# 本地 BERT 意图识别测试报告

## 概览
- 模型目录：`/Users/hwp/Documents/trae_projects/intelligent_cabin/models/local_bert_intent`
- 评测集：`/Users/hwp/Documents/trae_projects/intelligent_cabin/app/data/bert_intent_eval_independent.jsonl`
- 评测阈值：`0.0`
- 测试样本数：`42`
- 总体准确率：`0.9762`

## 训练摘要
- 基座模型：`hfl/chinese-macbert-base`
- 训练集 / 验证集：`1557 / 401`
- 最佳验证准确率：`0.9875`
- 训练设备：`mps`

## 分类别结果
- `business`: 33/34 = 0.9706
- `out_of_scope`: 4/4 = 1.0
- `social`: 4/4 = 1.0

## 分标签结果
- `__out_of_scope__` (out_of_scope): 4/4 = 1.0
- `__social__` (social): 4/4 = 1.0
- `cabin_ac_off` (business): 1/1 = 1.0
- `cabin_ac_on` (business): 1/1 = 1.0
- `cabin_defog_front_on` (business): 1/1 = 1.0
- `cabin_defog_rear_on` (business): 1/1 = 1.0
- `cabin_fan_down` (business): 1/1 = 1.0
- `cabin_fan_up` (business): 1/1 = 1.0
- `cabin_lights_off` (business): 1/1 = 1.0
- `cabin_lights_on` (business): 1/1 = 1.0
- `cabin_lock_doors` (business): 1/1 = 1.0
- `cabin_mirror_fold` (business): 1/1 = 1.0
- `cabin_mirror_unfold` (business): 1/1 = 1.0
- `cabin_nav_cancel` (business): 1/1 = 1.0
- `cabin_nav_to` (business): 1/1 = 1.0
- `cabin_next_track` (business): 1/1 = 1.0
- `cabin_pause_music` (business): 1/1 = 1.0
- `cabin_play_music` (business): 1/1 = 1.0
- `cabin_previous_track` (business): 1/1 = 1.0
- `cabin_seat_heat_off` (business): 1/1 = 1.0
- `cabin_seat_heat_on` (business): 1/1 = 1.0
- `cabin_set_ac` (business): 1/1 = 1.0
- `cabin_sunroof_close` (business): 1/1 = 1.0
- `cabin_sunroof_open` (business): 1/1 = 1.0
- `cabin_unlock_doors` (business): 1/1 = 1.0
- `cabin_volume_down` (business): 1/1 = 1.0
- `cabin_volume_mute` (business): 1/1 = 1.0
- `cabin_volume_up` (business): 1/1 = 1.0
- `cabin_window_close` (business): 1/1 = 1.0
- `cabin_window_open` (business): 0/1 = 0.0
- `cabin_wiper_off` (business): 1/1 = 1.0
- `cabin_wiper_on` (business): 1/1 = 1.0
- `cs_cancel_order` (business): 1/1 = 1.0
- `cs_query_logistics` (business): 1/1 = 1.0
- `cs_query_order` (business): 1/1 = 1.0
- `cs_transfer_human` (business): 1/1 = 1.0

## 错误样例
- 文本：`左前窗打开一点` | 类别：`business` | 期望：`cabin_window_open` | 预测：`cabin_defog_front_on` | 分数：`0.9951`

## 结论
- 当前本地 MacBERT 已具备较强的业务意图识别能力，可作为本地快链路分类器。
- 误判主要集中在方向相反或语义接近的控制指令，下一步应补充对抗样本和真实口语表达。
- 上线前建议继续补充 ASR 错字、多轮短句和多意图子句级样本。
