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mi-1.2-model

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metadata
license: apache-2.0
tags:
  - generated_from_trainer
base_model: bert-base-cased
metrics:
  - accuracy
model-index:
  - name: mi-1.2-model
    results: []

mi-1.2-model

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

  • Loss: 1.7264
  • Accuracy: 0.58

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:

  • learning_rate: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.6501 0.04 10 1.6095 0.235
1.655 0.08 20 1.5876 0.23
1.6465 0.12 30 1.5874 0.305
1.6577 0.16 40 1.6006 0.2325
1.5666 0.2 50 1.5611 0.245
1.5667 0.24 60 1.4245 0.44
1.4837 0.28 70 1.2916 0.4175
1.2603 0.32 80 1.3869 0.3925
1.2865 0.36 90 1.4055 0.3475
1.4037 0.4 100 1.3934 0.32
1.3201 0.44 110 1.4511 0.4125
1.3977 0.48 120 1.2251 0.44
1.1444 0.52 130 1.1517 0.5175
1.1627 0.56 140 1.1211 0.5225
1.21 0.6 150 1.1336 0.53
1.2211 0.64 160 1.4186 0.4
1.2985 0.68 170 1.1251 0.4725
1.1856 0.72 180 1.1138 0.5075
1.1027 0.76 190 1.0810 0.5075
1.0998 0.8 200 1.1034 0.5225
1.2546 0.84 210 1.1205 0.4925
1.0265 0.88 220 1.1996 0.4925
1.0898 0.92 230 1.1002 0.515
1.19 0.96 240 1.0805 0.4925
1.1456 1.0 250 1.0509 0.525
0.9265 1.04 260 1.1092 0.51
0.8554 1.08 270 1.0098 0.5325
0.8695 1.12 280 1.0991 0.4975
0.8505 1.16 290 1.0827 0.5075
0.8892 1.2 300 1.1195 0.52
0.8982 1.24 310 1.0691 0.51
0.9301 1.28 320 1.0236 0.545
1.052 1.32 330 1.0296 0.535
0.8072 1.3600 340 1.0227 0.55
0.8822 1.4 350 1.0494 0.53
1.1561 1.44 360 1.2036 0.4925
0.9526 1.48 370 1.0443 0.56
0.9916 1.52 380 1.0378 0.555
1.0388 1.56 390 1.0920 0.5375
0.9326 1.6 400 1.0510 0.5375
0.8453 1.6400 410 1.1247 0.5025
1.03 1.6800 420 1.0281 0.565
0.971 1.72 430 1.0322 0.54
0.941 1.76 440 0.9858 0.565
0.8615 1.8 450 0.9793 0.555
0.8815 1.8400 460 0.9778 0.56
0.7658 1.88 470 0.9760 0.56
1.0073 1.92 480 1.0747 0.5175
0.8929 1.96 490 0.9910 0.565
0.9089 2.0 500 1.0512 0.535
0.5102 2.04 510 1.0545 0.555
0.6748 2.08 520 1.1621 0.5175
0.5222 2.12 530 1.1038 0.5575
0.7978 2.16 540 1.1728 0.53
0.6749 2.2 550 1.1029 0.5475
0.6621 2.24 560 1.0977 0.5425
0.6808 2.2800 570 1.1776 0.545
0.5728 2.32 580 1.1747 0.5325
0.75 2.36 590 1.1707 0.5275
0.6622 2.4 600 1.1082 0.555
0.6008 2.44 610 1.0922 0.57
0.6491 2.48 620 1.1375 0.545
0.5876 2.52 630 1.0614 0.5675
0.5326 2.56 640 1.0460 0.58
0.4901 2.6 650 1.0864 0.58
0.6151 2.64 660 1.1919 0.58
0.6478 2.68 670 1.1301 0.5575
0.4841 2.7200 680 1.1451 0.58
0.6365 2.76 690 1.0701 0.575
0.5284 2.8 700 1.1674 0.5325
0.6506 2.84 710 1.1016 0.55
0.6446 2.88 720 1.1340 0.57
0.5193 2.92 730 1.1692 0.525
0.6129 2.96 740 1.1717 0.5325
0.6013 3.0 750 1.1374 0.55
0.3392 3.04 760 1.2702 0.515
0.3188 3.08 770 1.2584 0.515
0.3272 3.12 780 1.3520 0.5225
0.341 3.16 790 1.2752 0.5575
0.3826 3.2 800 1.3126 0.55
0.3062 3.24 810 1.4909 0.52
0.2657 3.2800 820 1.3804 0.5575
0.4609 3.32 830 1.3712 0.5625
0.3388 3.36 840 1.4701 0.5275
0.3007 3.4 850 1.3373 0.57
0.2732 3.44 860 1.3699 0.575
0.4551 3.48 870 1.3874 0.555
0.3048 3.52 880 1.4913 0.5625
0.4104 3.56 890 1.4586 0.565
0.2633 3.6 900 1.4353 0.565
0.4435 3.64 910 1.5246 0.555
0.282 3.68 920 1.6866 0.5275
0.5918 3.7200 930 1.5193 0.5525
0.315 3.76 940 1.4276 0.565
0.1276 3.8 950 1.4411 0.5625
0.3389 3.84 960 1.5420 0.5625
0.3248 3.88 970 1.4492 0.575
0.3051 3.92 980 1.4321 0.5925
0.3363 3.96 990 1.4374 0.5825
0.4602 4.0 1000 1.4581 0.57
0.1582 4.04 1010 1.4434 0.5675
0.2344 4.08 1020 1.4551 0.5975
0.2646 4.12 1030 1.4999 0.59
0.1948 4.16 1040 1.5550 0.5625
0.3058 4.2 1050 1.5955 0.5775
0.1569 4.24 1060 1.5721 0.575
0.1777 4.28 1070 1.6241 0.56
0.1256 4.32 1080 1.5711 0.575
0.2467 4.36 1090 1.5735 0.59
0.1964 4.4 1100 1.5924 0.585
0.0578 4.44 1110 1.6353 0.585
0.1358 4.48 1120 1.6710 0.5775
0.174 4.52 1130 1.6733 0.5725
0.2022 4.5600 1140 1.6658 0.585
0.028 4.6 1150 1.6708 0.585
0.1222 4.64 1160 1.6989 0.5875
0.2295 4.68 1170 1.7131 0.5825
0.374 4.72 1180 1.7197 0.5725
0.1342 4.76 1190 1.7237 0.575
0.079 4.8 1200 1.7267 0.58
0.154 4.84 1210 1.7204 0.585
0.0403 4.88 1220 1.7183 0.58
0.1964 4.92 1230 1.7253 0.5775
0.1297 4.96 1240 1.7252 0.5775
0.0834 5.0 1250 1.7264 0.58

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1