Pipeline to Detect Time-related Terminology

Description

This pretrained pipeline is built on the top of roberta_token_classifier_timex_semeval model.

Predicted Entities

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How to use

timex_pipeline = PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en")

timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.")
val timex_pipeline = new PretrainedPipeline("roberta_token_classifier_timex_semeval_pipeline", lang = "en")

timex_pipeline.annotate("Model training was started at 22:12C and it took 3 days from Tuesday to Friday.")

Results

Results




+-------+-----------------+
|chunk  |ner_label        |
+-------+-----------------+
|22:12C |Period           |
|3      |Number           |
|days   |Calendar-Interval|
|Tuesday|Day-Of-Week      |
|to     |Between          |
|Friday |Day-Of-Week      |
+-------+-----------------+


{:.model-param}

Model Information

Model Name: roberta_token_classifier_timex_semeval_pipeline
Type: pipeline
Compatibility: Spark NLP 4.4.2+
License: Open Source
Edition: Official
Language: en
Size: 439.5 MB

Included Models

  • DocumentAssembler
  • SentenceDetector
  • TokenizerModel
  • RoBertaForTokenClassification
  • NerConverter
  • Finisher