--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer datasets: - hdfs_log_summary_dataset metrics: - rouge model-index: - name: flan-log-sage results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: hdfs_log_summary_dataset type: hdfs_log_summary_dataset config: default split: test args: default metrics: - name: Rouge1 type: rouge value: 0.3588 --- # flan-log-sage This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the hdfs_log_summary_dataset dataset. It achieves the following results on the evaluation set: - Loss: 2.0031 - Rouge1: 0.3588 - Rouge2: 0.0956 - Rougel: 0.2747 - Rougelsum: 0.2883 - 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 10 | 2.9194 | 0.2508 | 0.0306 | 0.1977 | 0.2049 | 19.0 | | No log | 2.0 | 20 | 2.6227 | 0.2313 | 0.0208 | 0.1733 | 0.1895 | 17.9 | | No log | 3.0 | 30 | 2.4288 | 0.2135 | 0.0396 | 0.1577 | 0.1745 | 17.5 | | No log | 4.0 | 40 | 2.3059 | 0.3521 | 0.1294 | 0.303 | 0.3153 | 19.0 | | No log | 5.0 | 50 | 2.1717 | 0.42 | 0.1393 | 0.324 | 0.3342 | 19.0 | | No log | 6.0 | 60 | 2.0786 | 0.4378 | 0.1642 | 0.3662 | 0.3862 | 19.0 | | No log | 7.0 | 70 | 2.0037 | 0.4433 | 0.181 | 0.3792 | 0.3979 | 19.0 | | No log | 8.0 | 80 | 1.9295 | 0.4558 | 0.1789 | 0.3783 | 0.4009 | 19.0 | | No log | 9.0 | 90 | 1.8843 | 0.451 | 0.1719 | 0.3677 | 0.3912 | 19.0 | | No log | 10.0 | 100 | 1.8560 | 0.4587 | 0.1853 | 0.3546 | 0.3771 | 19.0 | | No log | 11.0 | 110 | 1.8437 | 0.4878 | 0.2028 | 0.3785 | 0.3975 | 19.0 | | No log | 12.0 | 120 | 1.8144 | 0.4679 | 0.1724 | 0.3588 | 0.3744 | 19.0 | | No log | 13.0 | 130 | 1.7968 | 0.4839 | 0.2225 | 0.3842 | 0.3995 | 19.0 | | No log | 14.0 | 140 | 1.7892 | 0.4571 | 0.154 | 0.3466 | 0.3732 | 19.0 | | No log | 15.0 | 150 | 1.7618 | 0.4605 | 0.1573 | 0.344 | 0.3693 | 19.0 | | No log | 16.0 | 160 | 1.7532 | 0.459 | 0.1738 | 0.3494 | 0.3647 | 19.0 | | No log | 17.0 | 170 | 1.7495 | 0.4583 | 0.1734 | 0.3638 | 0.3788 | 19.0 | | No log | 18.0 | 180 | 1.7276 | 0.4733 | 0.1754 | 0.37 | 0.3808 | 19.0 | | No log | 19.0 | 190 | 1.7532 | 0.4607 | 0.1752 | 0.3624 | 0.3741 | 19.0 | | No log | 20.0 | 200 | 1.7213 | 0.4554 | 0.17 | 0.3572 | 0.3694 | 19.0 | | No log | 21.0 | 210 | 1.7540 | 0.4589 | 0.1699 | 0.357 | 0.3722 | 19.0 | | No log | 22.0 | 220 | 1.7149 | 0.4634 | 0.1751 | 0.3622 | 0.3768 | 19.0 | | No log | 23.0 | 230 | 1.7007 | 0.4634 | 0.1751 | 0.3622 | 0.3768 | 19.0 | | No log | 24.0 | 240 | 1.7055 | 0.4579 | 0.1754 | 0.3566 | 0.3759 | 19.0 | | No log | 25.0 | 250 | 1.6835 | 0.4593 | 0.1736 | 0.361 | 0.375 | 19.0 | | No log | 26.0 | 260 | 1.7127 | 0.4561 | 0.1701 | 0.3619 | 0.3761 | 19.0 | | No log | 27.0 | 270 | 1.6924 | 0.4479 | 0.1616 | 0.3572 | 0.3665 | 19.0 | | No log | 28.0 | 280 | 1.6980 | 0.4148 | 0.142 | 0.3436 | 0.3509 | 19.0 | | No log | 29.0 | 290 | 1.7059 | 0.4207 | 0.1503 | 0.3469 | 0.3615 | 19.0 | | No log | 30.0 | 300 | 1.7335 | 0.3952 | 0.1316 | 0.3243 | 0.3387 | 19.0 | | No log | 31.0 | 310 | 1.7024 | 0.4241 | 0.1427 | 0.34 | 0.347 | 19.0 | | No log | 32.0 | 320 | 1.6907 | 0.4272 | 0.1474 | 0.34 | 0.3539 | 19.0 | | No log | 33.0 | 330 | 1.7033 | 0.4412 | 0.1431 | 0.3325 | 0.3588 | 19.0 | | No log | 34.0 | 340 | 1.6996 | 0.4455 | 0.1473 | 0.3475 | 0.3722 | 19.0 | | No log | 35.0 | 350 | 1.7147 | 0.4394 | 0.1472 | 0.3496 | 0.376 | 19.0 | | No log | 36.0 | 360 | 1.7269 | 0.4292 | 0.1398 | 0.3343 | 0.3601 | 19.0 | | No log | 37.0 | 370 | 1.7504 | 0.4278 | 0.1389 | 0.3351 | 0.3492 | 19.0 | | No log | 38.0 | 380 | 1.7343 | 0.4213 | 0.1387 | 0.3346 | 0.3486 | 19.0 | | No log | 39.0 | 390 | 1.7479 | 0.3881 | 0.1133 | 0.3063 | 0.3212 | 19.0 | | No log | 40.0 | 400 | 1.7642 | 0.3766 | 0.1141 | 0.3086 | 0.3168 | 19.0 | | No log | 41.0 | 410 | 1.7561 | 0.3904 | 0.1221 | 0.3195 | 0.3236 | 19.0 | | No log | 42.0 | 420 | 1.7221 | 0.3977 | 0.0999 | 0.2848 | 0.295 | 19.0 | | No log | 43.0 | 430 | 1.7372 | 0.4015 | 0.1318 | 0.3242 | 0.3338 | 19.0 | | No log | 44.0 | 440 | 1.7632 | 0.3735 | 0.107 | 0.2962 | 0.3118 | 19.0 | | No log | 45.0 | 450 | 1.7462 | 0.3735 | 0.107 | 0.2917 | 0.307 | 19.0 | | No log | 46.0 | 460 | 1.7303 | 0.3766 | 0.107 | 0.2857 | 0.3005 | 19.0 | | No log | 47.0 | 470 | 1.7847 | 0.3677 | 0.1123 | 0.2974 | 0.3136 | 19.0 | | No log | 48.0 | 480 | 1.7970 | 0.3804 | 0.113 | 0.3111 | 0.3255 | 19.0 | | No log | 49.0 | 490 | 1.7922 | 0.3821 | 0.1092 | 0.2993 | 0.3154 | 19.0 | | 1.2487 | 50.0 | 500 | 1.7925 | 0.3954 | 0.1084 | 0.3208 | 0.3394 | 19.0 | | 1.2487 | 51.0 | 510 | 1.7794 | 0.4406 | 0.1359 | 0.3583 | 0.3816 | 19.0 | | 1.2487 | 52.0 | 520 | 1.7403 | 0.3999 | 0.1083 | 0.3056 | 0.3281 | 19.0 | | 1.2487 | 53.0 | 530 | 1.7523 | 0.4071 | 0.121 | 0.3082 | 0.3304 | 19.0 | | 1.2487 | 54.0 | 540 | 1.8127 | 0.4044 | 0.1089 | 0.307 | 0.3297 | 19.0 | | 1.2487 | 55.0 | 550 | 1.8049 | 0.3544 | 0.0984 | 0.2974 | 0.3107 | 19.0 | | 1.2487 | 56.0 | 560 | 1.8057 | 0.3544 | 0.0984 | 0.2974 | 0.3107 | 19.0 | | 1.2487 | 57.0 | 570 | 1.8224 | 0.3769 | 0.1083 | 0.3146 | 0.3255 | 19.0 | | 1.2487 | 58.0 | 580 | 1.8068 | 0.3769 | 0.1083 | 0.3146 | 0.3255 | 19.0 | | 1.2487 | 59.0 | 590 | 1.7821 | 0.3672 | 0.0982 | 0.3037 | 0.3162 | 19.0 | | 1.2487 | 60.0 | 600 | 1.7814 | 0.4225 | 0.1263 | 0.3304 | 0.3518 | 19.0 | | 1.2487 | 61.0 | 610 | 1.8426 | 0.4182 | 0.1329 | 0.3144 | 0.3369 | 19.0 | | 1.2487 | 62.0 | 620 | 1.8970 | 0.4044 | 0.1293 | 0.3046 | 0.327 | 19.0 | | 1.2487 | 63.0 | 630 | 1.9199 | 0.4152 | 0.1138 | 0.3114 | 0.334 | 19.0 | | 1.2487 | 64.0 | 640 | 1.9089 | 0.4152 | 0.1138 | 0.3114 | 0.334 | 19.0 | | 1.2487 | 65.0 | 650 | 1.9055 | 0.387 | 0.1061 | 0.2863 | 0.3085 | 19.0 | | 1.2487 | 66.0 | 660 | 1.9152 | 0.3952 | 0.1106 | 0.2971 | 0.3184 | 19.0 | | 1.2487 | 67.0 | 670 | 1.9108 | 0.3636 | 0.0989 | 0.2852 | 0.2996 | 19.0 | | 1.2487 | 68.0 | 680 | 1.9012 | 0.343 | 0.0937 | 0.2678 | 0.2821 | 19.0 | | 1.2487 | 69.0 | 690 | 1.9481 | 0.3432 | 0.0756 | 0.2594 | 0.2723 | 19.0 | | 1.2487 | 70.0 | 700 | 1.9629 | 0.4152 | 0.1138 | 0.3114 | 0.334 | 19.0 | | 1.2487 | 71.0 | 710 | 1.9386 | 0.4123 | 0.1044 | 0.3149 | 0.3373 | 19.0 | | 1.2487 | 72.0 | 720 | 1.9092 | 0.3956 | 0.1015 | 0.3024 | 0.3236 | 19.0 | | 1.2487 | 73.0 | 730 | 1.8820 | 0.389 | 0.1015 | 0.3029 | 0.321 | 19.0 | | 1.2487 | 74.0 | 740 | 1.8895 | 0.3885 | 0.1111 | 0.2965 | 0.3155 | 19.0 | | 1.2487 | 75.0 | 750 | 1.9141 | 0.39 | 0.1113 | 0.293 | 0.3125 | 19.0 | | 1.2487 | 76.0 | 760 | 1.9794 | 0.3398 | 0.0937 | 0.272 | 0.2829 | 19.0 | | 1.2487 | 77.0 | 770 | 2.0373 | 0.3533 | 0.0952 | 0.2787 | 0.293 | 19.0 | | 1.2487 | 78.0 | 780 | 2.0286 | 0.3533 | 0.0952 | 0.2787 | 0.293 | 19.0 | | 1.2487 | 79.0 | 790 | 2.0324 | 0.3518 | 0.1013 | 0.2708 | 0.2858 | 19.0 | | 1.2487 | 80.0 | 800 | 1.9939 | 0.3518 | 0.1013 | 0.2708 | 0.2858 | 19.0 | | 1.2487 | 81.0 | 810 | 1.9930 | 0.3518 | 0.1013 | 0.2708 | 0.2858 | 19.0 | | 1.2487 | 82.0 | 820 | 1.9631 | 0.3518 | 0.1013 | 0.2708 | 0.2858 | 19.0 | | 1.2487 | 83.0 | 830 | 1.9396 | 0.3518 | 0.1013 | 0.2708 | 0.2858 | 19.0 | | 1.2487 | 84.0 | 840 | 1.9291 | 0.343 | 0.0937 | 0.2678 | 0.2821 | 19.0 | | 1.2487 | 85.0 | 850 | 1.9610 | 0.3374 | 0.0756 | 0.2535 | 0.2666 | 19.0 | | 1.2487 | 86.0 | 860 | 1.9802 | 0.343 | 0.0937 | 0.2678 | 0.2821 | 19.0 | | 1.2487 | 87.0 | 870 | 1.9926 | 0.3518 | 0.1013 | 0.2708 | 0.2858 | 19.0 | | 1.2487 | 88.0 | 880 | 1.9924 | 0.3518 | 0.1013 | 0.2708 | 0.2858 | 19.0 | | 1.2487 | 89.0 | 890 | 2.0042 | 0.3573 | 0.1018 | 0.2669 | 0.2811 | 19.0 | | 1.2487 | 90.0 | 900 | 2.0027 | 0.3573 | 0.1018 | 0.2669 | 0.2811 | 19.0 | | 1.2487 | 91.0 | 910 | 2.0046 | 0.3518 | 0.1013 | 0.2708 | 0.2858 | 19.0 | | 1.2487 | 92.0 | 920 | 2.0020 | 0.3533 | 0.0952 | 0.2787 | 0.293 | 19.0 | | 1.2487 | 93.0 | 930 | 2.0048 | 0.3533 | 0.0952 | 0.2787 | 0.293 | 19.0 | | 1.2487 | 94.0 | 940 | 1.9991 | 0.3588 | 0.0956 | 0.2747 | 0.2883 | 19.0 | | 1.2487 | 95.0 | 950 | 2.0008 | 0.3588 | 0.0956 | 0.2747 | 0.2883 | 19.0 | | 1.2487 | 96.0 | 960 | 2.0071 | 0.3588 | 0.0956 | 0.2747 | 0.2883 | 19.0 | | 1.2487 | 97.0 | 970 | 2.0050 | 0.3588 | 0.0956 | 0.2747 | 0.2883 | 19.0 | | 1.2487 | 98.0 | 980 | 2.0039 | 0.3588 | 0.0956 | 0.2747 | 0.2883 | 19.0 | | 1.2487 | 99.0 | 990 | 2.0033 | 0.3588 | 0.0956 | 0.2747 | 0.2883 | 19.0 | | 0.5044 | 100.0 | 1000 | 2.0031 | 0.3588 | 0.0956 | 0.2747 | 0.2883 | 19.0 | ### Framework versions - Transformers 4.39.0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2