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README.md

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+---
+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).