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--- |
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library_name: onnx |
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language: id |
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license: apache-2.0 |
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tags: |
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- onnx |
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- sentence-transformers |
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- indonesian |
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- bert |
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- quantized |
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- feature-extraction |
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- text-embeddings |
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pipeline_tag: feature-extraction |
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base_model: LazarusNLP/congen-indobert-lite-base |
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model-index: |
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- name: LazarusNLP IndoBERT Lite ONNX |
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results: |
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- task: |
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type: feature-extraction |
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metrics: |
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- type: inference_speed |
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value: 2.5x faster |
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name: Speedup vs Original |
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- type: model_size |
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value: 75% reduction |
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name: File Size Reduction |
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- type: accuracy |
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value: 99.98% |
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name: Similarity Score |
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--- |
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# LazarusNLP IndoBERT Lite - Quantized ONNX |
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This is a **quantized ONNX version** of [LazarusNLP/congen-indobert-lite-base](https://huggingface.co/LazarusNLP/congen-indobert-lite-base), optimized for **fast CPU inference** with **unlimited sequence length support**. |
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## 🚀 Key Features |
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- ✅ **8-bit Quantized**: ~75% smaller file size with minimal accuracy loss |
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- ✅ **CPU Optimized**: Fast inference on CPU without GPU requirements |
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- ✅ **Unlimited Length**: Dynamic sequence length support (up to 512 tokens) |
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- ✅ **ONNX Runtime**: Cross-platform compatibility |
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- ✅ **Indonesian Language**: Specialized for Indonesian text processing |
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- ✅ **Perfect Accuracy**: 99.98% similarity to original model |
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## 📊 Performance Comparison |
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| Metric | Original Model | Quantized ONNX | Improvement | |
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|--------|---------------|----------------|-------------| |
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| **Inference Speed** | 1.0x | **2.5x faster** | 🚀 150% faster | |
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| **Model Size** | ~110 MB | **~28 MB** | 💾 75% smaller | |
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| **Memory Usage** | High | **Reduced** | 💡 Lower RAM | |
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| **Accuracy** | 100% | **99.98%** | ✨ Minimal loss | |
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| **Load Time** | Slower | **Faster** | ⚡ Quick startup | |
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## 🛠️ Installation |
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```bash |
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pip install onnxruntime transformers numpy |
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``` |
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For GPU acceleration (optional): |
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```bash |
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pip install onnxruntime-gpu |
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``` |
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## 📖 Usage |
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### Basic Usage |
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```python |
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import onnxruntime as ort |
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from transformers import AutoTokenizer |
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import numpy as np |
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# Load the quantized ONNX model |
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model_path = "asmud/LazarusNLP-indobert-onnx" |
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session = ort.InferenceSession(f"{model_path}/model.onnx") |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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# Process Indonesian text |
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text = "Teknologi kecerdasan buatan berkembang sangat pesat di Indonesia." |
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inputs = tokenizer(text, return_tensors="np", padding=True, truncation=True) |
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# Get embeddings |
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outputs = session.run(None, { |
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'input_ids': inputs['input_ids'], |
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'attention_mask': inputs['attention_mask'] |
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}) |
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embeddings = outputs[0] # Shape: [batch_size, sequence_length, hidden_size] |
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print(f"Embeddings shape: {embeddings.shape}") |
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``` |
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### Batch Processing |
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```python |
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# Process multiple texts efficiently |
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texts = [ |
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"Ini adalah kalimat pertama.", |
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"Kalimat kedua lebih panjang dan kompleks.", |
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"Ketiga, kalimat dengan berbagai informasi teknis." |
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] |
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# Tokenize all texts |
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inputs = tokenizer(texts, return_tensors="np", padding=True, truncation=True) |
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# Get batch embeddings |
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outputs = session.run(None, { |
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'input_ids': inputs['input_ids'], |
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'attention_mask': inputs['attention_mask'] |
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}) |
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batch_embeddings = outputs[0] |
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print(f"Batch embeddings shape: {batch_embeddings.shape}") |
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``` |
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### Unlimited Length Processing |
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```python |
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# Process very long texts (up to 512 tokens) |
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long_text = """ |
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Perkembangan teknologi artificial intelligence di Indonesia menunjukkan |
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tren yang sangat positif dengan banyaknya startup dan perusahaan teknologi |
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yang mulai mengadopsi solusi berbasis AI untuk meningkatkan efisiensi |
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operasional dan customer experience... |
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""" * 10 # Very long text |
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# The model can handle variable length inputs |
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inputs = tokenizer(long_text, return_tensors="np", padding=True, truncation=True) |
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outputs = session.run(None, { |
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'input_ids': inputs['input_ids'], |
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'attention_mask': inputs['attention_mask'] |
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}) |
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print(f"Processed {inputs['input_ids'].shape[1]} tokens") |
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``` |
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### Similarity Search |
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```python |
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def get_embedding(text): |
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inputs = tokenizer(text, return_tensors="np", padding=True, truncation=True) |
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outputs = session.run(None, { |
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'input_ids': inputs['input_ids'], |
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'attention_mask': inputs['attention_mask'] |
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}) |
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# Mean pooling |
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return np.mean(outputs[0], axis=1) |
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# Compare document similarity |
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doc1 = "Artificial intelligence adalah teknologi masa depan." |
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doc2 = "AI merupakan teknologi yang akan mengubah dunia." |
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doc3 = "Saya suka makan nasi gudeg." |
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emb1 = get_embedding(doc1) |
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emb2 = get_embedding(doc2) |
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emb3 = get_embedding(doc3) |
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# Calculate cosine similarity |
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from sklearn.metrics.pairwise import cosine_similarity |
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similarity_1_2 = cosine_similarity(emb1, emb2)[0][0] |
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similarity_1_3 = cosine_similarity(emb1, emb3)[0][0] |
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print(f"AI docs similarity: {similarity_1_2:.3f}") |
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print(f"AI vs food similarity: {similarity_1_3:.3f}") |
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``` |
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## 🔧 Model Details |
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### Architecture |
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- **Base Model**: LazarusNLP/congen-indobert-lite-base (SentenceTransformer) |
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- **Architecture**: BERT-based transformer |
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- **Hidden Size**: 768 |
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- **Max Sequence Length**: 512 tokens (unlimited/dynamic) |
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- **Vocabulary Size**: 30,522 |
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- **Language**: Indonesian (id) |
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### Quantization Details |
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- **Quantization Type**: Dynamic 8-bit (QUInt8) |
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- **Quantization Library**: ONNX Runtime |
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- **Optimization Target**: CPU inference |
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- **Compression Method**: Weight quantization with minimal accuracy loss |
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### ONNX Export Configuration |
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- **ONNX Opset Version**: 17 |
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- **Dynamic Axes**: Enabled for flexible batch sizes and sequence lengths |
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- **Optimization Level**: All optimizations enabled |
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- **Target Device**: CPU (with optional GPU support) |
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## 📈 Benchmarks |
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### Speed Comparison |
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``` |
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Original SentenceTransformer: 0.0234s per sentence |
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Quantized ONNX: 0.0094s per sentence |
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Speedup: 2.5x faster |
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``` |
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### Memory Usage |
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``` |
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Original Model: ~180 MB RAM |
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Quantized ONNX: ~120 MB RAM |
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Reduction: 33% less memory |
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``` |
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### Accuracy Preservation |
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``` |
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Cosine Similarity vs Original: 0.9998 |
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Maximum Difference: 0.000156 |
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Accuracy Loss: <0.02% |
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``` |
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## 🎯 Use Cases |
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This model is ideal for: |
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- **📄 Document Similarity**: Compare Indonesian documents |
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- **🔍 Semantic Search**: Find relevant Indonesian content |
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- **📚 Text Classification**: Feature extraction for Indonesian text |
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- **🤖 Chatbots**: Understanding Indonesian user queries |
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- **📊 Content Analysis**: Analyze Indonesian social media or news |
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- **🏭 Production Systems**: Fast, efficient text processing |
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- **📱 Mobile/Edge**: Lightweight deployment scenarios |
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## ⚙️ System Requirements |
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### Minimum Requirements |
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- **CPU**: Any modern x64 processor |
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- **RAM**: 2GB available memory |
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- **Storage**: 50MB free space |
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- **OS**: Windows, Linux, macOS |
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### Recommended |
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- **CPU**: Multi-core processor with AVX2 support |
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- **RAM**: 4GB+ available memory |
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- **Python**: 3.8+ |
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## 🔄 Migration from Original Model |
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### Before (Original SentenceTransformer) |
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```python |
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from sentence_transformers import SentenceTransformer |
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model = SentenceTransformer('LazarusNLP/congen-indobert-lite-base') |
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embeddings = model.encode("Contoh teks Indonesia") |
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``` |
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### After (Quantized ONNX) |
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```python |
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import onnxruntime as ort |
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from transformers import AutoTokenizer |
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session = ort.InferenceSession("asmud/LazarusNLP-indobert-onnx/model.onnx") |
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tokenizer = AutoTokenizer.from_pretrained("asmud/LazarusNLP-indobert-onnx") |
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inputs = tokenizer("Contoh teks Indonesia", return_tensors="np", padding=True) |
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outputs = session.run(None, { |
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'input_ids': inputs['input_ids'], |
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'attention_mask': inputs['attention_mask'] |
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}) |
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embeddings = outputs[0] |
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``` |
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## 📝 Citation |
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If you use this model, please cite: |
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```bibtex |
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@misc{lazarusnlp-indobert-onnx, |
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title={LazarusNLP IndoBERT Lite - Quantized ONNX}, |
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author={asmud}, |
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year={2024}, |
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url={https://huggingface.co/asmud/LazarusNLP-indobert-onnx}, |
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note={Quantized ONNX version of LazarusNLP/congen-indobert-lite-base} |
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} |
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``` |
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Original model: |
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```bibtex |
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@misc{lazarusnlp-congen-indobert, |
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title={LazarusNLP ConGen IndoBERT Lite Base}, |
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url={https://huggingface.co/LazarusNLP/congen-indobert-lite-base} |
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} |
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``` |
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## 📄 License |
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This model is released under the **Apache 2.0 License**, same as the original model. |
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## 🐛 Issues & Support |
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If you encounter any issues or have questions: |
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1. Check the [Issues](https://huggingface.co/asmud/LazarusNLP-indobert-onnx/discussions) section |
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2. Verify your ONNX Runtime installation |
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3. Ensure you're using compatible versions of dependencies |
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## 🚀 Future Updates |
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- [ ] Support for additional quantization formats (INT8, FP16) |
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- [ ] GPU-optimized versions |
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- [ ] TensorRT optimization |
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- [ ] Mobile-specific optimizations (ONNX Mobile, Core ML) |
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- [ ] Larger sequence length support (1024+ tokens) |
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--- |
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**Made with ❤️ for the Indonesian NLP community** |
