Update README.md

README.md

@@ -2,4 +2,62 @@
 license: mit
 language:
 - en
----
+---
+
+
+```python
+import torch
+from torch.utils.data import DataLoader, Dataset
+from transformers import RobertaTokenizer, RobertaForSequenceClassification, AdamW
+from sklearn.model_selection import train_test_split
+import pandas as pd
+
+# Load the tokenizer
+tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
+
+# Load RoBERTa pre-trained model
+model = RobertaForSequenceClassification.from_pretrained('roberta-base', num_labels=2)
+model = model.to(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))
+
+
+
+
+```
+
+```python
+
+def predict_description(model, tokenizer, text, max_length=512):
+    model.eval()  # Set the model to evaluation mode
+    
+    # Ensure model is on the correct device
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = model.to(device)
+
+    # Encode the input text
+    inputs = tokenizer.encode_plus(
+        text,
+        None,
+        add_special_tokens=True,
+        max_length=max_length,
+        padding='max_length',
+        return_token_type_ids=False,
+        return_tensors='pt',
+        truncation=True
+    )
+
+    # Move tensors to the correct device
+    inputs = {key: value.to(device) for key, value in inputs.items()}
+
+    # Make prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probabilities = torch.softmax(logits, dim=-1)
+        predicted_class_id = torch.argmax(probabilities, dim=-1).item()
+
+    return predicted_class_id
+
+
+(['INCIDENT', 'REQUEST'])[predict_description(model, tokenizer, """My ID card is not being detected.""")]
+
+```