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tags: |
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- biology |
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- DNA |
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- genomics |
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--- |
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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 |
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# Import the tokenizer and the model |
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tokenizer = AutoTokenizer.from_pretrained("PoetschLab/GROVER") |
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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. |
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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. |
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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 |
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@article{sanabria2024dna, |
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title={DNA language model GROVER learns sequence context in the human genome}, |
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author={Sanabria, Melissa and Hirsch, Jonas and Joubert, Pierre M and Poetsch, Anna R}, |
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journal={Nature Machine Intelligence}, |
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pages={1--13}, |
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year={2024}, |
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publisher={Nature Publishing Group UK London} |
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} |
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``` |
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