Update README.md

README.md

@@ -86,19 +86,24 @@ Final Evaluation Accuracy**: **96.69%**
 ## Usage
 You can load and use the model with the Hugging Face `transformers` library as follows:
 ```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("AnkitAI/distilbert-base-uncased-financial-news-sentiment-analysis")
+model = AutoModelForSequenceClassification.from_pretrained("AnkitAI/distilbert-base-uncased-financial-news-sentiment-analysis")
 
-tokenizer = AutoTokenizer.from_pretrained('AnkitAI/distilbert-base-uncased-financial-news-sentiment-analysis')
-model = AutoModelForSequenceClassification.from_pretrained('AnkitAI/distilbert-base-uncased-financial-news-sentiment-analysis')
-
-text = "The company's quarterly earnings surpassed all estimates, indicating strong growth."
+text = "The company's revenue declined significantly due to market competition."
 inputs = tokenizer(text, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+
+logits = outputs.logits
+predicted_class_id = logits.argmax().item()
 
-outputs = model(**inputs)
-predictions = outputs.logits.argmax(dim=-1)
+label_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}
+predicted_label = label_mapping[predicted_class_id]
 
-label_mapping = {0: 'Negative', 1: 'Neutral', 2: 'Positive'}
-print(f"Sentiment: {label_mapping[predictions.item()]}")
+print(f"Text: {text}")
+print(f"Predicted Sentiment: {predicted_label}")
 ```
 
 ## License