| ---
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| license: mit
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| language: en
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| pipeline_tag: token-classification
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| tags:
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| - ner
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| - resume-parsing
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| - cv-parser
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| base_model: bert-base-uncased
|
| ---
|
|
|
| # CV Parser NER — bert-base-uncased (v2)
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|
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| Token-classification model that extracts **Job Titles**, **Skills**, and
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| **Education** from resumes/CVs using a BIO tag scheme.
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|
|
| ## Provenance
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| - **Trained from scratch on dataset 4** (`resume_bio_annotated_full.csv`,
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| 2,483 resumes — 1,739 train / 372 val / 372 test), the team's finalized
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| AI-Studio/Vertex-relabelled dataset.
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| - Reproduced end-to-end with the project notebooks/scripts
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| (`retokenize.py` + `train_bert_run.py`).
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| - Base model: `bert-base-uncased` · epochs: 4 · learning rate: 2e-5 ·
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| max_length 512 · stride 128 · seed 42.
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|
|
| ## Resume-level performance (dataset-4 splits)
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| | split | precision | recall | F1 |
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| |-------|-----------|--------|----|
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| | validation | — | — | 0.5540 |
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| | test | — | — | 0.5852 |
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|
|
| ## Labels
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| `O, B-JOB_TITLE, I-JOB_TITLE, B-SKILL, I-SKILL, B-EDUCATION, I-EDUCATION`
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| |
