| | --- |
| | dataset_info: |
| | features: |
| | - name: text |
| | dtype: string |
| | - name: label |
| | dtype: int64 |
| | splits: |
| | - name: train |
| | num_bytes: 64 |
| | num_examples: 2 |
| | - name: test |
| | num_bytes: 51 |
| | num_examples: 2 |
| | download_size: 2726 |
| | dataset_size: 115 |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: train |
| | path: data/train-* |
| | - split: test |
| | path: data/test-* |
| | --- |
| | |
| | This dataset has been generated using: |
| |
|
| | ``` |
| | from datasets import Dataset, DatasetDict |
| | |
| | # Create a very small dataset |
| | data = { |
| | "text": [ |
| | "Hello, how are you?", |
| | "I am fine, thank you!", |
| | "Good morning!", |
| | "See you later!", |
| | ], |
| | "label": [0, 1, 0, 1], # Example binary labels |
| | } |
| | |
| | # Convert the data into a Hugging Face Dataset |
| | dataset = Dataset.from_dict(data) |
| | |
| | # Split into train and test sets |
| | dataset_dict = DatasetDict( |
| | { |
| | "train": dataset.select([0, 1]), |
| | "test": dataset.select([2, 3]), |
| | } |
| | ) |
| | |
| | # Push the dataset to the Hugging Face Hub |
| | dataset_name = "flex-e2e-super-tiny-dataset" # Replace with your desired dataset name |
| | dataset_dict.push_to_hub(dataset_name) |
| | |
| | print(f"Dataset '{dataset_name}' has been pushed to the Hugging Face Hub.") |
| | |
| | ``` |
| |
|