
# Kemenkeu-Sentiment-Classifier

This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the MoF-DAC Mini Challenge#1 dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.66
- F1: 0.6368

Leaderboard score:
- Public score: 0.63733
- Private score: 0.65733

## Model description & limitations

- This model can be used to classify text with four possible outputs [netral, tdk-relevan, negatif, and positif]
- only for specific cases related to the Ministry Of Finance Indonesia

## How to use
You can use this model directly with a pipeline

```python
pretrained_name = "hanifnoerr/Kemenkeu-Sentiment-Classifier"
class_model = pipeline(tokenizer=pretrained_name, model=pretrained_name)

test_data = "Mengawal APBN, Indonesia Maju"
class_model(test_data)
```

## Training and evaluation data

The following hyperparameters were used during training:
- learning_rate: 1e-05
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0131        | 1.0   | 500  | 0.8590          | 0.644    | 0.5964 |
| 0.7133        | 2.0   | 1000 | 0.8639          | 0.63     | 0.5924 |
| 0.5261        | 3.0   | 1500 | 0.9002          | 0.66     | 0.6368 |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
{"language": ["id"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "pipeline_tag": "text-classification", "widget": [{"text": "sudah beli makan buat sahur?", "example_title": "contoh tidak relevan"}, {"text": "Mengawal APBN, Indonesia Maju", "example_title": "contoh kalimat"}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer id doi:10.57967/hf/0520 license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #id #doi-10.57967/hf/0520 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

Kemenkeu-Sentiment-Classifier
=============================


This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the MoF-DAC Mini Challenge#1 dataset.
It achieves the following results on the evaluation set:


* Accuracy: 0.66
* F1: 0.6368


Leaderboard score:


* Public score: 0.63733
* Private score: 0.65733


Model description & limitations
-------------------------------


* This model can be used to classify text with four possible outputs [netral, tdk-relevan, negatif, and positif]
* only for specific cases related to the Ministry Of Finance Indonesia


How to use
----------


You can use this model directly with a pipeline


Training and evaluation data
----------------------------


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #id #doi-10.57967/hf/0520 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #id #doi-10.57967/hf/0520 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3