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# 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

{"language": ["ko"], "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "\uc774 \ud68c\uc0ac\ub294 \ub7ec\uc2dc\uc544\uc758 \ud1b0\uc2a4\ud06c \uc9c0\uc5ed\uc5d0 \uc788\ub294 \ubca0\ub2c8\uc5b4 \uacf5\uc7a5\uc5d0 \uae30\uacc4\ub97c \ub0a9\ud488\ud558\uae30\ub85c \ub418\uc5b4 \uc788\uc5c8\ub2e4.", "example_title": "example01"}, {"text": "\uc0c8\ub85c\uc6b4 \uc0dd\uc0b0\uacf5\uc7a5\uc73c\ub85c \uc778\ud574 \ud68c\uc0ac\ub294 \uc608\uc0c1\ub418\ub294 \uc218\uc694 \uc99d\uac00\ub97c \ucda9\uc871\uc2dc\ud0ac \uc218 \uc788\ub294 \ub2a5\ub825\uc744 \uc99d\uac00\uc2dc\ud0a4\uace0 \uc6d0\uc790\uc7ac \uc0ac\uc6a9\uc744 \uac1c\uc120\ud558\uc5ec \uc0dd\uc0b0 \uc218\uc775\uc131\uc744 \ub192\uc77c \uac83\uc774\ub2e4.", "example_title": "example02"}, {"text": "\uad6d\uc81c \uc804\uc790\uc0b0\uc5c5 \ud68c\uc0ac\uc778 \uc5d8\ucf54\ud14d\uc740 \ud0c8\ub9b0 \uacf5\uc7a5\uc5d0\uc11c \uc218\uc2ed \uba85\uc758 \uc9c1\uc6d0\uc744 \ud574\uace0\ud588\uc73c\uba70, \uc774\uc804\uc758 \ud574\uace0\uc640\ub294 \ub2ec\ub9ac \ud68c\uc0ac\ub294 \uc0ac\ubb34\uc9c1 \uc9c1\uc6d0 \uc218\ub97c \uc904\uc600\ub2e4\uace0 \uc77c\uac04 \ud3ec\uc2a4\ud2f0\uba54\uc2a4\uac00 \ubcf4\ub3c4\ud588\ub2e4.", "example_title": "example03"}], "base_model": "cardiffnlp/twitter-xlm-roberta-base-sentiment", "model-index": [{"name": "ko-finance_news_classifier", "results": []}]}
transformers pytorch tensorboard safetensors xlm-roberta text-classification generated_from_trainer ko base_model:cardiffnlp/twitter-xlm-roberta-base-sentiment autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #ko #base_model-cardiffnlp/twitter-xlm-roberta-base-sentiment #autotrain_compatible #endpoints_compatible #has_space #region-us 

ko-finance\_news\_classifier
============================


This model is a fine-tuned version of 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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #ko #base_model-cardiffnlp/twitter-xlm-roberta-base-sentiment #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### 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 ### Framework versions


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