| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-t5/t5-small |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: Text_Summarization |
| 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. --> |
|
|
| # Text_Summarization |
| |
| This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.4199 |
| - Rouge1: 0.2439 |
| - Rouge2: 0.2006 |
| - Rougel: 0.2365 |
| - Rougelsum: 0.2366 |
| - Gen Len: 18.9994 |
| |
| ## 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: 12 |
| - eval_batch_size: 12 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
| | 1.9264 | 1.0 | 1580 | 1.6705 | 0.2329 | 0.1842 | 0.223 | 0.223 | 18.9994 | |
| | 1.8184 | 2.0 | 3160 | 1.5849 | 0.2376 | 0.1894 | 0.2287 | 0.2288 | 18.9988 | |
| | 1.7427 | 3.0 | 4740 | 1.5382 | 0.2379 | 0.1914 | 0.2296 | 0.2297 | 18.9994 | |
| | 1.7067 | 4.0 | 6320 | 1.5073 | 0.2397 | 0.1943 | 0.2318 | 0.2318 | 19.0 | |
| | 1.6783 | 5.0 | 7900 | 1.4873 | 0.2406 | 0.1957 | 0.2329 | 0.2329 | 19.0 | |
| | 1.6585 | 6.0 | 9480 | 1.4716 | 0.242 | 0.1976 | 0.2343 | 0.2343 | 19.0 | |
| | 1.6457 | 7.0 | 11060 | 1.4572 | 0.2427 | 0.1988 | 0.2351 | 0.2351 | 19.0 | |
| | 1.6129 | 8.0 | 12640 | 1.4488 | 0.2433 | 0.1995 | 0.2357 | 0.2358 | 19.0 | |
| | 1.6014 | 9.0 | 14220 | 1.4405 | 0.2435 | 0.1999 | 0.236 | 0.236 | 19.0 | |
| | 1.5851 | 10.0 | 15800 | 1.4337 | 0.2439 | 0.2002 | 0.2364 | 0.2365 | 18.9994 | |
| | 1.5859 | 11.0 | 17380 | 1.4281 | 0.2436 | 0.2 | 0.2362 | 0.2362 | 19.0 | |
| | 1.573 | 12.0 | 18960 | 1.4247 | 0.244 | 0.2005 | 0.2365 | 0.2366 | 18.9994 | |
| | 1.5826 | 13.0 | 20540 | 1.4220 | 0.244 | 0.2007 | 0.2365 | 0.2365 | 18.9994 | |
| | 1.5674 | 14.0 | 22120 | 1.4205 | 0.2439 | 0.2006 | 0.2365 | 0.2365 | 18.9994 | |
| | 1.572 | 15.0 | 23700 | 1.4199 | 0.2439 | 0.2006 | 0.2365 | 0.2366 | 18.9994 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.45.1 |
| - Pytorch 2.4.0 |
| - Datasets 3.0.1 |
| - Tokenizers 0.20.0 |
| |
