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license: mit |
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[BarcodeBERT](https://arxiv.org/pdf/2311.02401) model trained on all complete DNA sequences from the latest [BOLD database release](http://www.boldsystems.org/index.php/datapackages/Latest). We used the 'nucraw' column of DNA sequences and followed the preprocessing steps outlined in the BarcodeBERT paper. |
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The model has been trained for a total of 17 epochs. |
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## Example Usage |
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```py |
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from transformers import PreTrainedTokenizerFast, BertForMaskedLM |
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model = BertForMaskedLM.from_pretrained("LofiAmazon/BarcodeBERT-Entire-BOLD") |
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model.eval() |
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tokenizer = PreTrainedTokenizerFast.from_pretrained("LofiAmazon/BarcodeBERT-Entire-BOLD") |
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# The DNA sequence you want to predict. |
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# There should be a space after every 4 characters. |
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# The sequence may also have unknown characters which are not A,C,T,G. |
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# The maximum DNA sequence length (not counting spaces) should be 660 characters |
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dna_sequence = "AACA ATGT ATTT A-T- TTCG CCCT TGTG AATT TATT ..." |
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inputs = tokenizer(dna_sequence, return_tensors="pt") |
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# Obtain a DNA embedding, which is a vector of length 768. |
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# The embedding is a representation of this DNA sequence in the model's latent space. |
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embedding = model(**inputs).hidden_states[-1].mean(1).squeeze() |
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``` |
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## Results |
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