metadata
license: apache-2.0
size_categories: n<1K
dataset_info:
features:
- name: anchor
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'
- name: positive
dtype: string
- name: negative
dtype: string
- name: distilabel_metadata
struct:
- name: raw_output_generate_sentence_pair_0
dtype: string
- name: model_name
dtype: string
splits:
- name: train
num_bytes: 9267266
num_examples: 1656
download_size: 5939623
dataset_size: 9267266
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
Dataset Card for rag_qa_embedding_questions_0_60_0
This dataset has been created with distilabel.
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:
distilabel pipeline run --config "https://huggingface.co/datasets/zenml/rag_qa_embedding_questions_0_60_0/raw/main/pipeline.yaml"
or explore the configuration:
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
{
"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 context of ZenML?\n\n## Negative\n\nHow do I bake a cake using a conventional oven?"
},
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],
"filename": "https://docs.zenml.io/stack-components/artifact-stores",
"generated_questions": null,
"model_name": "gpt-4o",
"negative": "How do I bake a cake using a conventional oven?",
"parent_section": "stack-components",
"positive": "What is an example of a Validator in the context of ZenML?",
"token_count": 393,
"url": "https://docs.zenml.io/stack-components/artifact-stores"
}
This subset can be loaded as:
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:
from datasets import load_dataset
ds = load_dataset("zenml/rag_qa_embedding_questions_0_60_0")