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Efficient Splade V Large Query



The efficient-splade-V-large-query model is a language model that has been trained to perform the fill-mask task. Given an input sentence with a masked word, the model predicts the most likely word that fills the mask. It is designed to provide accurate and efficient predictions for large queries.

Use cases

The efficient-splade-V-large-query model can be useful in a variety of scenarios for technical users. One potential use case is in natural language processing applications, where the model can be employed to fill in missing information or complete sentences. It can be used in tasks such as sentiment analysis, text completion, or question answering systems. Another possible application is in content generation, where the model can suggest appropriate words or phrases to improve the quality and fluency of generated text. Furthermore, the model's efficiency and accuracy make it suitable for large queries, making it valuable in scenarios where quick and reliable predictions are crucial, such as in search engines or chatbots. By leveraging the capabilities of this model, developers can build products that enhance user experiences and provide valuable insights in various domains.



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Avg run time

Creator Models

Neuclir22 Splade Ru$?16
Trecdl22 Crossencoder Albert$?24
Neuclir22 Splade Zh$?16
Neuclir22 Pretrained Zh$?9
Trecdl22 Crossencoder Electra$?152

Similar Models

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Summary of this model and related resources.

Model NameEfficient Splade V Large Query
Platform did not provide a description for this model.
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided


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