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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: bart-JES-cnn_dailymail |
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results: |
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- task: |
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name: Summarization |
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type: summarization |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 43.9753 |
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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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# bart-JES-cnn_dailymail |
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This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1452 |
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- Rouge1: 43.9753 |
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- Rouge2: 19.7191 |
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- Rougel: 33.6236 |
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- Rougelsum: 41.1683 |
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- Gen Len: 80.1767 |
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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: 3e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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: 6.0 |
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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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| 1.2949 | 1.0 | 71779 | 1.2080 | 11.7171 | 3.3284 | 11.3209 | 11.4022 | 20.0 | |
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| 1.191 | 2.0 | 143558 | 1.1615 | 11.8484 | 3.363 | 11.4175 | 11.5037 | 20.0 | |
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| 1.0907 | 3.0 | 215337 | 1.1452 | 12.6221 | 3.773 | 12.1226 | 12.2359 | 20.0 | |
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| 0.9798 | 4.0 | 287116 | 1.1670 | 12.4306 | 3.7329 | 11.9497 | 12.0617 | 20.0 | |
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| 0.9112 | 5.0 | 358895 | 1.1667 | 12.5404 | 3.7842 | 12.0541 | 12.1643 | 20.0 | |
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| 0.8358 | 6.0 | 430674 | 1.1997 | 12.5153 | 3.778 | 12.0382 | 12.1332 | 20.0 | |
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### Framework versions |
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- Transformers 4.10.2 |
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- Pytorch 1.7.1+cu110 |
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- Datasets 1.11.0 |
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- Tokenizers 0.10.3 |
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