The AI technology implementation of self.so consists of three key components:
- information extraction layer: Based on Together.ai's Qwen 2.5 72B large language model specifically trained for resume parsing. The model ensures accuracy by:
- Structured Output: Force JSON format data generation, with canonical fields such as "company", "position", etc.
- Contextual understanding: the ability to correlate timeline information and intelligently complete incomplete date formats
- Multi-language support: can handle mixed resume content in Chinese/English/Japanese and other languages
- security filtration layer: Protect your system by detecting uploaded files with Llama Guard and blocking documents that may contain malicious code.
- data validation layer::
- Front-end validation: checking the integrity of required fields (e.g. name must be present)
- Manual correction: users can manually edit AI-generated content
- Preview mechanism: real-time display of parsing results to facilitate timely adjustments
In the actual test, the recognition accuracy of standard format resumes reaches more than 92%, and non-conventional formats can also maintain an accuracy of about 85%. When users encounter parsing deviations, they can quickly correct them through the editing interface.
This answer comes from the articleself.so: Generate a beautiful CV page with one clickThe
May not be reproduced without permission:AI productivity tools " How does self.so's AI technology work? How does it ensure the accuracy of information extraction?
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