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

> Source: <https://dev.to/adriens/find-the-pokemon-you-are-w-pokeapi-your-resume-embeddings-3bb5>
> Published: 2026-07-12 08:00:47+00:00

*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.