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---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: bart-large-cnn-prompt_generation-2.0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-prompt_generation-2.0
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/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
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