Author: Samer S. Najm (Sam)
Organization: AetherMind Project
Model Type: Knowledge-Distilled Transformer (Student Model)
Domain: Natural Language Inference (NLI) + Medical Reasoning (ADNI SRL)

🚀 Overview

AetherMind_SRL is the 12th-round refined version of AetherMind’s knowledge-distilled student model, trained using self-reflective learning (SRL), knowledge distillation, ADNI medical contradictions, and general-domain NLI datasets.

💡 Highlights

Improved contradiction detection
Strong general NLI performance
Lightweight and efficient
SRL-based iterative refinement

📊 Evaluation (Round 12 Final)

Dataset	Accuracy	Macro F1	Samples
SNLI	89.64%	89.55%	9,824
MNLI-M	90.20%	90.00%	9,815
MNLI-MM	89.61%	89.35%	9,832
ANLI R1	79.90%	79.89%	1,000
ANLI R2	67.50%	67.35%	1,000
ANLI R3	67.33%	66.81%	1,200

🧠 Self-Reflective Learning (SRL)

Train base model
Extract errors
Correct and retrain
Stabilize via KD + SRL loops

🔥 Teacher → Student Distillation

Teacher: microsoft/deberta-v3-base
Student: AetherMind_SRL

🛠 Usage Example

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "samerzaher80/AetherMind_SRL"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id).cuda()

premise = "The patient scored 28 on the MMSE last year."
hypothesis = "The patient shows signs of cognitive decline."

inputs = tokenizer(premise, hypothesis, return_tensors="pt").to("cuda")
with torch.no_grad():
    logits = model(**inputs).logits
    predicted = torch.argmax(logits, dim=-1).item()

labels = ["entailment", "neutral", "contradiction"]
print("Prediction:", labels[predicted])

🔧 Included Python Files

evaluate_round12.py
inference_srl_round12.py
train_round12_srl_kd.py
build_anli_global_error_buffer_round1.py
analyze_anli_errors_round1.py
srl_finetune_round5_smart.py

🧩 Metadata

json

{
  "tags": [
    "natural-language-inference",
    "knowledge-distillation",
    "biomedical-nlp",
    "aethermind",
    "nli",
    "self-reflective-learning",
    "transformers"
  ]
}