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.

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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

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Model Sources

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

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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")