metadata
license: apache-2.0
base_model: sshleifer/distilbart-cnn-12-6
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-cnn-12-6-finetuned-stocknews_200
results: []
pipeline_tag: summarization
distilbart-cnn-12-6-finetuned-stocknews_200
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0370
- Rouge1: 79.8682
- Rouge2: 71.4205
- Rougel: 75.6301
- Rougelsum: 77.0085
- Gen Len: 74.1543
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 80
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 169 | 0.5736 | 64.7045 | 47.6749 | 56.2681 | 59.2198 | 74.6113 |
| No log | 2.0 | 338 | 0.4806 | 72.0942 | 58.8471 | 65.4706 | 67.8252 | 71.5163 |
| 0.4734 | 3.0 | 507 | 0.4991 | 73.967 | 62.7751 | 68.5945 | 70.6273 | 74.724 |
| 0.4734 | 4.0 | 676 | 0.4965 | 76.8393 | 66.9993 | 72.19 | 73.864 | 72.7003 |
| 0.4734 | 5.0 | 845 | 0.5139 | 78.0584 | 68.124 | 73.447 | 75.0284 | 73.9466 |
| 0.1158 | 6.0 | 1014 | 0.5328 | 78.409 | 68.5496 | 73.4175 | 75.0927 | 72.6914 |
| 0.1158 | 7.0 | 1183 | 0.5370 | 77.5134 | 67.8142 | 72.7732 | 74.5942 | 71.5727 |
| 0.1158 | 8.0 | 1352 | 0.5872 | 78.01 | 68.8818 | 73.7514 | 75.3546 | 73.4036 |
| 0.0631 | 9.0 | 1521 | 0.5787 | 78.8662 | 69.9291 | 74.7183 | 76.1309 | 73.365 |
| 0.0631 | 10.0 | 1690 | 0.5887 | 78.5145 | 69.2414 | 73.9729 | 75.4945 | 73.3947 |
| 0.0631 | 11.0 | 1859 | 0.5866 | 77.9579 | 68.5705 | 73.2277 | 75.2179 | 72.4807 |
| 0.0456 | 12.0 | 2028 | 0.6155 | 79.4247 | 70.3457 | 75.0464 | 76.723 | 71.6261 |
| 0.0456 | 13.0 | 2197 | 0.6270 | 78.2792 | 69.1958 | 74.171 | 75.7049 | 72.9347 |
| 0.0456 | 14.0 | 2366 | 0.6342 | 78.6039 | 69.2197 | 74.2082 | 75.7638 | 74.543 |
| 0.0364 | 15.0 | 2535 | 0.6282 | 78.7977 | 69.8903 | 74.5441 | 76.4053 | 72.8961 |
| 0.0364 | 16.0 | 2704 | 0.6456 | 78.4486 | 69.2633 | 74.0665 | 75.4348 | 72.2819 |
| 0.0364 | 17.0 | 2873 | 0.6583 | 79.1083 | 70.2974 | 75.0199 | 76.544 | 72.6469 |
| 0.0282 | 18.0 | 3042 | 0.6477 | 78.7872 | 69.9616 | 74.6811 | 76.0256 | 72.8279 |
| 0.0282 | 19.0 | 3211 | 0.6716 | 78.7369 | 69.889 | 74.4537 | 75.9916 | 73.4214 |
| 0.0282 | 20.0 | 3380 | 0.6729 | 79.3218 | 70.2074 | 75.162 | 76.5582 | 73.7003 |
| 0.0222 | 21.0 | 3549 | 0.7011 | 77.7228 | 68.6481 | 73.4411 | 74.9113 | 74.4748 |
| 0.0222 | 22.0 | 3718 | 0.6763 | 79.47 | 70.7597 | 75.2025 | 76.8042 | 72.73 |
| 0.0222 | 23.0 | 3887 | 0.7025 | 79.8675 | 70.9624 | 75.4989 | 77.0572 | 72.8427 |
| 0.0196 | 24.0 | 4056 | 0.6746 | 79.1486 | 70.4134 | 74.9573 | 76.4961 | 73.0208 |
| 0.0196 | 25.0 | 4225 | 0.6750 | 79.774 | 71.187 | 75.6008 | 77.2557 | 72.1098 |
| 0.0196 | 26.0 | 4394 | 0.6921 | 79.5747 | 70.894 | 75.2295 | 76.7905 | 72.9318 |
| 0.0176 | 27.0 | 4563 | 0.7611 | 79.0068 | 70.1336 | 74.3258 | 75.9459 | 74.3501 |
| 0.0176 | 28.0 | 4732 | 0.7093 | 79.5467 | 70.8754 | 75.4346 | 77.2047 | 72.3116 |
| 0.0176 | 29.0 | 4901 | 0.7168 | 79.5496 | 70.5612 | 75.0587 | 76.6486 | 74.0415 |
| 0.0154 | 30.0 | 5070 | 0.7032 | 79.7382 | 71.0288 | 75.9411 | 77.103 | 72.5282 |
| 0.0154 | 31.0 | 5239 | 0.7206 | 79.3973 | 70.7136 | 75.1744 | 76.5041 | 72.5757 |
| 0.0154 | 32.0 | 5408 | 0.7478 | 79.6311 | 70.74 | 75.1728 | 76.8626 | 73.1395 |
| 0.013 | 33.0 | 5577 | 0.7279 | 79.9423 | 71.2295 | 75.7646 | 77.2329 | 70.8872 |
| 0.013 | 34.0 | 5746 | 0.7685 | 78.8995 | 70.121 | 74.4843 | 76.028 | 72.9763 |
| 0.013 | 35.0 | 5915 | 0.7498 | 79.6454 | 70.8632 | 75.4972 | 76.8668 | 72.0297 |
| 0.0126 | 36.0 | 6084 | 0.8016 | 78.8582 | 70.0804 | 74.5498 | 76.0402 | 74.8338 |
| 0.0126 | 37.0 | 6253 | 0.7923 | 78.8845 | 70.1465 | 74.837 | 76.2453 | 74.0742 |
| 0.0126 | 38.0 | 6422 | 0.7813 | 78.7254 | 70.0885 | 74.6831 | 76.1384 | 73.5994 |
| 0.0103 | 39.0 | 6591 | 0.7974 | 79.5855 | 70.7472 | 75.5436 | 76.9493 | 72.6795 |
| 0.0103 | 40.0 | 6760 | 0.7967 | 79.656 | 70.7795 | 75.2844 | 76.6875 | 72.3294 |
| 0.0103 | 41.0 | 6929 | 0.8029 | 79.8831 | 71.1647 | 75.697 | 77.0773 | 71.8872 |
| 0.0086 | 42.0 | 7098 | 0.8245 | 78.999 | 70.1721 | 74.8494 | 76.2723 | 72.7478 |
| 0.0086 | 43.0 | 7267 | 0.8459 | 79.052 | 70.2714 | 75.0921 | 76.4209 | 74.3828 |
| 0.0086 | 44.0 | 7436 | 0.8077 | 79.6009 | 70.4859 | 75.0207 | 76.7271 | 72.5163 |
| 0.0078 | 45.0 | 7605 | 0.8431 | 79.093 | 70.433 | 75.0361 | 76.589 | 73.3145 |
| 0.0078 | 46.0 | 7774 | 0.8794 | 79.1461 | 70.3654 | 74.845 | 76.3544 | 75.0415 |
| 0.0078 | 47.0 | 7943 | 0.8668 | 79.1443 | 70.2647 | 74.7967 | 76.3801 | 71.724 |
| 0.0076 | 48.0 | 8112 | 0.8347 | 78.6997 | 70.1008 | 74.6051 | 76.0351 | 73.9763 |
| 0.0076 | 49.0 | 8281 | 0.8544 | 78.9749 | 69.9824 | 74.6559 | 76.0268 | 74.6528 |
| 0.0076 | 50.0 | 8450 | 0.9060 | 79.5051 | 70.5755 | 75.3817 | 77.0026 | 71.1217 |
| 0.0065 | 51.0 | 8619 | 0.9501 | 79.2498 | 70.5003 | 75.1244 | 76.5023 | 75.0 |
| 0.0065 | 52.0 | 8788 | 0.8724 | 79.5012 | 70.4217 | 75.109 | 76.6551 | 73.73 |
| 0.0065 | 53.0 | 8957 | 0.8860 | 79.5313 | 71.0337 | 75.3122 | 76.928 | 72.7685 |
| 0.0053 | 54.0 | 9126 | 0.8859 | 79.674 | 71.0878 | 75.4582 | 76.925 | 73.3294 |
| 0.0053 | 55.0 | 9295 | 0.8965 | 78.5857 | 69.8599 | 74.2323 | 75.6027 | 75.7359 |
| 0.0053 | 56.0 | 9464 | 0.9871 | 79.8361 | 71.2171 | 75.8197 | 77.1182 | 74.0861 |
| 0.0052 | 57.0 | 9633 | 0.8972 | 79.8939 | 71.3469 | 75.9245 | 77.1549 | 72.8398 |
| 0.0052 | 58.0 | 9802 | 0.9693 | 79.5523 | 70.8739 | 75.2116 | 76.7137 | 74.3412 |
| 0.0052 | 59.0 | 9971 | 0.9605 | 79.483 | 70.6684 | 75.0183 | 76.3226 | 75.2522 |
| 0.0047 | 60.0 | 10140 | 0.9705 | 79.4894 | 70.6424 | 75.0833 | 76.504 | 74.8694 |
| 0.0047 | 61.0 | 10309 | 0.9730 | 79.4781 | 70.9014 | 75.4589 | 76.6387 | 75.0504 |
| 0.0047 | 62.0 | 10478 | 0.9284 | 79.485 | 70.6651 | 75.1062 | 76.4092 | 74.0148 |
| 0.0045 | 63.0 | 10647 | 0.9537 | 79.2664 | 70.4345 | 74.9998 | 76.4565 | 73.9199 |
| 0.0045 | 64.0 | 10816 | 0.9554 | 79.6061 | 70.8702 | 75.3191 | 76.6242 | 74.3145 |
| 0.0045 | 65.0 | 10985 | 1.0090 | 79.6107 | 70.9297 | 75.4102 | 76.9842 | 73.9466 |
| 0.0041 | 66.0 | 11154 | 0.9736 | 79.6246 | 70.8827 | 75.2682 | 76.7209 | 74.8131 |
| 0.0041 | 67.0 | 11323 | 0.9498 | 79.9549 | 71.3231 | 75.7987 | 77.2809 | 73.5371 |
| 0.0041 | 68.0 | 11492 | 0.9965 | 80.1403 | 71.4991 | 76.017 | 77.3741 | 74.2404 |
| 0.004 | 69.0 | 11661 | 1.0012 | 79.8784 | 71.444 | 75.827 | 77.1888 | 74.0059 |
| 0.004 | 70.0 | 11830 | 0.9888 | 80.1075 | 71.7102 | 75.9687 | 77.3636 | 72.9911 |
| 0.004 | 71.0 | 11999 | 0.9758 | 79.7998 | 71.3682 | 75.6694 | 77.0498 | 73.8991 |
| 0.0043 | 72.0 | 12168 | 0.9760 | 79.9748 | 71.4703 | 75.8148 | 77.1338 | 72.8843 |
| 0.0043 | 73.0 | 12337 | 0.9930 | 80.1032 | 71.6551 | 75.8235 | 77.1674 | 73.6499 |
| 0.0037 | 74.0 | 12506 | 1.0006 | 80.0302 | 71.5324 | 75.7755 | 77.2182 | 73.3027 |
| 0.0037 | 75.0 | 12675 | 0.9958 | 79.9088 | 71.313 | 75.7842 | 77.1939 | 73.362 |
| 0.0037 | 76.0 | 12844 | 0.9993 | 80.3059 | 71.7887 | 76.0696 | 77.5045 | 73.3086 |
| 0.0039 | 77.0 | 13013 | 1.0224 | 79.5564 | 71.1191 | 75.4324 | 76.7285 | 74.2344 |
| 0.0039 | 78.0 | 13182 | 1.0510 | 80.0006 | 71.4199 | 75.6626 | 77.006 | 74.0119 |
| 0.0039 | 79.0 | 13351 | 1.0410 | 79.7101 | 71.2137 | 75.5206 | 76.8997 | 74.4303 |
| 0.0036 | 80.0 | 13520 | 1.0370 | 79.8682 | 71.4205 | 75.6301 | 77.0085 | 74.1543 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2