Add model card and documentation

README.md

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+---
+language: en
+license: mit
+tags:
+- scibert
+- classification
+- technical-papers
+- machine-learning
+---
+
+# AEGIS SciBERT Technical Classifier
+
+## Model Description
+Fine-tuned SciBERT model for classifying technical papers into research categories.
+
+## Training Details
+- **Base Model**: allenai/scibert_scivocab_uncased
+- **Training Samples**: 500
+- **Number of Classes**: 6
+- **Classes**: cs.AI, cs.LG, quant-ph, cs.NE, stat.ML, cs.CV
+- **Validation Accuracy**: 1.0000
+
+## Usage
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("gsstec/aegis-scibert-technical")
+tokenizer = AutoTokenizer.from_pretrained("gsstec/aegis-scibert-technical")
+
+# Example inference
+text = "Quantum computing algorithms for machine learning"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+predictions = torch.softmax(outputs.logits, dim=-1)
+```
+
+## Classes
+- `cs.AI`: Class 0
+- `cs.LG`: Class 1
+- `quant-ph`: Class 2
+- `cs.NE`: Class 3
+- `stat.ML`: Class 4
+- `cs.CV`: Class 5