Switch Transformers

The bare SWITCH_TRANSFORMERS Model transformer outputting encoder's raw hidden-states without any specific head on top.

Pricing

Free

Tool Info

Rating: N/A (0 reviews)

Date Added: April 22, 2024

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Description

The SWITCH_TRANSFORMERS model is a transformer model that outputs the raw hidden-states of the encoder without any specific head on top. It was proposed in the paper 'Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity' by William Fedus, Barret Zoph, and Noam Shazeer. This model is an encoder-decoder T5-like model that can be used for various natural language processing tasks. It supports efficient sparsity and can scale up to trillion-parameter models, offering state-of-the-art performance in language-related tasks.

Key Features

Supports efficient sparsity.
Can scale up to trillion-parameter models.
Encoder-decoder T5-like model.
Adaptable for various NLP tasks.

Use Cases

Natural language understanding.
Machine translation.
Text summarization.
Question answering.

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