metadata

license: apache-2.0
base_model: LazarusNLP/IndoNanoT5-base
tags:
  - generated_from_trainer
language:
  - ind
datasets:
  - GEM/indonlg
metrics:
  - rouge
model-index:
  - name: IndoNanoT5-base-Liputan6-Canonical
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: indonlg
          type: indonlg
          config: liputan6_canonical
          split: test
          args: liputan6_canonical
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.3976
          - name: Rouge2
            type: rouge
            value: 0.2229
          - name: RougeL
            type: rouge
            value: 0.3346
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: indonlg
          type: indonlg
          config: liputan6_extreme
          split: test
          args: liputan6_extreme
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.3323
          - name: Rouge2
            type: rouge
            value: 0.1417
          - name: RougeL
            type: rouge
            value: 0.2621

LazarusNLP/IndoNanoT5-base-Liputan6-Canonical

This model is a fine-tuned version of LazarusNLP/IndoNanoT5-base on the indonlg dataset. It achieves the following results on the evaluation set:

Loss: 1.1194
Rouge1: 0.3976
Rouge2: 0.2229
Rougel: 0.3346
Rougelsum: 0.3345
Gen Len: 43.3808

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: 1e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
0.9693	1.0	24236	1.3245	0.3082	0.1585	0.2687	0.2688	18.9956
0.9338	2.0	48472	1.2759	0.3105	0.159	0.2705	0.2706	18.9985
0.8632	3.0	72708	1.2698	0.3094	0.1586	0.2701	0.2702	18.9995
0.8257	4.0	96944	1.2631	0.312	0.1603	0.2716	0.2715	18.9993
0.7789	5.0	121180	1.2642	0.3149	0.1625	0.2748	0.2747	18.9998
0.7595	6.0	145416	1.2587	0.3202	0.1658	0.279	0.2791	18.9995
0.7343	7.0	169652	1.2644	0.3183	0.1647	0.2773	0.2773	18.9996
0.7165	8.0	193888	1.2635	0.3141	0.1605	0.2732	0.2732	18.9993
0.6697	9.0	218124	1.2856	0.316	0.162	0.275	0.275	18.9998
0.6729	10.0	242360	1.2809	0.3195	0.164	0.2775	0.2776	18.9992
0.6471	11.0	266596	1.2833	0.3185	0.1636	0.2769	0.277	18.9982

Framework versions

Transformers 4.37.2
Pytorch 2.2.0+cu118
Datasets 2.16.1
Tokenizers 0.15.1