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metadata
license: apache-2.0
base_model: facebook/bart-base
tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: bart-base-finetuned-xsum
    results: []

bart-base-finetuned-xsum

This model is a fine-tuned version of facebook/bart-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0536
  • Rouge1: 30.3379
  • Rouge2: 28.823
  • Rougel: 30.2838
  • Rougelsum: 30.2947
  • Gen Len: 19.9298

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: 100

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 1.0 22 3.3585 9.7586 1.5287 7.4201 8.9593 19.4386
No log 2.0 44 3.1307 10.5799 1.8167 8.2614 9.6662 19.0175
No log 3.0 66 2.9495 11.7652 2.1847 8.7837 10.5655 19.3216
No log 4.0 88 2.7920 11.5464 2.4136 8.9111 10.5071 18.9415
No log 5.0 110 2.6630 12.2901 2.7634 9.4529 11.2263 18.9883
No log 6.0 132 2.5428 12.569 2.7898 9.4695 11.1434 19.5146
No log 7.0 154 2.4247 13.3365 2.8074 9.8493 11.7428 19.731
No log 8.0 176 2.3166 14.1582 3.3604 10.6909 12.6087 19.8538
No log 9.0 198 2.2134 14.4264 3.8769 11.0709 13.0822 19.7602
No log 10.0 220 2.1139 13.7978 3.1148 10.3251 12.2168 19.7778
No log 11.0 242 2.0238 14.1079 3.7373 10.9357 12.7981 19.8713
No log 12.0 264 1.9397 14.7055 4.3184 11.2992 13.5954 19.8772
No log 13.0 286 1.8451 14.6991 3.8832 11.2441 13.1588 19.848
No log 14.0 308 1.7630 15.0289 4.3497 11.5492 13.7312 19.8538
No log 15.0 330 1.6870 14.9628 4.4868 11.6513 13.7924 19.8538
No log 16.0 352 1.6129 15.8419 5.5329 12.5427 14.6066 19.8246
No log 17.0 374 1.5418 15.261 4.5563 11.8716 13.9079 19.8363
No log 18.0 396 1.4653 15.7719 5.561 12.6223 14.4619 19.8363
No log 19.0 418 1.3881 15.4202 5.1589 12.0679 14.3881 19.9064
No log 20.0 440 1.3274 16.3873 6.1422 13.2212 15.1157 19.8363
No log 21.0 462 1.2554 16.5245 6.1207 13.1476 15.2785 19.8772
No log 22.0 484 1.1957 17.1818 6.9277 13.756 16.0687 19.8713
2.6205 23.0 506 1.1303 16.4459 6.5034 13.2991 15.3716 19.8363
2.6205 24.0 528 1.0749 17.501 7.8981 14.3457 16.273 19.8947
2.6205 25.0 550 1.0179 18.0965 8.6491 15.0401 16.9821 19.8772
2.6205 26.0 572 0.9642 18.4629 8.2476 15.1398 17.1954 19.8246
2.6205 27.0 594 0.9065 18.9359 9.0813 15.7911 17.7216 19.9123
2.6205 28.0 616 0.8658 19.4514 10.5142 16.7065 18.4666 19.9123
2.6205 29.0 638 0.8187 19.7181 10.546 16.8919 18.5289 19.9123
2.6205 30.0 660 0.7741 19.8173 10.7183 17.1135 18.9732 19.9123
2.6205 31.0 682 0.7250 20.4778 12.0763 18.0214 19.641 19.9123
2.6205 32.0 704 0.6870 21.1546 12.9349 18.7801 20.2578 19.9123
2.6205 33.0 726 0.6507 22.6574 14.9931 20.5845 21.742 19.8713
2.6205 34.0 748 0.6117 22.5952 15.5575 20.4172 21.7037 19.8772
2.6205 35.0 770 0.5780 23.1109 16.2589 21.0752 22.3091 19.9181
2.6205 36.0 792 0.5464 23.1603 16.305 20.9824 22.2362 19.8713
2.6205 37.0 814 0.5142 24.162 17.7426 22.3464 23.3824 19.9181
2.6205 38.0 836 0.4892 24.2007 17.7402 22.3069 23.2964 19.9123
2.6205 39.0 858 0.4554 25.0508 19.2285 23.532 24.3425 19.8713
2.6205 40.0 880 0.4351 25.2177 19.7802 23.87 24.5619 19.8713
2.6205 41.0 902 0.4080 25.6072 20.2655 24.2885 25.012 19.8713
2.6205 42.0 924 0.3853 26.2649 21.7378 25.3695 25.8268 19.8713
2.6205 43.0 946 0.3644 26.5133 22.0374 25.6096 26.0746 19.8713
2.6205 44.0 968 0.3434 27.544 23.6211 26.7186 27.1989 19.9415
2.6205 45.0 990 0.3238 28.139 24.4929 27.4976 27.8511 19.9006
1.2313 46.0 1012 0.3078 28.3887 25.1736 27.9633 28.1744 19.9415
1.2313 47.0 1034 0.2926 28.6862 25.3258 28.1086 28.42 19.9123
1.2313 48.0 1056 0.2725 28.5213 25.4276 28.1376 28.3496 19.9006
1.2313 49.0 1078 0.2584 28.765 25.8043 28.2786 28.5328 19.9006
1.2313 50.0 1100 0.2450 28.8002 25.8598 28.4401 28.5732 19.9006
1.2313 51.0 1122 0.2314 29.1758 26.5723 28.7541 28.9057 19.9006
1.2313 52.0 1144 0.2193 29.2566 26.7015 28.8675 29.0212 19.9006
1.2313 53.0 1166 0.2095 29.1182 26.6288 28.8878 28.9966 19.9006
1.2313 54.0 1188 0.2014 29.9429 27.8068 29.8207 29.8447 19.9474
1.2313 55.0 1210 0.1868 29.0828 26.719 28.8889 28.9866 19.9006
1.2313 56.0 1232 0.1795 29.8194 27.8057 29.7228 29.7447 19.9006
1.2313 57.0 1254 0.1690 29.6354 27.4725 29.4187 29.4941 19.9006
1.2313 58.0 1276 0.1601 30.0272 27.9412 29.7751 29.8644 19.9298
1.2313 59.0 1298 0.1544 30.1801 28.3209 30.004 30.067 19.9474
1.2313 60.0 1320 0.1479 30.1894 28.3483 30.0292 30.0775 19.9298
1.2313 61.0 1342 0.1414 30.2365 28.4556 30.1151 30.1872 19.9298
1.2313 62.0 1364 0.1339 30.2558 28.4829 30.1638 30.2142 19.9474
1.2313 63.0 1386 0.1290 30.1935 28.3722 30.047 30.1198 19.9298
1.2313 64.0 1408 0.1232 30.2356 28.4823 30.1396 30.1941 19.9474
1.2313 65.0 1430 0.1181 30.2719 28.5342 30.176 30.245 19.9298
1.2313 66.0 1452 0.1125 30.2062 28.4951 30.1307 30.1736 19.9298
1.2313 67.0 1474 0.1069 30.2193 28.498 30.1143 30.1778 19.9298
1.2313 68.0 1496 0.1027 30.0782 28.4741 30.0127 30.0385 19.9006
0.6386 69.0 1518 0.1014 30.2859 28.7111 30.2224 30.2692 19.9298
0.6386 70.0 1540 0.0979 30.2806 28.6988 30.2214 30.2583 19.9298
0.6386 71.0 1562 0.0945 30.2865 28.7031 30.2387 30.2613 19.9298
0.6386 72.0 1584 0.0902 30.3134 28.8224 30.2775 30.2923 19.9474
0.6386 73.0 1606 0.0875 30.316 28.8558 30.2741 30.2958 19.9474
0.6386 74.0 1628 0.0848 30.2854 28.7254 30.2396 30.273 19.9298
0.6386 75.0 1650 0.0801 30.3158 28.8377 30.2739 30.2988 19.9474
0.6386 76.0 1672 0.0790 30.316 28.8628 30.2741 30.2958 19.9474
0.6386 77.0 1694 0.0770 30.3249 28.8701 30.2905 30.3056 19.9474
0.6386 78.0 1716 0.0745 30.3109 28.8801 30.2683 30.2891 19.9474
0.6386 79.0 1738 0.0735 30.3249 28.8732 30.2905 30.3056 19.9474
0.6386 80.0 1760 0.0704 30.3249 28.899 30.2905 30.3056 19.9474
0.6386 81.0 1782 0.0698 30.3183 28.9058 30.2817 30.2965 19.9474
0.6386 82.0 1804 0.0674 30.287 28.785 30.2354 30.2658 19.9298
0.6386 83.0 1826 0.0655 30.287 28.785 30.2354 30.2658 19.9298
0.6386 84.0 1848 0.0647 30.3006 28.8093 30.2572 30.2715 19.9298
0.6386 85.0 1870 0.0625 30.3006 28.8093 30.2572 30.2715 19.924
0.6386 86.0 1892 0.0620 30.287 28.7763 30.2354 30.2658 19.9298
0.6386 87.0 1914 0.0603 30.3006 28.8093 30.2572 30.2715 19.9298
0.6386 88.0 1936 0.0595 30.3006 28.8093 30.2572 30.2715 19.924
0.6386 89.0 1958 0.0584 30.3006 28.8093 30.2572 30.2715 19.924
0.6386 90.0 1980 0.0571 30.3006 28.8093 30.2572 30.2715 19.924
0.4186 91.0 2002 0.0566 30.3006 28.8093 30.2572 30.2715 19.9298
0.4186 92.0 2024 0.0560 30.3379 28.8176 30.272 30.2843 19.924
0.4186 93.0 2046 0.0557 30.3006 28.8093 30.2572 30.2715 19.924
0.4186 94.0 2068 0.0550 30.3379 28.823 30.2838 30.2947 19.924
0.4186 95.0 2090 0.0546 30.3528 28.9302 30.321 30.3214 19.9474
0.4186 96.0 2112 0.0542 30.3528 28.9255 30.3159 30.3159 19.9474
0.4186 97.0 2134 0.0540 30.3379 28.8176 30.272 30.2843 19.9298
0.4186 98.0 2156 0.0539 30.3379 28.8176 30.272 30.2843 19.9298
0.4186 99.0 2178 0.0537 30.3379 28.8176 30.272 30.2843 19.9298
0.4186 100.0 2200 0.0536 30.3379 28.823 30.2838 30.2947 19.9298

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.0
  • Tokenizers 0.13.3