| --- |
| license: mit |
| tags: |
| - resume-parsing |
| - information-extraction |
| - token-classification |
| - pytorch |
| - minilm |
| library_name: pytorch |
| --- |
| |
| # Parselex — Resume Parsing Model Weights |
|
|
| 13-stage PyTorch checkpoint set for [Parselex](https://github.com/karan-963/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 |
|
|
| ```bash |
| 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: |
|
|
| ```python |
| 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`](https://github.com/karan-963/parselex/blob/main/LICENSE). |
|
|
