t5-small-finetuned
This model is a fine-tuned version of google-t5/t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 13.3545
- Rouge1: 0.0324
- Rouge2: 0.0035
- Rougel: 0.0283
- Rougelsum: 0.0297
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 0.67 | 1 | 25.3754 | 0.0458 | 0.0078 | 0.038 | 0.0396 |
| No log | 2.0 | 3 | 23.7399 | 0.0458 | 0.0078 | 0.038 | 0.0396 |
| No log | 2.67 | 4 | 22.8640 | 0.0442 | 0.0053 | 0.0367 | 0.0384 |
| No log | 4.0 | 6 | 21.0827 | 0.0442 | 0.0053 | 0.0367 | 0.0384 |
| No log | 4.67 | 7 | 20.1867 | 0.0442 | 0.0053 | 0.0367 | 0.0384 |
| No log | 6.0 | 9 | 18.3401 | 0.0431 | 0.0109 | 0.0368 | 0.0388 |
| No log | 6.67 | 10 | 17.5540 | 0.0405 | 0.0054 | 0.0343 | 0.0346 |
| No log | 8.0 | 12 | 16.5123 | 0.0405 | 0.0054 | 0.0343 | 0.0346 |
| No log | 8.67 | 13 | 16.2865 | 0.0405 | 0.0054 | 0.0343 | 0.0346 |
| No log | 10.0 | 15 | 15.9394 | 0.0405 | 0.0054 | 0.0343 | 0.0346 |
| No log | 10.67 | 16 | 15.7787 | 0.0405 | 0.0054 | 0.0343 | 0.0346 |
| No log | 12.0 | 18 | 15.4614 | 0.0406 | 0.004 | 0.0331 | 0.0361 |
| No log | 12.67 | 19 | 15.3169 | 0.037 | 0.0012 | 0.0288 | 0.032 |
| 17.4357 | 14.0 | 21 | 15.0546 | 0.0372 | 0.0023 | 0.0302 | 0.0345 |
| 17.4357 | 14.67 | 22 | 14.9349 | 0.0372 | 0.0023 | 0.0302 | 0.0345 |
| 17.4357 | 16.0 | 24 | 14.7097 | 0.0372 | 0.0023 | 0.0302 | 0.0345 |
| 17.4357 | 16.67 | 25 | 14.6033 | 0.0372 | 0.0023 | 0.0302 | 0.0345 |
| 17.4357 | 18.0 | 27 | 14.4049 | 0.0365 | 0.0023 | 0.0298 | 0.0337 |
| 17.4357 | 18.67 | 28 | 14.3124 | 0.0365 | 0.0023 | 0.0298 | 0.0337 |
| 17.4357 | 20.0 | 30 | 14.1419 | 0.0324 | 0.0023 | 0.0271 | 0.0296 |
| 17.4357 | 20.67 | 31 | 14.0635 | 0.0324 | 0.0023 | 0.0272 | 0.0297 |
| 17.4357 | 22.0 | 33 | 13.9163 | 0.0324 | 0.0023 | 0.0272 | 0.0297 |
| 17.4357 | 22.67 | 34 | 13.8491 | 0.0324 | 0.0023 | 0.0272 | 0.0297 |
| 17.4357 | 24.0 | 36 | 13.7281 | 0.0324 | 0.0023 | 0.0272 | 0.0297 |
| 17.4357 | 24.67 | 37 | 13.6752 | 0.0324 | 0.0023 | 0.0272 | 0.0297 |
| 17.4357 | 26.0 | 39 | 13.5841 | 0.0324 | 0.0023 | 0.0272 | 0.0297 |
| 13.2934 | 26.67 | 40 | 13.5448 | 0.0324 | 0.0023 | 0.0272 | 0.0297 |
| 13.2934 | 28.0 | 42 | 13.4779 | 0.0324 | 0.0023 | 0.0272 | 0.0297 |
| 13.2934 | 28.67 | 43 | 13.4500 | 0.0324 | 0.0023 | 0.0272 | 0.0297 |
| 13.2934 | 30.0 | 45 | 13.4051 | 0.0324 | 0.0035 | 0.0283 | 0.0297 |
| 13.2934 | 30.67 | 46 | 13.3881 | 0.0324 | 0.0035 | 0.0283 | 0.0297 |
| 13.2934 | 32.0 | 48 | 13.3645 | 0.0324 | 0.0035 | 0.0283 | 0.0297 |
| 13.2934 | 32.67 | 49 | 13.3578 | 0.0324 | 0.0035 | 0.0283 | 0.0297 |
| 13.2934 | 33.33 | 50 | 13.3545 | 0.0324 | 0.0035 | 0.0283 | 0.0297 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.1