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bart-large-cnn-prompt_generation-2.0

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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-2.0
    results: []

bart-large-cnn-prompt_generation-2.0

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: 2.6403
  • Actual score: 0.8766
  • Predction score: 0.5039
  • Score difference: 0.3727

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: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 75
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Actual score Predction score Score difference
No log 1.0 8 3.6549 0.8766 -0.2093 1.0859
No log 2.0 16 3.6012 0.8766 -0.1961 1.0728
No log 3.0 24 3.5331 0.8766 -0.1613 1.0379
No log 4.0 32 3.4417 0.8766 -0.1132 0.9899
No log 5.0 40 3.3501 0.8766 -0.1821 1.0587
No log 6.0 48 3.2904 0.8766 -0.1653 1.0419
No log 7.0 56 3.2418 0.8766 -0.4566 1.3332
No log 8.0 64 3.1620 0.8766 -0.2897 1.1663
No log 9.0 72 3.0925 0.8766 -0.5185 1.3951
No log 10.0 80 3.0442 0.8766 -0.7127 1.5893
No log 11.0 88 3.0064 0.8766 -0.4893 1.3659
No log 12.0 96 2.9742 0.8766 -0.6391 1.5157
No log 13.0 104 2.9475 0.8766 -0.4873 1.3640
No log 14.0 112 2.9254 0.8766 -0.2786 1.1552
No log 15.0 120 2.9061 0.8766 -0.1893 1.0660
No log 16.0 128 2.8887 0.8766 -0.2202 1.0968
No log 17.0 136 2.8730 0.8766 -0.2009 1.0775
No log 18.0 144 2.8588 0.8766 -0.2101 1.0867
No log 19.0 152 2.8461 0.8766 -0.3374 1.2140
No log 20.0 160 2.8337 0.8766 -0.2005 1.0772
No log 21.0 168 2.8216 0.8766 -0.2570 1.1336
No log 22.0 176 2.8104 0.8766 -0.3601 1.2367
No log 23.0 184 2.7996 0.8766 -0.4823 1.3589
No log 24.0 192 2.7895 0.8766 -0.4451 1.3217
No log 25.0 200 2.7798 0.8766 -0.3621 1.2388
No log 26.0 208 2.7706 0.8766 -0.4108 1.2874
No log 27.0 216 2.7625 0.8766 -0.4750 1.3517
No log 28.0 224 2.7547 0.8766 -0.4004 1.2771
No log 29.0 232 2.7471 0.8766 -0.4535 1.3301
No log 30.0 240 2.7393 0.8766 -0.5414 1.4180
No log 31.0 248 2.7328 0.8766 -0.5666 1.4433
No log 32.0 256 2.7268 0.8766 -0.6630 1.5396
No log 33.0 264 2.7211 0.8766 -0.4073 1.2839
No log 34.0 272 2.7160 0.8766 -0.5464 1.4230
No log 35.0 280 2.7113 0.8766 -0.3629 1.2396
No log 36.0 288 2.7065 0.8766 -0.2926 1.1692
No log 37.0 296 2.7025 0.8766 -0.2596 1.1362
No log 38.0 304 2.6981 0.8766 -0.1478 1.0244
No log 39.0 312 2.6939 0.8766 -0.2252 1.1018
No log 40.0 320 2.6901 0.8766 -0.2750 1.1516
No log 41.0 328 2.6867 0.8766 -0.0900 0.9667
No log 42.0 336 2.6836 0.8766 -0.2377 1.1144
No log 43.0 344 2.6804 0.8766 -0.3135 1.1901
No log 44.0 352 2.6774 0.8766 -0.1023 0.9789
No log 45.0 360 2.6745 0.8766 -0.0386 0.9152
No log 46.0 368 2.6714 0.8766 0.1602 0.7164
No log 47.0 376 2.6689 0.8766 0.2508 0.6258
No log 48.0 384 2.6668 0.8766 0.1577 0.7190
No log 49.0 392 2.6648 0.8766 0.0565 0.8201
No log 50.0 400 2.6627 0.8766 0.2379 0.6387
No log 51.0 408 2.6607 0.8766 0.2343 0.6423
No log 52.0 416 2.6588 0.8766 0.2719 0.6048
No log 53.0 424 2.6570 0.8766 0.2214 0.6552
No log 54.0 432 2.6555 0.8766 0.2729 0.6037
No log 55.0 440 2.6541 0.8766 0.2798 0.5968
No log 56.0 448 2.6528 0.8766 0.0662 0.8104
No log 57.0 456 2.6514 0.8766 0.0377 0.8390
No log 58.0 464 2.6502 0.8766 0.2886 0.5880
No log 59.0 472 2.6491 0.8766 0.2257 0.6509
No log 60.0 480 2.6481 0.8766 0.2561 0.6206
No log 61.0 488 2.6471 0.8766 0.2683 0.6083
No log 62.0 496 2.6461 0.8766 0.2897 0.5869
2.5848 63.0 504 2.6453 0.8766 0.2974 0.5793
2.5848 64.0 512 2.6445 0.8766 0.2946 0.5820
2.5848 65.0 520 2.6438 0.8766 0.3021 0.5745
2.5848 66.0 528 2.6433 0.8766 0.2679 0.6087
2.5848 67.0 536 2.6428 0.8766 0.3133 0.5633
2.5848 68.0 544 2.6423 0.8766 0.3398 0.5368
2.5848 69.0 552 2.6418 0.8766 0.4149 0.4617
2.5848 70.0 560 2.6413 0.8766 0.4674 0.4092
2.5848 71.0 568 2.6410 0.8766 0.4929 0.3838
2.5848 72.0 576 2.6407 0.8766 0.4974 0.3793
2.5848 73.0 584 2.6406 0.8766 0.4948 0.3818
2.5848 74.0 592 2.6404 0.8766 0.4623 0.4143
2.5848 75.0 600 2.6403 0.8766 0.5039 0.3727

Framework versions

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