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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_13-19-03_n32kzn262d/events.out.tfevents.1708953549.n32kzn262d.980.0 +3 -0
  4. runs/Feb26_13-19-03_n32kzn262d/events.out.tfevents.1708954416.n32kzn262d.980.1 +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: 0.0599
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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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@@ -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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- | 1.3289 | 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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  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: 0.0362
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+ - Rouge1: 75.4021
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+ - Rouge2: 60.4781
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+ - Rougel: 69.4413
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+ - Rougelsum: 69.1531
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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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+ | 1.2638 | 1.0 | 13 | 0.7845 | 46.2506 | 27.5302 | 36.3031 | 36.1488 |
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+ | 0.6983 | 2.0 | 26 | 0.6852 | 46.9095 | 28.7666 | 36.3647 | 36.5142 |
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+ | 0.6086 | 3.0 | 39 | 0.5691 | 46.4844 | 31.1016 | 40.5842 | 40.5575 |
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+ | 0.545 | 4.0 | 52 | 0.5265 | 55.0792 | 41.0166 | 47.3565 | 47.125 |
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+ | 0.4797 | 5.0 | 65 | 0.4693 | 60.5996 | 45.0404 | 48.2476 | 48.193 |
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+ | 0.4172 | 6.0 | 78 | 0.3998 | 61.1898 | 44.5581 | 51.0268 | 50.9729 |
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+ | 0.3694 | 7.0 | 91 | 0.3464 | 65.6304 | 49.6498 | 55.2135 | 55.0835 |
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+ | 0.3175 | 8.0 | 104 | 0.2912 | 65.5462 | 49.7933 | 53.8552 | 53.9245 |
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+ | 0.2771 | 9.0 | 117 | 0.2090 | 66.7025 | 51.251 | 58.2184 | 57.8748 |
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+ | 0.2187 | 10.0 | 130 | 0.1532 | 72.6809 | 58.2315 | 63.6063 | 63.0489 |
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+ | 0.1809 | 11.0 | 143 | 0.0823 | 69.1654 | 54.3816 | 58.8206 | 58.877 |
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+ | 0.1138 | 12.0 | 156 | 0.0858 | 74.4507 | 58.8272 | 65.7722 | 65.371 |
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+ | 0.0813 | 13.0 | 169 | 0.0437 | 76.1358 | 60.4886 | 67.0897 | 66.9479 |
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+ | 0.0637 | 14.0 | 182 | 0.0561 | 74.8731 | 61.4541 | 67.0014 | 66.6877 |
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+ | 0.0523 | 15.0 | 195 | 0.0698 | 73.008 | 57.968 | 63.4556 | 63.2808 |
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+ | 0.0459 | 16.0 | 208 | 0.0556 | 73.1956 | 58.1032 | 65.9982 | 65.5125 |
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+ | 0.0352 | 17.0 | 221 | 0.0430 | 75.2864 | 61.8915 | 67.5246 | 67.3903 |
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+ | 0.0274 | 18.0 | 234 | 0.0495 | 75.2014 | 59.5957 | 67.6364 | 67.5241 |
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+ | 0.0203 | 19.0 | 247 | 0.0390 | 76.9033 | 63.5161 | 72.0725 | 72.1355 |
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+ | 0.0233 | 20.0 | 260 | 0.0362 | 75.4021 | 60.4781 | 69.4413 | 69.1531 |
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  ### Framework versions
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