--- 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'] | ### 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]