| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: bart-large-xsum-finetuned-natural-questions |
| 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-large-xsum-finetuned-natural-questions |
|
|
| This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2729 |
| - Rouge1: 19.7211 |
| - Rouge2: 17.4272 |
| - Rougel: 19.0681 |
| - Rougelsum: 19.3677 |
|
|
| ## 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: 5.6e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 8 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
| | No log | 0.99 | 34 | 0.2562 | 17.9806 | 15.2059 | 16.807 | 17.5533 | |
| | No log | 1.99 | 68 | 0.1845 | 14.6261 | 10.494 | 13.0132 | 13.8392 | |
| | No log | 2.98 | 102 | 0.2171 | 17.3737 | 14.7893 | 16.5485 | 16.8383 | |
| | No log | 4.0 | 137 | 0.3474 | 17.6187 | 14.727 | 16.5614 | 17.1476 | |
| | No log | 4.99 | 171 | 0.3462 | 17.7103 | 15.1403 | 16.9424 | 17.3123 | |
| | 0.1255 | 5.99 | 205 | 0.3355 | 19.2782 | 16.5525 | 18.4283 | 18.8422 | |
| | 0.1255 | 6.98 | 239 | 0.2281 | 19.8816 | 17.4387 | 19.238 | 19.552 | |
| | 0.1255 | 7.94 | 272 | 0.2729 | 19.7211 | 17.4272 | 19.0681 | 19.3677 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.30.2 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.13.1 |
| - Tokenizers 0.13.3 |
| |
