đ 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 can classify Korean financial news, achieving an accuracy of 0.8423 on the evaluation set, which helps users quickly understand the sentiment and category of financial news.
đ Quick Start
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
⨠Features
- Multilingual Support: Based on the XLM - RoBERTa model, it has certain multilingual processing capabilities.
- High Accuracy: Achieved an accuracy of 0.8423 on the evaluation set.
đĻ Installation
No installation steps are provided in the original document, so this section is skipped.
đģ Usage Examples
No code examples are provided in the original document, so this section is skipped.
đ Documentation
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
đ§ Technical Details
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
đ License
No license information is provided in the original document, so this section is skipped.
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