metadata

license: mit
tags:
  - summarization
  - generated_from_trainer
datasets:
  - ravkuk_summerize_dataset
metrics:
  - rouge
model-index:
  - name: le-fine-tune-mbart-large-50
    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: 0.2928

le-fine-tune-mbart-large-50

This model is a fine-tuned version of facebook/mbart-large-50 on the ravkuk_summerize_dataset dataset. It achieves the following results on the evaluation set:

Loss: 2.7762
Rouge1: 0.2928
Rouge2: 0.1926
Rougel: 0.2815
Rougelsum: 0.2816
Gen Len: 34.5028

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: 5.6e-05
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
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
3.1169	1.0	197	2.3604	0.1878	0.0725	0.1737	0.1737	33.7784
1.8945	2.0	394	2.2522	0.1897	0.0765	0.1776	0.1776	34.2074
1.3083	3.0	591	2.2886	0.2001	0.0927	0.1895	0.1892	35.4432
0.8693	4.0	788	2.3727	0.2243	0.1123	0.2122	0.2117	31.4943
0.5507	5.0	985	2.5059	0.2577	0.1527	0.2463	0.2466	34.3693
0.3385	6.0	1182	2.6032	0.2703	0.1672	0.2593	0.2584	33.5994
0.2031	7.0	1379	2.6518	0.2912	0.1932	0.2812	0.281	34.1676
0.1272	8.0	1576	2.7040	0.2891	0.1895	0.2799	0.2796	34.6761
0.0842	9.0	1773	2.7515	0.2978	0.198	0.2888	0.2887	34.1932
0.0605	10.0	1970	2.7762	0.2928	0.1926	0.2815	0.2816	34.5028

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2