{"text": "def fibonacci(n):\n    if n <= 1:\n        return n\n    return fibonacci(n-1) + fibonacci(n-2)"}
{"text": "The quick brown fox jumps over the lazy dog. This is a sample text for language modeling."}
{"text": "import torch\nimport torch.nn as nn\n\nclass TransformerModel(nn.Module):\n    def __init__(self, vocab_size, d_model):\n        super().__init__()"}
{"text": "# Machine Learning Pipeline\n\nThis document explains how to build an ML pipeline using Python and scikit-learn."}
{"text": "SELECT customer_id, COUNT(*) as order_count FROM orders WHERE order_date >= '2023-01-01' GROUP BY customer_id"}

{"target": [1.2, 1.5, 1.8, 2.0, 1.9, 1.7, 2.1, 1.8], "start": "2023-01-01", "freq": "D"}
{"target": [45.2, 48.1, 52.3, 49.7, 51.2, 47.8, 50.1], "start": "2023-01-01", "freq": "H"}
{"target": [100, 105, 110, 108, 112, 115, 118, 120], "start": "2023-01-01 00:00:00", "freq": "15T"}
{"target": [23.5, 24.1, 23.8, 24.7, 25.2, 24.9, 25.5], "start": "2023-06-15", "freq": "D"}