task_categories:text-generation task_categories:text2text-generation task_categories:question-answering size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif datacraft
---
size_categories: n<1K
task_categories:
- text-generation
- text2text-generation
- question-answering
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: completion
    dtype: string
  - name: system_prompt
    dtype: string
  splits:
  - name: train
    num_bytes: 39971
    num_examples: 10
  download_size: 18677
  dataset_size: 39971
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---

<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 my-distiset-abf7c8d7

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/2mohammad/my-distiset-abf7c8d7/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/2mohammad/my-distiset-abf7c8d7/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "completion": "You can use the `dict.items()`, `sorted`, and `dict.fromkeys` methods to sort the dictionary items by their values. However, the standard approach is to use the `dict(sorted(students.items(), key=lambda: x[1]))` method.\n\nHere\u0027s a concise solution:\n```python\nstudents = {\n    \"Alice\": 90,\n    \"Bob\": 70,\n    \"Charlie\": 80,\n    \"David\": 60,\n    \"Emily\": 95\n}\n\n# Using the sorted function and lambda function to sort the items in the dictionary based on their values\nsorted_students = dict(sorted(students.items(), key=lambda: x[1]))\n\n# Optional: convert it to item\n# sorted_students = dict(sorted(students.items(), key=lambda: x[1]))\nprint(sorted_students)\n```\n\nThis will output:\n```python\n{\u0027David\u0027: 60, \u0027Bob\u0027: 70, \u0027Charlie\u0027: 80, \u0027Alice\u0027: 90, \u0027Emily\u0027: 95}\n```\n\nThe lambda function here is sorting the items in the dictionary based on their values, so the items with the lowest value come first. The dict.fromkeys() and dict() will create and return a new ordered dictionary, which in this case does not have to be ordered but this depends on many factors such as for hashing tables and has base index of zero.   However this might not be desirable for other use cases. This is the more clear concise item for students.\n\nThe dict.fromkeys() let\u0027s you define what will be the keys, and in this case the keys will be the students item names (Bob, Alice, David). let\u0027s you define what will be the keys, and in this case the keys will be the students item names (Bob, Alice, David).  Since we are hashing table and we are itemizing students, so associative table. The more clear concise item for students.\n\nThe associative table here is let\u0027s you define what will be the keys, and in this item for students case ped associative table.  let\u0027s you define what will be the keys, and in this item for students case associative table. The associative table is by no default mean that  will be for hashing tables and has base index of zero.   However this might not be desirable for other cases. The more clear concise item for students.\n\nThe associative table here is let\u0027s you item for students case ped associative table.  let\u0027s you define what will be the keys, and in this item for students case ped associative table.  let\u0027s you define what will be the keys. item for students case ped associative table.  let\u0027s you define what will be the keys, and in this item for students case. item for students case ped associative table.  let\u0027s you define what will be the keys, and in this item for students case. item for students ped associative table. item for students case ped associative table.  let\u0027s you define what will be the keys, and in this item for students case. item for students ped associative table. let\u0027s you define what will be for hashing tables and has base index of zero.   However this might not be desirable for other cases. item for ped associative table. item for students case ped associative table. let\u0027s item for students case ped associative table.  let\u0027s item for students case ped associative table.  ped associative table. item for students case ped associative table.  let\u0027s item for students case ped associative table.  let\u0027s item for students case ped associative table. item for students case ped associative table.  ped associative for hashing table and has base item for students case ped associative table.  let\u0027s item for students case ped and granules of pe and sand stone item for students case. ped associative table. item for item for students case ped associative table.  let\u0027s item for students case ped and granules for pe and sand stone item for students case. ped associative table.  let\u0027s item for students case ped for hashing tables and has base index of zero.   However this might not be desirable for other cases. ped associative table. item for students case ped associative table.  let\u0027s item for students case ped and granules for pe and sand stone item for students case. ped associative table. let\u0027s item item for students case ped associative table.  let\u0027s item for students case ped and ped and granules item for students case ped associative table.  let\u0027s item for students case ped and granules for pe and sand stone item for students case. item for students ped and granules item for students case ped associative table.  let\u0027s item for students ped and ped and ped and granules item for students case ped associative table.  let\u0027s item for students case ped and granules for pe and sand stone item for students case. ped associative table.  let\u0027s item for students case ped for hashing tables and has base index of zero.   However this might not be desirable for other cases.  item for students case ped associative table. ",
    "prompt": "You have a dictionary named students in which the keys are names and the values are numbers. You want to sort the students in ascending order based on their grades. Here is your dictionary:\n```python\nstudents = {\n    \"Alice\": 90,\n    \"Bob\": 70,\n    \"Charlie\": 80,\n    \"David\": 60,\n    \"Emily\": 95\n}\n```\n\nHow can I sort the dictionary and return it.",
    "system_prompt": "You are an expert programmer with advanced knowledge of Python. Your task is to provide concise and easy-to-understand solutions. Please answer the following python question. User questions are clear and concise"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("2mohammad/my-distiset-abf7c8d7", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("2mohammad/my-distiset-abf7c8d7")
```


</details>




