Parselex — Resume Parsing Model Weights
13-stage PyTorch checkpoint set for Parselex, a deterministic, offline resume-to-JSON extraction pipeline. No LLM involved — each stage is a small MiniLM-backbone classifier (~23M params) fine-tuned for one step of the pipeline (section detection, entry boundary detection, field classification, run separately per resume section).
This repo hosts weights only — no inference code. Clone the code repo and point it at these weights: https://github.com/karan-963/parselex
Contents
Both FP32 and INT8 (quantized) checkpoints for all 13 stages, ~2.5GB total:
| Folder | Stage | Files |
|---|---|---|
section_p1/ |
Section heading detection | best_model_line_minilm.pt, best_model_line_minilm_int8.pt |
section_p2/ |
Section classification | best_model.pt, best_model_int8.pt |
education_phase1_segment/ |
Education phrase segmentation | best_model.pt, best_model_int8.pt |
education_phase2_divider/ |
Education entry boundaries | best_model.pt, best_model_int8.pt |
education_phase3_classify/ |
Education field classification | best_model.pt, best_model_int8.pt |
experience_phase1_segment/ |
Experience phrase segmentation | best_model.pt, best_model_int8.pt |
experience_phase2_divider/ |
Experience entry boundaries | best_model.pt, best_model_int8.pt |
experience_phase3_classify/ |
Experience field classification | best_model.pt, best_model_int8.pt |
project_phase1_segment/ |
Project phrase segmentation | best_model.pt, best_model_int8.pt |
project_phase2_divider/ |
Project entry boundaries | best_model.pt, best_model_int8.pt |
project_phase3_classify/ |
Project field classification | best_model.pt, best_model_int8.pt |
skills_classify/ |
Skills BIO tagging | best_model.pt, best_model_int8.pt |
personal_classify/ |
Personal info BIO tagging | best_model.pt, best_model_int8.pt |
best_model.pt = FP32 checkpoint. best_model_int8.pt = quantized (smaller, faster, slightly
lower accuracy). Folder layout matches model_weights/<stage>/ in the code repo exactly — no
renaming needed to use these.
Usage
git clone https://github.com/karan-963/parselex
cd parselex/model_weights
python3 download.py # pulls this repo via huggingface_hub, extracts into place
Or manually:
from huggingface_hub import snapshot_download
snapshot_download(repo_id="karan963/parselex-weights", local_dir="model_weights")
Training data & accuracy
See the paper for full methodology, held-out test numbers, and known limitations (small
training set, single-column resumes only, synthetic-data ceiling):
<arXiv link — filled in once submitted>
License
MIT — same as the code repository. See LICENSE.