Multimodal-Toolkit
tab-transformer-pytorch
| Multimodal-Toolkit | tab-transformer-pytorch | |
|---|---|---|
| 2 | 1 | |
| 553 | 698 | |
| 1.8% | - | |
| 7.6 | 4.5 | |
| about 2 months ago | 5 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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Multimodal-Toolkit
- [D]Are there any good solutions for multimodal classification? Libraries, AutoML tool?
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Classification problem with text and numerical features
A quick and dirty way of trying this is using this framework:https://github.com/georgian-io/Multimodal-Toolkit
tab-transformer-pytorch
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[P] pytorch-widedeep v1.0.9: the Perceiver and the FastFormer for tabular data are now available in the library
Code for https://arxiv.org/abs/2012.06678 found: https://github.com/lucidrains/tab-transformer-pytorch
What are some alternatives?
transformers - ð€ Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
tabnet - PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
autogluon - Fast and Accurate ML in 3 Lines of Code
rtdl - Research on Tabular Deep Learning (Python package & papers) [Moved to: https://github.com/Yura52/rtdl]
Transformers4Rec - Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.
routing-transformer - Fully featured implementation of Routing Transformer
pykale - Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the ð¥PyTorch ecosystem. â Star to support our work!
Linear-Multihead-Attention - Reproducing the Linear Multihead Attention introduced in Linformer paper (Linformer: Self-Attention with Linear Complexity)
multimodal - TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale.
perceiver-pytorch - Implementation of Perceiver, General Perception with Iterative Attention, in Pytorch
HTM-pytorch - Implementation of Hierarchical Transformer Memory (HTM) for Pytorch
conformer - Implementation of the convolutional module from the Conformer paper, for use in Transformers