Description
Implements Noisy Channel Model Spell Algorithm. Correction candidates are extracted combining context information and word information
Live Demo Open in Colab Copy S3 URI
How to use
model = ContextSpellCheckerModel.pretrained("spellcheck_clinical","en","clinical/models")
.setInputCols("token")
.setOutputCol("spell")
val model = ContextSpellCheckerModel.pretrained("spellcheck_clinical","en","clinical/models")
.setInputCols("token")
.setOutputCol("spell")
import nlu
nlu.load("en.spell.clinical").predict("""Put your text here.""")
Model Information
Name: | spellcheck_clinical |
Type: | ContextSpellCheckerModel |
Compatibility: | 2.4.2 |
License: | Licensed |
Edition: | Official |
Input labels: | [token] |
Output labels: | [spell] |
Language: | en |
Dependencies: | embeddings_clinical |
Data Source
Trained with augmented version of i2b2 and PubMed datasets.