asd

app.py

@@ -13,9 +13,9 @@ cuda_available = torch.cuda.is_available()
 device = torch.device("cuda" if cuda_available else "cpu")
 print(f"Using device: {device}")
 
-paraphraser_model_name = "Vamsi/T5_Paraphrase_Paws"
-paraphraser_tokenizer = AutoTokenizer.from_pretrained(paraphraser_model_name, token=hf_token)
-paraphraser_model = AutoModelForSeq2SeqLM.from_pretrained(paraphraser_model_name, token=hf_token).to(device)
+paraphraser_model_name = "facebook/bart-large-cnn"
+paraphraser_tokenizer = AutoTokenizer.from_pretrained(paraphraser_model_name)
+paraphraser_model = AutoModelForSeq2SeqLM.from_pretrained(paraphraser_model_name).to(device)
 
 classifier_model_name = "andreas122001/roberta-mixed-detector"
 classifier_tokenizer = AutoTokenizer.from_pretrained(classifier_model_name)
@@ -58,7 +58,7 @@ def generate_paraphrases(text, setting, output_format):
     json_output = {"original_text": text, "paraphrased_versions": [], "combined_versions": [], "human_like_versions": []}
     
     for i, sentence in enumerate(sentences):
-        inputs = paraphraser_tokenizer(f"paraphrase: {sentence}", return_tensors="pt", max_length=max_length, truncation=True).to(device)
+        inputs = paraphraser_tokenizer(sentence, return_tensors="pt", max_length=max_length, truncation=True).to(device)
         
         outputs = paraphraser_model.generate(
             **inputs,