metadata

license: apache-2.0
tags:
  - summarization
  - generated_from_trainer
datasets:
  - ravkuk_summerize_dataset
metrics:
  - rouge
model-index:
  - name: le-fine-tune-mt5-base
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: ravkuk_summerize_dataset
          type: ravkuk_summerize_dataset
          config: default
          split: train
          args: default
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.1555

le-fine-tune-mt5-base

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

Loss: 2.6590
Rouge1: 0.1555
Rouge2: 0.065
Rougel: 0.1489
Rougelsum: 0.149
Gen Len: 18.9858

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
5.0797	1.0	197	2.7316	0.1101	0.0319	0.1025	0.1024	18.9432
2.8975	2.0	394	2.6943	0.1239	0.0453	0.1207	0.1204	18.9688
2.7115	3.0	591	2.6143	0.1333	0.0505	0.1283	0.1289	18.9688
2.365	4.0	788	2.5704	0.125	0.0433	0.1201	0.1199	19.0
2.0738	5.0	985	2.5296	0.1341	0.0478	0.1284	0.1286	18.9858
1.6716	6.0	1182	2.4902	0.1451	0.0554	0.1397	0.1395	18.9886
1.2644	7.0	1379	2.5039	0.1446	0.0562	0.1407	0.1406	18.9744
0.9641	8.0	1576	2.6590	0.1555	0.065	0.1489	0.149	18.9858

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2