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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-4-16 |
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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-4-16 |
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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: 0.8760 |
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- Rouge1: 56.3338 |
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- Rouge2: 42.4032 |
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- Rougel: 45.9455 |
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- Rougelsum: 54.6488 |
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- Gen Len: 142.0 |
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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 | 398 | 0.9325 | 52.7796 | 33.0802 | 34.8217 | 50.2211 | 142.0 | |
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| 1.1317 | 2.0 | 796 | 0.8313 | 53.6274 | 35.3235 | 37.7077 | 51.0888 | 141.2963 | |
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| 0.6757 | 3.0 | 1194 | 0.7893 | 54.1449 | 34.7532 | 36.3211 | 51.781 | 142.0 | |
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| 0.4511 | 4.0 | 1592 | 0.7647 | 52.2694 | 34.2286 | 36.5736 | 49.7078 | 142.0 | |
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| 0.4511 | 5.0 | 1990 | 0.7596 | 55.1986 | 37.5865 | 41.406 | 53.1897 | 141.8333 | |
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| 0.3037 | 6.0 | 2388 | 0.7688 | 53.9367 | 36.8729 | 39.9456 | 51.5108 | 142.0 | |
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| 0.209 | 7.0 | 2786 | 0.7590 | 54.6867 | 37.6415 | 41.2602 | 52.746 | 142.0 | |
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| 0.1452 | 8.0 | 3184 | 0.7744 | 53.5374 | 36.3666 | 40.0432 | 51.3461 | 142.0 | |
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| 0.11 | 9.0 | 3582 | 0.8042 | 56.6623 | 40.4702 | 44.0028 | 54.5138 | 142.0 | |
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| 0.11 | 10.0 | 3980 | 0.8105 | 55.6002 | 40.5663 | 43.8119 | 53.9117 | 142.0 | |
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| 0.0833 | 11.0 | 4378 | 0.8230 | 56.2517 | 40.8567 | 44.0009 | 54.3271 | 142.0 | |
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| 0.0634 | 12.0 | 4776 | 0.8329 | 55.9228 | 40.6443 | 43.6161 | 54.0975 | 142.0 | |
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| 0.0474 | 13.0 | 5174 | 0.8570 | 55.4923 | 40.3683 | 43.4675 | 53.404 | 142.0 | |
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| 0.0349 | 14.0 | 5572 | 0.8658 | 56.4454 | 41.8069 | 44.2922 | 54.464 | 142.0 | |
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| 0.0349 | 15.0 | 5970 | 0.8754 | 56.3837 | 42.2025 | 45.7817 | 54.4912 | 142.0 | |
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| 0.0304 | 16.0 | 6368 | 0.8760 | 56.3338 | 42.4032 | 45.9455 | 54.6488 | 142.0 | |
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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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