---
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
```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"
  ]
}
```