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--- |
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metrics: |
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- matthews_correlation |
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- f1 |
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tags: |
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- biology |
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- medical |
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--- |
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This is the official pre-trained model introduced in [DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome |
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](https://arxiv.org/pdf/2306.15006.pdf). |
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We sincerely appreciate the MosaicML team for the [MosaicBERT](https://openreview.net/forum?id=5zipcfLC2Z) implementation, which serves as the base of DNABERT-2 development. |
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DNABERT-2 is a transformer-based genome foundation model trained on multi-species genome. |
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To load the model from huggingface: |
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``` |
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import torch |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("zhihan1996/DNABERT-2-117M", trust_remote_code=True) |
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model = AutoModel.from_pretrained("zhihan1996/DNABERT-2-117M", trust_remote_code=True) |
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``` |
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To calculate the embedding of a dna sequence |
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``` |
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dna = "ACGTAGCATCGGATCTATCTATCGACACTTGGTTATCGATCTACGAGCATCTCGTTAGC" |
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inputs = tokenizer(dna, return_tensors = 'pt')["input_ids"] |
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hidden_states = model(inputs)[0] # [1, sequence_length, 768] |
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# embedding with mean pooling |
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embedding_mean = torch.mean(hidden_states[0], dim=0) |
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print(embedding_mean.shape) # expect to be 768 |
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# embedding with max pooling |
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embedding_max = torch.max(hidden_states[0], dim=0)[0] |
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print(embedding_max.shape) # expect to be 768 |
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``` |
