transformers safetensors t5 text2text-generation generated_from_trainer summarization dataset:hdfs_log_summary_dataset base_model:google/flan-t5-base base_model:finetune:google/flan-t5-base license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: google/flan-t5-base
datasets:
- hdfs_log_summary_dataset
license: apache-2.0
metrics:
- rouge
pipeline_tag: summarization
tags:
- generated_from_trainer
model-index:
- name: flan-log-sage
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: hdfs_log_summary_dataset
      type: hdfs_log_summary_dataset
      config: default
      split: train
      args: default
    metrics:
    - type: rouge
      value: 0.4709
      name: Rouge1
---

<!-- 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. -->

# 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: 1.5181
- Rouge1: 0.4709
- Rouge2: 0.1615
- Rougel: 0.3748
- Rougelsum: 0.3905
- 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   | 2.9597          | 0.1985 | 0.0098 | 0.1629 | 0.1658    | 18.8    |
| No log        | 2.0   | 24   | 2.5389          | 0.3028 | 0.0271 | 0.2401 | 0.2492    | 17.8    |
| No log        | 3.0   | 36   | 2.2506          | 0.3349 | 0.0688 | 0.2549 | 0.2789    | 19.0    |
| No log        | 4.0   | 48   | 2.0524          | 0.4046 | 0.0982 | 0.3249 | 0.3409    | 19.0    |
| No log        | 5.0   | 60   | 1.9082          | 0.4479 | 0.1438 | 0.3449 | 0.3617    | 19.0    |
| No log        | 6.0   | 72   | 1.8325          | 0.4564 | 0.1577 | 0.3402 | 0.3562    | 18.8    |
| No log        | 7.0   | 84   | 1.7565          | 0.4441 | 0.1456 | 0.3335 | 0.351     | 19.0    |
| No log        | 8.0   | 96   | 1.7091          | 0.4691 | 0.1732 | 0.3486 | 0.3667    | 19.0    |
| No log        | 9.0   | 108  | 1.6683          | 0.4847 | 0.1645 | 0.3589 | 0.3667    | 19.0    |
| No log        | 10.0  | 120  | 1.5987          | 0.4847 | 0.1727 | 0.3667 | 0.3667    | 19.0    |
| No log        | 11.0  | 132  | 1.5606          | 0.4684 | 0.1935 | 0.3746 | 0.3751    | 19.0    |
| No log        | 12.0  | 144  | 1.5245          | 0.4749 | 0.193  | 0.3817 | 0.3894    | 19.0    |
| No log        | 13.0  | 156  | 1.4859          | 0.5163 | 0.2289 | 0.3802 | 0.3879    | 19.0    |
| No log        | 14.0  | 168  | 1.4950          | 0.4404 | 0.1522 | 0.3474 | 0.3474    | 19.0    |
| No log        | 15.0  | 180  | 1.4552          | 0.4609 | 0.1865 | 0.3573 | 0.362     | 19.0    |
| No log        | 16.0  | 192  | 1.4501          | 0.4521 | 0.1685 | 0.342  | 0.3423    | 19.0    |
| No log        | 17.0  | 204  | 1.3955          | 0.4763 | 0.1769 | 0.3788 | 0.379     | 19.0    |
| No log        | 18.0  | 216  | 1.4192          | 0.4602 | 0.199  | 0.3168 | 0.3178    | 19.0    |
| No log        | 19.0  | 228  | 1.3750          | 0.411  | 0.1258 | 0.3168 | 0.3269    | 19.0    |
| No log        | 20.0  | 240  | 1.3660          | 0.5038 | 0.2293 | 0.3638 | 0.3649    | 19.0    |
| No log        | 21.0  | 252  | 1.3610          | 0.4508 | 0.1364 | 0.3319 | 0.3397    | 19.0    |
| No log        | 22.0  | 264  | 1.3437          | 0.4495 | 0.1225 | 0.3217 | 0.3239    | 19.0    |
| No log        | 23.0  | 276  | 1.3394          | 0.4495 | 0.1225 | 0.3217 | 0.3239    | 19.0    |
| No log        | 24.0  | 288  | 1.3716          | 0.4499 | 0.1459 | 0.3562 | 0.3727    | 19.0    |
| No log        | 25.0  | 300  | 1.3673          | 0.4427 | 0.1585 | 0.3704 | 0.3784    | 19.0    |
| No log        | 26.0  | 312  | 1.3225          | 0.4427 | 0.1585 | 0.3704 | 0.3784    | 19.0    |
| No log        | 27.0  | 324  | 1.3041          | 0.4308 | 0.1457 | 0.3426 | 0.352     | 19.0    |
| No log        | 28.0  | 336  | 1.3350          | 0.4508 | 0.1459 | 0.3562 | 0.3647    | 19.0    |
| No log        | 29.0  | 348  | 1.3438          | 0.4243 | 0.1256 | 0.3364 | 0.3439    | 19.0    |
| No log        | 30.0  | 360  | 1.3332          | 0.4302 | 0.1262 | 0.3394 | 0.3474    | 19.0    |
| No log        | 31.0  | 372  | 1.3551          | 0.4647 | 0.1385 | 0.3595 | 0.3595    | 19.0    |
| No log        | 32.0  | 384  | 1.3822          | 0.4647 | 0.1385 | 0.3595 | 0.3595    | 19.0    |
| No log        | 33.0  | 396  | 1.3978          | 0.4647 | 0.1385 | 0.3595 | 0.3595    | 19.0    |
| No log        | 34.0  | 408  | 1.4044          | 0.4469 | 0.1331 | 0.3518 | 0.3518    | 19.0    |
| No log        | 35.0  | 420  | 1.3828          | 0.4614 | 0.1369 | 0.357  | 0.3727    | 19.0    |
| No log        | 36.0  | 432  | 1.3797          | 0.4551 | 0.1369 | 0.357  | 0.3727    | 19.0    |
| No log        | 37.0  | 444  | 1.3528          | 0.4493 | 0.124  | 0.3515 | 0.3669    | 19.0    |
| No log        | 38.0  | 456  | 1.3716          | 0.4493 | 0.124  | 0.3515 | 0.3669    | 19.0    |
| No log        | 39.0  | 468  | 1.4217          | 0.4429 | 0.124  | 0.3449 | 0.3606    | 19.0    |
| No log        | 40.0  | 480  | 1.4128          | 0.4429 | 0.124  | 0.3449 | 0.3606    | 19.0    |
| No log        | 41.0  | 492  | 1.3495          | 0.4429 | 0.124  | 0.3449 | 0.3606    | 19.0    |
| 1.33          | 42.0  | 504  | 1.3608          | 0.4397 | 0.1117 | 0.348  | 0.3636    | 19.0    |
| 1.33          | 43.0  | 516  | 1.4052          | 0.4605 | 0.1246 | 0.3688 | 0.3845    | 19.0    |
| 1.33          | 44.0  | 528  | 1.3969          | 0.4605 | 0.1435 | 0.3688 | 0.3845    | 19.0    |
| 1.33          | 45.0  | 540  | 1.3768          | 0.4551 | 0.1369 | 0.357  | 0.3727    | 19.0    |
| 1.33          | 46.0  | 552  | 1.3903          | 0.4429 | 0.124  | 0.3449 | 0.3606    | 19.0    |
| 1.33          | 47.0  | 564  | 1.3829          | 0.4458 | 0.1395 | 0.3547 | 0.3628    | 19.0    |
| 1.33          | 48.0  | 576  | 1.3972          | 0.4551 | 0.1369 | 0.357  | 0.3727    | 19.0    |
| 1.33          | 49.0  | 588  | 1.4015          | 0.4429 | 0.124  | 0.3449 | 0.3606    | 19.0    |
| 1.33          | 50.0  | 600  | 1.3791          | 0.4493 | 0.124  | 0.3515 | 0.3669    | 19.0    |
| 1.33          | 51.0  | 612  | 1.4205          | 0.4493 | 0.124  | 0.3515 | 0.3669    | 19.0    |
| 1.33          | 52.0  | 624  | 1.4269          | 0.4493 | 0.124  | 0.3515 | 0.3669    | 19.0    |
| 1.33          | 53.0  | 636  | 1.3988          | 0.4493 | 0.124  | 0.3515 | 0.3669    | 19.0    |
| 1.33          | 54.0  | 648  | 1.4126          | 0.4493 | 0.124  | 0.3515 | 0.3669    | 19.0    |
| 1.33          | 55.0  | 660  | 1.4178          | 0.4429 | 0.124  | 0.3449 | 0.3606    | 19.0    |
| 1.33          | 56.0  | 672  | 1.4674          | 0.4332 | 0.1189 | 0.3408 | 0.3565    | 19.0    |
| 1.33          | 57.0  | 684  | 1.4871          | 0.4543 | 0.1403 | 0.3546 | 0.3703    | 19.0    |
| 1.33          | 58.0  | 696  | 1.4709          | 0.4547 | 0.1365 | 0.3567 | 0.3723    | 19.0    |
| 1.33          | 59.0  | 708  | 1.4891          | 0.4493 | 0.124  | 0.3515 | 0.3669    | 19.0    |
| 1.33          | 60.0  | 720  | 1.5033          | 0.4398 | 0.1109 | 0.3289 | 0.3446    | 19.0    |
| 1.33          | 61.0  | 732  | 1.4830          | 0.4398 | 0.1109 | 0.3289 | 0.3446    | 19.0    |
| 1.33          | 62.0  | 744  | 1.4642          | 0.4246 | 0.1042 | 0.335  | 0.3507    | 19.0    |
| 1.33          | 63.0  | 756  | 1.4480          | 0.4246 | 0.1042 | 0.335  | 0.3507    | 19.0    |
| 1.33          | 64.0  | 768  | 1.4312          | 0.4493 | 0.124  | 0.3515 | 0.3669    | 19.0    |
| 1.33          | 65.0  | 780  | 1.4761          | 0.4378 | 0.1247 | 0.3458 | 0.3615    | 19.0    |
| 1.33          | 66.0  | 792  | 1.4705          | 0.4378 | 0.1247 | 0.3458 | 0.3615    | 19.0    |
| 1.33          | 67.0  | 804  | 1.4665          | 0.4493 | 0.124  | 0.3515 | 0.3669    | 19.0    |
| 1.33          | 68.0  | 816  | 1.4700          | 0.4493 | 0.124  | 0.3515 | 0.3669    | 19.0    |
| 1.33          | 69.0  | 828  | 1.4753          | 0.4493 | 0.124  | 0.3515 | 0.3669    | 19.0    |
| 1.33          | 70.0  | 840  | 1.4910          | 0.4351 | 0.113  | 0.3354 | 0.351     | 19.0    |
| 1.33          | 71.0  | 852  | 1.4857          | 0.4586 | 0.1505 | 0.3589 | 0.3746    | 19.0    |
| 1.33          | 72.0  | 864  | 1.4965          | 0.4481 | 0.1399 | 0.3585 | 0.3727    | 19.0    |
| 1.33          | 73.0  | 876  | 1.5141          | 0.4481 | 0.1399 | 0.3585 | 0.3727    | 19.0    |
| 1.33          | 74.0  | 888  | 1.5162          | 0.4407 | 0.1358 | 0.3534 | 0.3687    | 19.0    |
| 1.33          | 75.0  | 900  | 1.5005          | 0.4523 | 0.1439 | 0.3525 | 0.3682    | 19.0    |
| 1.33          | 76.0  | 912  | 1.4910          | 0.417  | 0.1126 | 0.3258 | 0.3396    | 19.0    |
| 1.33          | 77.0  | 924  | 1.4811          | 0.4174 | 0.1143 | 0.3375 | 0.3513    | 19.0    |
| 1.33          | 78.0  | 936  | 1.4698          | 0.4312 | 0.1281 | 0.3534 | 0.3687    | 19.0    |
| 1.33          | 79.0  | 948  | 1.4688          | 0.4298 | 0.1281 | 0.3522 | 0.3666    | 19.0    |
| 1.33          | 80.0  | 960  | 1.4665          | 0.4312 | 0.1281 | 0.3534 | 0.3687    | 19.0    |
| 1.33          | 81.0  | 972  | 1.4879          | 0.4601 | 0.1469 | 0.3684 | 0.3838    | 19.0    |
| 1.33          | 82.0  | 984  | 1.4899          | 0.4601 | 0.1469 | 0.3684 | 0.3838    | 19.0    |
| 1.33          | 83.0  | 996  | 1.4859          | 0.4601 | 0.1469 | 0.3684 | 0.3838    | 19.0    |
| 0.5425        | 84.0  | 1008 | 1.4906          | 0.4645 | 0.1549 | 0.3684 | 0.3838    | 19.0    |
| 0.5425        | 85.0  | 1020 | 1.4987          | 0.4547 | 0.1424 | 0.3567 | 0.3723    | 19.0    |
| 0.5425        | 86.0  | 1032 | 1.4982          | 0.4611 | 0.149  | 0.363  | 0.3787    | 19.0    |
| 0.5425        | 87.0  | 1044 | 1.4928          | 0.4611 | 0.149  | 0.363  | 0.3787    | 19.0    |
| 0.5425        | 88.0  | 1056 | 1.4995          | 0.4611 | 0.149  | 0.363  | 0.3787    | 19.0    |
| 0.5425        | 89.0  | 1068 | 1.4994          | 0.4547 | 0.1424 | 0.3567 | 0.3723    | 19.0    |
| 0.5425        | 90.0  | 1080 | 1.5050          | 0.4547 | 0.1424 | 0.3567 | 0.3723    | 19.0    |
| 0.5425        | 91.0  | 1092 | 1.5118          | 0.4611 | 0.149  | 0.363  | 0.3787    | 19.0    |
| 0.5425        | 92.0  | 1104 | 1.5085          | 0.4611 | 0.149  | 0.363  | 0.3787    | 19.0    |
| 0.5425        | 93.0  | 1116 | 1.5093          | 0.4611 | 0.149  | 0.363  | 0.3787    | 19.0    |
| 0.5425        | 94.0  | 1128 | 1.5149          | 0.4611 | 0.149  | 0.363  | 0.3787    | 19.0    |
| 0.5425        | 95.0  | 1140 | 1.5164          | 0.4611 | 0.149  | 0.363  | 0.3787    | 19.0    |
| 0.5425        | 96.0  | 1152 | 1.5165          | 0.4611 | 0.149  | 0.363  | 0.3787    | 19.0    |
| 0.5425        | 97.0  | 1164 | 1.5167          | 0.4611 | 0.149  | 0.363  | 0.3787    | 19.0    |
| 0.5425        | 98.0  | 1176 | 1.5171          | 0.4611 | 0.149  | 0.363  | 0.3787    | 19.0    |
| 0.5425        | 99.0  | 1188 | 1.5180          | 0.4709 | 0.1615 | 0.3748 | 0.3905    | 19.0    |
| 0.5425        | 100.0 | 1200 | 1.5181          | 0.4709 | 0.1615 | 0.3748 | 0.3905    | 19.0    |


### Framework versions

- Transformers 4.39.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2