Update README.md

README.md

@@ -10,36 +10,50 @@ license: apache-2.0
   The following code shows how to test in the model.
   python
   ```
-import torch
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
 
-# Load the model and tokenizer from the directory where it's saved
-model_path = "model"
+# Load model and tokenizer
+model_path = "model"  # Ensure this path points to the correct directory
 model = AutoModelForSequenceClassification.from_pretrained(model_path)
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 
-# Function to prepare and make predictions on text
-def predict_climate_att(text):
-    # Encode the text using the tokenizer
-    encoded_input = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=64)
-    
-    # Evaluate the model on the encoded text
-    model.eval()
-    with torch.no_grad():
-        outputs = model(**encoded_input)
-    
-    # Extract logits (the outputs of the model before any final activation function)
-    logits = outputs.logits.squeeze()
-
-    # (Optional) Apply a final activation function if necessary (e.g., softmax for classification)
-    # probabilities = torch.softmax(logits, dim=0)
-
-    # For now, let's just return the raw logits
-    return logits
-
-# Example usage
-text = "Your example text goes here."
-predictions = predict_climate_att(text)
-print(predictions)
-
-'''
+# Define the path to your text file
+file_path = 'cl.txt'
+
+# Read the content of the file
+with open(file_path, 'r', encoding='utf-8') as file:
+    new_text = file.read()
+
+# Encode the text using the tokenizer used during training
+encoded_input = tokenizer(new_text, return_tensors='pt', padding=True, truncation=True, max_length=64)
+
+# Move the model to the correct device (CPU or GPU if available)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)  # Move model to the correct device
+encoded_input = {k: v.to(device) for k, v in encoded_input.items()}  # Move tensor to the correct device
+
+model.eval()  # Set the model to evaluation mode
+
+# Perform the prediction
+with torch.no_grad():
+    outputs = model(**encoded_input)
+
+# Get the predictions (assumes classification with labels)
+predictions = outputs.logits.squeeze()
+
+# Assuming softmax is needed to interpret the logits as probabilities
+probabilities = torch.softmax(predictions, dim=0)
+
+# Define labels for each class index based on your classification categories
+labels = ["risk", "neutral", "opportunity"]
+predicted_index = torch.argmax(probabilities).item()  # Get the index of the max probability
+predicted_label = labels[predicted_index]
+predicted_probability = probabilities[predicted_index].item()
+
+# Print the predicted label and its probability
+print(f"Predicted Label: {predicted_label}, Probability: {predicted_probability:.4f}")
+
+##the output example: predicted Label: neutral, Probability: 0.8377
+
+```