--- license: odc-by task_categories: - text-generation language: - en --- # Dataset Card for Fineweb Ultra Mini Fineweb Ultra Mini is a dataset derived from the original Fineweb dataset made by huggingface (see here: https://huggingface.co/datasets/HuggingFaceFW/fineweb). The dataset focuses on extracting high quality data from the Fineweb dataset, from the 1-0.5% range. If you would like more data, though slightly sacrificing quality check out fineweb ultra mini, which focuses on the 2-3% of high quality data originally found in fineweb. ## Dataset Details ### Dataset Description Below outlines the steps which were taken to curate this dataset: 1. Data Source: The original FineWeb dataset from Hugging Face. 2. Filtering: A text classification model was trained on H100s to identify the top 2-3% of documents. 3. Data Preparation: The selected documents were processed to ensure consistency and quality. 4. Dataset Creation: The filtered and processed data was organized into the Hugging Face dataset format. - **Curated by:** ReflexAI Open Source Engineering Team - **Funded by:** Modal Grant Program - **Language(s) (NLP):** English (more coming soon) - **License:** odc-by ## Intended Uses The fineweb-ultra-mini dataset is intended for a variety of natural language processing tasks, including: - Language Modeling: Training large language models on high-quality text data. - Text Summarization: Extracting key information from web documents. - Question Answering: Answering questions based on the information in web documents. - Text Classification: Categorizing web documents based on their content. ### Out-of-Scope Use ##### Commercial Use Commercial use is permitted under the ODC-By (Open Database License - Attribution) license, allowing individuals and organizations to use, modify, and distribute the dataset for commercial purposes, provided they comply with the attribution requirements. This includes using the dataset in commercial products, services, research, or any other profit-generating activity. However, it is important to highlight the following limitations and conditions: 1. Attribution Requirement: Any use of the dataset, including commercial use, must provide proper attribution to the original creators of the dataset. This attribution must be clear and visible in any products or services that incorporate the dataset. 2. Redistribution and Derivative Works: While derivative works (e.g., modified versions of the dataset) are permitted, they must also adhere to the attribution requirement. Moreover, any redistribution of the dataset must include the same ODC-By license to ensure that others are also informed of these conditions. #### Unethical Activities Despite the commercial use allowance, certain activities involving the dataset remain strictly out of scope due to ethical concerns. These include, but are not limited to: 1. Data Manipulation for Malicious Purposes: Using the dataset to manipulate or fabricate data in a way that misleads or harms individuals, communities, or organizations, including but not limited to creating deep fakes, misinformation, or defamation. 2. Surveillance and Privacy Violations: Using the dataset for surveillance activities or any practices that infringe on the privacy rights of individuals, including profiling, tracking, or unauthorized data collection, is strictly prohibited. 3. Discriminatory or Harmful Practices: Employing the dataset in ways that may promote or perpetuate discrimination, hate speech, violence, or any other harmful practices, including using the data to reinforce or exacerbate biases. 4. Violation of Laws and Regulations: Any use of the dataset that violates local, national, or international laws, such as engaging in illegal surveillance or using the dataset in activities prohibited by data protection and privacy laws, is not allowed. By adhering to these guidelines, users of the dataset can ensure its ethical and responsible use while contributing positively to commercial endeavors and research. ## Bias, Risks, and Limitations #### Bias Datasets often reflect the biases inherent in the data collection process, sources, or the methodology used in their creation. It is crucial to be aware of the following potential biases in the dataset: 1. Selection Bias: The dataset may not be fully representative of the entire population or domain it is intended to represent. Certain groups or perspectives might be overrepresented or underrepresented, which can lead to skewed outcomes when the dataset is used in analysis, modeling, or training AI systems. 2. Cultural Bias: If the dataset is sourced from a particular cultural or geographical context, it may carry cultural biases that are not universally applicable. For example, language use, social norms, and values represented in the data may not align with those of different cultures or regions. 3. Algorithmic Bias: When the dataset is used in machine learning or algorithmic processes, any existing biases in the data can be amplified or perpetuated by algorithms, potentially leading to discriminatory or unfair outcomes in automated decisions or predictions. #### Risks The use of this dataset carries several risks that users should be aware of, especially when deploying the data in real-world applications or commercial products: 1. Model Performance Risks: If the dataset is incomplete, imbalanced, or biased, it may result in inaccurate or unreliable models. This can impact decision-making, predictions, or analysis, especially in critical sectors such as healthcare, finance, or legal systems. 2. Reinforcement of Existing Inequalities: When used to train models or systems, biased datasets can reinforce existing societal inequalities. For example, models trained on biased data can inadvertently perpetuate stereotypes or exacerbate disparities in areas such as hiring, lending, and law enforcement. 3. Reputation Risks: Misuse or unethical use of the dataset may lead to reputational damage for both individuals and organizations. Public backlash can arise if the dataset is used in ways that are perceived as discriminatory, harmful, or unethical. 4. Legal and Regulatory Risks: Depending on the dataset’s content and intended use, there may be legal risks associated with its use, especially when dealing with sensitive or personally identifiable information (PII). Users should be cautious of potential violations of data privacy laws, including GDPR or CCPA, and ensure compliance with relevant regulations. #### Limitations While the dataset provides valuable insights and utility, it is important to recognize its inherent limitations: 1. Scope and Completeness: The dataset may not cover all aspects of the domain it represents, leaving gaps that could affect its applicability in certain contexts. Users should assess whether the dataset aligns with their specific use case and complement it with additional data if necessary. 2. Quality and Accuracy: The accuracy of the dataset may vary, and errors, inconsistencies, or outdated information could impact its reliability. It is essential to validate the dataset before using it in high-stakes applications. 3. Generalization: Models trained on the dataset may struggle to generalize well to new or unseen data, especially if the dataset is narrow in scope or lacks diversity. Overfitting to the dataset’s specific patterns can lead to poor performance in real-world scenarios. 4. Maintenance and Updates: Datasets can become outdated over time as the world evolves. Users should be prepared to update the dataset regularly to ensure it remains relevant and reflects current trends, information, and developments in the field. By understanding and addressing these biases, risks, and limitations, users can take a more informed and responsible approach to utilizing the dataset, ensuring its effectiveness and minimizing potential negative consequences. ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation (Coming Soon) **BibTeX:** (Coming Soon) **APA:** (Coming Soon)