Dataset Card for Q1652

This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into your Argilla server as explained in Load with Argilla, or used directly with the datasets library in Load with datasets.

Using this dataset with Argilla

To load with Argilla, you'll just need to install Argilla as pip install argilla --upgrade and then use the following code:

import argilla as rg

ds = rg.Dataset.from_hub("ClimatePolicyRadar/Q1652", settings="auto")

This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation.

Using this dataset with datasets

To load the records of this dataset with datasets, you'll just need to install datasets as pip install datasets --upgrade and then use the following code:

from datasets import load_dataset

ds = load_dataset("ClimatePolicyRadar/Q1652")

This will only load the records of the dataset, but not the Argilla settings.

Dataset Structure

This dataset repo contains:

• Dataset records in a format compatible with HuggingFace datasets. These records will be loaded automatically when using rg.Dataset.from_hub and can be loaded independently using the datasets library via load_dataset.
• The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.
• A dataset configuration folder conforming to the Argilla dataset format in .argilla.

The dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.

Fields

The fields are the features or text of a dataset's records. For example, the 'text' column of a text classification dataset of the 'prompt' column of an instruction following dataset.

Field Name	Title	Type	Required	Markdown
text	Text	text	True	True

Questions

The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.

Question Name	Title	Type	Required	Description	Values/Labels
entities	entities	span	True	N/A	N/A

Data Instances

An example of a dataset instance in Argilla looks as follows:

{
    "_server_id": "4026704f-0659-4939-b886-904d6e239dd1",
    "fields": {
        "text": "26 Central Statistical Agency. \"Ethiopia 2013 Labour Force Survey Addis Ababa. Last modified 2014. https://www.ilo.org/surveyLib/index.php/ catalog/7142/download/43668\n-----\nThe objective of this chapter is threefold. First, it presents estimates of investments needed to achieve NZE by mid-century under the three selected scenarios. Second, it estimates the gap between the needed and available investments to meet the net-zero targets. Third, it reviews the strategy to close the financial gap by describing the financing options associated with costs estimates derived from the CBA. The technical annex shows the assumptions, modelling definitions, and interventions that guided the financing actions of the LT-LEDS.\n-----\nThis chapter provides a data-driven blueprint, aligned with and complementary to the existing national climate finance policies and strategies for financing the transition to a zero-emission and climate-resilient pathway by mid-century."
    },
    "id": "997ca298-0bad-41d5-a9b4-6b68343bf39e",
    "metadata": {
        "coords": [
            [
                322,
                479
            ],
            [
                539,
                479
            ],
            [
                539,
                616
            ],
            [
                322,
                616
            ]
        ],
        "document_content_type": "application/pdf",
        "document_id": "UNFCCC.party.1745.0",
        "document_metadata.author": [
            "Ethiopia"
        ],
        "document_metadata.author_is_party": true,
        "document_metadata.collection_id": "nan",
        "document_metadata.date": "2023-06-21",
        "document_metadata.family_id": "nan",
        "document_metadata.family_name": "Ethiopia\u0027s long term low emission and climate resilient development strategy (2020-2050)",
        "document_metadata.family_slug": "nan",
        "document_metadata.geography_iso": "ETH",
        "document_metadata.role": "MAIN",
        "document_metadata.source": "GST",
        "document_metadata.status": "nan",
        "document_metadata.types": [
            "Long-Term Low-Emission Development Strategy"
        ],
        "document_metadata.variant": "nan",
        "document_name": "Ethiopia\u0027s long term low emission and climate resilient development strategy (2020-2050)",
        "document_source_url": "t/sites/default/files/resource/ETHIOPIA_%20LONG%20TERM%20LOW%20EMISSION%20AND%20CLIMATE%20RESILIENT%20DEVELOPMENT%20STRATEGY.pdf",
        "has_valid_text": true,
        "language": "en",
        "languages": [
            "en"
        ],
        "page_number": 91,
        "text": [
            "echnical annex shows the assumptions, modelling definitions, and interventions that guided the financing actions of the LT-LEDS."
        ],
        "text_block_id": "p_91_b_0",
        "translated": false,
        "type": "Text",
        "type_confidence": 0.9975045323371887
    },
    "responses": {
        "entities": [
            {
                "user_id": "19cc536b-c98d-4033-9880-b40477b55515",
                "value": [
                    {
                        "end": 963,
                        "label": "Q1652",
                        "score": null,
                        "start": 904
                    }
                ]
            }
        ]
    },
    "status": "completed",
    "suggestions": {},
    "vectors": {}
}

While the same record in HuggingFace datasets looks as follows:

null

Data Splits

The dataset contains a single split, which is train.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation guidelines

Highlight the entity if it is present in the text

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

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Contributions

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