| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: bart-ingredients-extract |
| 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-ingredients-extract |
|
|
| This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-xsum-12-6) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3434 |
| - Rouge1: 44.3464 |
| - Rouge2: 25.67 |
| - Rougel: 44.3032 |
| - Rougelsum: 44.3007 |
| - Gen Len: 16.2697 |
|
|
| ## 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: 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: 4 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
| | 0.7151 | 1.0 | 1552 | 0.5275 | 53.7819 | 31.247 | 53.7202 | 53.7078 | 12.9069 | |
| | 0.5151 | 2.0 | 3104 | 0.4429 | 49.9951 | 28.9098 | 49.9357 | 49.9016 | 13.4797 | |
| | 0.4237 | 3.0 | 4656 | 0.3622 | 52.4925 | 31.4498 | 52.4645 | 52.4606 | 13.5396 | |
| | 0.3644 | 4.0 | 6208 | 0.3434 | 44.3464 | 25.67 | 44.3032 | 44.3007 | 16.2697 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.28.1 |
| - Pytorch 2.0.0+cu118 |
| - Datasets 2.11.0 |
| - Tokenizers 0.13.3 |
|
|
