---
license: apache-2.0
size_categories: n<1K
dataset_info:
features:
- name: page_content
dtype: string
- name: filename
dtype: string
- name: parent_section
dtype: string
- name: url
dtype: string
- name: embedding
sequence: float64
- name: token_count
dtype: int64
- name: generated_questions
dtype: 'null'
splits:
- name: train
num_bytes: 8661527.2
num_examples: 1656
- name: test
num_bytes: 2165381.8
num_examples: 414
download_size: 7242796
dataset_size: 10826909.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
tags:
- synthetic
- distilabel
- rlaif
---
# Dataset Card for rag_qa_embedding_questions_0_60_0
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/zenml/rag_qa_embedding_questions_0_60_0/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/zenml/rag_qa_embedding_questions_0_60_0/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
Configuration: default
```json
{
"anchor": "a Validator is such an example.\n\nRelated concepts:the Artifact Store is a type of Stack Component that needs to be registered as part of your ZenML Stack.\n\nthe objects circulated through your pipelines are serialized and stored in the Artifact Store using Materializers. Materializers implement the logic required to serialize and deserialize the artifact contents and to store them and retrieve their contents to/from the Artifact Store.\n\nWhen to use it\n\nThe Artifact Store is a mandatory component in the ZenML stack. It is used to store all artifacts produced by pipeline runs, and you are required to configure it in all of your stacks.\n\nArtifact Store Flavors\n\nOut of the box, ZenML comes with a local artifact store already part of the default stack that stores artifacts on your local filesystem. Additional Artifact Stores are provided by integrations:\n\nArtifact Store Flavor Integration URI Schema(s) Notes Local local built-in None This is the default Artifact Store. It stores artifacts on your local filesystem. Should be used only for running ZenML locally. Amazon S3 s3 s3 s3:// Uses AWS S3 as an object store backend Google Cloud Storage gcp gcp gs:// Uses Google Cloud Storage as an object store backend Azure azure azure abfs:// , az:// Uses Azure Blob Storage as an object store backend Custom Implementation custom custom Extend the Artifact Store abstraction and provide your own implementation\n\nIf you would like to see the available flavors of Artifact Stores, you can use the command:\n\nzenml artifact-store flavor list\n\nEvery Artifact Store has a path attribute that must be configured when it is registered with ZenML. This is a URI pointing to the root path where all objects are stored in the Artifact Store. It must use a URI schema that is supported by the Artifact Store flavor. For example, the S3 Artifact Store will need a URI that contains the s3:// schema:\n\nzenml artifact-store register s3_store -f s3 --path s3://my_bucket\n\nHow to use it",
"distilabel_metadata": {
"raw_output_generate_sentence_pair_0": "## Positive\n\nWhat is an example of a Validator in the ZenML documentation?\n\n## Negative\n\nHow can I implement a custom Artifact Store in AWS?"
},
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],
"filename": "https://docs.zenml.io/stack-components/artifact-stores",
"generated_questions": null,
"model_name": "gpt-4o",
"negative": "How can I implement a custom Artifact Store in AWS?",
"parent_section": "stack-components",
"positive": "What is an example of a Validator in the ZenML documentation?",
"token_count": 393,
"url": "https://docs.zenml.io/stack-components/artifact-stores"
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("zenml/rag_qa_embedding_questions_0_60_0", "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("zenml/rag_qa_embedding_questions_0_60_0")
```