| license: apache-2.0 | |
| base_model: t5-small | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - xsum | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: t5-small-finetuned-xsum | |
| results: | |
| - task: | |
| name: Sequence-to-sequence Language Modeling | |
| type: text2text-generation | |
| dataset: | |
| name: xsum | |
| type: xsum | |
| config: default | |
| split: validation | |
| args: default | |
| metrics: | |
| - name: Rouge1 | |
| type: rouge | |
| value: 28.2399 | |
| <!-- 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. --> | |
| # t5-small-finetuned-xsum | |
| This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.4782 | |
| - Rouge1: 28.2399 | |
| - Rouge2: 7.7274 | |
| - Rougel: 22.2538 | |
| - Rougelsum: 22.2486 | |
| - Gen Len: 18.8191 | |
| ## 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: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | | |
| |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | |
| | 2.7161 | 1.0 | 12753 | 2.4782 | 28.2399 | 7.7274 | 22.2538 | 22.2486 | 18.8191 | | |
| ### Framework versions | |
| - Transformers 4.31.0 | |
| - Pytorch 2.0.0+cu117 | |
| - Datasets 2.14.3 | |
| - Tokenizers 0.13.3 | |