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ptt5-xlsumm-cstnews-1024
This model is a fine-tuned version of arthurmluz/ptt5-xlsumm-30epochs on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2582
- Rouge1: 0.2812
- Rouge2: 0.213
- Rougel: 0.2582
- Rougelsum: 0.2747
- Gen Len: 19.0
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 47 | 1.4425 | 0.2155 | 0.1096 | 0.1749 | 0.2026 | 19.0 |
| No log | 2.0 | 94 | 1.3388 | 0.2385 | 0.1386 | 0.1902 | 0.2251 | 19.0 |
| No log | 3.0 | 141 | 1.2907 | 0.2491 | 0.1664 | 0.2162 | 0.2399 | 19.0 |
| No log | 4.0 | 188 | 1.2664 | 0.2588 | 0.1828 | 0.2282 | 0.2497 | 19.0 |
| 1.6936 | 5.0 | 235 | 1.2464 | 0.2636 | 0.1898 | 0.2363 | 0.256 | 19.0 |
| 1.6936 | 6.0 | 282 | 1.2430 | 0.2707 | 0.2005 | 0.2454 | 0.2645 | 19.0 |
| 1.6936 | 7.0 | 329 | 1.2320 | 0.2717 | 0.2023 | 0.2463 | 0.2654 | 19.0 |
| 1.6936 | 8.0 | 376 | 1.2276 | 0.2733 | 0.2054 | 0.2461 | 0.2692 | 19.0 |
| 1.2963 | 9.0 | 423 | 1.2237 | 0.2753 | 0.2114 | 0.2482 | 0.2701 | 19.0 |
| 1.2963 | 10.0 | 470 | 1.2225 | 0.2784 | 0.2143 | 0.2506 | 0.2732 | 19.0 |
| 1.2963 | 11.0 | 517 | 1.2247 | 0.2753 | 0.2117 | 0.2495 | 0.2702 | 19.0 |
| 1.2963 | 12.0 | 564 | 1.2244 | 0.2792 | 0.2137 | 0.2533 | 0.2738 | 19.0 |
| 1.1352 | 13.0 | 611 | 1.2285 | 0.2797 | 0.2125 | 0.2542 | 0.2742 | 19.0 |
| 1.1352 | 14.0 | 658 | 1.2287 | 0.2751 | 0.2096 | 0.2507 | 0.2684 | 19.0 |
| 1.1352 | 15.0 | 705 | 1.2325 | 0.2727 | 0.2089 | 0.2503 | 0.2672 | 19.0 |
| 1.1352 | 16.0 | 752 | 1.2330 | 0.2769 | 0.2143 | 0.2552 | 0.2711 | 19.0 |
| 1.1352 | 17.0 | 799 | 1.2353 | 0.2769 | 0.2143 | 0.2552 | 0.2711 | 19.0 |
| 1.0196 | 18.0 | 846 | 1.2352 | 0.2831 | 0.2176 | 0.261 | 0.2771 | 19.0 |
| 1.0196 | 19.0 | 893 | 1.2400 | 0.2838 | 0.2184 | 0.2611 | 0.2771 | 19.0 |
| 1.0196 | 20.0 | 940 | 1.2406 | 0.2838 | 0.2184 | 0.2611 | 0.2771 | 19.0 |
| 1.0196 | 21.0 | 987 | 1.2457 | 0.2771 | 0.2109 | 0.2554 | 0.2711 | 19.0 |
| 0.912 | 22.0 | 1034 | 1.2471 | 0.2771 | 0.2109 | 0.2554 | 0.2711 | 19.0 |
| 0.912 | 23.0 | 1081 | 1.2499 | 0.278 | 0.2094 | 0.2552 | 0.2714 | 19.0 |
| 0.912 | 24.0 | 1128 | 1.2508 | 0.278 | 0.2094 | 0.2552 | 0.2714 | 19.0 |
| 0.912 | 25.0 | 1175 | 1.2541 | 0.282 | 0.2139 | 0.2588 | 0.275 | 19.0 |
| 0.8667 | 26.0 | 1222 | 1.2563 | 0.282 | 0.2139 | 0.2588 | 0.275 | 19.0 |
| 0.8667 | 27.0 | 1269 | 1.2569 | 0.2812 | 0.213 | 0.2582 | 0.2747 | 19.0 |
| 0.8667 | 28.0 | 1316 | 1.2577 | 0.2812 | 0.213 | 0.2582 | 0.2747 | 19.0 |
| 0.8667 | 29.0 | 1363 | 1.2582 | 0.2812 | 0.213 | 0.2582 | 0.2747 | 19.0 |
| 0.8561 | 30.0 | 1410 | 1.2582 | 0.2812 | 0.213 | 0.2582 | 0.2747 | 19.0 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
