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--- |
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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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base_model: google/pegasus-newsroom |
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model-index: |
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- name: pegasus-newsroom-headline_writer_57k |
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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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# pegasus-newsroom-headline_writer_57k |
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This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3599 |
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- Rouge1: 42.2586 |
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- Rouge2: 23.2731 |
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- Rougel: 35.8685 |
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- Rougelsum: 36.0581 |
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- Gen Len: 34.3651 |
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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: 8 |
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- eval_batch_size: 8 |
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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 | Gen Len | |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 1.5213 | 1.0 | 5670 | 1.4040 | 41.8648 | 22.8205 | 35.3983 | 35.535 | 34.8817 | |
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| 1.4171 | 2.0 | 11340 | 1.3672 | 42.26 | 23.2611 | 35.8016 | 35.9753 | 34.3492 | |
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| 1.3722 | 3.0 | 17010 | 1.3599 | 42.2586 | 23.2731 | 35.8685 | 36.0581 | 34.3651 | |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.0 |
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- Tokenizers 0.13.1 |
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