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---
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 |