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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:

And the results of the test dataset:

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

Training Loss Epoch Step Validation Loss Accuracy F1
0.08 1.0 688 0.3532 0.9310 0.9053
0.0523 2.0 1376 0.3233 0.9317 0.9034
0.045 3.0 2064 0.3949 0.9286 0.8995
0.0252 4.0 2752 0.4662 0.9310 0.9049
0.0149 5.0 3440 0.6251 0.9246 0.8899
0.0091 6.0 4128 0.6148 0.9254 0.8928
0.0111 7.0 4816 0.6259 0.9222 0.8902
0.0106 8.0 5504 0.6123 0.9238 0.8882
0.0092 9.0 6192 0.6353 0.9230 0.8928
0.0085 10.0 6880 0.6733 0.9254 0.8989
0.0062 11.0 7568 0.6666 0.9302 0.9027
0.0036 12.0 8256 0.7578 0.9230 0.8962
0.0055 13.0 8944 0.7378 0.9270 0.8947
0.0023 14.0 9632 0.7758 0.9230 0.8978
0.0009 15.0 10320 0.7051 0.9278 0.9006
0.0033 16.0 11008 0.7442 0.9214 0.8902
0.0 17.0 11696 0.7513 0.9254 0.8974
0.0 18.0 12384 0.7554 0.9270 0.8999

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

Framework versions