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license: mit
language: en
pipeline_tag: token-classification
tags:
- ner
- resume-parsing
- cv-parser
base_model: bert-base-uncased
---
# 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`
|