MathGPT2 / README.md
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---
license: mit
base_model:
- distilbert/distilgpt2
tags:
- text-generation-inference
library_name: transformers
new_version: FlameF0X/MathGPT2.5
pipeline_tag: text-generation
---
# MathGPT-2 (distilgpt2 Fine-Tuned for Arithmetic)
This model is a **fine-tuned version of DistilGPT-2** on a custom dataset consisting exclusively of arithmetic problems and their answers. The goal of this model is to act as a **calculator** that can solve basic arithmetic problems.
## Benchmark
Link [here](https://huggingface.co/spaces/FlameF0X/Simple-Math-Benchmark).
## Model Description
The model was trained using a dataset of simple arithmetic expressions, including addition, subtraction, multiplication, and division. The training data was generated using Python and ensured to have **no duplicate expressions**.
### Key Features:
- **Solves basic arithmetic** (addition, subtraction, multiplication, division)
- Can **handle simple problems** like `12 + 5 =`
- Fine-tuned version of `distilgpt2` on a math-specific dataset
- Trained for **10 epochs** (further improvements can be made by training for more epochs)
## Model Details
- **Model architecture**: DistilGPT-2
- **Training duration**: 10 epochs (could be improved further)
- **Dataset**: Generated math expressions like `12 + 5 = 17`
- **Tokenization**: Standard GPT-2 tokenizer
- **Fine-tuned on**: Simple arithmetic operations
## Intended Use
This model is designed to:
- **Answer basic arithmetic problems** (addition, subtraction, multiplication, division).
- It can generate answers for simple problems like `12 * 6 = ?`.
### Example:
**Input**:
```
13 + 47 =
```
**Output**:
```
60
```
## Benchmark Results
We evaluated the model using a set of 10000 randomly generated math expressions to assess its performance. Here are the results:
- **Accuracy**: 76.3%
- **Average Inference Time**: 0.1448 seconds per question
---
## Training Data
The training dataset was generated using Python, consisting of random arithmetic expressions (addition, subtraction, multiplication, division) between numbers from 1 to 100. The expressions were formatted as:
```
2 + 3 = 5
100 - 25 = 75
45 * 5 = 225
100 / 25 = 4
```
No duplicate expressions were used, ensuring the model learns unique patterns.
## Fine-Tuning
This model was fine-tuned from the `distilgpt2` base model for 100 epochs.
---
## Limitations
- **Basic Arithmetic Only**: The model can only handle basic arithmetic problems like addition, subtraction, multiplication, and division. It does not handle more complex operations like exponentiation, logarithms, or advanced algebra.
- **Limited Training Duration**: While trained for 10 epochs, more epochs or data diversity may improve the model's performance further.
- **No real-time validation**: The model's performance varies, and there are still inaccuracies in answers for some problems.