| --- |
| datasets: |
| - kornwtp/indonlu-smsa |
| language: |
| - id |
| metrics: |
| - accuracy |
| base_model: |
| - indobenchmark/indobert-base-p1 |
| pipeline_tag: text-classification |
| --- |
| |
| # 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](https://metatext.io/datasets/smsa-(indonlu)) |
|
|
| ## 🔄 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 |
|
|
| ```python |
| 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) |
