{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"fineweb","slug":"fineweb","name":"FineWeb","type":"dataset","url":"https://huggingface.co/datasets/HuggingFaceFW/fineweb","page_url":"https://unfragile.ai/fineweb","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"fineweb__cap_0","uri":"capability://data.processing.analysis.multi.stage.web.data.filtering.pipeline","name":"multi-stage web data filtering pipeline","description":"Implements a cascading filtration architecture across 96 Common Crawl snapshots spanning 2013-2024, combining URL-level filtering, language detection via statistical classifiers, and learned quality classification using a trained neural model. Each stage progressively reduces noise before deduplication, enabling systematic removal of low-quality, non-English, and spam content at scale across petabyte-scale web corpora.","intents":["I need to curate high-quality English web text at scale for LLM pre-training without manual annotation","I want to understand which filtering stages remove the most noise and at what computational cost","I need to replicate this filtering approach on my own web crawl data"],"best_for":["ML teams training foundation models from scratch","researchers studying data quality impact on model performance","organizations building proprietary datasets using Common Crawl as a base"],"limitations":["Filtering pipeline is not open-sourced — only the final deduplicated dataset is released, preventing direct reproduction or modification of individual filter stages","Language detection and quality classifier details are undisclosed, limiting ability to adapt filters for non-English or domain-specific use cases","No incremental update mechanism — entire pipeline must be re-run for new Common Crawl snapshots rather than delta processing"],"requires":["Access to Common Crawl WARC files (2013-2024 snapshots)","Computational infrastructure for distributed processing of petabyte-scale data","Hugging Face account for dataset access"],"input_types":["WARC files from Common Crawl","Raw HTML/text content"],"output_types":["Filtered, deduplicated text documents","Metadata (source URL, quality scores)"],"categories":["data-processing-analysis","content-filtering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fineweb__cap_1","uri":"capability://data.processing.analysis.minhash.based.deduplication.at.petabyte.scale","name":"minhash-based deduplication at petabyte scale","description":"Applies MinHash locality-sensitive hashing to identify and remove duplicate and near-duplicate documents across the entire 15 trillion token corpus. This probabilistic fingerprinting approach enables efficient detection of duplicates without storing full document hashes, using a configurable number of hash functions to control false positive/negative rates while maintaining linear memory complexity relative to unique documents rather than total documents.","intents":["I need to remove duplicate documents from a massive web crawl without storing full hashes in memory","I want to understand the deduplication rate and its impact on model training efficiency","I need to deduplicate my own dataset using the same approach as FineWeb"],"best_for":["data engineers processing web-scale corpora (100GB+)","researchers studying the impact of deduplication on model convergence","teams building datasets where storage and memory efficiency are critical constraints"],"limitations":["MinHash is probabilistic — configurable false positive rate means some duplicates may be missed or some unique documents incorrectly flagged depending on hash function count","Deduplication parameters (number of hash functions, threshold) are not publicly disclosed, preventing exact reproduction","No streaming deduplication — entire corpus must be processed in a batch pass, requiring significant temporary storage"],"requires":["Sufficient RAM to store MinHash signatures for all unique documents (typically 100s of GB for 15T tokens)","Distributed computing framework (Spark, Ray, or custom) for parallel processing","Access to the filtered document corpus before deduplication"],"input_types":["Text documents (variable length)","Document identifiers or URLs"],"output_types":["Deduplicated document set","Deduplication statistics (duplicate rate, removed document count)"],"categories":["data-processing-analysis","deduplication"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fineweb__cap_2","uri":"capability://data.processing.analysis.temporal.web.crawl.composition.and.versioning","name":"temporal web crawl composition and versioning","description":"Aggregates and deduplicates content across 96 distinct Common Crawl snapshots spanning 12 years (2013-2024), maintaining temporal coherence while preventing snapshot-specific duplicates from inflating the corpus. The architecture treats each snapshot as an independent data source, applies deduplication across snapshot boundaries, and produces a unified dataset that captures the evolution of web content without temporal bias or redundancy.","intents":["I want to train on diverse web content from multiple time periods without snapshot-specific duplicates skewing the distribution","I need to understand how web content quality and composition changed over 2013-2024","I want to create a dataset that includes historical web content for temporal robustness"],"best_for":["researchers studying how model performance varies with training data temporal diversity","teams building models that need to understand web content evolution","organizations wanting a dataset less biased toward recent web content than single-snapshot approaches"],"limitations":["No snapshot-level granularity in the released dataset — cannot isolate or weight content by time period after deduplication","Deduplication across snapshots may remove legitimate temporal variations (e.g., updated versions of pages) that could be valuable for certain tasks","No metadata indicating which snapshot each document originated from, limiting temporal analysis or time-aware fine-tuning"],"requires":["Access to all 96 Common Crawl snapshots (2013-2024)","Distributed storage for intermediate snapshot processing","Deduplication infrastructure capable of cross-snapshot comparison"],"input_types":["Common Crawl WARC snapshots (96 distinct versions)"],"output_types":["Unified deduplicated corpus","Snapshot composition statistics"],"categories":["data-processing-analysis","dataset-composition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fineweb__cap_3","uri":"capability://data.processing.analysis.benchmark.validated.dataset.quality.assurance","name":"benchmark-validated dataset quality assurance","description":"Validates dataset quality through downstream model training and evaluation on aggregate benchmarks (MMLU, ARC, HellaSwag, TruthfulQA, Winogrande, GSM8K, and others), demonstrating that models trained on FineWeb consistently outperform those trained on alternative open datasets. This empirical validation approach uses standardized evaluation protocols to quantify the impact of filtering and deduplication choices on model capability.","intents":["I want to verify that my dataset filtering choices actually improve model performance before investing in large-scale training","I need to compare my dataset quality against established baselines like C4 and Dolma","I want to understand which filtering stages contribute most to downstream performance gains"],"best_for":["data scientists designing filtering pipelines for pre-training datasets","researchers publishing datasets and needing empirical validation of quality claims","teams deciding between open datasets for model training based on performance impact"],"limitations":["Benchmark validation is computationally expensive — requires training multiple models to convergence, limiting iteration speed during pipeline development","Aggregate benchmark performance may not reflect quality for specialized domains (code, scientific text, non-English) where FineWeb's general-purpose filtering may be suboptimal","No ablation study results published showing the individual contribution of each filtering stage, making it unclear which stages drive the performance gains","Benchmark results are snapshot-in-time — performance may vary with different model architectures, training procedures, or evaluation protocols"],"requires":["Computational resources for training multiple LLMs to convergence (100s of GPU hours)","Standardized benchmark suite (MMLU, ARC, HellaSwag, etc.)","Baseline results from competing datasets (C4, Dolma, RedPajama)"],"input_types":["Candidate datasets","Benchmark evaluation tasks"],"output_types":["Benchmark scores (accuracy, F1, etc.)","Comparative performance tables","Statistical significance tests"],"categories":["data-processing-analysis","quality-assurance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fineweb__cap_4","uri":"capability://data.processing.analysis.language.specific.content.filtering.and.detection","name":"language-specific content filtering and detection","description":"Applies statistical language detection to identify and filter for English-language content across the entire web crawl, removing non-English documents before quality classification and deduplication. The detection mechanism uses trained classifiers (likely based on character n-grams or neural models) to distinguish English from other languages with high precision, enabling the pipeline to focus computational resources on English content while maintaining dataset homogeneity.","intents":["I need to ensure my dataset contains only English text without manually reviewing millions of documents","I want to understand the language composition of my web crawl before and after filtering","I need to adapt language detection for a different language or multilingual dataset"],"best_for":["teams building English-specific LLMs and needing to filter multilingual web crawls","researchers studying the impact of language purity on model performance","organizations building datasets for non-English languages using similar pipelines"],"limitations":["Language detection classifier details are not disclosed — cannot assess false positive/negative rates or adapt for edge cases (code-heavy pages, transliterated text, mixed-language content)","No support for multilingual datasets — filtering is English-only, requiring separate pipelines for other languages","Language detection may incorrectly classify or remove content with significant code, mathematical notation, or transliterated text that should be retained","No confidence scores or language probability distributions provided — binary English/non-English classification without nuance"],"requires":["Trained language detection model (architecture and weights not provided)","Raw text or HTML content from web crawl","Computational resources for inference across petabyte-scale corpus"],"input_types":["Raw text documents","HTML content"],"output_types":["English-language documents","Language detection statistics (% English, % other languages)"],"categories":["data-processing-analysis","language-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fineweb__cap_5","uri":"capability://data.processing.analysis.trained.quality.classification.with.learned.patterns","name":"trained quality classification with learned patterns","description":"Applies a neural quality classifier trained on human-annotated examples to identify and filter low-quality documents, moving beyond heuristic rules to capture nuanced quality signals. The classifier learns patterns associated with spam, boilerplate, low-information content, and other quality issues, enabling detection of subtle quality problems that rule-based approaches miss. Classification scores are used to threshold documents, removing those below a learned quality boundary.","intents":["I want to remove low-quality web content without manually defining quality rules or heuristics","I need to understand what patterns the quality classifier learned and how to adapt it for my domain","I want to compare the quality filtering effectiveness against rule-based or statistical baselines"],"best_for":["teams building pre-training datasets and wanting to leverage learned quality signals rather than hand-crafted rules","researchers studying the relationship between document-level quality and downstream model performance","organizations with domain-specific quality requirements who want to fine-tune a quality classifier"],"limitations":["Quality classifier training data and annotation guidelines are not disclosed — cannot assess bias, coverage, or applicability to specialized domains","No classifier weights or architecture details provided — cannot inspect learned patterns, perform error analysis, or adapt for different quality definitions","Quality threshold is fixed and not tunable — users cannot adjust the precision/recall tradeoff for their use case","Classifier may be biased toward the annotation guidelines used during training, which may not align with all use cases (e.g., technical documentation, academic papers)"],"requires":["Trained quality classification model (not provided separately)","Human-annotated training data for quality assessment (not released)","Computational resources for inference across entire corpus"],"input_types":["Text documents","Document metadata (URL, source)"],"output_types":["Quality scores","Filtered documents (above quality threshold)","Quality filtering statistics"],"categories":["data-processing-analysis","quality-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fineweb__cap_6","uri":"capability://data.processing.analysis.distributed.dataset.hosting.and.streaming.access","name":"distributed dataset hosting and streaming access","description":"Hosts the 15 trillion token dataset on Hugging Face Hub infrastructure, enabling streaming download and access without requiring local storage of the entire corpus. The dataset is split into manageable chunks and can be accessed via the Hugging Face datasets library with automatic caching, allowing researchers to load subsets or stream data on-demand. This architecture supports both batch pre-training workflows and interactive exploration.","intents":["I want to train a model on FineWeb without downloading the entire 15 trillion token corpus to local storage","I need to explore a sample of the dataset to understand its content and quality before committing to full training","I want to integrate FineWeb into my training pipeline with minimal setup overhead"],"best_for":["researchers with limited local storage who want to stream data during training","teams using Hugging Face ecosystem tools (transformers, accelerate) for pre-training","organizations exploring the dataset before deciding on full-scale training runs"],"limitations":["Streaming access introduces network latency — not optimal for training pipelines with high I/O requirements unless data is cached locally","Dataset splits and chunking strategy are not documented — unclear how to efficiently sample specific subsets or control data distribution","No built-in support for custom filtering or sampling at access time — must download and process locally to apply domain-specific filters","Requires Hugging Face account and internet connectivity — not suitable for air-gapped or offline training environments"],"requires":["Hugging Face account","Python 3.7+","huggingface_hub library","Internet connectivity for streaming","Sufficient local storage for caching (optional but recommended for performance)"],"input_types":["Dataset configuration (split, subset selection)"],"output_types":["Streamed text documents","Batched data for training","Dataset metadata"],"categories":["data-processing-analysis","dataset-distribution"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fineweb__cap_7","uri":"capability://data.processing.analysis.reproducible.dataset.composition.documentation","name":"reproducible dataset composition documentation","description":"Provides detailed documentation of dataset composition, filtering stages, and benchmark validation results, enabling researchers to understand the dataset's construction and make informed decisions about its suitability for their use cases. Documentation includes filtering statistics (documents removed at each stage), deduplication rates, language composition, and comparative benchmark results against competing datasets.","intents":["I need to understand exactly how FineWeb was constructed to assess its suitability for my use case","I want to know the filtering statistics and deduplication rates to estimate data loss and quality tradeoffs","I need to compare FineWeb's composition against C4, Dolma, and RedPajama to choose the best dataset"],"best_for":["researchers making informed dataset selection decisions","teams building custom datasets and wanting to understand FineWeb's approach","organizations publishing datasets and needing to document their construction methodology"],"limitations":["Documentation does not include filtering pipeline code or trained model weights — cannot reproduce the exact filtering process","Quality classifier training data and annotation guidelines are not disclosed — cannot assess bias or adapt the classifier","No ablation studies showing the individual contribution of each filtering stage — unclear which stages drive performance gains","Benchmark results are limited to aggregate benchmarks — no domain-specific evaluation (code, scientific text, etc.)"],"requires":["Access to Hugging Face dataset card and documentation","Understanding of dataset filtering concepts and metrics"],"input_types":["Dataset documentation and metadata"],"output_types":["Composition statistics","Filtering stage breakdown","Benchmark comparison tables","Quality assessment insights"],"categories":["data-processing-analysis","documentation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fineweb__cap_8","uri":"capability://data.processing.analysis.open.source.dataset.release.with.reproducibility","name":"open-source dataset release with reproducibility","description":"Releases FineWeb as an open-source dataset on Hugging Face Hub with full documentation, enabling researchers to download, analyze, and build upon the curated corpus. The release includes dataset cards, filtering methodology documentation, and benchmark results, supporting reproducibility and enabling community contributions to data curation techniques. The dataset is versioned and maintained, with clear provenance tracking from Common Crawl snapshots to final corpus.","intents":["I need access to a large-scale, high-quality English pretraining dataset for LLM training","I want to understand the data curation methodology and filtering decisions","I need to reproduce or extend the FineWeb curation pipeline for my own datasets"],"best_for":["LLM researchers training foundation models with open-source data","Data engineers building custom pretraining datasets based on FineWeb methodology","Academic teams studying data curation and its impact on model performance"],"limitations":["Dataset is English-only; multilingual pretraining requires separate curation efforts","Full dataset (15 trillion tokens) is very large; downloading and processing requires significant storage and compute","Filtering methodology is documented but not fully open-sourced; reproducing exact pipeline requires reverse-engineering","Dataset is static; no incremental updates or versioning strategy for new Common Crawl snapshots","No built-in tools for dataset analysis or quality inspection; users must implement custom analysis"],"requires":["Hugging Face account for dataset access","Storage capacity for 15 trillion tokens (estimated 100+ TB)","Network bandwidth for downloading dataset","Compute resources for processing and analyzing dataset"],"input_types":["Hugging Face Hub API or direct download links"],"output_types":["FineWeb pretraining corpus in standard formats (JSONL, Parquet, etc.)","Dataset metadata and documentation"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fineweb__headline","uri":"capability://data.processing.analysis.high.quality.english.web.dataset.for.llm.pre.training","name":"high-quality english web dataset for llm pre-training","description":"FineWeb is a meticulously filtered 15 trillion token English web dataset derived from Common Crawl, setting a new standard for open LLM pre-training data with superior performance on benchmarks.","intents":["best dataset for LLM training","open web dataset for machine learning","high-quality web data for NLP","LLM pre-training data comparison","best dataset for language models"],"best_for":["large language model training","NLP research"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"low","permissions":["Access to Common Crawl WARC files (2013-2024 snapshots)","Computational infrastructure for distributed processing of petabyte-scale data","Hugging Face account for dataset access","Sufficient RAM to store MinHash signatures for all unique documents (typically 100s of GB for 15T tokens)","Distributed computing framework (Spark, Ray, or custom) for parallel processing","Access to the filtered document corpus before deduplication","Access to all 96 Common Crawl snapshots (2013-2024)","Distributed storage for intermediate snapshot processing","Deduplication infrastructure capable of cross-snapshot comparison","Computational resources for training multiple LLMs to convergence (100s of GPU hours)"],"failure_modes":["Filtering pipeline is not open-sourced — only the final deduplicated dataset is released, preventing direct reproduction or modification of individual filter stages","Language detection and quality classifier details are undisclosed, limiting ability to adapt filters for non-English or domain-specific use cases","No incremental update mechanism — entire pipeline must be re-run for new Common Crawl snapshots rather than delta processing","MinHash is probabilistic — configurable false positive rate means some duplicates may be missed or some unique documents incorrectly flagged depending on hash function count","Deduplication parameters (number of hash functions, threshold) are not publicly disclosed, preventing exact reproduction","No streaming deduplication — entire corpus must be processed in a batch pass, requiring significant temporary storage","No snapshot-level granularity in the released dataset — cannot isolate or weight content by time period after deduplication","Deduplication across snapshots may remove legitimate temporal variations (e.g., updated versions of pages) that could be valuable for certain tasks","No metadata indicating which snapshot each document originated from, limiting temporal analysis or time-aware fine-tuning","Benchmark validation is computationally expensive — requires training multiple models to convergence, limiting iteration speed during pipeline development","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.548Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=fineweb","compare_url":"https://unfragile.ai/compare?artifact=fineweb"}},"signature":"uj79Ynp5F+FTYYgMTSeDjaQaBsibr9HN2IyE/gGT0JKOm62ZAvpdWHKtOenBm8h1y5jPY/uVwpMAn/3A0WuBAg==","signedAt":"2026-06-21T13:07:24.638Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/fineweb","artifact":"https://unfragile.ai/fineweb","verify":"https://unfragile.ai/api/v1/verify?slug=fineweb","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}