---
license: apache-2.0
base_model: psyche/KoT5-summarization
tags:
- generated_from_trainer
model-index:
- name: KoT5-summarization-mydata
  results: []
---

<!-- 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. -->

# KoT5-summarization-mydata

This model is a fine-tuned version of [psyche/KoT5-summarization](https://huggingface.co/psyche/KoT5-summarization) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7827
- Rouge1 Precision: 0.5055
- Rouge1 Recall: 0.5287
- Rouge1 F1: 0.5102
- Rouge2 Precision: 0.3635
- Rouge2 Recall: 0.3790
- Rouge2 F1: 0.3660
- Rouge3 Precision: 0.2739
- Rouge3 Recall: 0.2852
- Rouge3 F1: 0.2753

## 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: 8e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 F1 | Rouge2 Precision | Rouge2 Recall | Rouge2 F1 | Rouge3 Precision | Rouge3 Recall | Rouge3 F1 |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------:|:----------------:|:-------------:|:---------:|:----------------:|:-------------:|:---------:|
| 0.8855        | 1.0   | 34033 | 0.7909          | 0.5110           | 0.5294        | 0.5134    | 0.3681           | 0.3805        | 0.3691    | 0.2803           | 0.2892        | 0.2805    |
| 0.8206        | 2.0   | 68066 | 0.7827          | 0.5055           | 0.5287        | 0.5102    | 0.3635           | 0.3790        | 0.3660    | 0.2739           | 0.2852        | 0.2753    |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.8.0+cu128
- Datasets 2.19.0
- Tokenizers 0.19.1