Update README.md

README.md

@@ -26,13 +26,11 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("metagene-ai/METAGENE-1")
 model = AutoModelForCausalLM.from_pretrained("metagene-ai/METAGENE-1", torch_dtype=torch.bfloat16)
 
-# Example input: Hexamita inflata 5.8S ribosomal RNA gene sequence
-input_sequence = (
-    "TCACCGTTCTACAATCCCAAGCTGGAGTCAAGCTCAACAGGGTCTTCTTGCCCCGCTGAGGGTTACACTCGCCCGTTCCCGAGTCTGTGGTTTCGCGAAGATATGACCAGGGACAGTAAGAACC"
-)
+# Example input sequence
+input_sequence = "TCACCGTTCTACAATCCCAAGCTGGAGTCAAGCTCAACAGGGTCTTC"
 
 # Tokenize the input sequence and truncate to the first 12 tokens
-input_tokens = tokenizer.encode(input_sequence, return_tensors="pt", add_special_tokens=False)[..., :12]
+input_tokens = tokenizer.encode(input_sequence, return_tensors="pt", add_special_tokens=False)
 
 # Generate output from the model with a max sequence length of 32 tokens
 generated_tokens = model.generate(input_tokens, max_length=32)
@@ -41,9 +39,9 @@ generated_tokens = model.generate(input_tokens, max_length=32)
 generated_sequence = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
 generated_sequence = generated_sequence.replace(" ", "").replace("_", "")
 
-# Print the original input and the model's output
-print(f"📄 Input Sequence:\n{input_sequence}")
+# Generated output: A Hexamita inflata 5.8S ribosomal RNA gene sequence
 print(f"🔬 Generated Sequence:\n{generated_sequence}")
+# TCACCGTTCTACAATCCCAAGCTGGAGTCAAGCTCAACAGGGTCTTCTTGCCCCGCTGAGGGTTACACTCGCCCGTTCCCGAGTCTGTGGTTTCGCGAAGATATGACCAGGGACAGTAAGAACC
 ```
 
 ## **Benchmark Performance**