🤗 Find the Pokemon you are w. PokéAPI, your resume & embeddings

A developer built an open-source tool that matches a person's resume to a Pokémon character using embeddings and a reranker. The project, hosted on Hugging Face Spaces, uses the BAAI/bge-m3 model to embed resume sections and Pokémon data from the PokeAPI, then applies cosine similarity and a cross-encoder for precise matching. The tool runs entirely on a laptop with no GPU, relying on pure open-source software.

This is a submission for Weekend Challenge: Passion Edition https://dev.to/challenges/weekend-2026-07-09 ❔ What I Built These two last weeks, my team mates started to to use Claude Code together with they yearly review : to discover which Pokemon they are... and why . I found that really really fun... and started to wonder if I could automate that with only onPrem resources, with embeddings, ML... only with a simple laptop without GPU, a simple core i5 and 8 Gib. And of course, only with pure Open Source software ❣️ What you'll discover below is how I started to prototype it and make it happen. 🍿 Demo 🤗 Code The whole code source is available as a HF Space, see rastadidi/resume-to-pokemon for more... or to play wth it 🤓 🧰 How I Built It To achieve this first prototype I : - Used the data I already prepared with my registry.jsonresume.org/adriens - Bundled dataset built once . build dataset.py fetches every species from the PokeAPI https://pokeapi.co/docs/v2 — name, types, base stats, sprite, genus and English Pokedex flavor text. For each Pokemon it also derives a professional-archetype profile from its types and stat spread e.g. a Steel type → "a disciplined, precise, robust engineer of structured systems" , so career resumes and monster biology meet in the same trait vocabulary. Description + profile are embedded with BAAI/bge-m3 and committed as data/pokemon.json + data/embeddings.npy — so the app makes no PokeAPI calls at runtime . - Resume → phrases. Sections that carry semantic signal — basics.summary , skills , work / volunteer , projects , interests — are each turned into a short phrase and embedded with the same model. Administrative sections like education, certificates and languages are skipped. - Retrieve → rerank. Cosine similarity over the embeddings retrieves a shortlist of the closest Pokemon; a cross-encoder BAAI/bge-reranker-v2-m3 then re-scores the resume, Pokemon pairs jointly for much sharper precision than cosine alone. The tool explains why by quoting the matched resume phrase and the Pokemon's own profile + Pokedex text. - Ranking + relative fit. Pokemon are ranked by a blend of the rerank match and their base stats adjustable . Because a broad resume matches many Pokemon similarly, raw scores cluster tightly and are unreadable — so the reported score is a relative fit : rerank scores are standardized across the shortlist and spread through a sigmoid, so the top clearly stands out ~100% and the tail drops off. It's a fit relative to the candidate pool , not an absolute probability. The two best-fitting types are derived from the same shortlist, so they always agree with the ranked Pokemon. - Calibrated confidence. Instead of a raw similarity number, a read-out reports how far the top match stands out from the field a z-score over the shortlist , flagging decisive vs. diffuse, multi-type profiles. Type scores and the ranked Pokemon are derived from the same reranked shortlist, so the "best-fit typing" always agrees with the cards.