.5361 5.5677 192
5.5355 5.5775 193
5.5372 5.5649 194
5.5355 5.5477 195
5.5328 5.5492 196
5.5342 5.5575 197
5.5331 5.5774 198
5.5362 5.5631 199
5.5350 5.5539 200
5.5365 5.5799 201
5.5372 5.5630 202
5.5341 5.5584 203
5.5353 5.5616 204
5.5351 5.5764 205
5.5374 5.5692 206
5.5363 5.5608 207
5.5345 5.5611 208
5.5381 5.5643 209
5.5363 5.5719 210
5.5386 5.5536 211
5.5329 5.5757 212
5.5360 5.5405 213
5.5356 5.5525 214
5.5354 5.5423 215
5.5382 5.5476 216
5.5353 5.5623 217
5.5344 5.5716 218
5.5361 5.5569 219
5.5369 5.5536 220
5.5370 5.5726 221
5.5366 5.5520 222
5.5370 5.5698 223
5.5342 5.5522 224
5.5367 5.5438 225
5.5373 5.5474 226
5.5317 5.5634 227
5.5350 5.5669 228
5.5360 5.5631 229
5.5370 5.5553 230
5.5347 5.5452 231
5.5347 5.5600 232
5.5351 5.5551 233
5.5360 5.5625 234
5.5409 5.5640 235
5.5362 5.5596 236
5.5340 5.5506 237
5.5372 5.5549 238
5.5340 5.5879 239
5.5355 5.5609 240
5.5376 5.5627 241
5.5354 5.5903 242
5.5358 5.5591 243
5.5327 5.5638 244
5.5334 5.5449 245
5.5330 5.5552 246
5.5338 5.5721 247
5.5359 5.5736 248
5.5361 5.5440 249
5.5377 5.5656 250
5.5353 5.5690 251
5.5375 5.5540 252
5.5357 5.5555 253
5.5349 5.5658 254
5.5365 5.5563 255
5.5327 5.5544 256
5.5346 5.5851 257
5.5372 5.5556 258
5.5373 5.5504 259
5.5361 5.5657 260
5.5348 5.5585 261
5.5349 5.5664 262
5.5343 5.5454 263
5.5351 5.5820 264
5.5334 5.5521 265
5.5361 5.5648 266
5.5375 5.5596 267
5.5363 5.5525 268
5.5377 5.5752 269
5.5359 5.5523 270
5.5347 5.5662 271
5.5363 5.5613 272
5.5370 5.5529 273
5.5360 5.5523 274
5.5363 5.5548 275
5.5342 5.5523 276
5.5318 5.5659 277
5.5376 5.5582 278
5.5327 5.5649 279
5.5339 5.5665 280
5.5373 5.5693 281
5.5324 5.5660 282
5.5352 5.5580 283
5.5362 5.5770 284
5.5383 5.5431 285
5.5337 5.5632 286
5.5376 5.5549 287

Framework versions

  • Transformers 4.40.2
  • TensorFlow 2.15.0
  • Datasets 2.19.1
  • Tokenizers 0.19.1