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license: apache-2.0 |
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--- |
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In this project, we have refined the capabilities of a pre-existing model to assess **the Big Five personality traits** for a given text/sentence. By meticulously fine-tuning this model using a specially curated dataset tailored for personality traits, it has learned to correlate specific textual inputs with distinct personality characteristics. This targeted approach has significantly enhanced the model's precision in identifying the Big Five personality traits from text, outperforming other models that were developed or fine-tuned on more generalized datasets. |
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The **accuracy** reaches 80%, and **F1 score** is 79%. Both are much higher than the similar personality-detection models hosted in huggingface. In other words, our model remarkably outperforms other models. |
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Due to the fact that the output values are continuous, it is better to use mean squared errors (MSE) or mean absolute error (MAE) to evaluate the model's performance. |
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When both metrics are smaller, it indciates that the model performs better. Our models performance: **MSE: 0.07**, **MAE: 0.14**. |
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Please **cite**: |
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``` |
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article{wang2024personality, |
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title={Continuous Output Personality Detection Models via Mixed Strategy Training}, |
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author={Rong Wang, Kun Sun}, |
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year={2024}, |
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journal={ArXiv}, |
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url={https://arxiv.org/abs/2406.16223} |
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} |
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``` |
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The project of predicting human cognition and emotion, and training details are available at: https://github.com/fivehills/detecting_personality |
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The following provides the code to implement the task of detecting personality from an input text. |
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```python |
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#import packages |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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import torch |
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model = AutoModelForSequenceClassification.from_pretrained("KevSun/Personality_LM") |
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tokenizer = AutoTokenizer.from_pretrained("KevSun/Personality_LM") |
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# Example new text input |
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#new_text = "I really enjoy working on complex problems and collaborating with others." |
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file_path = 'path/to/your/textfile.txt' |
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with open(file_path, 'r', encoding='utf-8') as file: |
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new_text = file.read() |
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# Encode the text using the same tokenizer used during training |
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encoded_input = tokenizer(new_text, return_tensors='pt', padding=True, truncation=True, max_length=64) |
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# Move the model to the correct device (CPU in this case, or GPU if available) |
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#model.eval() # Set the model to evaluation mode |
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# Perform the prediction |
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with torch.no_grad(): |
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outputs = model(**encoded_input) |
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# Get the predictions (the output here depends on whether you are doing regression or classification) |
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predictions = outputs.logits.squeeze() |
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# Assuming the model is a regression model and outputs raw scores |
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predicted_scores = predictions.numpy() # Convert to numpy array if necessary |
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trait_names = ["Agreeableness", "Openness", "Conscientiousness", "Extraversion", "Neuroticism"] |
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# Print the predicted personality traits scores |
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for trait, score in zip(trait_names, predicted_scores): |
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print(f"{trait}: {score:.4f}") |
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##"output": "agreeableness: 0.46; openness: 0.27; conscientiousness: 0.31; extraversion: 0.1; neuroticism: 0.84" |
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``` |
