Fine-tuned-Indonesian-Sentiment-Classifier

This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the IndoNLU's SmSA dataset. It achieves the following results on the evaluation dataset:

• Loss: 0.3233
• Accuracy: 0.9317
• F1: 0.9034

And the results of the test dataset:

• Accuracy: 0.928
• F1 macro: 0.9113470780757361
• F1 micro: 0.928
• F1 weighted: 0.9261959965604815

Model description

This model can be used to determine the sentiment of a text with three possible outputs [positive, negative, or neutral]

How to use

from transformers import AutoTokenizer, AutoModelForSequenceClassification

Pre-trained = "hanifnoerr/Fine-tuned-Indonesian-Sentiment-Classifier"
tokenizer = AutoTokenizer.from_pretrained(Pre-trained)
model = AutoModelForSequenceClassification.from_pretrained(Pre-trained)

make classification

pretrained_name = "hanifnoerr/Fine-tuned-Indonesian-Sentiment-Classifier"
sentimen = pipeline(tokenizer=pretrained_name, model=pretrained_name)

kalimat = "buku ini jelek sekali"
sentimen(kalimat)

output: [{'label': 'negative', 'score': 0.9996247291564941}]

Training results

Although trained with 18 epochs, this model uses the best weight (Epoch 2)

Framework versions

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