Update README.md

README.md

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 - pytorch_model_hub_mixin
 ---
 
-# segment-enformer
+# SegmentEnformer
 
 SegmentEnformer is a segmentation model leveraging [Enformer](https://www.nature.com/articles/s41592-021-01252-x) to predict the location of several types of genomics 
 elements in a sequence at a single nucleotide resolution. It was trained on 14 different classes, including gene (protein-coding genes, lncRNAs, 5’UTR, 3’UTR, exon, intron, splice acceptor and donor sites) and regulatory (polyA signal, tissue-invariant and 
 tissue-specific promoters and enhancers, and CTCF-bound sites) elements.
 
 
-**Developed by:** [InstaDeep](https://huggingface.co/InstaDeepAI)
+**Developed by:** [InstaDeep](https://huggingface.co/InstaDeepAI)
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer)
+- **Paper:** [Segmenting the genome at single-nucleotide resolution with DNA foundation models](https://www.biorxiv.org/content/biorxiv/early/2024/03/15/2024.03.14.584712.full.pdf) 
+
+### How to use
+
+To Be Done
+
+
+
+## Training data
+
+The **SegmentEnformer** model was trained on all human chromosomes except for chromosomes 20 and 21, kept as test set, and chromosome 22, used as a validation set.
+During training, sequences are randomly sampled in the genome with associated annotations. However, we keep the sequences in the validation and test set fixed by 
+using a sliding window of length 196kb (original enformer input length) over the chromosomes 20 and 21. The validation set was used to monitor training and for early stopping.
+
+## Training procedure
+
+### Preprocessing
+
+The DNA sequences are tokenized using one-hot encoding similar to the Enformer model
+
+### Architecture
+
+The model is composed of the Enformer backbone, from which we remove the heads and replaced it by a 1-dimensional U-Net segmentation head made of 2 downsampling convolutional blocks and 2 upsampling convolutional blocks. Each of these 
+blocks is made of 2 convolutional layers with 1, 024 and 2, 048 kernels respectively.
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{de2024segmentnt,
+  title={SegmentNT: annotating the genome at single-nucleotide resolution with DNA foundation models},
+  author={de Almeida, Bernardo P and Dalla-Torre, Hugo and Richard, Guillaume and Blum, Christopher and Hexemer, Lorenz and Gelard, Maxence and Pandey, Priyanka and Laurent, Stefan and Laterre, Alexandre and Lang, Maren and others},
+  journal={bioRxiv},
+  pages={2024--03},
+  year={2024},
+  publisher={Cold Spring Harbor Laboratory}
+}
+
+```