README.md

metadata

tags:
  - rlfh
  - argilla
  - human-feedback

Dataset Card for Batch_2

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("etdvprg/Batch_2", 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("etdvprg/Batch_2")

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

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
entity_type	Highlight entities using the following entity types.	span	True	N/A	['Person-Individual', 'Person-Collective', 'Organization-Political', 'Organization-Government', 'Organization-Military', 'Organization-Other', 'Location', 'Object', 'Time', 'Event-Local', 'Event-International', 'Production-Media', 'Production-Government', 'Production-Doctrine', 'Numerical Statistics']
note	Additional Notes	text	False	N/A	N/A

Metadata

The metadata is a dictionary that can be used to provide additional information about the dataset record.

Metadata Name	Title	Type	Values	Visible for Annotators
Source	Source	terms	-	True
Year	Year	terms	-	True
Publication	Publication	terms	-	True
Issue	Issue	terms	-	True
Page Number	Page Number	terms	-	True
Remarks	Remarks	terms	-	True
Row_Index	Row_Index	terms	-	False

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

Entity Annotation Guidelines

Highlight any valid named entities (proper names) you find in the given text snippet. Refer to the annotation guidelines for additional instructions and a more detailed description of each entity type.

Open this link for the article images.

Additional Instructions:

• Annotate nested entity types as a whole e.g. for Jose Rizal University, Jose Rizal would not be a separate entity.
• Annotate coordinated entities separately i.e. entities delimited by commas, coordinating conjunctions, and prepositions would all be separate individual entries.
• Annotate acronyms and abbreviations separately e.g. Department of Health (DOH) would be annotated as Department of Health and(<ORG-GOV> DOH </ORG-GOV>).

Brief Entity Type Description

---

Choose the most appropriate entity from the ff:

1. Person (PER): The name of a person.

   • Individual (PER - IND): The name of an individual person. Annotate fullname, include titles if any, separate other named entities. e.g. "Ferdinand E. Marcos", "Cardinal Jaime Sin"
   • Article Author (PER - AUTHOR): The author of the given article
   • Collective (PER - COLL): An entity that refers to more than one individual. Note that it must refer to actual people (not abstract entities or organizations). It must also have a proper name — something capitalized and specific, not just “the soldiers” or “the committee.” e.g. "the Beatles", "Lava Brothers", "Mga Marcos."

2. Organization (ORG): Commercial, educational, entertainment, government, media, medical-science, non-governmental, religious, and sports organizations

   • Political Organization (ORG - POL) - National/international political parties, progressive activist groups (e.g. "CPP", "Gabriela")
   • International/National Government Organization (ORG - GOV) - International/national government organization (e.g. DENR, DOST, "Estados Unidos" as a geopolitical entity)
   • Military organization (ORG - MIL) - Formal armed forces, branches, units, or militant groups, armed wings, geopolitical military alliances (e.g., AFP, NPA, NATO, 42nd Infantry Battalion)
   • Other Organizations / Groups (ORG - OTHER) - Companies, clubs, educational institutions (e.g. PLDT, CBCP, UP, Free Masons). * Don't annotate demonyms and generic non-proper name group mentions

3. Events (EVENT): Named occurrences significant historical, political, social, or cultural occurrences.

   • Local Event (EVENT- LOCAL) - Events that transpired place in the Philippines (e.g. "Plaza Miranda bombings", "Martial Law"). Concurrent events that are also taking place in other countries (e.g. "Pasko) are assumed to be local unless stated otherwise.
   • International Event (EVENT - INTL) - International events that occurred outside the Philippines (e.g. "Watergate Scandal", "Vietnam War"). Also includes any mentions of issues or conflicts that primarily impact the world at large (e.g. WWII, Global Warming, Spanish Influenza).

4. Location (LOC): buildings, cities, regions, streets, countries, bodies of water, land masses e.g. "Plaza Miranda", "Mt. Mayon", "Ongpin St."

5. Object (OBJ): physical object names, model or brand names e.g. "M-16", "Humvee", "Volkswagen Beetle". Do not annotate government documents and issuances as objects.

6. TIME (OBJ): specific dates, season, historical periods, or date ranges e.g. "September 21, 1972", "1972–1986", "‘Kapaskuhan’", "Araw ng Halalan". The time of day is not included

7. Production (PROD): media productions as well as ideologies e.g. "Tempo", "The Manila Times", "Pasismo"

   • Media Productions (PROD - MEDIA) - newspapers (+ names of publications), magazines, broadcasts (e.g., "Radio Veritas", "DZBB")
   • Government Issuances (PROD - GOV) - Republic acts, mandates, court orders, anything produced/legislated by the government as a whole.
   • Doctrines (PROD - DOCT) - Political, philosophical, religious, sectarian doctrines (e.g., "Sindicalismo", "Marxism-Leninism-Maoism", "Katolisismo")

8. Numerical Statistics (STAT) - Monetary amounts, prices, percentages, quantities e.g., ₱100, 500 pesos, 30%, 80 porsiyentong, 10 kilometro. Don't include ordinal, nominal, and positional numbers.

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]