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--- |
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license: apache-2.0 |
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tags: |
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- summarisation |
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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: distilbart-xsum-6-6-finetuned-bbc-news |
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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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# distilbart-xsum-6-6-finetuned-bbc-news |
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This model is a fine-tuned version of [sshleifer/distilbart-xsum-6-6](https://huggingface.co/sshleifer/distilbart-xsum-6-6) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2624 |
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- Rouge1: 62.1927 |
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- Rouge2: 54.4754 |
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- Rougel: 55.868 |
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- Rougelsum: 60.9345 |
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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: 5.6e-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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
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| 0.4213 | 1.0 | 445 | 0.2005 | 59.4886 | 51.7791 | 53.5126 | 58.3405 | |
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| 0.1355 | 2.0 | 890 | 0.1887 | 61.7474 | 54.2823 | 55.7324 | 60.5787 | |
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| 0.0891 | 3.0 | 1335 | 0.1932 | 61.1312 | 53.103 | 54.6992 | 59.8923 | |
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| 0.0571 | 4.0 | 1780 | 0.2141 | 60.8797 | 52.6195 | 54.4402 | 59.5298 | |
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| 0.0375 | 5.0 | 2225 | 0.2318 | 61.7875 | 53.8753 | 55.5068 | 60.5448 | |
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| 0.0251 | 6.0 | 2670 | 0.2484 | 62.3535 | 54.6029 | 56.2804 | 61.031 | |
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| 0.0175 | 7.0 | 3115 | 0.2542 | 61.6351 | 53.8248 | 55.6399 | 60.3765 | |
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| 0.0133 | 8.0 | 3560 | 0.2624 | 62.1927 | 54.4754 | 55.868 | 60.9345 | |
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### Framework versions |
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- Transformers 4.21.2 |
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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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