---
license: apache-2.0
language:
- zh
- en
tags:
  - intent-classification
  - chinese
  - albert
  - binary-classification
  - transformers
  - diner-call
base_model:
- ckiplab/albert-base-chinese
datasets:
  - Luigi/dinercall-intent
pipeline_tag: text-classification
model-index:
  - name: albert-base-chinese-dinercall-intent
    results:
      - task:
          name: Intent Classification
          type: text-classification
        dataset:
          name: DinerCall Intent
          type: Luigi/dinercall-intent
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.85
---

# albert-base-chinese-dinercall-intent

A binary intent classification model fine-tuned from [`ckiplab/albert-base-chinese`](https://huggingface.co/ckiplab/albert-base-chinese) on the [`Luigi/dinercall-intent`](https://huggingface.co/datasets/Luigi/dinercall-intent) dataset. This model identifies whether a Chinese restaurant phone call contains a reservation intent (`label=1`) or not (`label=0`).

## Model Details

- **Base model**: `ckiplab/albert-base-chinese`
- **Task**: Binary intent classification
- **Language**: Traditional Chinese
- **Labels**:
  - `0`: No intent to book a table
  - `1`: Intent to make a reservation

## Use Cases

This model is designed for voice AI assistants in restaurants to automatically identify reservation intents from spoken or transcribed customer sentences.

## Example Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("Luigi/albert-base-chinese-dinercall-intent")
model = AutoModelForSequenceClassification.from_pretrained("Luigi/albert-base-chinese-dinercall-intent")

inputs = tokenizer("你好，我想訂位，今天晚上七點兩位", return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
    predicted = torch.argmax(logits, dim=-1).item()

print(f"Prediction: {'Reservation intent' if predicted == 1 else 'No intent'}")