seksa

src/hf.py

@@ -2,41 +2,41 @@ import os.path
 
 from transformers import BertTokenizer, BertForSequenceClassification,TextClassificationPipeline, AutoModelForSequenceClassification
 # Load tokenizer and model from the fine-tuned directory
-model_path = './intent_classification/TinyBERT_106_V2'  # can try other checkpoints
-
-tokenizer = BertTokenizer.from_pretrained(model_path)
-# model = BertForSequenceClassification.from_pretrained(model_path)
-model = AutoModelForSequenceClassification.from_pretrained(model_path, local_files_only=True)
-print(os.path.exists(model_path))
-print("TInyBERT model is ready to use")
-
-
-# for classification pipeline
-text_pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)
-
-# function to generate response
-def generate_response(user_query):
-    response = text_pipeline(user_query)
-
-    # example of response: [{'label': 'LABEL_4', 'score': 0.9997817873954773}]
-    label_name = response[0].get('label')
-    score = response[0].get('score')
-
-    # label for each math topic based on label_name
-    topic_label='NA'
-
-    match label_name:
-        case "LABEL_0":
-            topic_label='RAG'
-
-        case "LABEL_1":
-            topic_label = 'Neo4j'
-
-    return topic_label, score
+# model_path = './intent_classification/TinyBERT_106_V2'  # can try other checkpoints
+#
+# tokenizer = BertTokenizer.from_pretrained(model_path)
+# # model = BertForSequenceClassification.from_pretrained(model_path)
+# model = AutoModelForSequenceClassification.from_pretrained(model_path, local_files_only=True)
+# print(os.path.exists(model_path))
+# print("TInyBERT model is ready to use")
+#
+#
+# # for classification pipeline
+# text_pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)
+#
+# # function to generate response
+# def generate_response(user_query):
+#     response = text_pipeline(user_query)
+#
+#     # example of response: [{'label': 'LABEL_4', 'score': 0.9997817873954773}]
+#     label_name = response[0].get('label')
+#     score = response[0].get('score')
+#
+#     # label for each math topic based on label_name
+#     topic_label='NA'
+#
+#     match label_name:
+#         case "LABEL_0":
+#             topic_label='RAG'
+#
+#         case "LABEL_1":
+#             topic_label = 'Neo4j'
+#
+#     return topic_label, score
 
 
 def get_dir():
     return os.getcwd()
 
-print(generate_response("Procedure to withdraw"))
+# print(generate_response("Procedure to withdraw"))
 get_dir()