|
--- |
|
language: |
|
- {en} |
|
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} |
|
--- |
|
|
|
|
|
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](https://ajitrajasekharan.github.io/2021/01/02/my-first-post.html) |
|
|
|
[Towards Data Science link](https://twitter.com/TDataScience/status/1486300137366466560?s=20) to the same post |
|
|
|
[Github link](https://github.com/ajitrajasekharan/unsupervised_NER) to NER using this model in an ensemble with bert-base cased to detect 69 entity types (17 broad entity groups) |
|
|
|
|
