| --- |
| 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 |
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| 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). |
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| ## Using this dataset with Argilla |
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| To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: |
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| ```python |
| import argilla as rg |
| |
| ds = rg.Dataset.from_hub("davanstrien/nob", settings="auto") |
| ``` |
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| This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation. |
|
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| ## Using this dataset with `datasets` |
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| 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: |
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| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("davanstrien/nob") |
| ``` |
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| This will only load the records of the dataset, but not the Argilla settings. |
|
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| ## Dataset Structure |
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| This dataset repo contains: |
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| * 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`. |
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| The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. |
|
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| ### Fields |
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| 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. |
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| | Field Name | Title | Type | Required | |
| | ---------- | ----- | ---- | -------- | |
| | text | text | text | True | |
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| ### Questions |
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| 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 | |
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| ### Data Splits |
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| The dataset contains a single split, which is `train`. |
|
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| ## Dataset Creation |
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| ### Curation Rationale |
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| [More Information Needed] |
|
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| ### Source Data |
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| #### Initial Data Collection and Normalization |
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| [More Information Needed] |
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| #### Who are the source language producers? |
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| [More Information Needed] |
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| ### Annotations |
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| #### Annotation guidelines |
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| ### Guidelines for Rating Educational Content |
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| Rate the content using these criteria: |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| #### Annotation process |
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| [More Information Needed] |
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| #### Who are the annotators? |
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| [More Information Needed] |
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| ### Personal and Sensitive Information |
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| [More Information Needed] |
|
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| ## Considerations for Using the Data |
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| ### Social Impact of Dataset |
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| [More Information Needed] |
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| ### Discussion of Biases |
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| [More Information Needed] |
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| ### Other Known Limitations |
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| [More Information Needed] |
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| ## Additional Information |
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| ### Dataset Curators |
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| [More Information Needed] |
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| ### Licensing Information |
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| [More Information Needed] |
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| ### Citation Information |
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| [More Information Needed] |
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| ### Contributions |
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| [More Information Needed] |
