| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: bigData_w9 |
| 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. --> |
|
|
| # bigData_w9 |
| |
| This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.0525 |
| - Bleu4: 0.1309 |
| - Rouge1: 0.4023 |
| - Rouge2: 0.1845 |
| - Rougel: 0.2757 |
| - Rougelsum: 0.2753 |
| - Gen Len: 76.4341 |
| |
| ## 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: 3e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 516 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 64 |
| - 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 | Bleu4 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:------:|:---------:|:-------:| |
| | 2.0178 | 0.45 | 100 | 1.4840 | 0.1337 | 0.3875 | 0.1805 | 0.2688 | 0.2689 | 67.5509 | |
| | 1.142 | 0.89 | 200 | 1.1196 | 0.1296 | 0.3897 | 0.1766 | 0.2669 | 0.2667 | 69.3817 | |
| | 1.0693 | 1.34 | 300 | 1.0796 | 0.1345 | 0.3951 | 0.1818 | 0.2727 | 0.2727 | 70.015 | |
| | 1.0536 | 1.78 | 400 | 1.0684 | 0.1284 | 0.399 | 0.1839 | 0.2731 | 0.2729 | 77.3069 | |
| | 1.0084 | 2.23 | 500 | 1.0624 | 0.1287 | 0.3977 | 0.1808 | 0.2729 | 0.2729 | 76.7904 | |
| | 0.9855 | 2.67 | 600 | 1.0575 | 0.1349 | 0.4005 | 0.1843 | 0.2789 | 0.2787 | 72.4521 | |
| | 0.9812 | 3.12 | 700 | 1.0568 | 0.1303 | 0.4009 | 0.1847 | 0.2756 | 0.2752 | 76.1781 | |
| | 0.9916 | 3.56 | 800 | 1.0507 | 0.1364 | 0.4014 | 0.1853 | 0.279 | 0.2789 | 72.9746 | |
| | 0.9856 | 4.01 | 900 | 1.0507 | 0.1327 | 0.4003 | 0.1833 | 0.276 | 0.2758 | 74.9461 | |
| | 0.9747 | 4.45 | 1000 | 1.0519 | 0.1328 | 0.4014 | 0.185 | 0.276 | 0.2757 | 75.9461 | |
| | 0.9519 | 4.9 | 1100 | 1.0525 | 0.1309 | 0.4023 | 0.1845 | 0.2757 | 0.2753 | 76.4341 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.29.1 |
| - Pytorch 2.0.0+cu118 |
| - Datasets 2.12.0 |
| - Tokenizers 0.13.3 |
|
|
