update model weight

schema_item_filter.py

@@ -18,7 +18,7 @@ def prepare_inputs_and_labels(sample, tokenizer):
     input_words = [sample["text"]]
     for table_id, table_name in enumerate(table_names):
         input_words.append("|")
-        input_words.append(table_name)
+        input_words.append(table_name)_
         table_name_word_indices.append(len(input_words) - 1)
         input_words.append(":")
         
@@ -238,14 +238,10 @@ def lista_contains_listb(lista, listb):
 class SchemaItemClassifierInference():
     def __init__(self, model_save_path):
         set_seed(42)
-        # load tokenizer
+        # load tokenizer from Hugging Face
         self.tokenizer = AutoTokenizer.from_pretrained(model_save_path, add_prefix_space = True)
-        # initialize model
+        # load model directly from Hugging Face
         self.model = SchemaItemClassifier(model_save_path, "test")
-        # load fine-tuned params
-        self.model.load_state_dict(torch.load(model_save_path + "/dense_classifier.pt", map_location=torch.device('cpu')), strict=False)
-        if torch.cuda.is_available():
-            self.model = self.model.cuda()
         self.model.eval()
     
     def predict_one(self, sample):

text2sql.py

@@ -111,9 +111,8 @@ class ChatBot():
         self.max_new_tokens = 256
         self.max_prefix_length = self.max_length - self.max_new_tokens
 
-        # self.sic = SchemaItemClassifierInference("Roxanne-WANG/LangSQL", token=os.getenv('HF_TOKEN'))
+        # Directly loading the model from Hugging Face
         self.sic = SchemaItemClassifierInference("Roxanne-WANG/LangSQL")
-
         self.db_id2content_searcher = dict()
         for db_id in os.listdir("db_contents_index"):
             schema = Schema(content=TEXT(stored=True))