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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/Mar09_16-30-04_nit3cw02yg/events.out.tfevents.1710001844.nit3cw02yg.254.0 +3 -0
  4. runs/Mar09_16-30-04_nit3cw02yg/events.out.tfevents.1710004946.nit3cw02yg.254.1 +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: 0.0384
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- - Rouge1: 67.0012
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- - Rouge2: 55.1201
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- - Rougel: 64.9916
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- - Rougelsum: 65.0
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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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- | 0.6964 | 1.0 | 23 | 0.3699 | 50.5808 | 36.0599 | 48.8381 | 48.7816 |
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- | 0.3654 | 2.0 | 46 | 0.3412 | 56.4293 | 40.3615 | 53.4553 | 53.366 |
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- | 0.3112 | 3.0 | 69 | 0.2891 | 55.2786 | 41.4255 | 52.7934 | 52.7485 |
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- | 0.2749 | 4.0 | 92 | 0.2826 | 61.501 | 42.4993 | 56.3623 | 56.2897 |
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- | 0.2534 | 5.0 | 115 | 0.2314 | 62.1301 | 45.1421 | 58.4136 | 58.6102 |
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- | 0.2363 | 6.0 | 138 | 0.2202 | 60.738 | 43.6776 | 56.4619 | 56.553 |
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- | 0.2015 | 7.0 | 161 | 0.1876 | 65.3434 | 48.4004 | 61.6649 | 61.6797 |
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- | 0.1911 | 8.0 | 184 | 0.1667 | 62.5351 | 48.4521 | 59.7955 | 59.7174 |
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- | 0.1587 | 9.0 | 207 | 0.1280 | 63.6654 | 48.5257 | 61.1761 | 61.3154 |
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- | 0.1419 | 10.0 | 230 | 0.0920 | 65.0905 | 50.0418 | 61.9516 | 62.1153 |
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- | 0.1105 | 11.0 | 253 | 0.0632 | 64.3945 | 51.397 | 61.1146 | 61.0697 |
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- | 0.0855 | 12.0 | 276 | 0.0448 | 66.9018 | 55.0888 | 65.0609 | 65.0079 |
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- | 0.0652 | 13.0 | 299 | 0.0601 | 64.0396 | 52.9896 | 62.2512 | 62.2246 |
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- | 0.0441 | 14.0 | 322 | 0.0398 | 66.3833 | 55.1127 | 64.038 | 64.0185 |
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- | 0.0366 | 15.0 | 345 | 0.0241 | 66.9502 | 55.7562 | 64.8033 | 64.8408 |
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- | 0.0268 | 16.0 | 368 | 0.0594 | 69.0772 | 56.148 | 66.4356 | 66.5236 |
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- | 0.02 | 17.0 | 391 | 0.0344 | 66.4522 | 55.175 | 64.7948 | 64.7399 |
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- | 0.0155 | 18.0 | 414 | 0.0456 | 68.6415 | 56.1231 | 66.1926 | 66.2718 |
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- | 0.0119 | 19.0 | 437 | 0.0392 | 66.9798 | 55.3614 | 65.0161 | 64.9401 |
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- | 0.0096 | 20.0 | 460 | 0.0384 | 67.0012 | 55.1201 | 64.9916 | 65.0 |
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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: 0.0339
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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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  | 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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