| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: random-all-q | |
| 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. --> | |
| # random-all-q | |
| This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.3793 | |
| - Rouge1: 0.1605 | |
| - Rouge2: 0.0487 | |
| - Rougel: 0.1314 | |
| - Rougelsum: 0.1314 | |
| - Gen Len: 17.3704 | |
| ## 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: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | |
| | 3.0528 | 1.0 | 11248 | 2.5314 | 0.1402 | 0.0407 | 0.1159 | 0.1159 | 15.8872 | | |
| | 2.951 | 2.0 | 22496 | 2.4406 | 0.1554 | 0.0462 | 0.1276 | 0.1276 | 17.0291 | | |
| | 2.847 | 3.0 | 33744 | 2.4066 | 0.1592 | 0.0485 | 0.1307 | 0.1307 | 17.2227 | | |
| | 2.7959 | 4.0 | 44992 | 2.3870 | 0.1597 | 0.0489 | 0.1309 | 0.131 | 17.3245 | | |
| | 2.8236 | 5.0 | 56240 | 2.3793 | 0.1605 | 0.0487 | 0.1314 | 0.1314 | 17.3704 | | |
| ### Framework versions | |
| - Transformers 4.28.0 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.12.0 | |
| - Tokenizers 0.13.3 | |