| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: bart-cnn-science-v3-e5 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # bart-cnn-science-v3-e5 |
|
|
| This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe/bart-cnn-science) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.8090 |
| - Rouge1: 54.0053 |
| - Rouge2: 35.5018 |
| - Rougel: 37.3204 |
| - Rougelsum: 51.5456 |
| - Gen Len: 142.0 |
|
|
| ## Model description |
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| More information needed |
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|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
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|
| More information needed |
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|
| ## Training procedure |
|
|
| ### Training hyperparameters |
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|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 2 |
| - eval_batch_size: 2 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| |
| | No log | 1.0 | 398 | 0.9935 | 51.9669 | 31.8139 | 34.4748 | 49.5311 | 141.7407 | |
| | 1.1747 | 2.0 | 796 | 0.8565 | 51.7344 | 31.7341 | 34.3917 | 49.2488 | 141.7222 | |
| | 0.7125 | 3.0 | 1194 | 0.8252 | 52.829 | 33.2332 | 35.8865 | 50.1883 | 141.5556 | |
| | 0.4991 | 4.0 | 1592 | 0.8222 | 53.582 | 33.4906 | 35.7232 | 50.589 | 142.0 | |
| | 0.4991 | 5.0 | 1990 | 0.8090 | 54.0053 | 35.5018 | 37.3204 | 51.5456 | 142.0 | |
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| ### Framework versions |
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|
| - Transformers 4.19.2 |
| - Pytorch 1.11.0+cu113 |
| - Datasets 2.2.2 |
| - Tokenizers 0.12.1 |
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|
