Datasets:

distilabel-internal-testing
/

deita-no-normalization

	---
	size_categories: n<1K
	config_names:
	- deita_filtering
	tags:
	- synthetic
	- distilabel
	- rlaif
	---

	<p align="left">
	<a href="https://github.com/argilla-io/distilabel">
	<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
	</a>
	</p>

	# Dataset Card for deita-no-normalization

	This dataset has been created with [Distilabel](https://distilabel.argilla.io/).

	## Dataset Summary

	This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

	```console
	distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/deita-no-normalization/raw/main/pipeline.yaml"
	```

	or explore the configuration:

	```console
	distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/deita-no-normalization/raw/main/pipeline.yaml"
	```

	## Dataset structure

	The examples have the following structure per configuration:


	<details><summary> Configuration: deita_filtering </summary><hr>

	```json
	{
	"answer": "The code snippet provided is correct and does not contain any logical errors.",
	"completion": "The original code counts the number of lowercase vowels in a string, but the count should probably be case-insensitive. Here is a case-insensitive version.\r\n\r\nfunction countVowels(str) {\r\n let vowels = [\u0027a\u0027, \u0027e\u0027, \u0027i\u0027, \u0027o\u0027, \u0027u\u0027];\r\n let count = 0;\r\n \r\n for (let i = 0; i \u003c str.length; i++) {\r\n if (vowels.includes(str[i].toLowerCase())) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}",
	"conversation": [
	{
	"content": "Can you find and correct any logical errors in the following code snippet and output the corrected code?\r\n\r\nfunction countVowels(str) {\r\n let vowels = [\u0027a\u0027, \u0027e\u0027, \u0027i\u0027, \u0027o\u0027, \u0027u\u0027];\r\n let count = 0;\r\n \r\n for (let i = 0; i \u003c str.length; i++) {\r\n if (vowels.includes(str[i])) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}",
	"role": "user"
	},
	{
	"content": "The code snippet provided is correct and does not contain any logical errors.",
	"role": "assistant"
	}
	],
	"deita_score": 1.0,
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	],
	"evol_instruction_score": 1.0,
	"evol_response_score": 1.0,
	"evolved_instruction": "Can you find and correct any logical errors in the following code snippet and output the corrected code?\r\n\r\nfunction countVowels(str) {\r\n let vowels = [\u0027a\u0027, \u0027e\u0027, \u0027i\u0027, \u0027o\u0027, \u0027u\u0027];\r\n let count = 0;\r\n \r\n for (let i = 0; i \u003c str.length; i++) {\r\n if (vowels.includes(str[i])) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}",
	"evolved_response": "The code snippet provided is correct and does not contain any logical errors.",
	"meta": {
	"category": "Natural language to code",
	"completion": "The original code counts the number of lowercase vowels in a string, but the count should probably be case-insensitive. Here is a case-insensitive version.\r\n\r\nfunction countVowels(str) {\r\n let vowels = [\u0027a\u0027, \u0027e\u0027, \u0027i\u0027, \u0027o\u0027, \u0027u\u0027];\r\n let count = 0;\r\n \r\n for (let i = 0; i \u003c str.length; i++) {\r\n if (vowels.includes(str[i].toLowerCase())) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}",
	"id": 6,
	"input": null,
	"motivation_app": null,
	"prompt": "Can you find and correct any logical errors in the following code snippet and output the corrected code?\r\n\r\nfunction countVowels(str) {\r\n let vowels = [\u0027a\u0027, \u0027e\u0027, \u0027i\u0027, \u0027o\u0027, \u0027u\u0027];\r\n let count = 0;\r\n \r\n for (let i = 0; i \u003c str.length; i++) {\r\n if (vowels.includes(str[i])) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}",
	"source": "surge",
	"subcategory": "Debugging"
	},
	"model_name": "gpt-3.5-turbo",
	"nearest_neighbor_distance": 0.12795357273014518
	}
	```

	This subset can be loaded as:

	```python
	from datasets import load_dataset

	ds = load_dataset("distilabel-internal-testing/deita-no-normalization", "deita_filtering")
	```

	</details>