| --- |
| language: |
| - es |
| - en |
|
|
| license: apache-2.0 |
|
|
| pipeline_tag: text-classification |
|
|
| library_name: transformers |
|
|
| tags: |
| - bert |
| - distilbert |
| - multilingual |
| - text-classification |
| - onnx |
| - android |
| - offline |
| --- |
| |
| # Travel Intent Classifier |
|
|
| Multilingual Multi-Task Intent Classifier for travel and local place recommendations. |
|
|
| The model is designed to classify natural language queries into travel-related intents and provide additional semantic information through multiple prediction heads. |
|
|
| ## Features |
|
|
| - 🌍 Multilingual (DistilBERT Multilingual) |
| - ⚡ Optimized for Android |
| - 📱 Offline inference with ONNX Runtime |
| - 🧠 Multi-Task architecture |
| - 🚀 Quantized ONNX model available |
|
|
| --- |
|
|
| # Predictions |
|
|
| The model performs three independent predictions from a single input. |
|
|
| ## 1. Intent |
|
|
| Examples: |
|
|
| - RESTAURANT |
| - CAFE |
| - FAST_FOOD |
| - BAR |
| - PUB |
| - SUPERMARKET |
| - PHARMACY |
| - HOSPITAL |
| - FUEL |
| - MUSEUM |
| - GALLERY |
| - PARK |
| - GARDEN |
| - VIEWPOINT |
| - CINEMA |
| - THEATRE |
| - MALL |
| - ATTRACTION |
| - NONE |
| |
| --- |
| |
| ## 2. Subcategory |
| |
| Predicts a more specific semantic category. |
| |
| Examples: |
| |
| - ITALIAN |
| - JAPANESE |
| - BURGER |
| - SEAFOOD |
| - VEGAN |
| |
| ... |
| |
| --- |
| |
| ## 3. Restaurant Type |
| |
| Additional prediction used only for restaurant-related queries. |
| |
| --- |
| |
| # Architecture |
| |
| ``` |
| User text |
| │ |
| ▼ |
| DistilBERT Multilingual |
| │ |
| ├────────► Intent |
| │ |
| ├────────► Subcategory |
| │ |
| └────────► Restaurant Type |
| ``` |
| |
| --- |
| |
| # Model Outputs |
| |
| The exported ONNX model exposes three outputs: |
| |
| ``` |
| label_logits |
| subcategory_logits |
| restaurant_type_logits |
| ``` |
| |
| Each output should be converted into probabilities using Softmax. |
| |
| --- |
| |
| # Confidence Gate |
| |
| The application using this model should apply a post-processing stage before returning the prediction. |
| |
| Recommended validations include: |
| |
| - Minimum confidence threshold |
| - Dynamic Top-1 / Top-2 gap validation |
| - Semantic consistency validation between the three prediction heads |
| |
| If the prediction does not satisfy these conditions, the recommended output is: |
| |
| ``` |
| NONE |
| ``` |
| |
| --- |
| |
| # ONNX |
| |
| The repository also provides an ONNX version optimized for mobile inference. |
| |
| Recommended files for Android: |
| |
| ``` |
| model_quantized.onnx |
| tokenizer.onnx |
| metadata.json |
| ``` |
| |
| --- |
| |
| # Intended Use |
| |
| This model is intended for: |
| |
| - Travel assistants |
| - Tourism applications |
| - Offline recommendation systems |
| - Android applications |
| - Kotlin Multiplatform projects |
| |
| --- |
| |
| # Training |
| |
| The model was trained using a custom multilingual dataset containing travel-related natural language requests. |
| |
| The training pipeline automatically generates: |
| |
| - PyTorch model |
| - ONNX model |
| - Quantized ONNX model |
| - ONNX tokenizer |
| - Metadata mappings |
| |
| --- |
| |
| # License |
| |
| Apache 2.0 |
