README.md

metadata

library_name: transformers
tags:
  - nli
  - natural-language-inference
  - knowledge-distillation
  - srl
  - self-reflective-learning
  - biomedical-nlp
  - aethermind
  - student-model
license: mit
datasets:
  - your-dataset-name-here
language:
  - en
pipeline_tag: text-classification
model_name: AetherMind_SRL
model_creator: samerzaher80

📘 AetherMind_SRL – Self-Reflective Learning NLI Model

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)

1. Train base model
2. Extract errors
3. Correct and retrain
4. Stabilize via KD + SRL loops

🔥 Teacher → Student Distillation

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

🛠 Usage Example

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

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