transformers safetensors bart text2text-generation generated_from_trainer base_model:gogamza/kobart-base-v2 license:mit autotrain_compatible endpoints_compatible region:us
---
license: mit
base_model: gogamza/kobart-base-v2
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: kobart-base-v2-2932-korean
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# kobart-base-v2-2932-korean

This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2633
- Rouge1: 0.36
- Rouge2: 0.1465
- Rougel: 0.3537
- Rougelsum: 0.3538
- Gen Len: 19.9992

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.2804        | 1.44  | 500  | 0.2805          | 0.3394 | 0.1272 | 0.3346 | 0.3344    | 20.0    |
| 0.2127        | 2.88  | 1000 | 0.2490          | 0.3512 | 0.1374 | 0.3461 | 0.3459    | 19.9992 |
| 0.1409        | 4.33  | 1500 | 0.2504          | 0.3594 | 0.1429 | 0.3527 | 0.3524    | 20.0    |
| 0.1208        | 5.77  | 2000 | 0.2506          | 0.3607 | 0.1436 | 0.3536 | 0.3538    | 20.0    |
| 0.0866        | 7.21  | 2500 | 0.2603          | 0.3657 | 0.1466 | 0.3589 | 0.3593    | 20.0    |
| 0.0782        | 8.65  | 3000 | 0.2633          | 0.36   | 0.1465 | 0.3537 | 0.3538    | 19.9992 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
