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update model card README.md

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@@ -16,11 +16,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7620
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- - Rouge1: 43.3685
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- - Rouge2: 21.9134
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- - Rougel: 40.2798
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- - Rougelsum: 43.3935
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  ## Model description
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@@ -45,16 +45,18 @@ 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: 3
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
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- | 5.6307 | 1.0 | 167 | 1.6589 | 17.9154 | 4.5628 | 17.3823 | 17.9298 |
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- | 1.5548 | 2.0 | 334 | 0.8930 | 37.8352 | 17.9295 | 35.5132 | 37.8323 |
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- | 1.1204 | 3.0 | 501 | 0.7620 | 43.3685 | 21.9134 | 40.2798 | 43.3935 |
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3512
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+ - Rouge1: 71.7164
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+ - Rouge2: 48.3026
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+ - Rougel: 60.9741
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+ - Rougelsum: 71.703
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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: 5
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
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+ | 5.526 | 1.0 | 167 | 1.5778 | 20.8927 | 6.5549 | 19.8894 | 20.8757 |
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+ | 1.2998 | 2.0 | 334 | 0.7396 | 47.1012 | 24.6065 | 42.5169 | 47.1201 |
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+ | 0.8173 | 3.0 | 501 | 0.4854 | 63.0251 | 38.8258 | 53.3364 | 63.0153 |
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+ | 0.6211 | 4.0 | 668 | 0.3797 | 70.1541 | 46.2974 | 59.4121 | 70.1327 |
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+ | 0.5504 | 5.0 | 835 | 0.3512 | 71.7164 | 48.3026 | 60.9741 | 71.703 |
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  ### Framework versions