Create README.md

README.md

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+---
+license: apache-2.0
+---
+
+The following provides the code to implement the task of detecting personality from an input text.
+
+
+#import packages
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+model = AutoModelForSequenceClassification.from_pretrained("Kevintu/Personality_LM")
+tokenizer = AutoTokenizer.from_pretrained("Kevintu/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
+
+# Perform the prediction
+with torch.no_grad():
+    outputs = model(**encoded_input)
+
+# 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"]
+
+# Print the predicted personality traits scores
+for trait, score in zip(trait_names, predicted_scores):
+    print(f"{trait}: {score:.4f}")
+
+##"output": "agreeableness: 0.4600000000; openness: 0.2700000000; conscientiousness: 0.3100000000; extraversion: 0.1000000000; neuroticism: 0.8400000000"