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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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- rouge
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model-index:
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- name: t5_finetuned_paraphrase-1024
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results: []
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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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should probably proofread and complete it, then remove this comment. -->
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# t5_finetuned_paraphrase-1024
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7026
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- Rouge1: 69.0908
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- Rouge2: 51.3373
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- Rougel: 65.9952
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- Rougelsum: 65.9963
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- Gen Len: 17.9946
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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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: 8
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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 | Gen Len |
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|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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| 0.9264 | 1.0 | 12500 | 0.8018 | 67.7403 | 48.7329 | 64.2684 | 64.273 | 18.0296 |
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| 0.8654 | 2.0 | 25000 | 0.7572 | 68.256 | 49.8753 | 64.9795 | 64.9743 | 18.0071 |
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| 0.8391 | 3.0 | 37500 | 0.7366 | 68.5695 | 50.4061 | 65.3434 | 65.3424 | 18.0047 |
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| 0.8151 | 4.0 | 50000 | 0.7225 | 68.7762 | 50.6912 | 65.5711 | 65.561 | 17.9929 |
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| 0.8068 | 5.0 | 62500 | 0.7134 | 68.8998 | 50.9785 | 65.7391 | 65.7475 | 17.9953 |
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| 0.801 | 6.0 | 75000 | 0.7076 | 69.0179 | 51.1629 | 65.8718 | 65.8738 | 17.9952 |
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| 0.7982 | 7.0 | 87500 | 0.7035 | 69.0643 | 51.2707 | 65.9447 | 65.9508 | 17.9959 |
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| 0.784 | 8.0 | 100000 | 0.7026 | 69.0908 | 51.3373 | 65.9952 | 65.9963 | 17.9946 |
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### Framework versions
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- Transformers 4.27.3
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- Pytorch 1.7.1+cu101
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- Datasets 2.13.2
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- Tokenizers 0.13.2
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