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+# Vehicle User Instructions Classification - BERT (Chinese)
+
+This repository contains a fine-tuned BERT model for classifying vehicle user instructions in Chinese. The model is trained on a dataset of user instructions related to various vehicle control commands.
+## Preface
+This fine-tuned model is for Our team's UOW CSIT998 Professional Capstone Project.
+## Dataset
+
+The dataset used for training and evaluation consists of Chinese text instructions corresponding to different vehicle control commands. The distribution of the dataset is as follows:
+
+- Training set: 4499 samples
+- Validation set: 2249 samples
+- Test set: 2250 samples
+
+The instructions cover a range of vehicle control commands, including:
+
+```
+{'开车窗': 0, '关左车门': 1, '关右前车窗': 2, '关闭引擎': 3, '关左前车窗': 4, '开右前车窗': 5, '关左后车窗': 6, '开左后车窗': 7, '开后备箱': 8, '关车门': 9, '关车窗': 10, '开左前车窗': 11, '关右后车窗': 12, '开敞篷': 13, '开左侧车窗': 14, '关敞篷': 15, '喇叭': 16, '开右后车窗': 17, '开右车门': 18, '停车点1': 19, '关后备箱': 20, '关右车门': 21, '开左车门': 22, '停车点2': 23, '开车门': 24, '打开引擎': 25, '关左侧车窗': 26}
+```
+
+## Model
+
+The model is based on the pre-trained Chinese BERT model (`bert-base-chinese`). It has been fine-tuned on the vehicle user instructions dataset using the following training arguments:
+
+```python
+training_args = TrainingArguments(
+    output_dir='',
+    do_train=True,
+    do_eval=True,
+    num_train_epochs=3,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=32,
+    warmup_steps=100,
+    weight_decay=0.01,
+    logging_strategy='steps',
+    logging_dir='',
+    logging_steps=50,
+    evaluation_strategy="steps",
+    eval_steps=50,
+    save_strategy="steps",
+    save_steps=200,
+    fp16=True,
+    load_best_model_at_end=True
+)
+```
+
+## Training Results
+
+The model was trained for 3 epochs, and the training progress can be summarized as follows:
+
+| Step | Training Loss | Validation Loss | Accuracy | F1     | Precision | Recall  |
+|------|---------------|-----------------|----------|--------|-----------|---------|
+| 50   | 3.257000      | 2.964479        | 0.168519 | 0.089801 | 0.229036 | 0.126555 |
+| 100  | 2.525000      | 1.711695        | 0.648288 | 0.532127 | 0.595545 | 0.590985 |
+| 150  | 1.197200      | 0.628560        | 0.921298 | 0.888212 | 0.892879 | 0.890719 |
+| ...  | ...           | ...             | ...      | ...    | ...       | ...     |
+| 8000 | 0.045900      | 0.136842        | 0.969320 | 0.969658 | 0.969638 | 0.970056 |
+
+## Evaluation
+
+The trained model was evaluated on the training, validation, and test sets, achieving the following performance:
+
+|       | eval_loss | eval_Accuracy | eval_F1  | eval_Precision | eval_Recall |
+|-------|-----------|---------------|----------|----------------|-------------|
+| train | 0.036020  | 0.991331      | 0.991048 | 0.991615       | 0.990673    |
+| val   | 0.136842  | 0.969320      | 0.969658 | 0.969638       | 0.970056    |
+| test  | 0.126695  | 0.974222      | 0.975473 | 0.975814       | 0.975435    |
+
+The model achieves high accuracy, F1 score, precision, and recall on all three datasets, indicating its effectiveness in classifying vehicle user instructions.
+
+## Usage
+
+To use the fine-tuned model for inference, you can utilize the Hugging Face Inference API. Here's an example of how to make a request to the API using Python:
+
+```python
+import requests
+
+API_URL = "https://api-inference.huggingface.co/models/lindsey-chang/vehicle-user-instructions-classification-bert-chinese"
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
+
+# Example usage
+input_text = "请打开车窗"
+output = query({"inputs": input_text})
+print(output)
+```
+
+Replace `your-username` with your Hugging Face username and `API_TOKEN` with your personal API token, which you can create in your Hugging Face account settings.
+
+The model will return the predicted class index for the input instruction. You can map the class index back to the corresponding vehicle control command using the provided class labels.
+