| --- |
| tags: |
| - biology |
| - DNA |
| - genomics |
| --- |
| This is the official pre-trained model introduced in [DNA language model GROVER learns sequence context in the human genome](https://www.nature.com/articles/s42256-024-00872-0) |
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| from transformers import AutoTokenizer, AutoModelForMaskedLM |
| |
| # Import the tokenizer and the model |
| tokenizer = AutoTokenizer.from_pretrained("PoetschLab/GROVER") |
| model = AutoModelForMaskedLM.from_pretrained("PoetschLab/GROVER") |
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| 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 guarantee 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). |
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| ### BibTeX entry and citation info |
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| ```bibtex |
| @article{sanabria2024dna, |
| title={DNA language model GROVER learns sequence context in the human genome}, |
| author={Sanabria, Melissa and Hirsch, Jonas and Joubert, Pierre M and Poetsch, Anna R}, |
| journal={Nature Machine Intelligence}, |
| pages={1--13}, |
| year={2024}, |
| publisher={Nature Publishing Group UK London} |
| } |
| ``` |
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