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long-t5-tglobal-base

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ubaada/pegasus-x-large-booksum-16k

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  1. README.md +12 -6
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -17,10 +17,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.9565
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- - Rouge1: 0.1423
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- - Rouge2: 0.0178
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- - Rougel: 0.0928
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  ## Model description
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@@ -39,7 +39,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 8e-05
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  - train_batch_size: 8
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  - eval_batch_size: 1
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  - seed: 42
@@ -50,7 +50,7 @@ The following hyperparameters were used during training:
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  - total_eval_batch_size: 2
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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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  ### Training results
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@@ -59,6 +59,12 @@ The following hyperparameters were used during training:
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  | 1.5731 | 0.9996 | 600 | 1.9730 | 0.1342 | 0.0151 | 0.0912 |
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  | 1.3694 | 1.9996 | 1200 | 1.9623 | 0.1371 | 0.0175 | 0.0909 |
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  | 1.9561 | 2.9992 | 1800 | 1.9565 | 0.1423 | 0.0178 | 0.0928 |
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.9401
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+ - Rouge1: 0.1934
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+ - Rouge2: 0.0269
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+ - Rougel: 0.1151
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 4e-05
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  - train_batch_size: 8
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  - eval_batch_size: 1
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  - seed: 42
 
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  - total_eval_batch_size: 2
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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: 9
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  ### Training results
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  | 1.5731 | 0.9996 | 600 | 1.9730 | 0.1342 | 0.0151 | 0.0912 |
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  | 1.3694 | 1.9996 | 1200 | 1.9623 | 0.1371 | 0.0175 | 0.0909 |
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  | 1.9561 | 2.9992 | 1800 | 1.9565 | 0.1423 | 0.0178 | 0.0928 |
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+ | 1.0882 | 3.9996 | 2400 | 1.9548 | 0.1417 | 0.0186 | 0.0900 |
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+ | 1.4872 | 4.9992 | 3000 | 1.9412 | 0.1581 | 0.0212 | 0.1006 |
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+ | 1.4126 | 5.9988 | 3600 | 1.9486 | 0.1589 | 0.0188 | 0.0986 |
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+ | 1.1634 | 7.0 | 4201 | 1.9464 | 0.1756 | 0.0229 | 0.1046 |
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+ | 0.9541 | 7.9996 | 4801 | 1.9401 | 0.1791 | 0.0243 | 0.1078 |
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+ | 0.9153 | 8.9975 | 5400 | 1.9401 | 0.1934 | 0.0269 | 0.1151 |
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  ### Framework versions
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