| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: model2-bart-reverse |
| 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. --> |
|
|
| # model2-bart-reverse |
|
|
| This model is a fine-tuned version of [theojolliffe/model-2-bart-reverse-raw](https://huggingface.co/theojolliffe/model-2-bart-reverse-raw) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5768 |
| - Rouge1: 50.0024 |
| - Rouge2: 44.5149 |
| - Rougel: 50.408 |
| - Rougelsum: 50.0015 |
| - Gen Len: 20.0 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
| | No log | 1.0 | 160 | 0.5107 | 54.2246 | 50.7271 | 51.1954 | 54.4944 | 20.0 | |
| | No log | 2.0 | 320 | 0.5895 | 48.1317 | 43.4207 | 48.6594 | 48.4308 | 20.0 | |
| | No log | 3.0 | 480 | 0.5833 | 51.7747 | 46.8312 | 52.47 | 52.1239 | 20.0 | |
| | 0.4286 | 4.0 | 640 | 0.5768 | 50.0024 | 44.5149 | 50.408 | 50.0015 | 20.0 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.22.1 |
| - Pytorch 1.12.1+cu113 |
| - Datasets 2.4.0 |
| - Tokenizers 0.12.1 |
|
|
