feverlash/Indonesian-SentimentAnalysis-Model

Indonesian Text Sentiment Analysis 🚀

📌 Overview

This project fine-tunes a transformer-based model to analyze sentiment for Indonesian text.

📥 Data Collection

The dataset used for fine-tuning was sourced from IndoNLU Datasets, specifically:
SmSA (IndoNLU) Dataset

🔄 Data Preparation

• Tokenization:
  • Used Indobert for efficient text processing.
• Train-Test Split:
  • The Dataset is already splitted into train, validation, and test.

🏋️ Fine-Tuning & Results

The model was fine-tuned using TensorFlow Hugging Face Transformers.

📊 Evaluation Metrics

Epoch	Train Loss	Train Accuracy	Eval Loss	Eval Accuracy	Training Time	Validation Time
1	0.2471	88.15%	0.2107	91.31%	7:55 min	10 sec
2	0.1844	90.41%	0.2107	92.39%	7:50 min	10 sec
3	0.1502	91.66%	0.2135	93.14%	7:51 min	9 sec
4	0.1285	92.50%	0.2192	93.69%	7:50 min	10 sec
5	0.1101	93.13%	0.2367	94.14%	7:48 min	9 sec

⚙️ Training Parameters

epochs = 5

learning_rate = 5e-5

seed_val = 42

max_length = 128

batch_size = 32

eval_batch_size = 32

🤖 How to use

import tensorflow as tf
from transformers import TFAutoModelForSequenceClassification, AutoTokenizer

# Load model dan tokenizer
model_name = "feverlash/Indonesian-SentimentAnalysis-Model"  # Ganti dengan path model yang telah disimpan
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModelForSequenceClassification.from_pretrained(model_name)

# Fungsi untuk melakukan prediksi sentimen
def predict(text):
    sentiment_mapping = {
        1: "positive",
        0: "negative",
        2: "neutral"
    }
    
    # Tokenisasi teks
    inputs = tokenizer(
        text,
        return_tensors="tf",
        truncation=True,
        padding="max_length",
        max_length=128
    )
    
    # Prediksi menggunakan model
    outputs = model(inputs)
    logits = outputs.logits

    # Menghitung probabilitas
    probabilities = tf.nn.softmax(logits).numpy()
    
    # Menentukan label prediksi
    predicted_index = int(tf.argmax(probabilities, axis=1).numpy()[0])
    predicted_label = sentiment_mapping.get(predicted_index, "unknown")
    
    # Keyakinan prediksi
    confidence = probabilities[0][predicted_index]

    print(f"Teks: {text}")
    print(f"Prediksi label: {predicted_label} (Confidence: {confidence:.2f})")

# Contoh penggunaan
text = "aku sedang jalan-jalan di Yogyakarta"
predict(text)