README.md

---
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](https://github.com/argilla-io/argilla). As shown in the sections below, this dataset can be loaded into your Argilla server as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#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:

```python
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:

```python
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](#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'] |


<!-- check length of metadata properties -->

### 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]