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---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: bart-large-cnn-prompt_generation
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
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: 2.5934
- 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
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