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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/Mar16_22-59-26_nbes0yzag8/events.out.tfevents.1710629973.nbes0yzag8.779.0 +3 -0
  4. runs/Mar16_23-06-09_nd7l5mu82g/events.out.tfevents.1710630423.nd7l5mu82g.214.0 +3 -0
  5. runs/Mar16_23-06-09_nd7l5mu82g/events.out.tfevents.1710634006.nd7l5mu82g.214.1 +3 -0
  6. tokenizer.json +2 -2
  7. 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: 0.0339
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- - Rouge1: 66.2674
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- - Rouge2: 53.24
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- - Rougel: 64.4312
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- - Rougelsum: 64.3801
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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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- | 0.6909 | 1.0 | 23 | 0.3786 | 48.9181 | 33.2327 | 47.3395 | 47.2726 |
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- | 0.368 | 2.0 | 46 | 0.3206 | 59.2983 | 39.75 | 55.6982 | 55.6325 |
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- | 0.3137 | 3.0 | 69 | 0.2792 | 56.4245 | 38.1385 | 53.2912 | 53.3048 |
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- | 0.2767 | 4.0 | 92 | 0.2686 | 62.4747 | 41.0411 | 57.1997 | 57.3046 |
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- | 0.246 | 5.0 | 115 | 0.2285 | 57.7108 | 38.4945 | 52.2872 | 52.374 |
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- | 0.2337 | 6.0 | 138 | 0.2097 | 59.1384 | 39.0569 | 54.3129 | 54.3312 |
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- | 0.1937 | 7.0 | 161 | 0.1818 | 60.471 | 43.523 | 56.1358 | 56.1602 |
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- | 0.181 | 8.0 | 184 | 0.1502 | 62.2563 | 44.1243 | 58.5507 | 58.4703 |
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- | 0.1529 | 9.0 | 207 | 0.1383 | 60.1078 | 45.3623 | 57.2384 | 57.1999 |
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- | 0.1344 | 10.0 | 230 | 0.1241 | 63.3003 | 46.5418 | 58.4059 | 58.5223 |
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- | 0.1062 | 11.0 | 253 | 0.1008 | 61.2042 | 47.5235 | 58.2944 | 58.3185 |
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- | 0.084 | 12.0 | 276 | 0.0526 | 67.0006 | 53.4416 | 63.5881 | 63.5149 |
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- | 0.0625 | 13.0 | 299 | 0.0504 | 67.9255 | 54.3837 | 63.909 | 63.9992 |
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- | 0.0437 | 14.0 | 322 | 0.0328 | 67.6534 | 55.7668 | 65.242 | 65.269 |
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- | 0.035 | 15.0 | 345 | 0.0515 | 66.4682 | 53.8452 | 64.2248 | 64.1449 |
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- | 0.0262 | 16.0 | 368 | 0.0600 | 67.4167 | 54.0939 | 64.3996 | 64.3916 |
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- | 0.0193 | 17.0 | 391 | 0.0200 | 67.6849 | 55.4936 | 65.648 | 65.6463 |
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- | 0.015 | 18.0 | 414 | 0.0422 | 66.9699 | 54.6991 | 64.6387 | 64.5737 |
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- | 0.0116 | 19.0 | 437 | 0.0320 | 67.5409 | 54.6431 | 65.1123 | 65.0982 |
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- | 0.0104 | 20.0 | 460 | 0.0339 | 66.2674 | 53.24 | 64.4312 | 64.3801 |
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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: 0.0463
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+ - Rouge1: 21.8581
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+ - Rouge2: 15.7643
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+ - Rougel: 20.2702
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+ - Rougelsum: 20.1664
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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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+ | 0.6051 | 1.0 | 23 | 0.2654 | 27.0125 | 16.5829 | 25.1919 | 25.0995 |
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+ | 0.2383 | 2.0 | 46 | 0.2412 | 29.8849 | 18.9938 | 28.1456 | 28.0365 |
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+ | 0.2181 | 3.0 | 69 | 0.2270 | 28.3746 | 17.9884 | 26.5822 | 26.5863 |
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+ | 0.2068 | 4.0 | 92 | 0.2129 | 28.5887 | 18.4472 | 26.6067 | 26.4441 |
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+ | 0.1951 | 5.0 | 115 | 0.1929 | 28.7548 | 19.5159 | 27.0567 | 26.9487 |
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+ | 0.1891 | 6.0 | 138 | 0.1865 | 27.9473 | 19.347 | 26.3571 | 26.2061 |
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+ | 0.1767 | 7.0 | 161 | 0.1808 | 27.5207 | 18.474 | 25.0888 | 24.8773 |
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+ | 0.17 | 8.0 | 184 | 0.1682 | 28.0519 | 19.2238 | 25.9605 | 25.8616 |
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+ | 0.1587 | 9.0 | 207 | 0.1516 | 30.3229 | 20.6628 | 28.0404 | 27.9676 |
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+ | 0.1544 | 10.0 | 230 | 0.1511 | 23.3044 | 15.9156 | 21.8476 | 21.7132 |
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+ | 0.145 | 11.0 | 253 | 0.1277 | 28.9406 | 21.4792 | 27.4752 | 27.4783 |
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+ | 0.1387 | 12.0 | 276 | 0.1178 | 23.6338 | 16.3257 | 22.4785 | 22.3574 |
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+ | 0.1281 | 13.0 | 299 | 0.1041 | 24.6693 | 17.2313 | 23.1714 | 23.0528 |
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+ | 0.1137 | 14.0 | 322 | 0.0909 | 23.2186 | 15.4009 | 21.4084 | 21.4144 |
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+ | 0.1105 | 15.0 | 345 | 0.0819 | 20.3483 | 14.3868 | 19.2546 | 19.181 |
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+ | 0.0979 | 16.0 | 368 | 0.0718 | 20.8701 | 13.8019 | 19.0012 | 19.0207 |
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+ | 0.0896 | 17.0 | 391 | 0.0576 | 21.626 | 15.2753 | 19.9486 | 19.887 |
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+ | 0.0775 | 18.0 | 414 | 0.0530 | 23.5035 | 17.2154 | 21.6261 | 21.6594 |
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+ | 0.0736 | 19.0 | 437 | 0.0493 | 22.8066 | 16.6016 | 21.3275 | 21.3432 |
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+ | 0.0673 | 20.0 | 460 | 0.0463 | 21.8581 | 15.7643 | 20.2702 | 20.1664 |
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  ### Framework versions
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