Add some usage code

README.md

@@ -1,3 +1,88 @@
 ---
 license: mit
 ---
+
+
+```python
+class HelpdeskReviewModel(nn.Module):
+    def __init__(self):
+        super(HelpdeskReviewModel, self).__init__()
+        self.bert = RobertaModel.from_pretrained('roberta-base')
+        self.drop = nn.Dropout(p=0.2)
+        
+        # Fully connected layers
+        self.fc1 = nn.Linear(self.bert.config.hidden_size, 512)  # First fully connected layer
+        self.fc2 = nn.Linear(512, 256)  # Second fully connected layer
+        self.fc3 = nn.Linear(256, 128)  # Third fully connected layer
+
+        # Activation function
+        self.relu = nn.ReLU()
+        
+        self.output = nn.Linear(128, 4)  # 4 outputs
+        self.sigmoid = nn.Sigmoid()
+
+        state_dict = torch.hub.load_state_dict_from_url(f"https://huggingface.co/KameronB/SITCCSA/resolve/main/pytorch_model.bin")
+        # if running on cpu
+        # state_dict = torch.hub.load_state_dict_from_url(f"https://huggingface.co/KameronB/SITCCSA/resolve/main/pytorch_model.bin", map_location=torch.device('cpu'))
+
+        self.load_state_dict(state_dict)
+
+    def forward(self, input_ids, attention_mask):
+        _, pooled_output = self.bert(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            return_dict=False
+        )
+        output = self.drop(pooled_output)
+        
+        # apply new hidden layers
+        output = self.relu(self.fc1(output))
+        output = self.relu(self.fc2(output))
+        output = self.relu(self.fc3(output))
+
+        return self.sigmoid(self.output(output))
+```
+
+```python
+def make_prediction(model, tokenizer, text, max_length=512):
+    # Tokenize the input text and convert to tensor
+    inputs = tokenizer.encode_plus(
+        text,
+        add_special_tokens=True,  # Add '[CLS]' and '[SEP]'
+        max_length=max_length,    # Pad & truncate all sentences.
+        padding='max_length',
+        truncation=True,
+        return_tensors="pt"       # Return PyTorch tensors.
+    )
+
+    # Move tensors to the same device as model
+    input_ids = inputs['input_ids']
+    attention_mask = inputs['attention_mask']
+    if torch.cuda.is_available():
+        input_ids = input_ids.cuda()
+        attention_mask = attention_mask.cuda()
+        model = model.cuda()
+
+    # Make prediction
+    with torch.no_grad():
+        outputs = model(input_ids, attention_mask=attention_mask)
+
+    # Return probabilities
+    return outputs.cpu().numpy()[0]  # Return to CPU and convert to numpy array if not running on CPU
+
+# Example usage of the function
+texts = [
+    "This Agent is TERRIBLE!: The agent I spoke to on the phone did not seem to have any idea of what he was doing.",
+    "Excellent work!: The tech that installed my software was amazing! Thank you!",
+    "Good Work: The person who anwsered the phone did a pretty good job. It took a bit longer than I would have liked, but they got the job done.",
+    "Bad Computer: My Computer is a piece of junk!!!",
+    "Poor Service: I sent David and email and it took him over 30 seconds to respond. The service is so slow that I missed the solar eclipse.",
+    "Very Slow: The technician was very slow.",
+    "Thank you!: Stanley did a great job installing my software!",
+    "You need better training: These agents need better training, they cant even seem to do simple troubleshooting.",
+    "The technician threatened my life: The technician threatened my life!"
+]
+for text in texts:
+    probabilities = make_prediction(model, tokenizer, text)
+    print(probabilities)
+```