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A recent wildfire in the Los Ríos region of Chile has caused significant damage to a 2,500-hectare area of native forest, leading to the displacement of several local families and the closure of a major highway. The fire is believed to have been caused by human activity, with an investigation underway to determine the exact cause.	[]
A wildfire that broke out in the Los Ríos region on February 10, 2022, covered an area of approximately 12,500 hectares, affecting 3,200 hectares of agricultural land and 7,000 hectares of protected forest. Fortunately, the fire was brought under control within 72 hours, with only 1 reported injury and no fatalities. The economic impact is estimated to be around $3.5 million, mainly due to the loss of agricultural crops.	[]

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A recent wildfire in the Los Ríos region of Chile has caused significant damage to a 2,500-hectare area of native forest, leading to the displacement of several local families and the closure of a major highway. The fire is believed to have been caused by human activity, with an investigation underway to determine the exact cause.

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A wildfire that broke out in the Los Ríos region on February 10, 2022, covered an area of approximately 12,500 hectares, affecting 3,200 hectares of agricultural land and 7,000 hectares of protected forest. Fortunately, the fire was brought under control within 72 hours, with only 1 reported injury and no fatalities. The economic impact is estimated to be around $3.5 million, mainly due to the loss of agricultural crops.

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Dataset Card for my-distiset-e9ebee4c

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/RexCats/my-distiset-e9ebee4c/raw/main/pipeline.yaml"

or explore the configuration:

distilabel pipeline info --config "https://huggingface.co/datasets/RexCats/my-distiset-e9ebee4c/raw/main/pipeline.yaml"

Dataset structure

The examples have the following structure per configuration:

Configuration: default

{
    "labels": [],
    "text": "A recent wildfire in the Los R\u00edos region of Chile has caused significant damage to a 2,500-hectare area of native forest, leading to the displacement of several local families and the closure of a major highway. The fire is believed to have been caused by human activity, with an investigation underway to determine the exact cause."
}

This subset can be loaded as:

from datasets import load_dataset

ds = load_dataset("RexCats/my-distiset-e9ebee4c", "default")

Or simply as it follows, since there's only one configuration and is named default:

from datasets import load_dataset

ds = load_dataset("RexCats/my-distiset-e9ebee4c")