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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cnn_dailymail |
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metrics: |
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- rouge |
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model-index: |
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- name: pegasus-newsroom-cnn-adam8bit-bs4x64acc |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: cnn_dailymail |
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type: cnn_dailymail |
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args: 3.0.0 |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 44.2881 |
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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-cnn-adam8bit-bs4x64acc |
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This model is a fine-tuned version of [oMateos2020/pegasus-newsroom-cnn-adam8bit-bs16x64acc](https://huggingface.co/oMateos2020/pegasus-newsroom-cnn-adam8bit-bs16x64acc) on the cnn_dailymail dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8608 |
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- Rouge1: 44.2881 |
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- Rouge2: 21.5487 |
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- Rougel: 31.3798 |
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- Rougelsum: 41.2326 |
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- Gen Len: 71.7744 |
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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: 6.4e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 64 |
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- total_train_batch_size: 256 |
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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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- lr_scheduler_warmup_ratio: 0.4 |
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- num_epochs: 1 |
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- mixed_precision_training: Native AMP |
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- label_smoothing_factor: 0.1 |
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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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| 2.9307 | 1.0 | 1121 | 2.8608 | 44.2881 | 21.5487 | 31.3798 | 41.2326 | 71.7744 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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