Create README.md

README.md

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+from transformers import AutoTokenizer, AutoModelForMaskedLM
+import numpy as np
+import torch
+
+# Import the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained("isoformer-anonymous/Isoformer", trust_remote_code=True)
+model = AutoModelForMaskedLM.from_pretrained("isoformer-anonymous/Isoformer",trust_remote_code=True)
+
+protein_sequences = ["RSRSRSRSRSRSRSRSRSRSRL" * 9]
+rna_sequences = ["ATTCCGGTTTTCA" * 9]
+sequence_length = 196_608
+rng = np.random.default_rng(seed=0)
+dna_sequences = ["".join(rng.choice(list("ATCGN"), size=(sequence_length,)))]
+
+torch_tokens = tokenizer(
+    dna_input=dna_sequences, rna_input=rna_sequences, protein_input=protein_sequences
+)
+dna_torch_tokens = torch.tensor(torch_tokens[0]["input_ids"])
+rna_torch_tokens = torch.tensor(torch_tokens[1]["input_ids"])
+protein_torch_tokens = torch.tensor(torch_tokens[2]["input_ids"])
+
+torch_output = model.forward(
+    tensor_dna=dna_torch_tokens,
+    tensor_rna=rna_torch_tokens,
+    tensor_protein=protein_torch_tokens,
+    attention_mask_rna=rna_torch_tokens != 1,
+    attention_mask_protein=protein_torch_tokens != 1,
+)
+
+print(f"Gene expression predictions: {torch_output['gene_expression_predictions']}")
+print(f"Final DNA embedding: {torch_output['final_dna_embeddings']}")