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.
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.
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.
