language:
- id
metrics:
- accuracy
- precision
- recall
- f1
base_model:
- indobenchmark/indobert-base-p1
pipeline_tag: text-classification
library_name: transformers
tags:
- NLP
- indobert
- sentimen
IndoBERT Sentiment Analysis Model
Model ini adalah hasil fine-tuning model IndoBERT base untuk tugas klasifikasi sentimen bahasa Indonesia.
Penggunaan
Use a pipeline as a high-level helper
from transformers import pipeline pipe = pipeline("text-classification", model="Ha1dir/sentimen-indobert")
Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ha1dir/sentimen-indobert") model = AutoModelForSequenceClassification.from_pretrained("Ha1dir/sentimen-indobert")
Label Kelas
- 0: Positive
- 1: Negative
- 2: Neutral
Tentang Model
- Base Model: indobenchmark/indobert-base-p1
- Training Epochs: 5
- Optimizer: Adam, LR = 3e-6
Hasil Training
(Epoch 1) TRAIN LOSS: 0.2962 Acc: 0.8896 Precision: 0.8893 Recall: 0.8896 F1: 0.8876
(Epoch 2) TRAIN LOSS: 0.1450 Acc: 0.9514 Precision: 0.9513 Recall: 0.9514 F1: 0.9513
(Epoch 3) TRAIN LOSS: 0.1053 Acc: 0.9646 Precision: 0.9646 Recall: 0.9646 F1: 0.9646
(Epoch 4) TRAIN LOSS: 0.0722 Acc: 0.9781 Precision: 0.9781 Recall: 0.9781 F1: 0.9781
(Epoch 5) TRAIN LOSS: 0.0468 Acc: 0.9874 Precision: 0.9874 Recall: 0.9874 F1: 0.9874
Validasi Mode
VAL LOSS: 0.0234 Acc: 0.9955 Precision: 0.9955 Recall: 0.9955 F1: 0.9954
Evaluasi
VAL LOSS: 0.0234 Acc: 0.9982 Precision: 0.9982 Recall: 0.9982 F1: 0.9982