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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: _id
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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: _id
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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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---
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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 cohere-wikipedia-2023-11-no-queries
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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/rasdani/cohere-wikipedia-2023-11-no-queries/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/rasdani/cohere-wikipedia-2023-11-no-queries/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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"_id": "20231101.no_262035_4",
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"model_name": "gpt-4o",
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"query": "Hva er eksekutivfunksjoner i nevropsykologi?",
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"score": 1.0,
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"text": "Evne til \u00e5 strukturere, planlegge, igangsette og kontrollere sammensatte handlinger (eksekutivfunksjoner).",
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"title": "Nevropsykologi",
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"url": "https://no.wikipedia.org/wiki/Nevropsykologi",
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"views": 728.6967671316081
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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("rasdani/cohere-wikipedia-2023-11-no-queries", "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("rasdani/cohere-wikipedia-2023-11-no-queries")
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```
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</details>
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