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--- |
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license: mit |
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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-large-cnn-finetuned-roundup-16 |
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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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# bart-large-cnn-finetuned-roundup-16 |
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8957 |
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- Rouge1: 49.4097 |
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- Rouge2: 29.3516 |
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- Rougel: 31.527 |
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- Rougelsum: 46.4241 |
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- Gen Len: 141.9 |
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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: 2 |
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- eval_batch_size: 2 |
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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: 16 |
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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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| No log | 1.0 | 132 | 1.3170 | 48.412 | 29.2017 | 31.6679 | 45.494 | 141.85 | |
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| No log | 2.0 | 264 | 1.2292 | 49.0133 | 29.6645 | 30.7612 | 46.1673 | 142.0 | |
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| No log | 3.0 | 396 | 1.2670 | 49.183 | 29.4104 | 31.573 | 46.7082 | 142.0 | |
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| 0.9596 | 4.0 | 528 | 1.3059 | 47.3854 | 26.6865 | 28.4666 | 44.4934 | 141.8 | |
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| 0.9596 | 5.0 | 660 | 1.3288 | 48.1189 | 26.9242 | 31.2938 | 45.3462 | 142.0 | |
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| 0.9596 | 6.0 | 792 | 1.4084 | 47.5713 | 26.7488 | 29.2959 | 45.1764 | 141.3 | |
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| 0.9596 | 7.0 | 924 | 1.5043 | 46.5407 | 26.0995 | 29.9007 | 43.9335 | 142.0 | |
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| 0.3369 | 8.0 | 1056 | 1.5115 | 49.6891 | 29.0514 | 32.33 | 46.9357 | 142.0 | |
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| 0.3369 | 9.0 | 1188 | 1.6131 | 47.5773 | 27.6348 | 30.5294 | 45.1151 | 142.0 | |
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| 0.3369 | 10.0 | 1320 | 1.6837 | 46.5699 | 26.3805 | 29.8581 | 43.5252 | 142.0 | |
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| 0.3369 | 11.0 | 1452 | 1.7874 | 47.1383 | 26.535 | 30.1724 | 44.2508 | 142.0 | |
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| 0.148 | 12.0 | 1584 | 1.7776 | 49.8061 | 30.1994 | 33.2405 | 47.6102 | 142.0 | |
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| 0.148 | 13.0 | 1716 | 1.8144 | 48.4451 | 28.2949 | 30.9026 | 45.6614 | 142.0 | |
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| 0.148 | 14.0 | 1848 | 1.8646 | 50.1964 | 30.4426 | 32.8156 | 47.4134 | 142.0 | |
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| 0.148 | 15.0 | 1980 | 1.8829 | 48.8129 | 29.2358 | 32.3247 | 46.2233 | 142.0 | |
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| 0.0726 | 16.0 | 2112 | 1.8957 | 49.4097 | 29.3516 | 31.527 | 46.4241 | 141.9 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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