Description
The pipeline uses regex <DT/>?/<JJ/>*<NN>+
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline_local = PretrainedPipeline('match_chunks')
result = pipeline_local.annotate("David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.")
result['chunk']
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline import com.johnsnowlabs.nlp.SparkNLP
SparkNLP.version()
val testData = spark.createDataFrame(Seq( (1, "David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow."))).toDF("id", "text")
val pipeline = PretrainedPipeline("match_chunks", lang="en")
val annotation = pipeline.transform(testData)
annotation.show()
import nlu
nlu.load("en.match.chunks").predict("""David visited the restaurant yesterday with his family. He also visited and the day before, but at that time he was alone. David again visited today with his colleagues. He and his friends really liked the food and hoped to visit again tomorrow.""")
Results
Results
['the restaurant yesterday',
'family',
'the day',
'that time',
'today',
'the food',
'tomorrow']
{:.model-param}
Model Information
| Model Name: | match_chunks |
| Type: | pipeline |
| Compatibility: | Spark NLP 4.4.2+ |
| License: | Open Source |
| Edition: | Official |
| Language: | en |
| Size: | 4.1 MB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- PerceptronModel
- Chunker