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---
tags:
- summarization
- generated_from_trainer
model-index:
- name: finetune-longt5
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. -->
# finetune-longt5
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9551
- Rouge1 Precision: 0.2602
- Rouge1 Recall: 0.3322
- Rouge1 Fmeasure: 0.2861
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 Fmeasure | Rouge1 Precision | Rouge1 Recall |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:----------------:|:-------------:|
| 2.5865 | 0.03 | 10 | 2.4019 | 0.2349 | 0.2481 | 0.2353 |
| 2.3384 | 0.06 | 20 | 2.2294 | 0.2404 | 0.2571 | 0.2385 |
| 2.0876 | 0.1 | 30 | 2.2887 | 0.2467 | 0.2653 | 0.2432 |
| 2.1205 | 0.13 | 40 | 2.2194 | 0.2517 | 0.2731 | 0.246 |
| 2.0637 | 0.16 | 50 | 2.2172 | 0.2577 | 0.2868 | 0.246 |
| 1.8623 | 0.19 | 60 | 2.2273 | 0.2613 | 0.2903 | 0.2497 |
| 0.0 | 0.22 | 70 | nan | 0.262 | 0.2911 | 0.25 |
| 1.8591 | 0.26 | 80 | 2.1895 | 0.2604 | 0.2901 | 0.2481 |
| 1.9819 | 0.29 | 90 | 2.1492 | 0.2663 | 0.2917 | 0.2575 |
| 1.9326 | 0.32 | 100 | 2.1248 | 0.2698 | 0.2964 | 0.2608 |
| 1.888 | 0.35 | 110 | 2.1253 | 0.2698 | 0.2954 | 0.2614 |
| 1.8934 | 0.38 | 120 | 2.0993 | 0.2705 | 0.3006 | 0.2589 |
| 1.8357 | 0.42 | 130 | 2.1050 | 0.2744 | 0.3001 | 0.267 |
| 1.8061 | 0.45 | 140 | 2.0705 | 0.2787 | 0.2924 | 0.2829 |
| 1.8444 | 0.48 | 150 | 2.1156 | 0.2739 | 0.2895 | 0.2762 |
| 1.83 | 0.51 | 160 | 2.0636 | 0.2773 | 0.2895 | 0.2831 |
| 1.7433 | 0.54 | 170 | 2.0857 | 0.2767 | 0.2811 | 0.29 |
| 1.7523 | 0.58 | 180 | 2.0809 | 0.2766 | 0.2798 | 0.2913 |
| 1.764 | 0.61 | 190 | 2.0351 | 0.2799 | 0.2751 | 0.3041 |
| 1.8441 | 0.64 | 200 | 2.0460 | 0.2804 | 0.2772 | 0.3025 |
| 1.7118 | 0.67 | 210 | 2.0319 | 0.2798 | 0.2767 | 0.3024 |
| 1.818 | 0.7 | 220 | 2.0287 | 0.2823 | 0.2666 | 0.318 |
| 1.7484 | 0.74 | 230 | 2.0084 | 0.2822 | 0.2653 | 0.3188 |
| 1.8224 | 0.77 | 240 | 2.0372 | 0.2787 | 0.2634 | 0.3132 |
| 1.6475 | 0.4 | 250 | 2.0281 | 0.2768 | 0.2594 | 0.3141 |
| 1.6915 | 0.42 | 260 | 1.9941 | 0.2851 | 0.2637 | 0.3269 |
| 1.7106 | 0.43 | 270 | 2.0204 | 0.2862 | 0.2718 | 0.3198 |
| 1.6382 | 0.45 | 280 | 2.0073 | 0.288 | 0.2657 | 0.3307 |
| 1.6017 | 0.46 | 290 | 2.0242 | 0.2847 | 0.2584 | 0.3328 |
| 1.8248 | 0.48 | 300 | 1.9996 | 0.2821 | 0.2595 | 0.3249 |
| 1.687 | 0.5 | 310 | 1.9801 | 0.2857 | 0.2664 | 0.3243 |
| 1.6993 | 0.51 | 320 | 1.9979 | 0.2837 | 0.2608 | 0.3271 |
| 1.6191 | 0.53 | 330 | 2.0025 | 0.285 | 0.2618 | 0.329 |
| 1.5409 | 0.54 | 340 | 1.9968 | 0.2851 | 0.2616 | 0.3293 |
| 1.6279 | 0.56 | 350 | 1.9940 | 0.2824 | 0.2601 | 0.3259 |
| 1.7538 | 0.58 | 360 | 1.9907 | 0.2803 | 0.2574 | 0.3234 |
| 1.6781 | 1.19 | 370 | 1.9684 | 0.2805 | 0.2565 | 0.3249 |
| 1.6106 | 1.22 | 380 | 1.9798 | 0.2842 | 0.2584 | 0.3314 |
| 1.5798 | 1.25 | 390 | 1.9940 | 0.2842 | 0.2581 | 0.3321 |
| 1.7134 | 1.28 | 400 | 1.9634 | 0.2851 | 0.2596 | 0.3313 |
| 1.6301 | 1.31 | 410 | 1.9644 | 0.2866 | 0.2606 | 0.3342 |
| 1.6657 | 1.35 | 420 | 1.9775 | 0.2861 | 0.2604 | 0.3325 |
| 1.7028 | 1.38 | 430 | 1.9792 | 0.2838 | 0.26 | 0.3275 |
| 1.7063 | 1.41 | 440 | 1.9728 | 0.2838 | 0.2598 | 0.3279 |
| 1.5825 | 1.44 | 450 | 1.9644 | 0.2838 | 0.259 | 0.3291 |
| 1.5955 | 1.47 | 460 | 1.9674 | 0.2864 | 0.2605 | 0.3329 |
| 1.6774 | 1.51 | 470 | 1.9718 | 0.288 | 0.2616 | 0.3354 |
| 1.7171 | 1.54 | 480 | 1.9602 | 0.2882 | 0.2619 | 0.3358 |
| 1.6631 | 1.57 | 490 | 1.9592 | 0.2873 | 0.2614 | 0.3335 |
| 1.5405 | 1.6 | 500 | 1.9625 | 0.2868 | 0.261 | 0.3329 |
| 1.5711 | 1.63 | 510 | 1.9690 | 0.2872 | 0.2614 | 0.3337 |
| 1.7382 | 1.67 | 520 | 1.9669 | 0.2873 | 0.262 | 0.3326 |
| 1.6345 | 1.7 | 530 | 1.9564 | 0.2867 | 0.2615 | 0.3323 |
| 1.6289 | 1.73 | 540 | 1.9558 | 0.2856 | 0.2604 | 0.3309 |
| 1.575 | 1.76 | 550 | 1.9620 | 0.2872 | 0.2616 | 0.333 |
| 1.682 | 1.79 | 560 | 1.9613 | 0.287 | 0.2615 | 0.3326 |
| 1.5385 | 1.83 | 570 | 1.9616 | 0.2869 | 0.2614 | 0.3324 |
| 1.62 | 1.86 | 580 | 1.9603 | 0.2868 | 0.2611 | 0.3325 |
| 1.6026 | 1.89 | 590 | 1.9589 | 0.2867 | 0.2611 | 0.3325 |
| 1.6511 | 1.92 | 600 | 1.9575 | 0.2865 | 0.2608 | 0.3322 |
| 1.5688 | 1.95 | 610 | 1.9555 | 0.2866 | 0.2608 | 0.3327 |
| 1.6092 | 1.99 | 620 | 1.9553 | 0.2863 | 0.2604 | 0.3324 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.15.1
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