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instruction null | model_name null | distilabel_metadata dict | response null |
|---|---|---|---|
null | null | {
"raw_input_text_generation_0": null,
"raw_input_text_generation_1": null,
"raw_output_text_generation_0": null,
"raw_output_text_generation_1": null
} | null |
Dataset Card for uplimit-synthetic-data-week-1-basic
This dataset has been created with distilabel.
The pipeline script was uploaded to easily reproduce the dataset: colab_kernel_launcher.py.
It can be run directly using the CLI:
distilabel pipeline run --script "https://huggingface.co/datasets/ramonactruta/uplimit-synthetic-data-week-1-basic/raw/main/colab_kernel_launcher.py"
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/ramonactruta/uplimit-synthetic-data-week-1-basic/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config "https://huggingface.co/datasets/ramonactruta/uplimit-synthetic-data-week-1-basic/raw/main/pipeline.yaml"
Dataset structure
The examples have the following structure per configuration:
Configuration: default
{
"distilabel_metadata": {
"raw_input_text_generation_0": null,
"raw_input_text_generation_1": null,
"raw_output_text_generation_0": null,
"raw_output_text_generation_1": null
},
"instruction": null,
"model_name": null,
"response": null
}
This subset can be loaded as:
from datasets import load_dataset
ds = load_dataset("ramonactruta/uplimit-synthetic-data-week-1-basic", "default")
Or simply as it follows, since there's only one configuration and is named default:
from datasets import load_dataset
ds = load_dataset("ramonactruta/uplimit-synthetic-data-week-1-basic")
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