---
license: apache-2.0
base_model: KETI-AIR/ke-t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: ke_t5_base_aihub
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ke_t5_base_aihub

This model is a fine-tuned version of [KETI-AIR/ke-t5-base](https://huggingface.co/KETI-AIR/ke-t5-base) 
on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 0.0

## Model description

KE-T5 is a pretrained-model of t5 text-to-text transfer transformers using the Korean and English corpus developed by KETI (한국전자연구원). The vocabulary used by KE-T5 consists of 64,000 sub-word tokens and was created using Google's sentencepiece. The Sentencepiece model was trained to cover 99.95% of a 30GB corpus with an approximate 7:3 mix of Korean and English.

## Intended uses & limitations

This is an excersize for ke-t5 summarization finetuning using pre-trained ke-t5-base
using the data from aihub.

## Training and evaluation data



## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0           | 1.0   | 743  | nan             | 0.0    | 0.0    | 0.0    | 0.0       | 0.0     |
| 0.0           | 2.0   | 1486 | nan             | 0.0    | 0.0    | 0.0    | 0.0       | 0.0     |
| 0.0           | 3.0   | 2229 | nan             | 0.0    | 0.0    | 0.0    | 0.0       | 0.0     |
| 0.0           | 4.0   | 2972 | nan             | 0.0    | 0.0    | 0.0    | 0.0       | 0.0     |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3