Complete solutions from selection to deployment
Building an intelligent customer service system involves three key aspects: model selection, data docking, and service deployment:
- Model selection phase: Filter NLP models (e.g. BCinks Intelligent Dialogue Model) in the Model Square and quickly match them with the "Applicable Scenarios" label, and it is recommended to select pre-trained models that have been labeled with "Intelligent Customer Service" scenarios.
- Data docking phase::
- Organize historical customer service conversations into CSV files in QA format (question columns + standard response columns)
- Use the model fine-tuning function to upload data to fine-tune the model, it is recommended to set the 5e-5 learning rate and 32 batch sizes to balance the effect with the speed
- System deployment phase::
- Get exclusive key through API docking function, SDK supports Java/Python/Go and other mainstream languages
- Calling examples (Python):
from volcengine.ark import ArkClient
client = ArkClient(api_key='your_key')
response = client.inference(model_id='Customer Service Model ID', input=User Question)
Note: The first time you use it, you can apply for 150 RMB computing power coupon to test the basic process, and the formal environment is recommended to turn on the data encryption function of "Security Management".
This answer comes from the articleVolcano Ark: Big Model Training and Cloud Computing Service, Sign Up for $150 Equivalent ArithmeticThe
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