---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# kobart-base-v2-finetuned-paper

This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/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