Update README.md

README.md

@@ -8,31 +8,42 @@ pipeline_tag: text-generation
 tags:
 - MBTI
 - psychology
-- Q-generation
+- question-generation
 - profiling
+- random-question-generator
 ---
 
-# Flan-T5 Base — MBTI Question Generator
-Fine-tuned version of **Flan-T5-Base** on a custom MBTI dataset for generating personality-related interview questions.
+# Flan-T5 Base — MBTI Random Question Generator
+
+This repository hosts a fine-tuned version of **google/flan-t5-base**, adapted for generating *random, personality-themed questions* in the context of the **Myers–Briggs Type Indicator (MBTI)** framework.
+
+The model produces short, standalone prompts designed to encourage self-reflection and discussion related to personality traits, emotions, and decision-making.  
+It operates as a **randomized question generator** rather than an interactive conversational model.
+
 ---
-## 🧩 Model Overview
 
-**Task:** Text Generation  
-**Architecture:** Encoder-Decoder (Seq2Seq)  
-**Framework:** 🤗 Hugging Face Transformers  
-**Platform:** Kaggle GPU (A100)  
-**Author:** [f3nsmart](https://huggingface.co/f3nsmart)
+## Model Purpose
+
+The goal of this model is to generate concise, psychologically relevant questions similar to those found in MBTI-style interviews or self-assessment forms.  
+Each output question is intended to provoke reflection or reveal an aspect of human cognition, motivation, or behavior.
+
+**Key Characteristics:**
+- Generates *independent questions* — no memory or contextual carryover between generations.  
+- Optimized for **single-turn usage** (no long-term dialogue support).  
+- Produces diverse questions across multiple MBTI domains (e.g., intuition, sensing, thinking, feeling, judging, perceiving).  
+- Ideal for personality research tools, psychological chatbots, or training datasets for reflective AI dialogue.
 
-This model is trained to generate **thoughtful, personality-driven interview questions** similar to MBTI self-assessment prompts.
 ---
-## 🧠 Example Usage
+
+## Example Usage
+
 ```python
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 model = AutoModelForSeq2SeqLM.from_pretrained("f3nsmart/ft-flan-t5-base-qgen")
 tokenizer = AutoTokenizer.from_pretrained("f3nsmart/ft-flan-t5-base-qgen")
 
-prompt = "Describe how you make decisions under pressure."
+prompt = "Generate a question about emotional decision-making."
 inputs = tokenizer(prompt, return_tensors="pt")
 outputs = model.generate(**inputs, max_new_tokens=60)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))