# EleutherAI /enformer-corr_coef_obj

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 a8e9718             4aad70e a8e9718 5389c39 a8e9718                 4aad70e a8e9718                     12345678910111213141516171819202122232425262728293031 --- license: apache-2.0 inference: false --- # Enformer Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/enformer). This particular model was trained on sequences of 131,072 basepairs, target length 896 on v3-64 TPUs for 3 days with sequence augmentations and pearson correlation objective. This repo contains the weights of the PyTorch implementation by Phil Wang as seen in the [enformer-pytorch repository](https://github.com/lucidrains/enformer-pytorch). Disclaimer: The team releasing Enformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Enformer is a neural network architecture based on the Transformer that led to greatly increased accuracy in predicting gene expression from DNA sequence. We refer to the [paper](https://www.nature.com/articles/s41592-021-01252-x) published in Nature for details. ### How to use Refer to the README of [enformer-pytorch](https://github.com/lucidrains/enformer-pytorch) regarding usage. ### Citation info  Avsec, Ž., Agarwal, V., Visentin, D. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat Methods 18, 1196–1203 (2021). https://doi.org/10.1038/s41592-021-01252-x