| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - samsum | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: flan-t5-base-samsum | |
| results: | |
| - task: | |
| name: Sequence-to-sequence Language Modeling | |
| type: text2text-generation | |
| dataset: | |
| name: samsum | |
| type: samsum | |
| config: samsum | |
| split: test | |
| args: samsum | |
| metrics: | |
| - name: Rouge1 | |
| type: rouge | |
| value: 46.5595 | |
| <!-- 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. --> | |
| # flan-t5-base-samsum | |
| This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.3822 | |
| - Rouge1: 46.5595 | |
| - Rouge2: 22.9243 | |
| - Rougel: 39.0902 | |
| - Rougelsum: 42.934 | |
| - Gen Len: 17.2308 | |
| ## 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: 5e-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: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | |
| | 1.5156 | 1.0 | 1842 | 1.3822 | 46.5595 | 22.9243 | 39.0902 | 42.934 | 17.2308 | | |
| ### Framework versions | |
| - Transformers 4.27.2 | |
| - Pytorch 1.13.1+cu116 | |
| - Datasets 2.10.1 | |
| - Tokenizers 0.13.2 | |