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import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("RaphaelMourad/ModernBert-DNA-v1-117M-hg38", trust_remote_code=True)
model = AutoModel.from_pretrained("RaphaelMourad/ModernBert-DNA-v1-117M-hg38", trust_remote_code=True)DNAseq = "TGATGATTGGCGCGGCTAGGATCGGCT"
inputs = tokenizer(DNAseq, return_tensors = 'pt')["input_ids"]
hidden_states = model(inputs)[0] # [1, sequence_length, 256]
# embedding with max pooling
embedding_max = torch.max(hidden_states[0], dim=0)[0]
print(embedding_max.shape) # expect to be 256