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Download README.md from flexaihq/flex-e2e-super-tiny-dataset: direct link, hf CLI and curl.
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https://huggingface.co/datasets/flexaihq/flex-e2e-super-tiny-dataset/resolve/main/README.md
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curl -L -o README.md https://huggingface.co/datasets/flexaihq/flex-e2e-super-tiny-dataset/resolve/main/README.md
1.18 kB
| 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 = "flexsystems/flex-e2e-super-tiny-dataset" | |
| dataset_dict.push_to_hub(dataset_name, private=False) | |
| print(f"Dataset '{dataset_name}' has been pushed to the Hugging Face Hub.") | |
| ``` |