Update README.md

README.md

@@ -7,4 +7,55 @@ base_model:
 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)
+
+## 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)