metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - aihub_paper_summarization
metrics:
  - rouge
model-index:
  - name: kobart-base-v2-finetuned-paper
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: aihub_paper_summarization
          type: aihub_paper_summarization
          config: default
          split: train
          args: default
        metrics:
          - name: Rouge1
            type: rouge
            value: 6.2883

kobart-base-v2-finetuned-paper

This model is a fine-tuned version of gogamza/kobart-base-v2 on the aihub_paper_summarization dataset. It achieves the following results on the evaluation set:

Loss: 1.2966
Rouge1: 6.2883
Rouge2: 1.7038
Rougel: 6.2556
Rougelsum: 6.2618
Gen Len: 20.0

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.2215	1.0	8831	1.3293	6.2425	1.7317	6.2246	6.2247	20.0
1.122	2.0	17662	1.3056	6.2298	1.7005	6.2042	6.2109	20.0
1.0914	3.0	26493	1.2966	6.2883	1.7038	6.2556	6.2618	20.0

Framework versions

Transformers 4.25.1
Pytorch 1.13.1
Datasets 2.8.0
Tokenizers 0.13.2