This model is a fine-tuned version of google/mt5-small on the ravkuk_summerize_dataset dataset. It achieves the following results on the evaluation set:

Loss: 2.4588
Rouge1: 0.16
Rouge2: 0.0655
Rougel: 0.1519
Rougelsum: 0.1522
Gen Len: 18.9886

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: 0.0014142135623730952
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.3
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
7.6563	1.0	197	3.0371	0.1002	0.028	0.0941	0.0941	18.8949
3.4565	2.0	394	2.8403	0.1204	0.0362	0.1154	0.1152	18.9744
3.0924	3.0	591	2.7173	0.1276	0.0431	0.1199	0.1202	18.9631
2.7357	4.0	788	2.5831	0.1494	0.0555	0.1415	0.1416	18.9744
2.4543	5.0	985	2.5135	0.1437	0.0545	0.1345	0.135	18.9886
2.2055	6.0	1182	2.5031	0.1544	0.0642	0.147	0.1472	18.9773
2.0147	7.0	1379	2.4554	0.158	0.0643	0.1484	0.1487	18.9688
1.8746	8.0	1576	2.4588	0.16	0.0655	0.1519	0.1522	18.9886

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2

thaomer/le-fine-tune-mt5-small

le-fine-tune-mt5-small

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

thaomer/
le-fine-tune-mt5-small