| license: apache-2.0 | |
| tags: | |
| - summarization | |
| - generated_from_trainer | |
| datasets: | |
| - cnn_dailymail | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: t5-small-finetuned-summarization-cnn | |
| results: | |
| - task: | |
| name: Sequence-to-sequence Language Modeling | |
| type: text2text-generation | |
| dataset: | |
| name: cnn_dailymail | |
| type: cnn_dailymail | |
| config: 3.0.0 | |
| split: train[:2%] | |
| args: 3.0.0 | |
| metrics: | |
| - name: Rouge1 | |
| type: rouge | |
| value: 24.4825 | |
| <!-- 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-summarization-cnn | |
| This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.0105 | |
| - Rouge1: 24.4825 | |
| - Rouge2: 9.1573 | |
| - Rougel: 19.7135 | |
| - Rougelsum: 22.2551 | |
| ## 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: 5.6e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | |
| | 2.0389 | 1.0 | 718 | 2.0150 | 24.4413 | 9.1782 | 19.7202 | 22.2225 | | |
| | 1.9497 | 2.0 | 1436 | 2.0105 | 24.4825 | 9.1573 | 19.7135 | 22.2551 | | |
| ### Framework versions | |
| - Transformers 4.24.0 | |
| - Pytorch 1.12.1+cu113 | |
| - Datasets 2.7.0 | |
| - Tokenizers 0.13.2 | |