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led-large-16384-cnn_dailymail

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  1. README.md +11 -7
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Rouge1
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  type: rouge
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- value: 0.38275620598885174
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,11 +31,11 @@ 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 cnn_dailymail dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.6093
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- - Rouge1: 0.3828
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- - Rouge2: 0.1701
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- - Rougel: 0.2561
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- - Rougelsum: 0.3613
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  ## Model description
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@@ -62,7 +62,7 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 128
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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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  ### Training results
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  | 1.6491 | 4.46 | 10000 | 1.6172 | 0.3799 | 0.1681 | 0.2540 | 0.3586 |
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  | 1.5994 | 4.68 | 10500 | 1.6132 | 0.3825 | 0.1702 | 0.2560 | 0.3610 |
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  | 1.6493 | 4.9 | 11000 | 1.6093 | 0.3828 | 0.1701 | 0.2561 | 0.3613 |
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Rouge1
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  type: rouge
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+ value: 0.38289524455734836
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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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 cnn_dailymail dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.5981
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+ - Rouge1: 0.3829
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+ - Rouge2: 0.1704
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+ - Rougel: 0.2569
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+ - Rougelsum: 0.3614
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  ## Model description
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  - total_train_batch_size: 128
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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: 6
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  ### Training results
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  | 1.6491 | 4.46 | 10000 | 1.6172 | 0.3799 | 0.1681 | 0.2540 | 0.3586 |
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  | 1.5994 | 4.68 | 10500 | 1.6132 | 0.3825 | 0.1702 | 0.2560 | 0.3610 |
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  | 1.6493 | 4.9 | 11000 | 1.6093 | 0.3828 | 0.1701 | 0.2561 | 0.3613 |
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+ | 1.6769 | 5.13 | 11500 | 1.6074 | 0.3831 | 0.1706 | 0.2569 | 0.3619 |
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+ | 1.6554 | 5.35 | 12000 | 1.6044 | 0.3817 | 0.1695 | 0.2559 | 0.3605 |
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+ | 1.6155 | 5.57 | 12500 | 1.6010 | 0.3825 | 0.1700 | 0.2561 | 0.3608 |
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+ | 1.5863 | 5.8 | 13000 | 1.5981 | 0.3829 | 0.1704 | 0.2569 | 0.3614 |
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  ### Framework versions
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