This model is a fine-tuned version of 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

sunnyshi/flan-log-sage

flan-log-sage

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sunnyshi/
flan-log-sage