🧠 Alexithymia & ADHD Predictor
A probabilistic MLP+SMOTED SVM Based classifier for early screening insights into emotional awareness and attention-related traits.
🧩 Model Details
- Model Type:
bertopic-based transformer classifier - Dataset: Alexithymia-ADHD
- Task:
text-classification - Languages: English 🇬🇧
- Metrics Tracked:
Accuracy,Precision,Recall,F1
🔍 Use Cases
- 🧠 Early research-based screening tool
- 📊 Self-reflection on attention/emotion patterns
- 🧪 Experimental psychology & behavioral NLP
🚀 Quick Start
from transformers import pipeline
classifier = pipeline("text-classification", model="sankalp-indish/alexithymia-adhd-predictor")
classifier("I often find it hard to describe how I feel.")
📈 Example Output
The model takes in short user inputs and returns a probabilistic score for both Alexithymia and ADHD traits.
| Input Text | Alexithymia Score | ADHD Score |
|---|---|---|
| "I often struggle to explain what I'm feeling." | 1 | 0 |
| "I get distracted easily and can't finish tasks on time." | 0 | 1 |
| "Sometimes I can't tell if I'm anxious or excited." | 1 | 0 |
| "I daydream a lot and forget what I'm doing." | 0 | 1 |
🧪 These values are probabilities, not diagnoses.
Scores above 0.5 and 0.3 (based on the availability of the dataset) suggest strong presence of that trait.
🧠 Behind the Model
- This classifier is trained on combined NLP embeddings from real-world psychological scale responses. It uses probabilistic classification to predict traits, not diagnoses. And currently is scale and input of text based
- Framework: bertopic + sklearn
- Fine-tuned over custom-labeled dataset
- Designed for interpretability & simplicity
⚠️ Disclaimer
This tool is not a substitute for clinical diagnosis. It is provided for research and educational use only. Please consult a qualified mental health professional for clinical assessment.👨💻 Author
Developed by Sankalp Indish [sankalp_indish] https://huggingface.co/sankalp-indish