Dataset Card for scilake-maritime

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("SIRIS-Lab/scilake-maritime", 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("SIRIS-Lab/scilake-maritime")

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
text	Text	text	True
links	Linked entities	text	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
span_label	Select and classify the tokens according to the specified categories.	span	True	N/A	['vesselType']
assess_ner	Extracted entity validation	label_selection	True	Are the extracted entities correct?	['Correct', 'Partially correct', 'Incorrect']
assess_nel	Linked AIS entity validation	label_selection	True	Are the linked entities in the AIS taxonomy correct?	['Correct', 'Partially correct', 'Incorrect']
comments	Comments	text	False	Additional comments	N/A

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

Vessel type validation guidelines

Task Description

Your task is to validate the extraction of vessel type entities and their linking to their closest matching entries in the AIS taxonomy.

What to Validate

For each record, please verify the following:

1. Entity Spans: Are all text spans correctly identified? Are the span boundaries accurate?
2. Entity Types: Are entity types correctly assigned?
3. Entity Linking: Are the matching entities in the AIS taxonomy correctly assigned?

Instructions

1. Carefully read the texts.
2. Review the NER spans and correct them if:

• The boundaries (start/end) are incorrect
• The entity label is wrong

3. Verify that the extracted entities are correctly linked to their closest match in the AIS taxonomy
4. Add any comments or feedback you deem relevant

Validation Guidelines

• Entity Annotations: Mark spans as "Correct" only if boundaries and labels are accurate.
• Entity Extraction: Mark as "Correct" if all energy (storage) types mentioned are extracted; "Partially correct" if any are missing or incorrect.
• IRENA Linking: Mark as "Correct" if all links are to the appropriate entries. Use "Partially correct" if any are incorrect.

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

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

[More Information Needed]

Contributions

[More Information Needed]