| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - rouge |
| | model-index: |
| | - name: BART_reddit_other |
| | 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_reddit_other |
| |
|
| | This model is a fine-tuned version of [sshleifer/distilbart-xsum-6-6](https://huggingface.co/sshleifer/distilbart-xsum-6-6) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 3.5792 |
| | - Rouge1: 18.5705 |
| | - Rouge2: 5.0107 |
| | - Rougel: 15.2581 |
| | - Rougelsum: 16.082 |
| | - Gen Len: 19.402 |
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 2e-05 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 3 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| |
| | | 3.7887 | 1.0 | 1875 | 3.6044 | 18.4668 | 5.182 | 15.359 | 16.169 | 19.341 | |
| | | 3.3816 | 2.0 | 3750 | 3.5628 | 18.0998 | 4.8937 | 15.0179 | 15.7615 | 17.789 | |
| | | 3.134 | 3.0 | 5625 | 3.5792 | 18.5705 | 5.0107 | 15.2581 | 16.082 | 19.402 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.20.1 |
| | - Pytorch 1.12.0+cu113 |
| | - Datasets 2.3.2 |
| | - Tokenizers 0.12.1 |
| |
|
