shamEiNew/symbolic-math-classifier

🔢 Symbolic Math Expression Classifier (Experimental)

This model is an early-stage classifier for SymPy-style symbolic expressions. It predicts the type of symbolic mathematical input so that the it can route the query to the appropriate symbolic solver (convexity, derivatives, integrals, equations, systems, etc.).

👉 This model lives in the development branch `ml-classify` on github and is still under active experimentation.

⚠️ This is NOT production-ready. Accuracy is still low on complex expressions and multi-variable cases. Bugs and misclassifications are expected.

Model Details

Model Description

This model takes pure SymPy syntax strings as input and classifies them into categories like:

• —expression
• —equation
• —system_of_equations
• —linear_equation
• —nonlinear_equation
• —derivative (e.g., diff(x**3, x))
• —integral (e.g., Integral(sin(x), x))
• —convexity_problem

It is trained on synthetic symbolic data generated using SymPy templates.

Metadata

• —Developed for: Math Verification of LLM response
• —Finetuned from: RoBERTa (depending on your notebook)
• —Language: English + SymPy tokens
• —Format: Plain text
• —License: MIT

Model Sources

• —Repository: https://github.com/math_neuro-ao
• —Training Notebook: symbolic_classifier.ipynb
• —Branch: ml-classify

Intended Uses

Direct Use

Load the classifier like any HuggingFace model:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("<your-username>/symbolic-math-classifier")
model = AutoModelForSequenceClassification.from_pretrained("<your-username>/symbolic-math-classifier")