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bart-large-cnn-finetuned-scope-summarization

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  1. README.md +25 -25
  2. model.safetensors +1 -1
  3. runs/Feb26_13-02-47_n32kzn262d/events.out.tfevents.1708952573.n32kzn262d.727.0 +3 -0
  4. runs/Feb26_13-02-47_n32kzn262d/events.out.tfevents.1708953060.n32kzn262d.727.1 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -17,11 +17,11 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.0626
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- - Rouge1: 71.1924
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- - Rouge2: 56.5743
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- - Rougel: 58.4583
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- - Rougelsum: 58.5974
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  ## Model description
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@@ -52,26 +52,26 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
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- | 1.2505 | 1.0 | 13 | 0.6728 | 42.395 | 24.0426 | 32.0348 | 32.0715 |
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- | 0.6526 | 2.0 | 26 | 0.5205 | 48.8672 | 34.9826 | 40.7332 | 40.7674 |
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- | 0.5492 | 3.0 | 39 | 0.4153 | 54.2423 | 40.876 | 44.9622 | 44.9886 |
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- | 0.4827 | 4.0 | 52 | 0.3740 | 53.5596 | 39.3867 | 44.9257 | 45.057 |
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- | 0.4119 | 5.0 | 65 | 0.3762 | 52.0881 | 36.8225 | 40.5069 | 40.4043 |
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- | 0.3804 | 6.0 | 78 | 0.2344 | 57.5203 | 43.3466 | 46.4542 | 46.4936 |
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- | 0.3039 | 7.0 | 91 | 0.2151 | 57.6824 | 41.4977 | 45.5073 | 45.5051 |
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- | 0.2578 | 8.0 | 104 | 0.1730 | 61.3858 | 47.3075 | 52.4419 | 52.3631 |
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- | 0.2457 | 9.0 | 117 | 0.1536 | 64.3023 | 49.3955 | 52.6827 | 52.7162 |
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- | 0.1829 | 10.0 | 130 | 0.0971 | 66.5754 | 50.3852 | 55.1851 | 55.2008 |
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- | 0.1417 | 11.0 | 143 | 0.0924 | 68.1503 | 52.7339 | 57.7043 | 57.8818 |
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- | 0.0868 | 12.0 | 156 | 0.0604 | 71.9267 | 58.4041 | 63.0512 | 63.4469 |
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- | 0.071 | 13.0 | 169 | 0.0762 | 65.9864 | 50.9967 | 55.1759 | 55.5074 |
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- | 0.0494 | 14.0 | 182 | 0.0393 | 65.5931 | 51.3148 | 58.276 | 58.1242 |
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- | 0.0455 | 15.0 | 195 | 0.0660 | 70.7603 | 58.125 | 64.1561 | 64.1192 |
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- | 0.0348 | 16.0 | 208 | 0.0579 | 69.4807 | 55.6829 | 60.014 | 60.1088 |
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- | 0.0267 | 17.0 | 221 | 0.0711 | 73.3778 | 58.7117 | 58.6246 | 58.8468 |
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- | 0.0235 | 18.0 | 234 | 0.0643 | 73.5436 | 58.5648 | 58.4584 | 58.6235 |
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- | 0.0207 | 19.0 | 247 | 0.0665 | 72.4756 | 57.2448 | 59.1107 | 59.2048 |
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- | 0.0177 | 20.0 | 260 | 0.0626 | 71.1924 | 56.5743 | 58.4583 | 58.5974 |
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  ### Framework versions
 
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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: 0.0599
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+ - Rouge1: 64.9799
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+ - Rouge2: 49.2784
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+ - Rougel: 48.926
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+ - Rougelsum: 49.272
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
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+ | 1.3289 | 1.0 | 13 | 0.6583 | 35.8242 | 18.5105 | 27.8428 | 27.8714 |
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+ | 0.7168 | 2.0 | 26 | 0.5198 | 47.465 | 27.9541 | 34.9344 | 34.9803 |
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+ | 0.6214 | 3.0 | 39 | 0.4560 | 52.0755 | 33.7932 | 39.6654 | 39.7413 |
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+ | 0.5597 | 4.0 | 52 | 0.4063 | 51.5423 | 34.7987 | 41.9346 | 41.8684 |
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+ | 0.4795 | 5.0 | 65 | 0.3596 | 54.6926 | 37.776 | 40.0876 | 40.186 |
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+ | 0.436 | 6.0 | 78 | 0.3229 | 57.6804 | 40.6145 | 44.5193 | 44.4836 |
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+ | 0.3833 | 7.0 | 91 | 0.2669 | 57.3979 | 43.131 | 48.7031 | 48.9129 |
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+ | 0.32 | 8.0 | 104 | 0.2239 | 61.0803 | 45.3751 | 47.042 | 46.9104 |
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+ | 0.2917 | 9.0 | 117 | 0.1564 | 62.586 | 46.5086 | 52.1972 | 52.3728 |
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+ | 0.233 | 10.0 | 130 | 0.1522 | 60.6708 | 42.2612 | 44.4825 | 44.8483 |
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+ | 0.1999 | 11.0 | 143 | 0.0914 | 65.2236 | 49.7723 | 53.2089 | 53.1985 |
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+ | 0.1335 | 12.0 | 156 | 0.0682 | 64.3116 | 48.1972 | 50.4081 | 50.4617 |
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+ | 0.0983 | 13.0 | 169 | 0.0780 | 64.3727 | 48.4211 | 49.9703 | 50.1001 |
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+ | 0.0729 | 14.0 | 182 | 0.0757 | 63.7552 | 47.3836 | 52.3954 | 52.415 |
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+ | 0.0597 | 15.0 | 195 | 0.0647 | 65.7902 | 50.1794 | 54.2573 | 54.3065 |
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+ | 0.0399 | 16.0 | 208 | 0.0472 | 67.9871 | 50.7219 | 48.2455 | 48.3936 |
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+ | 0.0393 | 17.0 | 221 | 0.0547 | 68.1776 | 51.7682 | 49.719 | 50.0039 |
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+ | 0.0329 | 18.0 | 234 | 0.0482 | 68.8524 | 51.5706 | 48.9778 | 49.2514 |
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+ | 0.0245 | 19.0 | 247 | 0.0610 | 63.7113 | 47.7176 | 46.8786 | 47.2048 |
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+ | 0.0232 | 20.0 | 260 | 0.0599 | 64.9799 | 49.2784 | 48.926 | 49.272 |
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  ### Framework versions
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