Is it possible to create a Résumé parser using a Huggingface model?

Jungroo-AI released ReDefiNER, a 0.8B-parameter model under Apache 2.0 that extracts entities defined in a plain-English rulebook and runs on a CPU, as an alternative to training a résumé parser. The model returns exact spans with a type and confidence, but spans stop at 16 tokens, so it suits fields rather than whole paragraphs, and grouping spans into Education and Experience entries remains the caller's code. The release matters for developers who have résumé text and want field extraction without training a custom model.

For anyone reading this now: if you have the résumé as text, you can try a model that takes the field definitions as input instead of training one. I built ReDefiNER HF - jungroo-ai/redefiner-0.8b · Hugging Face https://huggingface.co/jungroo-ai/redefiner-0.8b GitHub - Jungroo-AI/redefiner: A 0.8B model that extracts the entities you define in a plain-English rulebook. It runs on a CPU and is free under Apache 2.0. · GitHub http://github.com/jungroo-ai/redefiner , Apache 2.0, 0.8B, runs on a CPU . You list the types and rules in plain English: RULES Extract every entity of the supplied types. Return exact spans from the text. TYPE DEGREE DESCRIPTION A degree or qualification, such as BS, MS or PhD. TYPE SCHOOL DESCRIPTION A university, college or school. TYPE COMPANY DESCRIPTION An employer. CO1 Do not mark schools or clients as COMPANY. TYPE DATE RANGE DESCRIPTION A year or range of years, such as 2012-2016 or 2016 - Present. It returns exact spans with a type and a confidence. Grouping them into Education and Experience entries is still your code, for example by section headings and line order. Spans stop at 16 tokens, so it suits fields, not whole paragraphs. You can also add precedence and examples for good accuracy on complex rules.