README.md

metadata

tags:
  - rlfh
  - argilla
  - human-feedback
dataset_info:
  features:
    - name: text
      dtype: string
    - name: id
      dtype: string
    - name: metadata
      struct:
        - name: data_id
          dtype: string
        - name: date
          dtype: string
        - name: dump
          dtype: string
        - name: file_path
          dtype: string
        - name: lang_code
          dtype: string
        - name: language
          dtype: string
        - name: language_score
          dtype: float64
        - name: language_script
          dtype: string
        - name: minhash_cluster_size
          dtype: int64
        - name: url
          dtype: string
  splits:
    - name: train
      num_bytes: 4095429
      num_examples: 1000
  download_size: 2391077
  dataset_size: 4095429
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Dataset Card for nob

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("davanstrien/nob", 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("davanstrien/nob")

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
Educational Value	Educational Value of the content	label_selection	True	N/A	['None', 'Minimal', 'Basic', 'Good', 'Excellent', '❗ Problematic Content ❗']
Language ID correct?	Is this text in the expected language	label_selection	True	N/A	['yes', 'no']

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
language_score	Language Score	float	-	True

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

Guidelines for Rating Educational Content

Rate the content using these criteria:

1️⃣ NO EDUCATIONAL VALUE

• No educational purpose whatsoever
• Pure entertainment, ads, or personal content
• Nothing to learn from this content ✓ Examples: • Social media conversations about daily life • Online shopping product listings • Advertisement pages • Personal blog posts about someone's day • Forum discussions about entertainment • Comment sections • Sports match reports

2️⃣ MINIMAL EDUCATIONAL VALUE

• Contains a few facts or pieces of information
• Mostly non-educational content
• Information is incidental or not the main focus ✓ Examples: • News article that mentions some historical facts • Travel blog with basic information about a location • Product review with some technical details • Company website with brief industry information • Recipe that briefly explains a cooking technique • Entertainment article with occasional facts

3️⃣ BASIC EDUCATIONAL CONTENT

• Attempts to explain or teach something
• Information might be scattered or disorganized
• Mixed with non-educational content ✓ Examples: • Basic how-to guide with ads • Simple Wikipedia-style article • Blog post explaining a concept but lacking depth • Amateur tutorial video transcript • Brief explanation of a scientific concept • Quick overview of a historical event

4️⃣ GOOD EDUCATIONAL CONTENT

• Clear teaching purpose
• Well-organized information
• Suitable for learning
• May have some minor limitations ✓ Examples: • Detailed tutorial with clear steps • Well-written educational blog post • Comprehensive guide to a topic • Clear explanation of a scientific process • Structured learning material • Educational website article with examples

5️⃣ EXCELLENT EDUCATIONAL CONTENT

• Outstanding teaching material
• Clear structure and thorough explanations
• Includes helpful examples
• No distracting content ✓ Examples: • Professional educational resource • Well-crafted learning module • In-depth guide with clear examples • Comprehensive educational article • High-quality teaching material • Expert explanation with practical applications

6️⃣ PROBLEMATIC CONTENT

• Wrong language
• Unreadable or corrupted text
• Inappropriate content
• Machine-generated nonsense ✓ Examples: • Text in a different language than expected • Garbled characters or formatting • Clearly AI-generated spam content • Inappropriate or offensive material • Broken/partial webpage content • Content that's too technical to evaluate

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]