| --- |
| license: apache-2.0 |
| base_model: google-t5/t5-small |
| tags: |
| - generated_from_trainer |
| datasets: |
| - lilferrit/xsum_t5_distillation |
| metrics: |
| - rouge |
| model-index: |
| - name: xsum_aligned_smallT5_full |
| results: |
| - task: |
| name: Summarization |
| type: summarization |
| dataset: |
| name: lilferrit/xsum_t5_distillation |
| type: lilferrit/xsum_t5_distillation |
| metrics: |
| - name: Rouge1 |
| type: rouge |
| value: 22.8498 |
| --- |
| |
| <!-- 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. --> |
|
|
| # xsum_aligned_smallT5_full |
| |
| This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the lilferrit/xsum_t5_distillation dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.4093 |
| - Rouge1: 22.8498 |
| - Rouge2: 4.7818 |
| - Rougel: 17.2861 |
| - Rougelsum: 18.0665 |
| - Gen Len: 33.6366 |
| |
| ## 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: 0.0002 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 16 |
| - optimizer: Adafactor |
| - lr_scheduler_type: constant |
| - training_steps: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| |
| | No log | 0.0 | 5 | 2.6444 | 22.3341 | 4.3395 | 16.2507 | 17.8303 | 46.2437 | |
| | No log | 0.0 | 10 | 2.4093 | 22.8498 | 4.7818 | 17.2861 | 18.0665 | 33.6366 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.39.3 |
| - Pytorch 2.2.2+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
|
|
