UltraFastBERT-1x11-long
Maintainer: pbelcak
72
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
UltraFastBERT-1x11-long is a compact BERT model that uses fast feedforward networks (FFF) instead of traditional feedforward layers. This allows the model to selectively engage just 12 out of 4095 neurons for each layer inference, using only 0.3% of its neurons during inference. The model was described in the paper "Exponentially Faster Language Modelling" and was pretrained similarly to crammedBERT but with the FFF substitution.
Model inputs and outputs
Inputs
- Text: The model takes in text as input, which can be used for various natural language processing tasks.
Outputs
- Predictions: The model outputs predictions based on the input text, which can be used for tasks like masked language modeling.
Capabilities
The UltraFastBERT-1x11-long model is capable of performing on par with similar BERT models while using a fraction of the computational resources. This makes it a promising candidate for applications where efficiency is a priority, such as on-device inference or real-time processing.
What can I use it for?
You can use the UltraFastBERT-1x11-long model for various natural language processing tasks by fine-tuning it on a downstream dataset, as discussed in the paper. The model can be particularly useful in scenarios where computational resources are limited, such as on mobile devices or in edge computing environments.
Things to try
One interesting aspect of the UltraFastBERT-1x11-long model is its selective engagement of neurons during inference. You could experiment with understanding the significance of this technique and how it impacts the model's performance and efficiency across different tasks and datasets.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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