singhsourabh/Resume-NER
Applying BERT for named entity recognition on resumes.
This tool helps HR professionals and recruiters quickly extract key details from resumes. You provide a resume document, and it automatically identifies and categorizes information like the candidate's name, designation, degrees, skills, and years of experience. This allows for faster screening and data entry, streamlining the hiring process.
No commits in the last 6 months.
Use this if you need to rapidly process a large volume of resumes and automatically pull out specific information without manual review.
Not ideal if you only handle a few resumes occasionally or require highly nuanced, subjective evaluations beyond simple data extraction.
Stars
68
Forks
26
Language
Python
License
MIT
Category
Last pushed
Jun 12, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/singhsourabh/Resume-NER"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
chakki-works/seqeval
A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
Hironsan/anago
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.
jbesomi/texthero
Text preprocessing, representation and visualization from zero to hero.
hamelsmu/ktext
Utilities for preprocessing text for deep learning with Keras
asahi417/tner
Language model fine-tuning on NER with an easy interface and cross-domain evaluation. "T-NER: An...