| --- |
| 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. |
