| --- |
| license: afl-3.0 |
| language: |
| - en |
| base_model: |
| - google/flan-t5-xl |
| pipeline_tag: text-classification |
| tags: |
| - personality |
| --- |
| |
|
|
|
|
| ## Model Details |
|
|
| * **Model Type:** PersonalityClassifier is a fine-tuned model from `google/flan-t5-xl` using annotation data for personality classification. |
| * **Model Date:** PersonalityClassifier was trained in Jan 2024. |
| * **Paper or resources for more information:** [https://arxiv.org/abs/2504.06868](https://arxiv.org/abs/2504.06868) |
| * **Train data:** [https://huggingface.co/datasets/mirlab/personality_120000](https://huggingface.co/datasets/mirlab/personality_120000) |
| ## Requirements |
|
|
| * `torch==2.1.0` |
| * `transformers==4.29.0` |
|
|
| ## How to use the model |
|
|
| ```python |
| import torch |
| from transformers import T5ForConditionalGeneration, AutoTokenizer |
| |
| # Set device to CUDA if available, otherwise use CPU |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| |
| # Load model and tokenizer |
| model_name = "mirlab/PersonalityClassifier" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = T5ForConditionalGeneration.from_pretrained(model_name).to(device) |
| |
| # Define model inference function |
| def modelGenerate(input_text, lm, tokenizer): |
| # Tokenize input text and move to device |
| input_ids = tokenizer(input_text, truncation=True, padding=True, return_tensors='pt')['input_ids'].to(device) |
| |
| # Generate text using the model |
| model_output = lm.generate(input_ids) |
| |
| # Decode generated tokens into text |
| model_answer = tokenizer.batch_decode(model_output, skip_special_tokens=True) |
| |
| return model_answer |
| |
| # Example input text |
| # Format: "[Valence] Statement: [Your Statement]. Trait: [Target Trait]" |
| # Target Trait is among ["Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism", "Machiavellianism", "Narcissism", "Psychopathy"]. |
| # Valence indicates positive (+) or negative (-) alignment with the trait. |
| |
| input_texts = "[Valence] Statement: I am outgoing. Trait: Extraversion" |
| |
| # Generate output using the model and print |
| output_texts = modelGenerate(input_texts, model, tokenizer) |
| print(output_texts) |
