---
library_name: transformers
tags:
- symbolic-math
- sympy
- equation-classification
- text-classification
- math-assistant
- math-ml
- experimental
license: mit
---

# 🔢 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](https://colab.research.google.com/drive/1ONuLHDp8Y93U_RYXAwJANP8-FD4kpxmX#scrollTo=hxWHsGjJM1Wx)
- **Branch:** `ml-classify`   

---

# Intended Uses

## Direct Use
Load the classifier like any HuggingFace model:

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

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