Overview of Funnel Transformer
Funnel Transformer is a type of machine learning model designed to reduce the cost of computation while increasing model capacity for tasks such as pretraining. This is achieved by compressing the sequence of hidden states to a shorter one, saving the FLOPs, and re-investing them in constructing a deeper or wider model.
The proposed model maintains the same overall structure as Transformer, with interleaved self-attention and feed-forward sub-modules wrapped by residual connections and layer normalization. However, the encoder gradually reduces the sequence length of the hidden states as the layers get deeper to achieve representation compression and computation reduction.
The Benefits of Funnel Transformer
The use of Funnel Transformer has several key benefits. Firstly, this model allows for greater efficiency in computation and memory usage, helping streamline the training process. By compressing the sequence of hidden states to a shorter one, the Funnel Transformer reduces the number of computations required to produce accurate results, freeing up additional resources to be used elsewhere in the model construction process.
Moreover, since Funnel Transformer reduces the computation cost without sacrificing the model's depth or width, the model's capacity is further enhanced. This increased capacity can lead to improved performance and accuracy for a given task, making it an attractive option for those looking to perform well on high-level machine learning challenges.
How Funnel Transformer Works
Funnel Transformer works by reusing the saved FLOPs from sequence length reduction in the construction process of deeper or wider models. This reinvestment of resources can result in models that are more robust, powerful, and accurate than traditional models.
Moreover, Funnel Transformer is designed specifically to handle token-level predictions that are required by many pre-training objectives. This is done by enabling the model to recover a deep representation for each token from the compressed hidden sequence via a decoder. The resulting decoder generates a full sequence of token-level representations from the compressed encoder output, making it possible to complete many of the most common pre-training tasks with less computation time and more accuracy.
In the fast-moving world of machine learning, models that offer greater efficiency, robustness, and accuracy are always in high demand. Funnel Transformer offers a unique solution to the challenge of reducing computation cost without sacrificing model depth or width, making it an attractive option for those looking to achieve high-level results on complex tasks. With its ability to handle token-level predictions with ease, Funnel Transformer is quickly becoming one of the most popular machine learning models available today.
