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prokbert-mini-phage

Text Classification

sequence embedding

genomic language models

promoter-prediction

Inference Endpoints

Model card Files Files and versions Community

ligeti commited on Feb 16

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4732d24

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1 Parent(s): 2055e51

Update README.md

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@@ -44,7 +44,7 @@ shift= 1
 tok_params = {'kmer' : kmer,
              'shift' : shift}
 tokenizer = ProkBERTTokenizer(tokenization_params=tok_params)
-model = BertForBinaryClassificationWithPooling.from_pretrained(finetuned_model)
 sequence = 'CACCGCATGGAGATCGGCACCTACTTCGACAAGCTGGAGGCGCTGCTGAAGGAGTGGTACGAGGCGCGCGGGGGTGAGGCATGACGGACTGGCAAGAGGAGCAGCGTCAGCGC'
 inputs = tokenizer(sequence, return_tensors="pt")
 # Ensure that inputs have a batch dimension

 tok_params = {'kmer' : kmer,
              'shift' : shift}
 tokenizer = ProkBERTTokenizer(tokenization_params=tok_params)
+model = MegatronBertForSequenceClassification.from_pretrained(finetuned_model)
 sequence = 'CACCGCATGGAGATCGGCACCTACTTCGACAAGCTGGAGGCGCTGCTGAAGGAGTGGTACGAGGCGCGCGGGGGTGAGGCATGACGGACTGGCAAGAGGAGCAGCGTCAGCGC'
 inputs = tokenizer(sequence, return_tensors="pt")
 # Ensure that inputs have a batch dimension