| --- |
| license: mit |
| base_model: VietAI/vit5-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: vit5-base-transcript-summarizer |
| 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. --> |
|
|
| # vit5-base-transcript-summarizer |
|
|
| This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5995 |
| - Rouge1: 52.1518 |
| - Rouge2: 28.7254 |
| - Rougel: 41.1877 |
| - Rougelsum: 46.0726 |
| - Gen Len: 16.5342 |
|
|
| ## Model description |
|
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| More information needed |
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|
| ## Intended uses & limitations |
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|
| More information needed |
|
|
| ## Training and evaluation data |
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| More information needed |
|
|
| ## Training procedure |
|
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| ### Training hyperparameters |
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| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
| | 0.6432 | 1.0 | 1842 | 0.6072 | 50.2818 | 27.4589 | 39.9803 | 44.436 | 15.7775 | |
| | 0.5389 | 2.0 | 3684 | 0.5928 | 51.449 | 28.8498 | 41.1803 | 45.7102 | 16.2433 | |
| | 0.4847 | 3.0 | 5526 | 0.5941 | 51.2837 | 28.3449 | 40.5158 | 45.1193 | 16.0562 | |
| | 0.4398 | 4.0 | 7368 | 0.5995 | 52.1518 | 28.7254 | 41.1877 | 46.0726 | 16.5342 | |
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| ### Framework versions |
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|
| - Transformers 4.38.2 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
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|
