Description
The onto_recognize_entities_bert_base 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
How to use
from sparknlp.pretrained import PretrainedPipelinein
pipeline = PretrainedPipeline('onto_recognize_entities_bert_base', lang = 'en')
annotations = pipeline.fullAnnotate(""Hello from John Snow Labs ! "")[0]
annotations.keys()
val pipeline = new PretrainedPipeline("onto_recognize_entities_bert_base", 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.bert.base').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', '!'] | [[-0.085488274693489,.,...]] | ['O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O'] | ['John Snow Labs'] |
{:.model-param}
Model Information
| Model Name: | onto_recognize_entities_bert_base |
| Type: | pipeline |
| Compatibility: | Spark NLP 4.4.2+ |
| License: | Open Source |
| Edition: | Official |
| Language: | en |
| Size: | 424.9 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- BertEmbeddings
- NerDLModel
- NerConverter