Description
The check_spelling is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and recognizes entities . It performs most of the common text processing tasks on your dataframe
Live Demo Open in Colab Download Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipelinein
pipeline = PretrainedPipeline('check_spelling', lang = 'en')
annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
annotations.keys()
val pipeline = new PretrainedPipeline("check_spelling", lang = "en")
val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
import nlu
text = [""Hello from John Snow Labs ! ""]
result_df = nlu.load('').predict(text)
result_df
Results
| | document | sentence | token | checked |
|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------------------------|
| 0 | ['I liek to live dangertus ! '] | ['I liek to live dangertus !'] | ['I', 'liek', 'to', 'live', 'dangertus', '!'] | ['I', 'like', 'to', 'live', 'dangerous', '!'] |
Model Information
| Model Name: | check_spelling |
| Type: | pipeline |
| Compatibility: | Spark NLP 3.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Language: | en |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- NorvigSweetingModel