tab-transformer-pytorch VS conformer

Compare tab-transformer-pytorch vs conformer and see what are their differences.

tab-transformer-pytorch

Implementation of TabTransformer, attention network for tabular data, in Pytorch (by lucidrains)

conformer

Implementation of the convolutional module from the Conformer paper, for use in Transformers (by lucidrains)
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tab-transformer-pytorch conformer
1 1
892 380
8.3% 3.7%
4.3 3.1
about 1 month ago almost 2 years ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

tab-transformer-pytorch

Posts with mentions or reviews of tab-transformer-pytorch. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-07.

conformer

Posts with mentions or reviews of conformer. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing tab-transformer-pytorch and conformer you can also consider the following projects:

tabnet - PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf

routing-transformer - Fully featured implementation of Routing Transformer

rtdl - Research on Tabular Deep Learning (Python package & papers) [Moved to: https://github.com/Yura52/rtdl]

Perceiver - Implementation of Perceiver, General Perception with Iterative Attention

Multimodal-Toolkit - Multimodal model for text and tabular data with HuggingFace transformers as building block for text data

Nystromformer - An implementation of the Nyströmformer, using Nystrom method to approximate standard self attention

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