metadata
language:
- en: null
license: mit
widget:
- text: Lou Gehrig who works for XCorp and lives in New York suffers from [MASK]
example_title: 'Test for entity type: Disease'
- text: Overexpression of [MASK] occurs across a wide range of cancers
example_title: 'Test for entity type: Gene'
- text: Patients treated with [MASK] are vulnerable to infectious diseases
example_title: 'Test for entity type: Drug'
- text: A eGFR level below [MASK] indicates chronic kidney disease
example_title: 'Test for entity type: Measure '
- text: In the [MASK], increased daily imatinib dose induced MMR
example_title: 'Test for entity type: STUDY/TRIAL'
- text: Paul Erdos died at [MASK]
example_title: 'Test for entity type: TIME'
tags:
- fill-mask: null
This model was pretrained from scratch on a custom vocabulary on Pubmed, Clinical trials corpus, and a small subset of Bookcorpus
It was used to do NER as is, with no fine-tuning as described in this post
Towards Data Science link to the same post
Github link to NER using this model in an ensemble with bert-base cased to detect 69 entity types (17 broad entity groups)
