---
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