metadata
language:
- en
tags:
- retrained
- SpanBERT
SpanBERT
This is the SpanBERT model from:
Mike Zhang, Kristian Nørgaard Jensen, Sif Dam Sonniks, and Barbara Plank. SkillSpan: Hard and Soft Skill Extraction from Job Postings. To appear at the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). 2022.
This model is pre-trained from scratch on the BookCorpus and WikiData. To pre-train from scratch we use the code from Splinter: https://github.com/oriram/splinter.
On our job posting dataset, we found that our spanbert-base-cased model works better than the original.
More information can be found in the paper (which should be released when the NAACL proceedings are online).
If you use this model, please cite the following paper:
@misc{https://doi.org/10.48550/arxiv.2204.12811,
doi = {10.48550/ARXIV.2204.12811},
url = {https://arxiv.org/abs/2204.12811},
author = {Zhang, Mike and Jensen, Kristian Nørgaard and Sonniks, Sif Dam and Plank, Barbara},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {SkillSpan: Hard and Soft Skill Extraction from English Job Postings},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}