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bart-large-cnn-finetuned-scope-summarization

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  1. README.md +27 -27
  2. model.safetensors +1 -1
  3. runs/Mar17_06-59-53_n6objsn46m/events.out.tfevents.1710658797.n6objsn46m.332.1 +2 -2
  4. runs/Mar17_06-59-53_n6objsn46m/events.out.tfevents.1710662485.n6objsn46m.332.2 +3 -0
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: 0.0463
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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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@@ -41,8 +41,8 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 5.6e-05
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- - train_batch_size: 15
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- - eval_batch_size: 15
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
@@ -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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- | 0.6051 | 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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  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: 0.0552
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+ - Rouge1: 49.8374
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+ - Rouge2: 38.0885
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+ - Rougel: 42.6985
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+ - Rougelsum: 42.4809
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5.6e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
 
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
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+ | 0.6831 | 1.0 | 43 | 0.3928 | 40.6965 | 25.3494 | 30.1716 | 29.9938 |
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+ | 0.3578 | 2.0 | 86 | 0.3598 | 43.284 | 27.9071 | 32.9941 | 32.9077 |
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+ | 0.3302 | 3.0 | 129 | 0.3362 | 45.2375 | 30.4709 | 34.8733 | 34.6801 |
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+ | 0.309 | 4.0 | 172 | 0.3136 | 44.928 | 30.8601 | 34.7804 | 34.6754 |
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+ | 0.2948 | 5.0 | 215 | 0.2919 | 44.5169 | 30.2429 | 34.5979 | 34.4672 |
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+ | 0.2841 | 6.0 | 258 | 0.2755 | 45.7172 | 31.6555 | 34.9668 | 34.9069 |
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+ | 0.268 | 7.0 | 301 | 0.2618 | 46.4085 | 32.782 | 35.804 | 35.6348 |
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+ | 0.252 | 8.0 | 344 | 0.2424 | 47.8634 | 33.6728 | 36.9559 | 36.9081 |
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+ | 0.2405 | 9.0 | 387 | 0.2286 | 46.8182 | 34.4363 | 37.7534 | 37.6356 |
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+ | 0.2255 | 10.0 | 430 | 0.2276 | 46.8516 | 33.3166 | 37.6246 | 37.5024 |
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+ | 0.2125 | 11.0 | 473 | 0.1946 | 47.6772 | 33.9627 | 37.8554 | 37.7735 |
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+ | 0.1918 | 12.0 | 516 | 0.1682 | 46.851 | 33.6098 | 38.2906 | 38.24 |
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+ | 0.1726 | 13.0 | 559 | 0.1442 | 48.8833 | 36.4235 | 39.4263 | 39.1955 |
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+ | 0.152 | 14.0 | 602 | 0.1305 | 50.5835 | 39.2008 | 43.3793 | 43.1671 |
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+ | 0.1344 | 15.0 | 645 | 0.1109 | 47.3517 | 35.4446 | 38.0845 | 38.0578 |
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+ | 0.116 | 16.0 | 688 | 0.0842 | 48.9774 | 37.6705 | 41.6306 | 41.4792 |
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+ | 0.1007 | 17.0 | 731 | 0.0762 | 49.9775 | 38.4186 | 42.647 | 42.4334 |
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+ | 0.0899 | 18.0 | 774 | 0.0623 | 50.1358 | 38.9943 | 43.4025 | 43.1603 |
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+ | 0.0805 | 19.0 | 817 | 0.0571 | 51.5974 | 40.1928 | 44.1821 | 43.9354 |
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+ | 0.0753 | 20.0 | 860 | 0.0552 | 49.8374 | 38.0885 | 42.6985 | 42.4809 |
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  ### Framework versions
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