Language Models in deepset Cloud

deepset Cloud loads models directly from Hugging Face. You can use publically available models but also your private ones if you connect deepset Cloud with Hugging Face.

If you're trying to find the right model on Hugging Face, go to the Models menu and you'll find a list of tasks by which you can search for your model.

A screen shot of model tasks on Hugging FaceA screen shot of model tasks on Hugging Face

Model tasks on Hugging Face

In deepset Cloud, there are two types of pipeline nodes that use models: dense retrievers and readers. Readers use models for question answering, while retrievers use sentence similarity or DPR models. For information about choosing the models for pipeline nodes, see EmbeddingRetriever, DensePassageRetriever, and Reader.

Recommended Models

If you don't know which model to start with, you can use one of the models we recommend.

Models for Question Answering

This table describes the models that we recommend for the Question Answering task:

Model URLDescriptionLanguage distilled model, relatively fast and with good performance.English large model with good performance. Slower than the distilled one.English base model with good speed and performance.Multilingual very fast model.English

You can also view state-of-the-art question answering models on the HuggingFace leaderboard.

Models for Information Retrieval

This table describes the models that we recommend for the Information Retrieval task:

Model URLDescriptionLanguage model with good speed and performance.English model with good speed and performance.English model faster than the base models with still good performance.English fast multilingual model.Multilingual relatively big model, slower than the mini one but with better performance.Multilingual

Using a Model

To use a model, simply provide its Hugging Face location as a parameter to the node and deepset Cloud will take care of loading it. For example:

- name: Retriever 
    type: EmbeddingRetriever 
      document_store: DocumentStore
      embedding_model: sentence-transformers/multi-qa-mpnet-base-dot-v1 
      model_format: sentence_transformers
      pooling_strategy: cls_token 
      top_k: 20

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