gsstec/aegis-scibert-technical

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

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