distilabel-internal-testing/fine-preferences-test-7

---
size_categories: n<1K
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 fine-preferences-test-7

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/fine-preferences-test-7/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/fine-preferences-test-7/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:

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

```json
{
"context": "Previous abstract Next abstract\nSession 40 - The Interstellar Medium.\nDisplay session, Tuesday, June 09\nGamma Ray Burst (GRB) explosions can make kpc-size shells and holes in the interstellar media (ISM) of spiral galaxies if much of the energy heats the local gas to above 10^7 K. Disk blowout is probably the major cause for energy loss in this case, but the momentum acquired during the pressurized expansion phase can be large enough that the bubble still snowplows to a kpc diameter. This differs from the standard model for the origin of such shells by multiple supernovae, which may have problems with radiative cooling, evaporative losses, and disk blow-out. Evidence for giant shells with energies of \\sim10^53 ergs are summarized. Some contain no obvious central star clusters and may be GRB remnants, although sufficiently old clusters would be hard to detect. The expected frequency of GRBs in normal galaxies can account for the number of such shells.\nProgram listing for Tuesday",
"conversation": null,
"distilabel_metadata": {
"raw_output_generate_conv_with_context_0": "What is the biggest explosion that can happen in space?\nAssistant: Gamma Ray Bursts (GRBs) are considered to be the most powerful explosions in the universe, releasing an enormous amount of energy in a very short period of time.\nHow do GRBs affect the surrounding space?\nAssistant: GRBs can create huge shells and holes in the interstellar medium, which is the material that fills the space between stars. This is because the energy released by the GRB heats up the local gas to extremely high temperatures.\nCan you tell me more about these shells and holes?\nAssistant: Yes, these shells and holes can be enormous, stretching up to a kiloparsec in size. They can also have a significant impact on the surrounding galaxy, affecting the formation of new stars and the overall structure of the galaxy.\nWhat causes these shells and holes to form?\nAssistant: The formation of these shells and holes is thought to be the result of the GRB\u0027s energy blowing out a huge bubble in the interstellar medium. This bubble can then expand and cool, forming a shell or hole.\nDo astronomers think that GRBs are common in the universe?\nAssistant: Yes, astronomers believe that GRBs are relatively common in the universe, and that they can occur in any galaxy that has stars forming in it. In fact, the frequency of GRBs is thought to be high enough to account for the number of giant shells and holes that are observed in galaxies.\nAre there any other ways that GRBs can affect the universe?"
},
"dump": "CC-MAIN-2013-20",
"file_path": "s3://commoncrawl/crawl-data/CC-MAIN-2013-20/segments/1368696381249/warc/CC-MAIN-20130516092621-00000-ip-10-60-113-184.ec2.internal.warc.gz",
"id": "\u003curn:uuid:e2300ad5-01dd-4e80-92b3-7ec88785cc9d\u003e",
"int_score": 3,
"language": "en",
"language_score": 0.9126408100128174,
"model_name": "meta-llama/Meta-Llama-3-70B-Instruct",
"score": 2.765625,
"token_count": 208,
"url": "http://aas.org/archives/BAAS/v30n2/aas192/abs/S040015.html"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("distilabel-internal-testing/fine-preferences-test-7", "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("distilabel-internal-testing/fine-preferences-test-7")
```

</details>

原始信息汇总

Dataset Card for fine-preferences-test-7

Dataset Summary

• Creation Tool: This dataset was created using distilabel.
• Reproduction: The dataset includes a pipeline.yaml file that can be used to reproduce the pipeline that generated it using the distilabel CLI.

Dataset Structure

• Configuration: The dataset has a default configuration.
• Example Structure: Each example in the dataset includes fields such as context, conversation, distilabel_metadata, dump, file_path, id, int_score, language, language_score, model_name, score, token_count, and url.

Loading the Dataset

• Default Configuration: The dataset can be loaded using the datasets library with the following code: python from datasets import load_dataset

  ds = load_dataset("distilabel-internal-testing/fine-preferences-test-7")