Update README.md

README.md

@@ -38,19 +38,12 @@ E5-EG-small (E5 EverGreen - Small) is an efficient multilingual text classificat
 ## How to Get Started with the Model
 
 ```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
 import torch
-import time
 
 # Load model and tokenizer
-model_name = "s-nlp/E5-EG-small"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-# For optimal performance, use GPU if available
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = model.to(device)
-model.eval()
+model_name = "s-nlp/E5-EverGreen-Multilingual-Small"
+pipe = pipeline("text-classification", model_name)
 
 # Batch classification example
 questions = [
@@ -58,35 +51,13 @@ questions = [
     "Who won the latest World Cup?",
     "What is the speed of light?",
     "What is the current Bitcoin price?"
+    "How old is Elon Musk",
+    "How old was Leo Tolstoy when he died?"
 ]
 
-# Tokenize all questions
-inputs = tokenizer(
-    questions, 
-    return_tensors="pt", 
-    padding=True, 
-    truncation=True, 
-    max_length=64
-).to(device)
-
 # Classify
-start_time = time.time()
-with torch.no_grad():
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-    predicted_classes = torch.argmax(predictions, dim=-1)
-
-inference_time = (time.time() - start_time) * 1000  # ms
-
-# Display results
-class_names = ["Immutable", "Mutable"]
-for i, question in enumerate(questions):
-    print(f"Q: {question}")
-    print(f"   Classification: {class_names[predicted_classes[i].item()]}")
-    print(f"   Confidence: {predictions[i][predicted_classes[i]].item():.2f}")
-
-print(f"\nTotal inference time: {inference_time:.2f}ms")
-print(f"Average per question: {inference_time/len(questions):.2f}ms")
+results = pipe(questions)
+
 ```
 
 ## Training Details