Token Classification
Transformers
ONNX
Safetensors
English
distilbert
resume-parsing
ner
resume
cv
information-extraction
Instructions to use oksomu/resume-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oksomu/resume-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="oksomu/resume-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("oksomu/resume-ner") model = AutoModelForTokenClassification.from_pretrained("oksomu/resume-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Make text preprocessor config-driven via resume_config.json
Somasundaram Ayyappan +1: Claude Opus 4.6 (1M context)All preprocessing rules now in pre_processing config section: - dash_replacements: em-dash → hyphen mappings - bullet_chars + bullet_replacement: bullet normalization - strip_labels: remove "Phone:", "Email:" prefixes - skill_table_categories: known category keywords for table expansion - table_prose_max_words: threshold to skip prose lines - table_continuation_max_chars: max length for continuation lines ResumeTextPreprocessor class reads config on init. Any language implementation can apply same rules from the JSON config. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>e6b7b6f -
Add text preprocessor for PDF extraction artifacts
Somasundaram Ayyappan +1: Claude Opus 4.6 (1M context)Normalizes CRLF, em-dashes, bullets, phone/email labels, and detects flattened two-column skill tables using known category keywords. Converts 'Category Skill1, Skill2' to 'Category: Skill1, Skill2'. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>b34f048