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--- |
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license: mit |
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base_model: facebook/bart-large-cnn |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bart-large-cnn-prompt_generation-2.0 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bart-large-cnn-prompt_generation-2.0 |
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6403 |
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- Actual score: 0.8766 |
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- Predction score: 0.5039 |
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- Score difference: 0.3727 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-07 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 75 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Actual score | Predction score | Score difference | |
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|:-------------:|:-----:|:----:|:---------------:|:------------:|:---------------:|:----------------:| |
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| No log | 1.0 | 8 | 3.6549 | 0.8766 | -0.2093 | 1.0859 | |
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| No log | 2.0 | 16 | 3.6012 | 0.8766 | -0.1961 | 1.0728 | |
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| No log | 3.0 | 24 | 3.5331 | 0.8766 | -0.1613 | 1.0379 | |
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| No log | 4.0 | 32 | 3.4417 | 0.8766 | -0.1132 | 0.9899 | |
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| No log | 5.0 | 40 | 3.3501 | 0.8766 | -0.1821 | 1.0587 | |
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| No log | 6.0 | 48 | 3.2904 | 0.8766 | -0.1653 | 1.0419 | |
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| No log | 7.0 | 56 | 3.2418 | 0.8766 | -0.4566 | 1.3332 | |
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| No log | 8.0 | 64 | 3.1620 | 0.8766 | -0.2897 | 1.1663 | |
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| No log | 9.0 | 72 | 3.0925 | 0.8766 | -0.5185 | 1.3951 | |
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| No log | 10.0 | 80 | 3.0442 | 0.8766 | -0.7127 | 1.5893 | |
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| No log | 11.0 | 88 | 3.0064 | 0.8766 | -0.4893 | 1.3659 | |
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| No log | 12.0 | 96 | 2.9742 | 0.8766 | -0.6391 | 1.5157 | |
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| No log | 13.0 | 104 | 2.9475 | 0.8766 | -0.4873 | 1.3640 | |
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| No log | 14.0 | 112 | 2.9254 | 0.8766 | -0.2786 | 1.1552 | |
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| No log | 15.0 | 120 | 2.9061 | 0.8766 | -0.1893 | 1.0660 | |
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| No log | 16.0 | 128 | 2.8887 | 0.8766 | -0.2202 | 1.0968 | |
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| No log | 17.0 | 136 | 2.8730 | 0.8766 | -0.2009 | 1.0775 | |
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| No log | 18.0 | 144 | 2.8588 | 0.8766 | -0.2101 | 1.0867 | |
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| No log | 19.0 | 152 | 2.8461 | 0.8766 | -0.3374 | 1.2140 | |
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| No log | 20.0 | 160 | 2.8337 | 0.8766 | -0.2005 | 1.0772 | |
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| No log | 21.0 | 168 | 2.8216 | 0.8766 | -0.2570 | 1.1336 | |
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| No log | 22.0 | 176 | 2.8104 | 0.8766 | -0.3601 | 1.2367 | |
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| No log | 23.0 | 184 | 2.7996 | 0.8766 | -0.4823 | 1.3589 | |
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| No log | 24.0 | 192 | 2.7895 | 0.8766 | -0.4451 | 1.3217 | |
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| No log | 25.0 | 200 | 2.7798 | 0.8766 | -0.3621 | 1.2388 | |
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| No log | 26.0 | 208 | 2.7706 | 0.8766 | -0.4108 | 1.2874 | |
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| No log | 27.0 | 216 | 2.7625 | 0.8766 | -0.4750 | 1.3517 | |
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| No log | 28.0 | 224 | 2.7547 | 0.8766 | -0.4004 | 1.2771 | |
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| No log | 29.0 | 232 | 2.7471 | 0.8766 | -0.4535 | 1.3301 | |
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| No log | 30.0 | 240 | 2.7393 | 0.8766 | -0.5414 | 1.4180 | |
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| No log | 31.0 | 248 | 2.7328 | 0.8766 | -0.5666 | 1.4433 | |
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| No log | 32.0 | 256 | 2.7268 | 0.8766 | -0.6630 | 1.5396 | |
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| No log | 33.0 | 264 | 2.7211 | 0.8766 | -0.4073 | 1.2839 | |
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| No log | 34.0 | 272 | 2.7160 | 0.8766 | -0.5464 | 1.4230 | |
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| No log | 35.0 | 280 | 2.7113 | 0.8766 | -0.3629 | 1.2396 | |
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| No log | 36.0 | 288 | 2.7065 | 0.8766 | -0.2926 | 1.1692 | |
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| No log | 37.0 | 296 | 2.7025 | 0.8766 | -0.2596 | 1.1362 | |
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| No log | 38.0 | 304 | 2.6981 | 0.8766 | -0.1478 | 1.0244 | |
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| No log | 39.0 | 312 | 2.6939 | 0.8766 | -0.2252 | 1.1018 | |
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| No log | 40.0 | 320 | 2.6901 | 0.8766 | -0.2750 | 1.1516 | |
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| No log | 41.0 | 328 | 2.6867 | 0.8766 | -0.0900 | 0.9667 | |
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| No log | 42.0 | 336 | 2.6836 | 0.8766 | -0.2377 | 1.1144 | |
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| No log | 43.0 | 344 | 2.6804 | 0.8766 | -0.3135 | 1.1901 | |
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| No log | 44.0 | 352 | 2.6774 | 0.8766 | -0.1023 | 0.9789 | |
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| No log | 45.0 | 360 | 2.6745 | 0.8766 | -0.0386 | 0.9152 | |
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| No log | 46.0 | 368 | 2.6714 | 0.8766 | 0.1602 | 0.7164 | |
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| No log | 47.0 | 376 | 2.6689 | 0.8766 | 0.2508 | 0.6258 | |
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| No log | 48.0 | 384 | 2.6668 | 0.8766 | 0.1577 | 0.7190 | |
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| No log | 49.0 | 392 | 2.6648 | 0.8766 | 0.0565 | 0.8201 | |
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| No log | 50.0 | 400 | 2.6627 | 0.8766 | 0.2379 | 0.6387 | |
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| No log | 51.0 | 408 | 2.6607 | 0.8766 | 0.2343 | 0.6423 | |
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| No log | 52.0 | 416 | 2.6588 | 0.8766 | 0.2719 | 0.6048 | |
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| No log | 53.0 | 424 | 2.6570 | 0.8766 | 0.2214 | 0.6552 | |
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| No log | 54.0 | 432 | 2.6555 | 0.8766 | 0.2729 | 0.6037 | |
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| No log | 55.0 | 440 | 2.6541 | 0.8766 | 0.2798 | 0.5968 | |
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| No log | 56.0 | 448 | 2.6528 | 0.8766 | 0.0662 | 0.8104 | |
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| No log | 57.0 | 456 | 2.6514 | 0.8766 | 0.0377 | 0.8390 | |
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| No log | 58.0 | 464 | 2.6502 | 0.8766 | 0.2886 | 0.5880 | |
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| No log | 59.0 | 472 | 2.6491 | 0.8766 | 0.2257 | 0.6509 | |
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| No log | 60.0 | 480 | 2.6481 | 0.8766 | 0.2561 | 0.6206 | |
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| No log | 61.0 | 488 | 2.6471 | 0.8766 | 0.2683 | 0.6083 | |
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| No log | 62.0 | 496 | 2.6461 | 0.8766 | 0.2897 | 0.5869 | |
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| 2.5848 | 63.0 | 504 | 2.6453 | 0.8766 | 0.2974 | 0.5793 | |
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| 2.5848 | 64.0 | 512 | 2.6445 | 0.8766 | 0.2946 | 0.5820 | |
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| 2.5848 | 65.0 | 520 | 2.6438 | 0.8766 | 0.3021 | 0.5745 | |
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| 2.5848 | 66.0 | 528 | 2.6433 | 0.8766 | 0.2679 | 0.6087 | |
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| 2.5848 | 67.0 | 536 | 2.6428 | 0.8766 | 0.3133 | 0.5633 | |
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| 2.5848 | 68.0 | 544 | 2.6423 | 0.8766 | 0.3398 | 0.5368 | |
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| 2.5848 | 69.0 | 552 | 2.6418 | 0.8766 | 0.4149 | 0.4617 | |
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| 2.5848 | 70.0 | 560 | 2.6413 | 0.8766 | 0.4674 | 0.4092 | |
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| 2.5848 | 71.0 | 568 | 2.6410 | 0.8766 | 0.4929 | 0.3838 | |
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| 2.5848 | 72.0 | 576 | 2.6407 | 0.8766 | 0.4974 | 0.3793 | |
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| 2.5848 | 73.0 | 584 | 2.6406 | 0.8766 | 0.4948 | 0.3818 | |
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| 2.5848 | 74.0 | 592 | 2.6404 | 0.8766 | 0.4623 | 0.4143 | |
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| 2.5848 | 75.0 | 600 | 2.6403 | 0.8766 | 0.5039 | 0.3727 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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