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bart-large-cnn-prompt_generation

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metadata
license: mit
base_model: facebook/bart-large-cnn
tags:
  - generated_from_trainer
model-index:
  - name: bart-large-cnn-prompt_generation
    results: []

bart-large-cnn-prompt_generation

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

  • Loss: 2.5934
  • Actual score: 0.8766
  • Predction score: 1.3535
  • Score difference: -0.4769

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: 3e-07
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Actual score Predction score Score difference
No log 1.0 15 3.6224 0.8766 -0.4105 1.2871
No log 2.0 30 3.5086 0.8766 -0.2477 1.1243
No log 3.0 45 3.3524 0.8766 -0.3119 1.1886
No log 4.0 60 3.2496 0.8766 -0.1139 0.9905
No log 5.0 75 3.1300 0.8766 -0.3163 1.1929
No log 6.0 90 3.0445 0.8766 -0.4738 1.3504
No log 7.0 105 2.9855 0.8766 -0.5561 1.4327
No log 8.0 120 2.9429 0.8766 -0.6262 1.5028
No log 9.0 135 2.9103 0.8766 -0.4633 1.3399
No log 10.0 150 2.8818 0.8766 -0.5404 1.4170
No log 11.0 165 2.8567 0.8766 -0.7534 1.6300
No log 12.0 180 2.8327 0.8766 -0.7283 1.6049
No log 13.0 195 2.8114 0.8766 -0.5976 1.4742
No log 14.0 210 2.7917 0.8766 -0.7693 1.6460
No log 15.0 225 2.7749 0.8766 -0.5831 1.4597
No log 16.0 240 2.7596 0.8766 -0.5963 1.4729
No log 17.0 255 2.7458 0.8766 -0.5232 1.3998
No log 18.0 270 2.7329 0.8766 -0.1795 1.0562
No log 19.0 285 2.7211 0.8766 -0.2189 1.0955
No log 20.0 300 2.7111 0.8766 -0.3411 1.2177
No log 21.0 315 2.7022 0.8766 -0.3058 1.1824
No log 22.0 330 2.6936 0.8766 -0.3270 1.2036
No log 23.0 345 2.6853 0.8766 -0.1728 1.0494
No log 24.0 360 2.6771 0.8766 -0.2413 1.1179
No log 25.0 375 2.6700 0.8766 0.0077 0.8689
No log 26.0 390 2.6641 0.8766 -0.0744 0.9510
No log 27.0 405 2.6589 0.8766 0.0078 0.8689
No log 28.0 420 2.6540 0.8766 0.0711 0.8055
No log 29.0 435 2.6493 0.8766 0.2289 0.6477
No log 30.0 450 2.6443 0.8766 0.1096 0.7670
No log 31.0 465 2.6393 0.8766 0.1335 0.7431
No log 32.0 480 2.6355 0.8766 0.3491 0.5275
No log 33.0 495 2.6321 0.8766 0.4268 0.4498
2.6272 34.0 510 2.6288 0.8766 0.3806 0.4960
2.6272 35.0 525 2.6258 0.8766 0.8496 0.0271
2.6272 36.0 540 2.6231 0.8766 0.6446 0.2321
2.6272 37.0 555 2.6204 0.8766 0.6268 0.2498
2.6272 38.0 570 2.6176 0.8766 0.8588 0.0178
2.6272 39.0 585 2.6159 0.8766 0.9990 -0.1224
2.6272 40.0 600 2.6132 0.8766 1.0628 -0.1862
2.6272 41.0 615 2.6111 0.8766 0.9146 -0.0380
2.6272 42.0 630 2.6092 0.8766 1.0457 -0.1691
2.6272 43.0 645 2.6078 0.8766 0.9640 -0.0874
2.6272 44.0 660 2.6059 0.8766 1.0378 -0.1612
2.6272 45.0 675 2.6047 0.8766 1.0599 -0.1833
2.6272 46.0 690 2.6034 0.8766 1.1746 -0.2980
2.6272 47.0 705 2.6019 0.8766 1.1497 -0.2730
2.6272 48.0 720 2.6002 0.8766 1.2987 -0.4221
2.6272 49.0 735 2.5988 0.8766 1.2149 -0.3383
2.6272 50.0 750 2.5982 0.8766 1.2456 -0.3690
2.6272 51.0 765 2.5973 0.8766 1.2476 -0.3709
2.6272 52.0 780 2.5958 0.8766 1.2934 -0.4168
2.6272 53.0 795 2.5948 0.8766 1.2370 -0.3604
2.6272 54.0 810 2.5937 0.8766 1.2163 -0.3397
2.6272 55.0 825 2.5926 0.8766 1.2636 -0.3869
2.6272 56.0 840 2.5923 0.8766 1.3040 -0.4273
2.6272 57.0 855 2.5921 0.8766 1.3694 -0.4928
2.6272 58.0 870 2.5916 0.8766 1.1951 -0.3185
2.6272 59.0 885 2.5916 0.8766 1.3291 -0.4525
2.6272 60.0 900 2.5914 0.8766 1.3288 -0.4521
2.6272 61.0 915 2.5914 0.8766 1.3867 -0.5101
2.6272 62.0 930 2.5916 0.8766 1.4165 -0.5399
2.6272 63.0 945 2.5915 0.8766 1.4103 -0.5337
2.6272 64.0 960 2.5910 0.8766 1.3960 -0.5194
2.6272 65.0 975 2.5908 0.8766 1.3134 -0.4368
2.6272 66.0 990 2.5903 0.8766 1.3638 -0.4872
1.9897 67.0 1005 2.5900 0.8766 1.3875 -0.5109
1.9897 68.0 1020 2.5901 0.8766 1.2404 -0.3637
1.9897 69.0 1035 2.5900 0.8766 1.4162 -0.5396
1.9897 70.0 1050 2.5899 0.8766 1.4048 -0.5281
1.9897 71.0 1065 2.5900 0.8766 1.3967 -0.5201
1.9897 72.0 1080 2.5900 0.8766 1.4208 -0.5442
1.9897 73.0 1095 2.5903 0.8766 1.4418 -0.5651
1.9897 74.0 1110 2.5903 0.8766 1.4656 -0.5890
1.9897 75.0 1125 2.5905 0.8766 1.4504 -0.5738
1.9897 76.0 1140 2.5910 0.8766 1.3669 -0.4903
1.9897 77.0 1155 2.5912 0.8766 1.3362 -0.4595
1.9897 78.0 1170 2.5917 0.8766 1.3196 -0.4430
1.9897 79.0 1185 2.5918 0.8766 1.3537 -0.4770
1.9897 80.0 1200 2.5921 0.8766 1.3136 -0.4370
1.9897 81.0 1215 2.5923 0.8766 1.3806 -0.5039
1.9897 82.0 1230 2.5926 0.8766 1.3900 -0.5134
1.9897 83.0 1245 2.5924 0.8766 1.3907 -0.5141
1.9897 84.0 1260 2.5924 0.8766 1.3785 -0.5019
1.9897 85.0 1275 2.5926 0.8766 1.4009 -0.5243
1.9897 86.0 1290 2.5928 0.8766 1.4108 -0.5342
1.9897 87.0 1305 2.5929 0.8766 1.3947 -0.5180
1.9897 88.0 1320 2.5929 0.8766 1.3845 -0.5078
1.9897 89.0 1335 2.5928 0.8766 1.4045 -0.5279
1.9897 90.0 1350 2.5929 0.8766 1.3804 -0.5038
1.9897 91.0 1365 2.5931 0.8766 1.3962 -0.5195
1.9897 92.0 1380 2.5931 0.8766 1.3801 -0.5034
1.9897 93.0 1395 2.5932 0.8766 1.3664 -0.4897
1.9897 94.0 1410 2.5933 0.8766 1.3716 -0.4950
1.9897 95.0 1425 2.5933 0.8766 1.3935 -0.5169
1.9897 96.0 1440 2.5933 0.8766 1.3676 -0.4910
1.9897 97.0 1455 2.5934 0.8766 1.3914 -0.5148
1.9897 98.0 1470 2.5933 0.8766 1.3912 -0.5146
1.9897 99.0 1485 2.5934 0.8766 1.3930 -0.5164
1.7966 100.0 1500 2.5934 0.8766 1.3535 -0.4769

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1