Update README.md

README.md

@@ -15,28 +15,22 @@ The following provides the code to implement the task of detecting personality f
 
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import torch
-model = AutoModelForSequenceClassification.from_pretrained("Kevintu/Personality_LM")
+model = AutoModelForSequenceClassification.from_pretrained("KevSun/Personality_LM")
 tokenizer = AutoTokenizer.from_pretrained("KevSun/Personality_LM")
 
-
 # Example new text input
 #new_text = "I really enjoy working on complex problems and collaborating with others."
 
-
-# Define the path to your text file
 file_path = 'path/to/your/textfile.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 same 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 in this case, or GPU if available)
-model.eval()  # Set the model to evaluation mode
+#model.eval()  # Set the model to evaluation mode
 
 # Perform the prediction
 with torch.no_grad():
@@ -45,7 +39,6 @@ with torch.no_grad():
 # Get the predictions (the output here depends on whether you are doing regression or classification)
 predictions = outputs.logits.squeeze()
 
-
 # Assuming the model is a regression model and outputs raw scores
 predicted_scores = predictions.numpy()  # Convert to numpy array if necessary
 trait_names = ["Agreeableness", "Openness", "Conscientiousness", "Extraversion", "Neuroticism"]