🔢 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")

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Safetensors

Model size

0.1B params

Tensor type

F32