Recognize Entities OntoNotes pipeline - ELECTRA Large

Description

The onto_recognize_entities_electra_large 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

Predicted Entities

Download Copy S3 URI

How to use

from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline('onto_recognize_entities_electra_large', lang = 'en')
annotations =  pipeline.fullAnnotate("Hello from John Snow Labs!")[0]
annotations.keys()
val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_large", lang = "en")
val result = pipeline.fullAnnotate("Hello from John Snow Labs ! ")(0)
import nlu
text = [""Hello from John Snow Labs ! ""]
result_df = nlu.load('en.ner.onto.large').predict(text)
result_df

Results

Results


|    | document                         | sentence                        | token                                          | embeddings                   | ner                                        | entities           |
|---:|:---------------------------------|:--------------------------------|:-----------------------------------------------|:-----------------------------|:-------------------------------------------|:-------------------|
|  0 | ['Hello from John Snow Labs ! '] | ['Hello from John Snow Labs !'] | ['Hello', 'from', 'John', 'Snow', 'Labs', '!'] | [[-0.264069110155105,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] |


{:.model-param}

Model Information

Model Name: onto_recognize_entities_electra_large
Type: pipeline
Compatibility: Spark NLP 4.4.2+
License: Open Source
Edition: Official
Language: en
Size: 1.3 GB

Included Models

  • DocumentAssembler
  • SentenceDetectorDLModel
  • TokenizerModel
  • BertEmbeddings
  • NerDLModel
  • NerConverter