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--- |
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tags: |
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- summarization |
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- generated_from_trainer |
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model-index: |
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- name: finetune-longt5 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# finetune-longt5 |
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This model was trained from scratch on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9551 |
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- Rouge1 Precision: 0.2602 |
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- Rouge1 Recall: 0.3322 |
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- Rouge1 Fmeasure: 0.2861 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 Fmeasure | Rouge1 Precision | Rouge1 Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------:|:----------------:|:-------------:| |
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| 2.5865 | 0.03 | 10 | 2.4019 | 0.2349 | 0.2481 | 0.2353 | |
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| 2.3384 | 0.06 | 20 | 2.2294 | 0.2404 | 0.2571 | 0.2385 | |
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| 2.0876 | 0.1 | 30 | 2.2887 | 0.2467 | 0.2653 | 0.2432 | |
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| 2.1205 | 0.13 | 40 | 2.2194 | 0.2517 | 0.2731 | 0.246 | |
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| 2.0637 | 0.16 | 50 | 2.2172 | 0.2577 | 0.2868 | 0.246 | |
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| 1.8623 | 0.19 | 60 | 2.2273 | 0.2613 | 0.2903 | 0.2497 | |
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| 0.0 | 0.22 | 70 | nan | 0.262 | 0.2911 | 0.25 | |
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| 1.8591 | 0.26 | 80 | 2.1895 | 0.2604 | 0.2901 | 0.2481 | |
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| 1.9819 | 0.29 | 90 | 2.1492 | 0.2663 | 0.2917 | 0.2575 | |
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| 1.9326 | 0.32 | 100 | 2.1248 | 0.2698 | 0.2964 | 0.2608 | |
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| 1.888 | 0.35 | 110 | 2.1253 | 0.2698 | 0.2954 | 0.2614 | |
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| 1.8934 | 0.38 | 120 | 2.0993 | 0.2705 | 0.3006 | 0.2589 | |
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| 1.8357 | 0.42 | 130 | 2.1050 | 0.2744 | 0.3001 | 0.267 | |
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| 1.8061 | 0.45 | 140 | 2.0705 | 0.2787 | 0.2924 | 0.2829 | |
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| 1.8444 | 0.48 | 150 | 2.1156 | 0.2739 | 0.2895 | 0.2762 | |
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| 1.83 | 0.51 | 160 | 2.0636 | 0.2773 | 0.2895 | 0.2831 | |
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| 1.7433 | 0.54 | 170 | 2.0857 | 0.2767 | 0.2811 | 0.29 | |
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| 1.7523 | 0.58 | 180 | 2.0809 | 0.2766 | 0.2798 | 0.2913 | |
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| 1.764 | 0.61 | 190 | 2.0351 | 0.2799 | 0.2751 | 0.3041 | |
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| 1.8441 | 0.64 | 200 | 2.0460 | 0.2804 | 0.2772 | 0.3025 | |
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| 1.7118 | 0.67 | 210 | 2.0319 | 0.2798 | 0.2767 | 0.3024 | |
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| 1.818 | 0.7 | 220 | 2.0287 | 0.2823 | 0.2666 | 0.318 | |
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| 1.7484 | 0.74 | 230 | 2.0084 | 0.2822 | 0.2653 | 0.3188 | |
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| 1.8224 | 0.77 | 240 | 2.0372 | 0.2787 | 0.2634 | 0.3132 | |
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| 1.6475 | 0.4 | 250 | 2.0281 | 0.2768 | 0.2594 | 0.3141 | |
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| 1.6915 | 0.42 | 260 | 1.9941 | 0.2851 | 0.2637 | 0.3269 | |
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| 1.7106 | 0.43 | 270 | 2.0204 | 0.2862 | 0.2718 | 0.3198 | |
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| 1.6382 | 0.45 | 280 | 2.0073 | 0.288 | 0.2657 | 0.3307 | |
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| 1.6017 | 0.46 | 290 | 2.0242 | 0.2847 | 0.2584 | 0.3328 | |
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| 1.8248 | 0.48 | 300 | 1.9996 | 0.2821 | 0.2595 | 0.3249 | |
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| 1.687 | 0.5 | 310 | 1.9801 | 0.2857 | 0.2664 | 0.3243 | |
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| 1.6993 | 0.51 | 320 | 1.9979 | 0.2837 | 0.2608 | 0.3271 | |
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| 1.6191 | 0.53 | 330 | 2.0025 | 0.285 | 0.2618 | 0.329 | |
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| 1.5409 | 0.54 | 340 | 1.9968 | 0.2851 | 0.2616 | 0.3293 | |
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| 1.6279 | 0.56 | 350 | 1.9940 | 0.2824 | 0.2601 | 0.3259 | |
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| 1.7538 | 0.58 | 360 | 1.9907 | 0.2803 | 0.2574 | 0.3234 | |
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| 1.6781 | 1.19 | 370 | 1.9684 | 0.2805 | 0.2565 | 0.3249 | |
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| 1.6106 | 1.22 | 380 | 1.9798 | 0.2842 | 0.2584 | 0.3314 | |
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| 1.5798 | 1.25 | 390 | 1.9940 | 0.2842 | 0.2581 | 0.3321 | |
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| 1.7134 | 1.28 | 400 | 1.9634 | 0.2851 | 0.2596 | 0.3313 | |
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| 1.6301 | 1.31 | 410 | 1.9644 | 0.2866 | 0.2606 | 0.3342 | |
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| 1.6657 | 1.35 | 420 | 1.9775 | 0.2861 | 0.2604 | 0.3325 | |
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| 1.7028 | 1.38 | 430 | 1.9792 | 0.2838 | 0.26 | 0.3275 | |
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| 1.7063 | 1.41 | 440 | 1.9728 | 0.2838 | 0.2598 | 0.3279 | |
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| 1.5825 | 1.44 | 450 | 1.9644 | 0.2838 | 0.259 | 0.3291 | |
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| 1.5955 | 1.47 | 460 | 1.9674 | 0.2864 | 0.2605 | 0.3329 | |
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| 1.6774 | 1.51 | 470 | 1.9718 | 0.288 | 0.2616 | 0.3354 | |
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| 1.7171 | 1.54 | 480 | 1.9602 | 0.2882 | 0.2619 | 0.3358 | |
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| 1.6631 | 1.57 | 490 | 1.9592 | 0.2873 | 0.2614 | 0.3335 | |
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| 1.5405 | 1.6 | 500 | 1.9625 | 0.2868 | 0.261 | 0.3329 | |
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| 1.5711 | 1.63 | 510 | 1.9690 | 0.2872 | 0.2614 | 0.3337 | |
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| 1.7382 | 1.67 | 520 | 1.9669 | 0.2873 | 0.262 | 0.3326 | |
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| 1.6345 | 1.7 | 530 | 1.9564 | 0.2867 | 0.2615 | 0.3323 | |
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| 1.6289 | 1.73 | 540 | 1.9558 | 0.2856 | 0.2604 | 0.3309 | |
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| 1.575 | 1.76 | 550 | 1.9620 | 0.2872 | 0.2616 | 0.333 | |
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| 1.682 | 1.79 | 560 | 1.9613 | 0.287 | 0.2615 | 0.3326 | |
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| 1.5385 | 1.83 | 570 | 1.9616 | 0.2869 | 0.2614 | 0.3324 | |
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| 1.62 | 1.86 | 580 | 1.9603 | 0.2868 | 0.2611 | 0.3325 | |
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| 1.6026 | 1.89 | 590 | 1.9589 | 0.2867 | 0.2611 | 0.3325 | |
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| 1.6511 | 1.92 | 600 | 1.9575 | 0.2865 | 0.2608 | 0.3322 | |
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| 1.5688 | 1.95 | 610 | 1.9555 | 0.2866 | 0.2608 | 0.3327 | |
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| 1.6092 | 1.99 | 620 | 1.9553 | 0.2863 | 0.2604 | 0.3324 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.15.1 |
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