transformers pytorch bert fill-mask biology DNA genomics autotrain_compatible endpoints_compatible region:us
---
tags:
- biology
- DNA
- genomics
---
This is the official pre-trained model introduced in [GROVER : A foundation DNA language with optimized vocabulary learns sequence context in the human genome](https://www.biorxiv.org/content/10.1101/2023.07.19.549677v2)



    from transformers import AutoTokenizer, AutoModelForMaskedLM
    import torch

    # Import the tokenizer and the model
    tokenizer = AutoTokenizer.from_pretrained("PoetschLab/GROVER")
    model = AutoModelForMaskedLM.from_pretrained("PoetschLab/GROVER")


Some preliminary analysis shows that sequence re-tokenization using Byte Pair Encoding (BPE) changes significantly if the sequence is less than 50 nucleotides long. Longer than 50 nucleotides, you should still be careful with sequence edges.
We advice to add 100 nucleotides at the beginning and end of every sequence in order to garantee that your sequence is represented with the same tokens as the original tokenization.
We also provide the tokenized chromosomes with their respective nucleotide mappers (They are available in the folder tokenized chromosomes).
