---
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


---