metadata
license: mit
datasets:
- SetFit/amazon_massive_scenario_zh-CN
language:
- zh
metrics:
- accuracy
base_model:
- BAAI/bge-m3
BBCIM: BGE-Embedding Based Chinese Intent Model
Model Details
Model Description
A lightweight intent classification model for chinese. It is designed to be modular, easy to integrate, and optimized for both performance and inference speed.
You can easily influence the model on CPU.
- Developed by: [ken000666@outlook.com]
Model Sources
- Repository: Gihub Repo
Uses
from inference import EmbeddingBasedIntentModelWrapper
device = "cpu"
embedding_path = 'YOUR_PATH_TO_BGE_EMBEDDING'
model_checkpoint = "YOUR_PATH_TO_THE_MODEL"
model = EmbeddingBasedIntentModelWrapper(embedding_path, model_checkpoint, device)
while True:
input_text = input("Enter input: ")
result = model.classify(input_text)
print(result)
Results
| Intent | Accuracy |
|---|---|
| News | 0.847 |
| 0.963 | |
| IOT | 0.968 |
| Play | 0.946 |
| General | 0.608 |
| Calendar | 0.925 |
| Weather | 0.936 |
| QA | 0.878 |
| Takeway | 0.895 |
| Lists | 0.852 |
| Transports | 0.919 |
| Social | 0.877 |
| Datetime | 0.951 |
| Music | 0.840 |
| Cooking | 0.847 |
| Alram | 0.990 |
| Recommendation | 0.830 |
| Audio | 0.935 |
| Average | 0.889 |