--- 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: 0.0737 --- # 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: nan - Rouge1: 0.0737 - Rouge2: 0.0154 - Rougel: 0.0737 - Rougelsum: 0.0741 - 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: 2 - eval_batch_size: 2 - 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 | 23 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 2.0 | 46 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 3.0 | 69 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 4.0 | 92 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 5.0 | 115 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 6.0 | 138 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 7.0 | 161 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 8.0 | 184 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 9.0 | 207 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 10.0 | 230 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 11.0 | 253 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 12.0 | 276 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 13.0 | 299 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 14.0 | 322 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 15.0 | 345 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 16.0 | 368 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 17.0 | 391 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 18.0 | 414 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 19.0 | 437 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 20.0 | 460 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | No log | 21.0 | 483 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 22.0 | 506 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 23.0 | 529 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 24.0 | 552 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 25.0 | 575 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 26.0 | 598 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 27.0 | 621 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 28.0 | 644 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 29.0 | 667 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 30.0 | 690 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 31.0 | 713 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 32.0 | 736 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 33.0 | 759 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 34.0 | 782 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 35.0 | 805 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 36.0 | 828 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 37.0 | 851 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 38.0 | 874 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 39.0 | 897 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 40.0 | 920 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 41.0 | 943 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 42.0 | 966 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 43.0 | 989 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 44.0 | 1012 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 45.0 | 1035 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 46.0 | 1058 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 47.0 | 1081 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 48.0 | 1104 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 49.0 | 1127 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 50.0 | 1150 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 51.0 | 1173 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 52.0 | 1196 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 53.0 | 1219 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 54.0 | 1242 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 55.0 | 1265 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 56.0 | 1288 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 57.0 | 1311 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 58.0 | 1334 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 59.0 | 1357 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 60.0 | 1380 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 61.0 | 1403 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 62.0 | 1426 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 63.0 | 1449 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 64.0 | 1472 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 65.0 | 1495 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 66.0 | 1518 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 67.0 | 1541 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 68.0 | 1564 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 69.0 | 1587 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 70.0 | 1610 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 71.0 | 1633 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 72.0 | 1656 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 73.0 | 1679 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 74.0 | 1702 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 75.0 | 1725 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 76.0 | 1748 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 77.0 | 1771 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 78.0 | 1794 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 79.0 | 1817 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 80.0 | 1840 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 81.0 | 1863 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 82.0 | 1886 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 83.0 | 1909 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 84.0 | 1932 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 85.0 | 1955 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 86.0 | 1978 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 87.0 | 2001 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 88.0 | 2024 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 89.0 | 2047 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 90.0 | 2070 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 91.0 | 2093 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 92.0 | 2116 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 93.0 | 2139 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 94.0 | 2162 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 95.0 | 2185 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 96.0 | 2208 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 97.0 | 2231 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 98.0 | 2254 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 99.0 | 2277 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | | 0.0 | 100.0 | 2300 | nan | 0.0737 | 0.0154 | 0.0737 | 0.0741 | 19.0 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1