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--- |
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license: mit |
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tags: |
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- biology |
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- genomics |
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- long-context |
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library_name: transformers |
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--- |
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# DNAFlash |
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## Abouts |
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### Dependencies |
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``` |
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rotary_embedding_torch |
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einops |
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``` |
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## How to use |
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### Simple example: embedding |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModel |
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# Load the tokenizer and model using the pretrained model name |
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tokenizer = AutoTokenizer.from_pretrained("isyslab/DNAFlash") |
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model = AutoModel.from_pretrained("isyslab/DNAFlash", trust_remote_code=True) |
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# Define input sequences |
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sequences = [ |
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"GAATTCCATGAGGCTATAGAATAATCTAAGAGAAATATATATATATTGAAAAAAAAAAAAAAAAAAAAAAAGGGG" |
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] |
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# Tokenize the sequences |
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inputs = tokenizer( |
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sequences, |
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add_special_tokens=True, |
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return_tensors="pt", |
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padding=True, |
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truncation=True |
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) |
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# Perform a forward pass through the model to obtain the outputs, including hidden states |
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with torch.inference_mode(): |
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outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"]) |
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``` |
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## Citation |
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