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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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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-99581698
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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/zero-to-agi/my-distiset-99581698/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/zero-to-agi/my-distiset-99581698/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": 3,
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"text": "Unfortunately, I\u0027ve been trying to purchase the new Apple Watch for weeks, but every time I visit the website, it says that it\u0027s out of stock. I\u0027ve even tried visiting stores, but they claim to have none in stock either. Is it really sold out, or is there a problem with the inventory system?"
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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("zero-to-agi/my-distiset-99581698", "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("zero-to-agi/my-distiset-99581698")
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```
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</details>
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