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---
license: mit
---
[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.
The model has been trained for a total of 17 epochs.
## Example Usage
```py
from transformers import PreTrainedTokenizerFast, BertForMaskedLM
model = BertForMaskedLM.from_pretrained("LofiAmazon/BarcodeBERT-Entire-BOLD")
model.eval()
tokenizer = PreTrainedTokenizerFast.from_pretrained("LofiAmazon/BarcodeBERT-Entire-BOLD")
# The DNA sequence you want to predict.
# There should be a space after every 4 characters.
# The sequence may also have unknown characters which are not A,C,T,G.
# The maximum DNA sequence length (not counting spaces) should be 660 characters
dna_sequence = "AACA ATGT ATTT A-T- TTCG CCCT TGTG AATT TATT ..."
inputs = tokenizer(dna_sequence, return_tensors="pt")
# Obtain a DNA embedding, which is a vector of length 768.
# The embedding is a representation of this DNA sequence in the model's latent space.
embedding = model(**inputs).hidden_states[-1].mean(1).squeeze()
```
## Results
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