README.md

---
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: output
    dtype: string
  splits:
  - name: train
    num_bytes: 15268888.05
    num_examples: 487500
  - name: test
    num_bytes: 391509.95
    num_examples: 12500
  download_size: 12160789
  dataset_size: 15660398.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# Simple Math

Just like my teacher gave me homework, i thought maybe we can also add some of these basics on the trainings of our models.

It was created with this code, if you add more complex operations and so.. please share the code :D thank you
```py
import random
# Define the number of samples you want to generate
num_samples = 500000
# Define the range for the random numbers
min_value = -99.99
max_value = 99.99
# Define the arithmetic operations
operations = ['+', '-', '*', '/']
# Generate data
data = []
for _ in range(num_samples):
    num1 = float("%.3f" % random.uniform(min_value, max_value))
    num2 = float("%.3f" % random.uniform(min_value, max_value))
    while num2 == 0.0:
         num2 = float("%.3f" % random.uniform(min_value, max_value))
    while num1 == 0.0:
         num1 = float("%.3f" % random.uniform(min_value, max_value))
    operation = random.choice(operations)
    if operation == '/':
        result = num1 / num2
    elif operation == '-':
        result = num1 - num2
    elif operation == '*':
        result = num1 * num2
    elif operation == '+':
        result = num1 + num2
    output = "%.4f" % result
    instruction = f"{num1} {operation} {num2}"
    data.append({'instruction': instruction, 'output': output})
# Create the dataset
import json
out_file = 'arithmetic-float4a.json'
with open(out_file, 'w') as f:
    json.dump(data, f)
```

If you use Simple Math o train your model, please cite on the modelcard or the paper.
Thank you