---
language:
- ko
tags:
- generated_from_trainer
metrics:
- accuracy
widget:
- text: 이 회사는 러시아의 톰스크 지역에 있는 베니어 공장에 기계를 납품하기로 되어 있었다.
  example_title: example01
- text: 새로운 생산공장으로 인해 회사는 예상되는 수요 증가를 충족시킬 수 있는 능력을 증가시키고 원자재 사용을 개선하여 생산 수익성을 높일
    것이다.
  example_title: example02
- text: 국제 전자산업 회사인 엘코텍은 탈린 공장에서 수십 명의 직원을 해고했으며, 이전의 해고와는 달리 회사는 사무직 직원 수를 줄였다고 일간
    포스티메스가 보도했다.
  example_title: example03
base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment
model-index:
- name: ko-finance_news_classifier
  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. -->

# ko-finance_news_classifier

This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4474
- Accuracy: 0.8423

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 243  | 1.0782          | 0.8010   |
| No log        | 2.0   | 486  | 1.0328          | 0.8381   |
| 0.0766        | 3.0   | 729  | 1.2348          | 0.8330   |
| 0.0766        | 4.0   | 972  | 1.3915          | 0.8052   |
| 0.046         | 5.0   | 1215 | 1.2995          | 0.8474   |
| 0.046         | 6.0   | 1458 | 1.2926          | 0.8361   |
| 0.0512        | 7.0   | 1701 | 1.2889          | 0.8330   |
| 0.0512        | 8.0   | 1944 | 1.3107          | 0.8392   |
| 0.0415        | 9.0   | 2187 | 1.4514          | 0.8309   |
| 0.0415        | 10.0  | 2430 | 1.2869          | 0.8381   |
| 0.0279        | 11.0  | 2673 | 1.2874          | 0.8526   |
| 0.0279        | 12.0  | 2916 | 1.4731          | 0.8423   |
| 0.0126        | 13.0  | 3159 | 1.3956          | 0.8443   |
| 0.0126        | 14.0  | 3402 | 1.4211          | 0.8454   |
| 0.0101        | 15.0  | 3645 | 1.3686          | 0.8474   |
| 0.0101        | 16.0  | 3888 | 1.4412          | 0.8423   |
| 0.0114        | 17.0  | 4131 | 1.4376          | 0.8423   |
| 0.0114        | 18.0  | 4374 | 1.4566          | 0.8423   |
| 0.0055        | 19.0  | 4617 | 1.4439          | 0.8443   |
| 0.0055        | 20.0  | 4860 | 1.4474          | 0.8423   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3