Instructions to use EleutherAI/enformer-official-rough with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EleutherAI/enformer-official-rough with Transformers:
# Load model directly from transformers import Enformer model = Enformer.from_pretrained("EleutherAI/enformer-official-rough", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
95cfa64
1
Parent(s): bc41bb6
update readme
Browse files
README.md
CHANGED
|
@@ -1,3 +1,27 @@
|
|
| 1 |
---
|
| 2 |
license: cc-by-4.0
|
|
|
|
| 3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
license: cc-by-4.0
|
| 3 |
+
inference: false
|
| 4 |
---
|
| 5 |
+
|
| 6 |
+
# Enformer
|
| 7 |
+
|
| 8 |
+
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).
|
| 9 |
+
|
| 10 |
+
This repo contains the official weights released by Deepmind, ported over to Pytorch.
|
| 11 |
+
|
| 12 |
+
## Model description
|
| 13 |
+
|
| 14 |
+
Enformer is a neural network architecture based on the Transformer that led to greatly increased accuracy in predicting gene expression from DNA sequence.
|
| 15 |
+
|
| 16 |
+
We refer to the [paper](https://www.nature.com/articles/s41592-021-01252-x) published in Nature for details.
|
| 17 |
+
|
| 18 |
+
### How to use
|
| 19 |
+
|
| 20 |
+
Refer to the README of [enformer-pytorch](https://github.com/lucidrains/enformer-pytorch) regarding usage.
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
### Citation info
|
| 24 |
+
|
| 25 |
+
```
|
| 26 |
+
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
|
| 27 |
+
```
|