--- 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: train args: default metrics: - name: Rouge1 type: rouge value: 0.4164 --- # 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: 2.1319 - Rouge1: 0.4164 - Rouge2: 0.1024 - Rougel: 0.3062 - Rougelsum: 0.2972 - 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 | 12 | 2.7041 | 0.2055 | 0.0262 | 0.1605 | 0.1605 | 19.0 | | No log | 2.0 | 24 | 2.5221 | 0.2579 | 0.043 | 0.1681 | 0.188 | 18.4 | | No log | 3.0 | 36 | 2.3734 | 0.2267 | 0.0312 | 0.1471 | 0.142 | 15.0 | | No log | 4.0 | 48 | 2.2547 | 0.3859 | 0.0906 | 0.284 | 0.2976 | 19.0 | | No log | 5.0 | 60 | 2.1711 | 0.4009 | 0.1082 | 0.3073 | 0.3049 | 17.2 | | No log | 6.0 | 72 | 2.1276 | 0.451 | 0.1003 | 0.2929 | 0.3133 | 19.0 | | No log | 7.0 | 84 | 2.0695 | 0.4656 | 0.1099 | 0.2987 | 0.3184 | 19.0 | | No log | 8.0 | 96 | 2.0238 | 0.4656 | 0.1199 | 0.2987 | 0.3184 | 19.0 | | No log | 9.0 | 108 | 1.9969 | 0.4656 | 0.1292 | 0.308 | 0.3288 | 19.0 | | No log | 10.0 | 120 | 1.9705 | 0.4646 | 0.1422 | 0.3081 | 0.3262 | 19.0 | | No log | 11.0 | 132 | 1.9569 | 0.4364 | 0.1155 | 0.3061 | 0.3259 | 19.0 | | No log | 12.0 | 144 | 1.9347 | 0.4459 | 0.1249 | 0.2999 | 0.3207 | 19.0 | | No log | 13.0 | 156 | 1.8836 | 0.4459 | 0.1249 | 0.2999 | 0.3207 | 19.0 | | No log | 14.0 | 168 | 1.8488 | 0.4245 | 0.0965 | 0.289 | 0.3086 | 19.0 | | No log | 15.0 | 180 | 1.8547 | 0.4253 | 0.1076 | 0.3125 | 0.3101 | 19.0 | | No log | 16.0 | 192 | 1.8332 | 0.452 | 0.1166 | 0.2952 | 0.3096 | 19.0 | | No log | 17.0 | 204 | 1.8252 | 0.4578 | 0.1247 | 0.3185 | 0.3285 | 19.0 | | No log | 18.0 | 216 | 1.8042 | 0.4656 | 0.1343 | 0.3267 | 0.3333 | 19.0 | | No log | 19.0 | 228 | 1.7856 | 0.4578 | 0.1247 | 0.3185 | 0.3285 | 19.0 | | No log | 20.0 | 240 | 1.8086 | 0.4451 | 0.1308 | 0.3241 | 0.3328 | 19.0 | | No log | 21.0 | 252 | 1.8156 | 0.433 | 0.1389 | 0.3247 | 0.3279 | 19.0 | | No log | 22.0 | 264 | 1.7810 | 0.4429 | 0.1135 | 0.3005 | 0.3155 | 19.0 | | No log | 23.0 | 276 | 1.7715 | 0.3852 | 0.072 | 0.2633 | 0.2655 | 19.0 | | No log | 24.0 | 288 | 1.8142 | 0.4176 | 0.1092 | 0.2922 | 0.2922 | 19.0 | | No log | 25.0 | 300 | 1.8024 | 0.4111 | 0.11 | 0.2811 | 0.2811 | 19.0 | | No log | 26.0 | 312 | 1.7650 | 0.404 | 0.1024 | 0.2776 | 0.2756 | 19.0 | | No log | 27.0 | 324 | 1.7557 | 0.4032 | 0.0963 | 0.2769 | 0.2733 | 19.0 | | No log | 28.0 | 336 | 1.7856 | 0.4282 | 0.1475 | 0.3136 | 0.3129 | 19.0 | | No log | 29.0 | 348 | 1.7468 | 0.4325 | 0.1374 | 0.3167 | 0.3167 | 19.0 | | No log | 30.0 | 360 | 1.7433 | 0.4258 | 0.1562 | 0.3007 | 0.3007 | 19.0 | | No log | 31.0 | 372 | 1.7651 | 0.4325 | 0.1556 | 0.3253 | 0.3253 | 19.0 | | No log | 32.0 | 384 | 1.7467 | 0.3914 | 0.0991 | 0.2751 | 0.2751 | 19.0 | | No log | 33.0 | 396 | 1.7758 | 0.3914 | 0.0991 | 0.2751 | 0.2751 | 19.0 | | No log | 34.0 | 408 | 1.7551 | 0.3858 | 0.0991 | 0.269 | 0.269 | 19.0 | | No log | 35.0 | 420 | 1.7500 | 0.3999 | 0.1255 | 0.2931 | 0.2931 | 19.0 | | No log | 36.0 | 432 | 1.7631 | 0.4176 | 0.1446 | 0.3276 | 0.3273 | 19.0 | | No log | 37.0 | 444 | 1.7702 | 0.406 | 0.1277 | 0.2883 | 0.2883 | 19.0 | | No log | 38.0 | 456 | 1.8084 | 0.3933 | 0.1088 | 0.2771 | 0.2771 | 19.0 | | No log | 39.0 | 468 | 1.8104 | 0.3999 | 0.1308 | 0.3018 | 0.3018 | 19.0 | | No log | 40.0 | 480 | 1.8087 | 0.3864 | 0.1097 | 0.2785 | 0.2785 | 19.0 | | No log | 41.0 | 492 | 1.8254 | 0.3974 | 0.1277 | 0.2883 | 0.2883 | 19.0 | | 1.3176 | 42.0 | 504 | 1.8406 | 0.4042 | 0.1385 | 0.2955 | 0.2955 | 19.0 | | 1.3176 | 43.0 | 516 | 1.8620 | 0.3864 | 0.1097 | 0.2785 | 0.2785 | 19.0 | | 1.3176 | 44.0 | 528 | 1.8932 | 0.3855 | 0.108 | 0.2872 | 0.2867 | 19.0 | | 1.3176 | 45.0 | 540 | 1.8810 | 0.3911 | 0.108 | 0.2994 | 0.2994 | 19.0 | | 1.3176 | 46.0 | 552 | 1.8600 | 0.3985 | 0.1095 | 0.298 | 0.2969 | 19.0 | | 1.3176 | 47.0 | 564 | 1.8706 | 0.3937 | 0.1178 | 0.2932 | 0.2932 | 19.0 | | 1.3176 | 48.0 | 576 | 1.8394 | 0.4061 | 0.1178 | 0.306 | 0.3056 | 19.0 | | 1.3176 | 49.0 | 588 | 1.8910 | 0.3929 | 0.0813 | 0.2833 | 0.2822 | 19.0 | | 1.3176 | 50.0 | 600 | 1.9152 | 0.3808 | 0.0807 | 0.281 | 0.2805 | 19.0 | | 1.3176 | 51.0 | 612 | 1.9092 | 0.3883 | 0.0918 | 0.289 | 0.289 | 19.0 | | 1.3176 | 52.0 | 624 | 1.8571 | 0.3877 | 0.1009 | 0.2971 | 0.2971 | 19.0 | | 1.3176 | 53.0 | 636 | 1.8913 | 0.3985 | 0.1254 | 0.3069 | 0.3052 | 19.0 | | 1.3176 | 54.0 | 648 | 1.9744 | 0.3985 | 0.1254 | 0.3069 | 0.3052 | 19.0 | | 1.3176 | 55.0 | 660 | 1.9156 | 0.3975 | 0.1024 | 0.292 | 0.2837 | 19.0 | | 1.3176 | 56.0 | 672 | 1.8886 | 0.3937 | 0.1183 | 0.306 | 0.303 | 19.0 | | 1.3176 | 57.0 | 684 | 1.9325 | 0.3883 | 0.1178 | 0.306 | 0.3056 | 19.0 | | 1.3176 | 58.0 | 696 | 1.9252 | 0.3994 | 0.1183 | 0.3175 | 0.3158 | 19.0 | | 1.3176 | 59.0 | 708 | 1.9159 | 0.3883 | 0.1178 | 0.306 | 0.3056 | 19.0 | | 1.3176 | 60.0 | 720 | 2.0071 | 0.4097 | 0.1369 | 0.3281 | 0.3261 | 19.0 | | 1.3176 | 61.0 | 732 | 1.9834 | 0.4164 | 0.1358 | 0.3266 | 0.3247 | 19.0 | | 1.3176 | 62.0 | 744 | 1.9928 | 0.4378 | 0.1185 | 0.3342 | 0.3261 | 19.0 | | 1.3176 | 63.0 | 756 | 1.9718 | 0.4267 | 0.1149 | 0.3306 | 0.3239 | 19.0 | | 1.3176 | 64.0 | 768 | 1.9513 | 0.4267 | 0.1149 | 0.3306 | 0.3239 | 19.0 | | 1.3176 | 65.0 | 780 | 1.9836 | 0.4067 | 0.1021 | 0.2976 | 0.29 | 19.0 | | 1.3176 | 66.0 | 792 | 1.9588 | 0.4134 | 0.1015 | 0.3069 | 0.2992 | 19.0 | | 1.3176 | 67.0 | 804 | 1.9513 | 0.4098 | 0.101 | 0.3142 | 0.3072 | 19.0 | | 1.3176 | 68.0 | 816 | 2.0276 | 0.4146 | 0.1015 | 0.3142 | 0.3045 | 19.0 | | 1.3176 | 69.0 | 828 | 2.0201 | 0.4134 | 0.1015 | 0.3069 | 0.2992 | 19.0 | | 1.3176 | 70.0 | 840 | 2.0082 | 0.4001 | 0.1022 | 0.3051 | 0.3051 | 19.0 | | 1.3176 | 71.0 | 852 | 2.0198 | 0.4004 | 0.1022 | 0.3124 | 0.3115 | 19.0 | | 1.3176 | 72.0 | 864 | 2.0386 | 0.4209 | 0.1015 | 0.3251 | 0.3172 | 19.0 | | 1.3176 | 73.0 | 876 | 2.0227 | 0.4057 | 0.1022 | 0.3221 | 0.3221 | 19.0 | | 1.3176 | 74.0 | 888 | 2.0413 | 0.4014 | 0.1031 | 0.3045 | 0.3024 | 19.0 | | 1.3176 | 75.0 | 900 | 2.0415 | 0.4014 | 0.1031 | 0.3045 | 0.3024 | 19.0 | | 1.3176 | 76.0 | 912 | 2.0913 | 0.4014 | 0.1031 | 0.3045 | 0.3024 | 19.0 | | 1.3176 | 77.0 | 924 | 2.0887 | 0.4014 | 0.1031 | 0.3045 | 0.3024 | 19.0 | | 1.3176 | 78.0 | 936 | 2.0997 | 0.4004 | 0.1022 | 0.3124 | 0.3115 | 19.0 | | 1.3176 | 79.0 | 948 | 2.0907 | 0.4014 | 0.1031 | 0.3045 | 0.3024 | 19.0 | | 1.3176 | 80.0 | 960 | 2.1398 | 0.4164 | 0.1024 | 0.3062 | 0.2972 | 19.0 | | 1.3176 | 81.0 | 972 | 2.1364 | 0.4164 | 0.1024 | 0.3062 | 0.2972 | 19.0 | | 1.3176 | 82.0 | 984 | 2.1417 | 0.4164 | 0.1024 | 0.3062 | 0.2972 | 19.0 | | 1.3176 | 83.0 | 996 | 2.1454 | 0.4014 | 0.1031 | 0.3045 | 0.3024 | 19.0 | | 0.5445 | 84.0 | 1008 | 2.1506 | 0.4014 | 0.1031 | 0.3045 | 0.3024 | 19.0 | | 0.5445 | 85.0 | 1020 | 2.1224 | 0.4151 | 0.1184 | 0.3106 | 0.3036 | 19.0 | | 0.5445 | 86.0 | 1032 | 2.0857 | 0.4151 | 0.1184 | 0.3106 | 0.3036 | 19.0 | | 0.5445 | 87.0 | 1044 | 2.0810 | 0.3939 | 0.103 | 0.2969 | 0.2898 | 19.0 | | 0.5445 | 88.0 | 1056 | 2.0854 | 0.4007 | 0.103 | 0.2969 | 0.2898 | 19.0 | | 0.5445 | 89.0 | 1068 | 2.1048 | 0.4098 | 0.103 | 0.2969 | 0.2898 | 19.0 | | 0.5445 | 90.0 | 1080 | 2.1153 | 0.4007 | 0.103 | 0.2969 | 0.2898 | 19.0 | | 0.5445 | 91.0 | 1092 | 2.1200 | 0.3971 | 0.1018 | 0.2939 | 0.2873 | 19.0 | | 0.5445 | 92.0 | 1104 | 2.1221 | 0.3971 | 0.1018 | 0.2939 | 0.2873 | 19.0 | | 0.5445 | 93.0 | 1116 | 2.1291 | 0.4007 | 0.103 | 0.2969 | 0.2898 | 19.0 | | 0.5445 | 94.0 | 1128 | 2.1419 | 0.4164 | 0.1024 | 0.3062 | 0.2972 | 19.0 | | 0.5445 | 95.0 | 1140 | 2.1438 | 0.4073 | 0.1024 | 0.3062 | 0.2972 | 19.0 | | 0.5445 | 96.0 | 1152 | 2.1381 | 0.4164 | 0.1024 | 0.3062 | 0.2972 | 19.0 | | 0.5445 | 97.0 | 1164 | 2.1349 | 0.4164 | 0.1024 | 0.3062 | 0.2972 | 19.0 | | 0.5445 | 98.0 | 1176 | 2.1347 | 0.4164 | 0.1024 | 0.3062 | 0.2972 | 19.0 | | 0.5445 | 99.0 | 1188 | 2.1322 | 0.4164 | 0.1024 | 0.3062 | 0.2972 | 19.0 | | 0.5445 | 100.0 | 1200 | 2.1319 | 0.4164 | 0.1024 | 0.3062 | 0.2972 | 19.0 | ### Framework versions - Transformers 4.39.0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2