Updated

app.py

@@ -223,9 +223,11 @@ def split_text_allow_complete_sentences_nltk(text, max_length=256, tolerance=30,
     
     if type_det == 'bc':
         tokenizer = text_bc_tokenizer
+        max_length = 333
         
     elif type_det == 'mc':
         tokenizer = text_mc_tokenizer
+        max_length = 256
     
     for sentence in sentences:
         tokens = tokenizer.tokenize(sentence)
@@ -277,7 +279,7 @@ def predict_quillbot(text):
 
 def predict_bc(model, tokenizer, text):
     tokens = text_bc_tokenizer(
-        text, padding='max_length', truncation=True, max_length=256, return_tensors="pt"
+        text, padding='max_length', truncation=True, max_length=333, return_tensors="pt"
     ).to(device)["input_ids"]
     output = model(tokens)
     output_norm = softmax(output.logits.detach().cpu().numpy(), 1)[0]