NMTBaliIndoBART / README.md
pijarcandra22's picture
Training in progress epoch 329
325f60f
metadata
license: apache-2.0
base_model: facebook/bart-base
tags:
  - generated_from_keras_callback
model-index:
  - name: pijarcandra22/NMTBaliIndoBART
    results: []

pijarcandra22/NMTBaliIndoBART

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: 5.4651
  • Validation Loss: 6.1406
  • Epoch: 329

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
9.3368 5.6757 0
5.5627 5.5987 1
5.5311 5.5419 2
5.5152 5.5201 3
5.5005 5.6477 4
5.4704 5.5914 5
5.4610 6.0922 6
5.4584 5.7137 7
5.4528 5.8658 8
5.4820 5.5628 9
5.4874 5.5309 10
5.4917 5.7595 11
5.4898 5.7333 12
5.4833 5.6789 13
5.4767 5.9588 14
5.4883 5.9895 15
5.4694 6.0100 16
5.4663 6.0316 17
5.4602 5.9233 18
5.4576 6.0051 19
5.4559 5.9966 20
5.4651 6.0025 21
5.4660 6.0160 22
5.4626 5.8324 23
5.4647 5.8383 24
5.4695 6.0272 25
5.4614 6.0724 26
5.4623 5.9454 27
5.4678 6.0196 28
5.4860 5.5949 29
5.4851 5.8838 30
5.4666 5.8506 31
5.4715 6.0391 32
5.4630 6.0870 33
5.4646 6.2195 34
5.4574 5.9696 35
5.4564 5.8970 36
5.4570 5.9522 37
5.4559 6.1518 38
5.4584 6.1860 39
5.4732 6.1168 40
5.4625 6.1588 41
5.4601 5.9868 42
5.4645 5.9606 43
5.4664 6.1495 44
5.4698 6.0152 45
5.4666 6.2713 46
5.4557 6.2708 47
5.4557 6.0003 48
5.4693 5.9321 49
5.4928 5.8971 50
5.5032 6.0766 51
5.4749 5.8919 52
5.4689 5.9853 53
5.4665 5.9329 54
5.4574 5.9770 55
5.4686 6.1022 56
5.4727 5.8973 57
5.4692 5.9633 58
5.4608 6.0480 59
5.4613 5.9596 60
5.4607 6.1158 61
5.4531 6.0617 62
5.4610 6.0375 63
5.4631 6.1184 64
5.4627 6.0465 65
5.4685 6.0011 66
5.4642 6.0828 67
5.4577 6.0883 68
5.4615 5.9523 69
5.4673 5.7216 70
5.4724 6.0274 71
5.4601 6.0344 72
5.4640 5.9661 73
5.4590 6.0013 74
5.4622 6.0172 75
5.4666 5.8407 76
5.4669 6.0261 77
5.4859 5.9295 78
5.5042 6.1254 79
5.4845 5.8930 80
5.5001 5.8867 81
5.4923 5.9480 82
5.4909 6.0475 83
5.4780 5.9289 84
5.4867 5.8134 85
5.4877 6.0032 86
5.4806 6.0884 87
5.4784 6.0567 88
5.4830 5.9790 89
5.4894 5.8919 90
5.4890 5.9626 91
5.4774 6.0267 92
5.5033 6.1150 93
5.4765 5.9776 94
5.4657 6.1395 95
5.4720 5.9938 96
5.4748 5.9656 97
5.4701 6.0163 98
5.4718 6.1462 99
5.4672 6.0804 100
5.4775 6.1055 101
5.4775 6.0936 102
5.4673 5.9839 103
5.4691 5.8972 104
5.4694 5.8271 105
5.5106 5.5305 106
5.5135 5.8806 107
5.4786 6.1380 108
5.4770 5.9899 109
5.4709 6.1072 110
5.4701 5.9356 111
5.4636 5.8304 112
5.4670 6.0451 113
5.4598 6.0311 114
5.4731 5.9862 115
5.4798 5.9589 116
5.4674 5.9356 117
5.4634 6.0088 118
5.4709 5.9534 119
5.4891 5.9995 120
5.4737 5.8611 121
5.4725 6.0112 122
5.4835 5.6280 123
5.5217 5.6917 124
5.4821 5.9458 125
5.4898 5.7593 126
5.4866 5.9110 127
5.4744 5.9463 128
5.4673 6.0359 129
5.4838 6.0166 130
5.4864 6.0046 131
5.4896 5.9479 132
5.4722 6.0699 133
5.4627 6.0684 134
5.4690 6.0577 135
5.4666 6.1473 136
5.4655 6.0441 137
5.4665 5.9313 138
5.4588 6.1375 139
5.4575 6.1655 140
5.4609 5.9701 141
5.4666 6.0677 142
5.4672 6.1272 143
5.4776 6.2186 144
5.4769 5.9815 145
5.4666 6.0674 146
5.4670 6.0282 147
5.4868 5.7416 148
5.4901 6.0836 149
5.4877 5.9086 150
5.4842 5.8724 151
5.5167 5.7298 152
5.5043 5.7802 153
5.4737 6.0805 154
5.4805 6.0888 155
5.4765 5.9967 156
5.4691 5.9332 157
5.4697 6.0675 158
5.4648 6.0689 159
5.4658 5.9954 160
5.4721 5.8917 161
5.4641 5.8973 162
5.4703 6.0126 163
5.4753 5.9064 164
5.4731 6.0835 165
5.5094 5.5720 166
5.5355 5.9077 167
5.4791 6.0669 168
5.4690 6.0729 169
5.4635 5.9580 170
5.4698 6.1453 171
5.4668 5.9952 172
5.4728 6.0041 173
5.5062 6.1592 174
5.4944 5.9536 175
5.4802 5.9673 176
5.4710 5.9888 177
5.4653 6.0656 178
5.4618 6.0278 179
5.4659 5.9563 180
5.4596 6.0022 181
5.4627 5.9594 182
5.4688 5.8462 183
5.4662 5.9550 184
5.4646 5.9757 185
5.4753 5.9400 186
5.4911 5.7438 187
5.4681 6.0941 188
5.4719 6.0324 189
5.4692 6.0313 190
5.4634 5.9874 191
5.4639 5.9928 192
5.4714 6.0265 193
5.4569 5.8387 194
5.4606 6.0462 195
5.4667 5.9636 196
5.4653 6.0299 197
5.4623 6.0311 198
5.4629 5.9745 199
5.4630 5.9398 200
5.4618 5.9005 201
5.4611 5.8718 202
5.4979 5.7893 203
5.4995 5.8556 204
5.4949 5.9533 205
5.4806 6.0033 206
5.4700 6.0395 207
5.4601 6.0592 208
5.4605 6.1408 209
5.4638 6.0469 210
5.4592 6.1216 211
5.4646 6.0284 212
5.4607 5.8940 213
5.4573 5.8946 214
5.4690 5.8057 215
5.5077 5.8491 216
5.4734 5.9847 217
5.4859 5.9075 218
5.4889 6.0483 219
5.4837 6.0959 220
5.4878 5.9962 221
5.4854 5.9575 222
5.4763 6.0648 223
5.4890 5.9731 224
5.4866 5.9771 225
5.4906 5.8407 226
5.4735 5.9678 227
5.4777 5.9756 228
5.4718 6.2007 229
5.5181 6.2549 230
5.4902 5.9385 231
5.4804 5.8927 232
5.4670 5.9336 233
5.4641 6.0430 234
5.4797 5.9510 235
5.4735 6.0544 236
5.4720 6.1127 237
5.4669 5.9939 238
5.4735 6.0469 239
5.4671 6.0462 240
5.4701 5.9689 241
5.4629 6.1712 242
5.4697 5.8240 243
5.4705 5.9930 244
5.4638 5.9622 245
5.4558 6.0722 246
5.4628 5.9254 247
5.5040 5.5639 248
5.5086 5.6835 249
5.4892 5.8721 250
5.4737 5.7408 251
5.4715 5.7788 252
5.4698 6.0910 253
5.4714 6.0434 254
5.4702 5.9299 255
5.4653 5.8748 256
5.4639 5.9960 257
5.4674 5.9360 258
5.4700 5.8395 259
5.4724 5.9795 260
5.4697 5.9666 261
5.4753 6.0311 262
5.4763 6.2138 263
5.4732 5.9983 264
5.4672 6.1064 265
5.4640 6.1435 266
5.4687 6.0045 267
5.4682 5.9584 268
5.4629 5.8993 269
5.4575 5.9650 270
5.4612 5.9068 271
5.4643 5.8807 272
5.4904 6.1078 273
5.4683 6.0270 274
5.4759 5.9261 275
5.4712 6.0527 276
5.4673 5.9386 277
5.4624 6.0371 278
5.4631 6.0731 279
5.4628 6.1382 280
5.4681 6.0160 281
5.4631 6.0364 282
5.4745 6.1409 283
5.4783 5.9656 284
5.4972 5.8866 285
5.4840 5.9830 286
5.4811 5.9043 287
5.4728 6.0377 288
5.4732 5.9237 289
5.4851 6.2526 290
5.4867 5.8407 291
5.4796 6.1529 292
5.4948 5.7028 293
5.4849 5.9857 294
5.4844 6.0176 295
5.4786 6.0555 296
5.4669 6.0944 297
5.4658 6.1695 298
5.4630 6.0527 299
5.4640 6.0363 300
5.4657 6.0326 301
5.4641 6.0652 302
5.4697 6.1227 303
5.4632 6.0833 304
5.4589 6.3688 305
5.4627 5.9862 306
5.4695 5.9722 307
5.4629 6.1108 308
5.4686 5.9089 309
5.4580 6.2293 310
5.4608 5.9682 311
5.4715 5.9653 312
5.4710 6.2234 313
5.4719 6.1679 314
5.4841 5.7812 315
5.4806 5.7937 316
5.4864 5.8997 317
5.4724 5.9115 318
5.4691 5.9373 319
5.4752 6.0193 320
5.4800 6.0091 321
5.4766 6.0992 322
5.4684 6.0849 323
5.4689 5.9258 324
5.4670 6.0871 325
5.4678 6.0564 326
5.4651 5.9685 327
5.4649 6.0744 328
5.4651 6.1406 329

Framework versions

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