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README.md
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This finetuned model is specifically designed for promoter identification and is based on the [ProkBERT-mini-long model](https://huggingface.co/neuralbioinfo/prokbert-mini-long).
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For more details, refer to the [
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### Example Usage
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tok_params = {'kmer' : kmer,
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'shift' : shift}
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tokenizer = ProkBERTTokenizer(tokenization_params=tok_params)
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model =
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sequence = 'CACCGCATGGAGATCGGCACCTACTTCGACAAGCTGGAGGCGCTGCTGAAGGAGTGGTACGAGGCGCGCGGGGGTGAGGCATGACGGACTGGCAAGAGGAGCAGCGTCAGCGC'
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inputs = tokenizer(sequence, return_tensors="pt")
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# Ensure that inputs have a batch dimension
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This finetuned model is specifically designed for promoter identification and is based on the [ProkBERT-mini-long model](https://huggingface.co/neuralbioinfo/prokbert-mini-long).
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For more details, refer to the [phage dataset description](https://huggingface.co/datasets/neuralbioinfo/phage-test-10k) used for training and evaluating this model.
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### Example Usage
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tok_params = {'kmer' : kmer,
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'shift' : shift}
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tokenizer = ProkBERTTokenizer(tokenization_params=tok_params)
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model = MegatronBertForSequenceClassification.from_pretrained(finetuned_model)
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sequence = 'CACCGCATGGAGATCGGCACCTACTTCGACAAGCTGGAGGCGCTGCTGAAGGAGTGGTACGAGGCGCGCGGGGGTGAGGCATGACGGACTGGCAAGAGGAGCAGCGTCAGCGC'
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inputs = tokenizer(sequence, return_tensors="pt")
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# Ensure that inputs have a batch dimension
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