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Kemenkeu-Sentiment-Classifier
hanifnoerr/Kemenkeu-Sentiment-Classifier
Kemenkeu-Sentiment-Classifier is a text classification model from hanifnoerr. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the MoF-DAC Mini Challenge1 dataset. It achieves the following results on the evaluation set: - Accuracy: 0.66 - F1: 0.6368
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Apr 8, 2023
- Updated
- Apr 8, 2023
- SHA
- 06eaeaf7
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Model card
From the Hugging Face model README
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
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
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Languages
- id
- Created
- Apr 8, 2023
- Updated
- Apr 8, 2023