task_categories:text-generation task_categories:text2text-generation task_categories:text-retrieval task_categories:question-answering language:ru size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif datacraft
---
language:
- ru
size_categories: n<1K
task_categories:
- text-generation
- text2text-generation
- text-retrieval
- question-answering
dataset_info:
  features:
  - name: context
    dtype: string
  - name: question
    dtype: string
  - name: response
    dtype: string
  splits:
  - name: train
    num_bytes: 126511
    num_examples: 100
  download_size: 34878
  dataset_size: 126511
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-32091474

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/Ivan000/my-distiset-32091474/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/Ivan000/my-distiset-32091474/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "context": "15 \u0442\u0440\u0435\u043d\u0434\u043e\u0432\u044b\u0445 \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0435\u0439 \u043d\u0430 2025 \u0433\u043e\u0434\n\n\u0421 \u043a\u0430\u0436\u0434\u044b\u043c \u0433\u043e\u0434\u043e\u043c \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438 \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442\u0430 \u0438 \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0435\u0439 \u0441\u0442\u0430\u043d\u043e\u0432\u044f\u0442\u0441\u044f \u0432\u0441\u0435 \u0431\u043e\u043b\u0435\u0435 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u044b\u043c\u0438 \u0438 \u0440\u0430\u0437\u043d\u043e\u043e\u0431\u0440\u0430\u0437\u043d\u044b\u043c\u0438. \u0412 2025 \u0433\u043e\u0434\u0443 \u043c\u044b \u043d\u0430\u0431\u043b\u044e\u0434\u0430\u0435\u043c \u0437\u043d\u0430\u0447\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0439 \u0440\u043e\u0441\u0442 \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u0430 \u043a \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u044f\u043c, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043d\u0430\u0445\u043e\u0434\u044f\u0442 \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u0432 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u0441\u0444\u0435\u0440\u0430\u0445, \u043e\u0442 \u0431\u0438\u0437\u043d\u0435\u0441\u0430 \u0434\u043e \u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u044f. \u0412 \u044d\u0442\u043e\u0439 \u0441\u0442\u0430\u0442\u044c\u0435 \u043c\u044b \u0440\u0430\u0441\u0441\u043c\u043e\u0442\u0440\u0438\u043c 15 \u0442\u0440\u0435\u043d\u0434\u043e\u0432\u044b\u0445 \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0435\u0439, \u0440\u0430\u0437\u0431\u0438\u0432 \u0438\u0445 \u043d\u0430 \u0442\u0440\u0438 \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0438\u0438: \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044f \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439, \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044f \u0432\u0438\u0434\u0435\u043e \u0438 \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044f \u0442\u0435\u043a\u0441\u0442\u0430.",
    "question": "What are the 15 trending neural networks of 2025 and how are they categorized into image generation, video generation, and text generation?",
    "response": "Unfortunately, the provided text snippet does not explicitly list the 15 trending neural networks of 2025. However, it does mention that they will be categorized into three categories: \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044f \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439 (image generation), \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044f \u0432\u0438\u0434\u0435\u043e (video generation), and \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044f \u0442\u0435\u043a\u0441\u0442\u0430 (text generation).\n\nIt seems that the article is promising to discuss these 15 trends but does not provide the specific names of the neural networks. To provide a more accurate answer, I would need more information from the document or the full article."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("Ivan000/my-distiset-32091474", "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("Ivan000/my-distiset-32091474")
```


</details>




