hanifnoerr/Kemenkeu-Sentiment-Classifier

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

Framework versions

• —Transformers 4.27.4
• —Pytorch 2.0.0+cu118
• —Datasets 2.11.0
• —Tokenizers 0.13.3