Update README.md

README.md

@@ -34,7 +34,48 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 - **Demo [optional]:** [More Information Needed]
 
 ## Uses
-
+```
+from transformers import BertTokenizer, BertForSequenceClassification
+
+# Load the model and tokenizer from the Hugging Face model hub
+mymodel = BertForSequenceClassification.from_pretrained("pritam2014/SentimentBERT")
+mytokenizer = BertTokenizer.from_pretrained("bert-base-uncased",use_auth_token=True)
+```
+```
+def preprocess_text(text):
+    # Preprocess the input text
+    inputs = mytokenizer.encode_plus(
+        text,
+        max_length=512,
+        padding='max_length',
+        truncation=True,
+        return_attention_mask=True,
+        return_tensors='pt'
+    )
+    return inputs
+```
+```
+def make_prediction(text):
+    # Preprocess the input text
+    inputs = preprocess_text(text)
+
+    # Make predictions using the loaded model
+    with torch.no_grad():
+        outputs = mymodel(inputs['input_ids'], attention_mask=inputs['attention_mask'])
+        logits = outputs.logits
+        predicted_class_id = torch.argmax(logits).item()
+
+    # Map the predicted class ID to a sentiment label
+    sentiment_labels = {0: 'Negative', 1: 'Positive'}
+    predicted_sentiment = sentiment_labels[predicted_class_id]
+
+    return predicted_sentiment
+```
+```
+text = "I love this product"
+predicted_sentiment = make_prediction(text)
+print(predicted_sentiment)
+```
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 ### Direct Use