Update README.md

README.md

@@ -35,7 +35,7 @@ from transformers import T5Tokenizer, T5ForConditionalGeneration
 tokenizer = T5Tokenizer.from_pretrained('dkarthikeyan1/tcrt5_ft_tcrdb')
 tcrt5 = T5ForConditionalGeneration.from_pretrained("dkarthikeyan1/tcrt5_ft_tcrdb")
 pmhc = "[PMHC]KLGGALQAK[SEP]YFAMYQENVAQTDVDTLYIIYRDYTWAELAYTWY[EOS]"
-encoded_pmhc = tokenizer(text, return_tensors='pt')
+encoded_pmhc = tokenizer(pmhc, return_tensors='pt')
 
 # Define the number of TCRs you would like to generate ()
 num_tcrs = 10
@@ -45,7 +45,7 @@ num_beams = 30
 outputs = tcrt5.generate(**encoded_pmhc, max_new_tokens=25, num_return_sequences=num_tcrs, num_beams=num_beams)
 
 # Use regex to get out the [TCR] tag
-cdr3b_sequences = [re.sub(r'\[.*\]', '', x) for x in tokenizer.batch_decode(model.generate(num_beams=300, num_return_sequences=100, max_new_tokens=25), skip_special_tokens=True)]
+cdr3b_sequences = [re.sub(r'\[.*\]', '', x) for x in tokenizer.batch_decode(outputs, skip_special_tokens=True)]
 
 >>> cdr3b_sequences