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"