Pipelines and Models

Pretrained Pipelines

Pretrained Models

English

Italian

French

How to use Models and Pipelines

Online

To use Spark NLP pretrained pipelines, you can call PretrainedPipeline with pipeline’s name and its language (default is en):

pipeline = PretrainedPipeline('explain_document_dl', lang='en')

Same in Scala

val pipeline = PretrainedPipeline("explain_document_dl", lang="en")

You can follow the same approach to use Spark NLP pretrained models:

# load NER model trained by deep learning approach and GloVe word embeddings
ner_dl = NerDLModel.pretrained('ner_dl')
# load NER model trained by deep learning approach and BERT word embeddings
ner_crf = NerDLModel.pretrained('ner_dl_bert')

The default language is English, so for other laguages you should set the language:

// load French POS tagger model trained by Universal Dependencies
val french_pos = PerceptronModel.pretrained("pos_ud_gsd", lang="fr")
// load Italain LemmatizerModel
val italian_lemma = LemmatizerModel.pretrained("lemma_dxc", lang="it")

Offline

If you have any trouble using online pipelines or models in your environment (maybe it’s air-gapped), you can directly download them for offline use.

After downloading offline models/pipelines and extracting them, here is how you can use them iside your code (the path could be a shared storage like HDFS in a cluster):

• Loading PerceptronModel annotator model inside Spark NLP Pipeline

val french_pos = PerceptronModel.load("/tmp/pos_ud_gsd_fr_2.0.2_2.4_1556531457346/")
      .setInputCols("document", "token")
      .setOutputCol("pos")

• Loading Offline Pipeline

val advancedPipeline = PipelineModel.load("/tmp/explain_document_dl_en_2.0.2_2.4_1556530585689/")
// To use the loaded Pipeline for prediction
advancedPipeline.transform(predictionDF)