Zeqhx/cv-parser-bert-v2

CV Parser NER — bert-base-uncased (v2)

Token-classification model that extracts Job Titles, Skills, and Education from resumes/CVs using a BIO tag scheme.

Provenance

• Trained from scratch on dataset 4 (resume_bio_annotated_full.csv, 2,483 resumes — 1,739 train / 372 val / 372 test), the team's finalized AI-Studio/Vertex-relabelled dataset.
• Reproduced end-to-end with the project notebooks/scripts (retokenize.py + train_bert_run.py).
• Base model: bert-base-uncased · epochs: 4 · learning rate: 2e-5 · max_length 512 · stride 128 · seed 42.

Resume-level performance (dataset-4 splits)

split	precision	recall	F1
validation	—	—	0.5540
test	—	—	0.5852

Labels

O, B-JOB_TITLE, I-JOB_TITLE, B-SKILL, I-SKILL, B-EDUCATION, I-EDUCATION