README.md

---
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