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README.md
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---
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dataset_info:
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features:
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- name: text
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data_files:
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- split: train
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path: data/train-*
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---
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---
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size_categories: n<1K
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task_categories:
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- text-classification
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dataset_info:
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features:
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- name: text
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data_files:
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- split: train
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path: data/train-*
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tags:
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- synthetic
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- distilabel
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- rlaif
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- datacraft
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---
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<p align="left">
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<a href="https://github.com/argilla-io/distilabel">
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<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
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</a>
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</p>
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# Dataset Card for my-distiset-acc23032
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This dataset has been created with [distilabel](https://distilabel.argilla.io/).
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## Dataset Summary
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This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
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```console
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distilabel pipeline run --config "https://huggingface.co/datasets/hendyfergus93/my-distiset-acc23032/raw/main/pipeline.yaml"
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```
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or explore the configuration:
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```console
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distilabel pipeline info --config "https://huggingface.co/datasets/hendyfergus93/my-distiset-acc23032/raw/main/pipeline.yaml"
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```
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## Dataset structure
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The examples have the following structure per configuration:
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<details><summary> Configuration: default </summary><hr>
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```json
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{
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"label": 1,
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"text": "I\u0027ve noticed that vaping products are gaining popularity among my younger cousins, but I\u0027m worried about the health risks associated with them. Has anyone tried quitting vape? How difficult is it to stop?"
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}
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```
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This subset can be loaded as:
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```python
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from datasets import load_dataset
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ds = load_dataset("hendyfergus93/my-distiset-acc23032", "default")
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```
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Or simply as it follows, since there's only one configuration and is named `default`:
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```python
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from datasets import load_dataset
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ds = load_dataset("hendyfergus93/my-distiset-acc23032")
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```
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</details>
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