IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.
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Elastic Docs ›Machine Learning in the Elastic Stack [8.1] ›Natural language processing ›Resources

Third party NLP models

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Third party NLP models

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The Elastic Stack machine learning features support transformer models that conform to the standard BERT model interface and use the WordPiece tokenization algorithm.

The current list of supported architectures is:

  • BERT
  • DPR bi-encoders
  • DistilBERT
  • ELECTRA
  • MobileBERT
  • RetriBERT
  • MPNet
  • SentenceTransformers bi-encoders with the above transformer architectures

In general, any trained model that has a supported architecture is deployable in Elasticsearch by using eland. However, it is not possible to test every third party model. The following lists are therefore provided for informational purposes only and may not be current. Elastic makes no warranty or assurance that the machine learning features will continue to interoperate with these third party models in the way described, or at all.

These models are listed by NLP task; for more information about those tasks, refer to Overview.

Third party fill-mask models

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Third party named entity recognition models

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Third party text embedding models

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Using SentenceTransformerWrapper:

Using DPREncoderWrapper:

Third party text classification models
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Third party zero-shot text classification models

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