README.md

---
license: apache-2.0
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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"