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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Add section-aware chunked inference for resumes exceeding 512 tokens
Somasundaram Ayyappan +1: Claude Opus 4.6 (1M context)Splits text at paragraph boundaries (double newlines) and packs sections into chunks that fit within the model's context window. Character offsets are mapped back to the original text. Falls back to single-pass for short inputs. Benchmark uses chunked inference — no regression on val set. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>f10912e -
Improve structured benchmark analysis and robustness
Somasundaram Ayyappan4129d85 -
Clean up training pipeline and add export benchmarks
Somasundaram Ayyappan750e1a2