Update README.md

README.md

@@ -78,25 +78,25 @@ fine-tuned further for similar tasks in different contexts.
 Use the code snippet below to get started with the model:
 
 ```python
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 import torch
+from peft import AutoPeftModelForSequenceClassification
+
 
 model_name = "tferhan/Intent-GovMa-v1"
 
-# Load the tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-# Example inference
-questions = ["qu'est ce que open data",
-           "je veux les informations de l'eau potable"]
-for question in questions:
-        inputs = tokenizer(question, return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
-        with torch.no_grad():
-            outputs = model(**inputs)
-        
-        print(f"question: {question}")
-        print(f"predicted type of question: {class_names[torch.argmax(outputs.logits).item()]}\n")
+model = AutoPeftModelForSequenceClassification.from_pretrained(model_name)
+nlp_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
+
+questions = ["qu'est ce que open data", "je veux les informations de l'eau potable"]
+results = nlp_pipeline_class(questions)
+
+for result in results:
+    print(result)
+
+#{'label': 'LABEL_0', 'score': 0.9999700784683228} === general
+#{'label': 'LABEL_1', 'score': 0.9994990825653076} === request_data
 ```
 
 ## Training Details