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led-base-finetune-xsum

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update model card README.md

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@@ -16,12 +16,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.5945
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- - Rouge1: 33.044
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- - Rouge2: 10.1279
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- - Rougel: 26.0726
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- - Rougelsum: 26.1473
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- - Gen Len: 19.88
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  ## Model description
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@@ -46,22 +46,32 @@ The following hyperparameters were used during training:
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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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- - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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- | No log | 1.0 | 125 | 2.0340 | 33.5205 | 11.6068 | 27.034 | 27.1108 | 19.4 |
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- | No log | 2.0 | 250 | 2.0703 | 33.4026 | 11.5155 | 26.8554 | 26.9315 | 19.52 |
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- | No log | 3.0 | 375 | 2.1928 | 31.8924 | 11.2046 | 25.5199 | 25.4997 | 19.86 |
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- | 1.4951 | 4.0 | 500 | 2.2934 | 32.8838 | 11.2708 | 26.4849 | 26.5854 | 19.78 |
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- | 1.4951 | 5.0 | 625 | 2.3796 | 32.3596 | 11.1823 | 25.8718 | 25.9102 | 19.92 |
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- | 1.4951 | 6.0 | 750 | 2.4533 | 32.3313 | 11.008 | 25.9119 | 25.9228 | 19.89 |
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- | 1.4951 | 7.0 | 875 | 2.5151 | 31.6539 | 9.9426 | 25.1465 | 25.265 | 19.89 |
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- | 0.4719 | 8.0 | 1000 | 2.5631 | 32.2152 | 10.4829 | 25.808 | 25.9387 | 19.79 |
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- | 0.4719 | 9.0 | 1125 | 2.5777 | 31.8661 | 9.6903 | 25.7577 | 25.7874 | 19.89 |
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- | 0.4719 | 10.0 | 1250 | 2.5945 | 33.044 | 10.1279 | 26.0726 | 26.1473 | 19.88 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 3.3325
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+ - Rouge1: 31.3157
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+ - Rouge2: 9.2183
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+ - Rougel: 23.7641
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+ - Rougelsum: 23.8202
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+ - Gen Len: 19.89
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  ## Model description
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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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+ - num_epochs: 20
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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+ | No log | 1.0 | 125 | 2.6311 | 32.5653 | 10.8601 | 25.3811 | 25.5187 | 19.84 |
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+ | No log | 2.0 | 250 | 2.7544 | 31.6321 | 9.9595 | 25.0264 | 25.0779 | 19.85 |
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+ | No log | 3.0 | 375 | 2.8261 | 32.0246 | 10.1415 | 25.2121 | 25.2632 | 19.89 |
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+ | 0.1515 | 4.0 | 500 | 2.9240 | 31.6961 | 11.1892 | 25.0684 | 25.1019 | 19.92 |
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+ | 0.1515 | 5.0 | 625 | 3.0229 | 31.1022 | 9.294 | 24.3075 | 24.309 | 19.9 |
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+ | 0.1515 | 6.0 | 750 | 3.0900 | 31.7063 | 10.2344 | 25.1885 | 25.3359 | 19.89 |
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+ | 0.1515 | 7.0 | 875 | 3.0958 | 31.6973 | 10.2856 | 25.5433 | 25.6242 | 19.91 |
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+ | 0.0437 | 8.0 | 1000 | 3.1248 | 30.9445 | 10.3904 | 24.0861 | 24.109 | 19.91 |
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+ | 0.0437 | 9.0 | 1125 | 3.1542 | 31.4694 | 9.4087 | 24.3248 | 24.4039 | 19.97 |
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+ | 0.0437 | 10.0 | 1250 | 3.1986 | 30.428 | 9.6657 | 24.2568 | 24.4035 | 19.86 |
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+ | 0.0437 | 11.0 | 1375 | 3.2040 | 32.3325 | 9.8754 | 25.117 | 25.1563 | 19.95 |
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+ | 0.0229 | 12.0 | 1500 | 3.2044 | 30.8435 | 8.6959 | 23.4129 | 23.5211 | 19.99 |
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+ | 0.0229 | 13.0 | 1625 | 3.2419 | 31.8807 | 9.6734 | 24.5748 | 24.6672 | 19.96 |
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+ | 0.0229 | 14.0 | 1750 | 3.2926 | 31.8181 | 9.5238 | 24.3606 | 24.4569 | 19.88 |
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+ | 0.0229 | 15.0 | 1875 | 3.2935 | 30.7551 | 8.9042 | 23.9581 | 24.1074 | 19.98 |
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+ | 0.0107 | 16.0 | 2000 | 3.3219 | 31.3919 | 9.3308 | 24.1432 | 24.2162 | 19.93 |
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+ | 0.0107 | 17.0 | 2125 | 3.3167 | 31.7918 | 9.4813 | 23.9672 | 24.0244 | 19.9 |
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+ | 0.0107 | 18.0 | 2250 | 3.3281 | 31.0624 | 9.3608 | 23.6247 | 23.6658 | 19.89 |
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+ | 0.0107 | 19.0 | 2375 | 3.3248 | 31.7832 | 9.5257 | 23.9738 | 24.0255 | 19.96 |
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+ | 0.0063 | 20.0 | 2500 | 3.3325 | 31.3157 | 9.2183 | 23.7641 | 23.8202 | 19.89 |
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  ### Framework versions