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---
tags:
- summarization
- generated_from_trainer
model-index:
- name: finetune-led-thousanddata
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-led-thousanddata
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9539
- Rouge1 Precision: 0.2722
- Rouge1 Recall: 0.3458
- Rouge1 Fmeasure: 0.3011
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 Fmeasure | Rouge1 Precision | Rouge1 Recall |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:----------------:|:-------------:|
| 2.0529 | 0.13 | 10 | 2.6191 | 0.3014 | 0.2948 | 0.324 |
| 1.778 | 0.26 | 20 | 2.4690 | 0.2947 | 0.2802 | 0.3213 |
| 1.7425 | 0.38 | 30 | 2.3989 | 0.3037 | 0.2734 | 0.3524 |
| 1.7006 | 0.51 | 40 | 2.3216 | 0.2941 | 0.2665 | 0.3386 |
| 1.6751 | 0.64 | 50 | 2.3027 | 0.3101 | 0.282 | 0.3551 |
| 1.6887 | 0.77 | 60 | 2.2911 | 0.3058 | 0.2731 | 0.3577 |
| 1.6008 | 0.89 | 70 | 2.2476 | 0.3016 | 0.272 | 0.3487 |
| 1.5767 | 1.02 | 80 | 2.2167 | 0.3043 | 0.2775 | 0.3465 |
| 1.5046 | 1.15 | 90 | 2.2185 | 0.3004 | 0.2721 | 0.3458 |
| 1.5394 | 1.28 | 100 | 2.1977 | 0.2991 | 0.2696 | 0.3463 |
| 1.5449 | 1.41 | 110 | 2.1823 | 0.2978 | 0.2704 | 0.341 |
| 1.5073 | 1.53 | 120 | 2.1832 | 0.3057 | 0.276 | 0.3527 |
| 1.5232 | 0.42 | 130 | 2.2091 | 0.2955 | 0.2664 | 0.3424 |
| 1.4896 | 0.45 | 140 | 2.2069 | 0.2905 | 0.2574 | 0.3424 |
| 1.4848 | 0.48 | 150 | 2.1913 | 0.2868 | 0.2567 | 0.3356 |
| 1.5084 | 0.51 | 160 | 2.1826 | 0.3006 | 0.2755 | 0.3406 |
| 1.4322 | 0.54 | 170 | 2.2525 | 0.3049 | 0.2716 | 0.3582 |
| 1.4672 | 0.58 | 180 | 2.1890 | 0.2919 | 0.2663 | 0.3322 |
| 1.4543 | 0.61 | 190 | 2.1487 | 0.3022 | 0.276 | 0.344 |
| 1.5446 | 0.64 | 200 | 2.1496 | 0.2993 | 0.273 | 0.3418 |
| 1.412 | 0.67 | 210 | 2.1837 | 0.2976 | 0.268 | 0.3439 |
| 1.5241 | 0.7 | 220 | 2.1423 | 0.2913 | 0.2665 | 0.3305 |
| 1.4806 | 0.74 | 230 | 2.1303 | 0.2997 | 0.2736 | 0.3411 |
| 1.5405 | 0.77 | 240 | 2.1205 | 0.2966 | 0.2668 | 0.3428 |
| 1.4287 | 0.8 | 250 | 2.1322 | 0.2976 | 0.268 | 0.3442 |
| 1.4977 | 0.83 | 260 | 2.1334 | 0.2979 | 0.2665 | 0.3477 |
| 1.4171 | 0.86 | 270 | 2.1184 | 0.3043 | 0.2741 | 0.3509 |
| 1.4491 | 0.9 | 280 | 2.1038 | 0.2868 | 0.2628 | 0.3253 |
| 1.4316 | 0.93 | 290 | 2.1254 | 0.2958 | 0.2678 | 0.3393 |
| 1.4689 | 0.96 | 300 | 2.1052 | 0.299 | 0.2685 | 0.3471 |
| 1.4347 | 0.99 | 310 | 2.0815 | 0.3019 | 0.273 | 0.3476 |
| 1.3285 | 1.02 | 320 | 2.0877 | 0.2981 | 0.2695 | 0.3427 |
| 1.2636 | 1.06 | 330 | 2.0740 | 0.2933 | 0.2645 | 0.3382 |
| 1.32 | 1.09 | 340 | 2.0755 | 0.2997 | 0.2689 | 0.3487 |
| 1.357 | 1.12 | 350 | 2.0594 | 0.301 | 0.2743 | 0.3434 |
| 1.3412 | 1.15 | 360 | 2.0660 | 0.2961 | 0.2677 | 0.3405 |
| 1.327 | 1.18 | 370 | 2.0649 | 0.2912 | 0.263 | 0.335 |
| 1.3193 | 1.22 | 380 | 2.0842 | 0.2952 | 0.2673 | 0.3392 |
| 1.2961 | 1.25 | 390 | 2.0749 | 0.2957 | 0.2705 | 0.3342 |
| 1.3093 | 1.28 | 400 | 2.0715 | 0.2997 | 0.272 | 0.3441 |
| 1.3403 | 1.31 | 410 | 2.0671 | 0.3119 | 0.2823 | 0.3584 |
| 1.3685 | 1.34 | 420 | 2.0580 | 0.2973 | 0.2695 | 0.3409 |
| 1.2913 | 1.38 | 430 | 2.0685 | 0.2926 | 0.2632 | 0.339 |
| 1.3796 | 1.41 | 440 | 2.0339 | 0.2962 | 0.2697 | 0.3387 |
| 1.354 | 1.44 | 450 | 2.0371 | 0.2953 | 0.2665 | 0.3412 |
| 1.3268 | 1.47 | 460 | 2.0309 | 0.2957 | 0.2681 | 0.3395 |
| 1.3706 | 1.5 | 470 | 2.0215 | 0.2932 | 0.2685 | 0.3315 |
| 1.3288 | 1.54 | 480 | 2.0044 | 0.2948 | 0.2674 | 0.3374 |
| 1.4102 | 1.57 | 490 | 2.0046 | 0.2998 | 0.271 | 0.3446 |
| 1.3952 | 1.6 | 500 | 2.0044 | 0.3063 | 0.2794 | 0.3487 |
| 1.2994 | 1.63 | 510 | 1.9993 | 0.3052 | 0.2787 | 0.3461 |
| 1.2948 | 1.66 | 520 | 2.0168 | 0.3 | 0.2743 | 0.3406 |
| 1.2972 | 1.7 | 530 | 2.0290 | 0.3003 | 0.2734 | 0.342 |
| 1.3181 | 1.73 | 540 | 2.0234 | 0.2949 | 0.2676 | 0.338 |
| 1.3505 | 1.76 | 550 | 1.9942 | 0.301 | 0.2737 | 0.3436 |
| 1.3163 | 1.79 | 560 | 1.9983 | 0.2963 | 0.2705 | 0.3366 |
| 1.2876 | 1.82 | 570 | 2.0206 | 0.303 | 0.2739 | 0.3486 |
| 1.2895 | 1.86 | 580 | 2.0131 | 0.2958 | 0.2652 | 0.3443 |
| 1.3257 | 1.89 | 590 | 1.9888 | 0.3022 | 0.2743 | 0.3455 |
| 1.2891 | 1.92 | 600 | 1.9928 | 0.2972 | 0.2694 | 0.3408 |
| 1.3152 | 1.95 | 610 | 1.9785 | 0.292 | 0.2653 | 0.334 |
| 1.2834 | 1.98 | 620 | 2.0105 | 0.3039 | 0.2735 | 0.3511 |
| 1.2373 | 2.02 | 630 | 2.0023 | 0.3019 | 0.2735 | 0.346 |
| 1.2569 | 2.05 | 640 | 2.0006 | 0.3029 | 0.2753 | 0.3463 |
| 1.2337 | 2.08 | 650 | 1.9919 | 0.3006 | 0.2746 | 0.3416 |
| 1.1274 | 2.11 | 660 | 2.0095 | 0.3015 | 0.2732 | 0.3457 |
| 1.2178 | 2.14 | 670 | 1.9974 | 0.3031 | 0.275 | 0.3475 |
| 1.22 | 2.18 | 680 | 1.9924 | 0.3059 | 0.2777 | 0.3501 |
| 1.2913 | 2.21 | 690 | 1.9880 | 0.3044 | 0.2745 | 0.351 |
| 1.2441 | 2.24 | 700 | 1.9886 | 0.299 | 0.2721 | 0.3412 |
| 1.3258 | 2.27 | 710 | 1.9772 | 0.2956 | 0.2686 | 0.3377 |
| 1.158 | 2.3 | 720 | 2.0003 | 0.2983 | 0.2702 | 0.3424 |
| 1.1908 | 2.34 | 730 | 1.9845 | 0.2975 | 0.2705 | 0.3398 |
| 1.2411 | 2.37 | 740 | 1.9768 | 0.304 | 0.275 | 0.3493 |
| 1.1936 | 2.4 | 750 | 2.0065 | 0.293 | 0.2628 | 0.3403 |
| 1.1578 | 2.44 | 760 | 2.0199 | 0.301 | 0.2713 | 0.3473 |
| 1.2086 | 2.47 | 770 | 1.9949 | 0.2921 | 0.2664 | 0.3323 |
| 1.2574 | 2.5 | 780 | 1.9806 | 0.297 | 0.2693 | 0.3405 |
| 1.2331 | 2.53 | 790 | 2.0100 | 0.3012 | 0.2733 | 0.3446 |
| 1.2522 | 2.56 | 800 | 1.9969 | 0.301 | 0.2716 | 0.3468 |
| 1.2508 | 2.6 | 810 | 1.9931 | 0.3016 | 0.2719 | 0.3471 |
| 1.1558 | 2.63 | 820 | 1.9873 | 0.2986 | 0.2725 | 0.3402 |
| 1.2721 | 2.66 | 830 | 1.9763 | 0.2988 | 0.2671 | 0.348 |
| 1.2817 | 2.69 | 840 | 1.9713 | 0.2961 | 0.2688 | 0.3388 |
| 1.2183 | 2.72 | 850 | 1.9783 | 0.2985 | 0.2709 | 0.3416 |
| 1.2278 | 2.76 | 860 | 1.9757 | 0.2964 | 0.2681 | 0.3402 |
| 1.2087 | 2.79 | 870 | 1.9818 | 0.304 | 0.2735 | 0.3516 |
| 1.1838 | 2.82 | 880 | 1.9845 | 0.2916 | 0.2659 | 0.3312 |
| 1.1185 | 2.85 | 890 | 1.9912 | 0.3044 | 0.2759 | 0.3492 |
| 1.1214 | 2.88 | 900 | 1.9838 | 0.2995 | 0.2692 | 0.3468 |
| 1.2341 | 2.92 | 910 | 1.9685 | 0.296 | 0.2713 | 0.3344 |
| 1.1808 | 2.95 | 920 | 1.9803 | 0.3008 | 0.2725 | 0.345 |
| 1.2843 | 2.98 | 930 | 1.9645 | 0.3041 | 0.2745 | 0.3504 |
| 1.1824 | 3.01 | 940 | 1.9750 | 0.2985 | 0.2713 | 0.3412 |
| 1.1399 | 3.04 | 950 | 1.9762 | 0.2943 | 0.264 | 0.3416 |
| 1.1347 | 3.08 | 960 | 1.9841 | 0.2971 | 0.2685 | 0.3419 |
| 1.2298 | 3.11 | 970 | 1.9526 | 0.2993 | 0.2701 | 0.3448 |
| 1.1731 | 3.14 | 980 | 1.9787 | 0.304 | 0.2726 | 0.3531 |
| 1.1819 | 3.17 | 990 | 1.9570 | 0.2995 | 0.2715 | 0.3437 |
| 1.2072 | 3.2 | 1000 | 1.9613 | 0.3004 | 0.2705 | 0.3472 |
| 1.1214 | 3.24 | 1010 | 1.9670 | 0.3 | 0.2723 | 0.3432 |
| 1.226 | 3.27 | 1020 | 1.9676 | 0.2945 | 0.2639 | 0.3422 |
| 1.1956 | 3.3 | 1030 | 1.9721 | 0.2949 | 0.2657 | 0.3406 |
| 1.2286 | 3.33 | 1040 | 1.9572 | 0.3046 | 0.2759 | 0.3489 |
| 1.1786 | 3.36 | 1050 | 1.9549 | 0.3009 | 0.2728 | 0.3448 |
| 1.1512 | 3.4 | 1060 | 1.9609 | 0.2989 | 0.2699 | 0.3441 |
| 1.1897 | 3.43 | 1070 | 1.9626 | 0.2983 | 0.2697 | 0.3427 |
| 1.187 | 3.46 | 1080 | 1.9612 | 0.3016 | 0.2731 | 0.3457 |
| 1.1394 | 3.49 | 1090 | 1.9519 | 0.3015 | 0.2746 | 0.3431 |
| 1.1088 | 3.52 | 1100 | 1.9674 | 0.301 | 0.2709 | 0.3477 |
| 1.1787 | 3.56 | 1110 | 1.9549 | 0.3009 | 0.2728 | 0.3449 |
| 1.1961 | 3.59 | 1120 | 1.9545 | 0.3016 | 0.2722 | 0.3476 |
| 1.1194 | 3.62 | 1130 | 1.9693 | 0.3028 | 0.2735 | 0.3484 |
| 1.1991 | 3.65 | 1140 | 1.9538 | 0.3002 | 0.2706 | 0.3461 |
| 1.2109 | 3.68 | 1150 | 1.9428 | 0.3018 | 0.2729 | 0.3465 |
| 1.1389 | 3.72 | 1160 | 1.9578 | 0.3008 | 0.2723 | 0.3452 |
| 1.1922 | 3.75 | 1170 | 1.9576 | 0.2992 | 0.2701 | 0.3446 |
| 1.1002 | 3.78 | 1180 | 1.9571 | 0.299 | 0.2696 | 0.3445 |
| 1.1407 | 3.81 | 1190 | 1.9530 | 0.2979 | 0.2692 | 0.3422 |
| 1.1882 | 3.84 | 1200 | 1.9491 | 0.3009 | 0.2725 | 0.345 |
| 1.1755 | 3.88 | 1210 | 1.9562 | 0.3024 | 0.2735 | 0.3468 |
| 1.062 | 3.91 | 1220 | 1.9577 | 0.302 | 0.2722 | 0.3478 |
| 1.1965 | 3.94 | 1230 | 1.9575 | 0.3013 | 0.2716 | 0.3472 |
| 1.1255 | 3.97 | 1240 | 1.9550 | 0.3014 | 0.272 | 0.3466 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.15.1
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