{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"the-pile","slug":"the-pile","name":"The Pile","type":"dataset","url":"https://pile.eleuther.ai/","page_url":"https://unfragile.ai/the-pile","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"the-pile__cap_0","uri":"capability://data.processing.analysis.multi.domain.pretraining.corpus.assembly","name":"multi-domain pretraining corpus assembly","description":"Combines 22 discrete, curated text datasets (academic papers, books, code, web text, specialized sources) into a single 825 GiB jsonlines corpus compressed with zstandard. The assembly approach prioritizes diversity across domains rather than size maximization, enabling language models trained on this corpus to develop broad cross-domain knowledge and generalization capabilities. Data is provided as-is without documented preprocessing, deduplication, or filtering pipelines, placing responsibility for data cleaning on downstream users.","intents":["I need a diverse, high-quality pretraining dataset that covers academic, code, web, and specialized text domains for training a general-purpose language model from scratch","I want to train a model that generalizes well across multiple text domains without overfitting to a single domain or data distribution","I need a benchmark dataset to evaluate whether my model has learned broad knowledge across diverse text types"],"best_for":["researchers and teams training large language models from scratch with compute budgets >100 GPU-hours","open-source model developers building alternatives to proprietary LLMs (GPT, Claude, Gemini)","academic institutions studying language model pretraining and generalization"],"limitations":["English-only; no multilingual coverage or non-English language support","Static snapshot with no versioning, update mechanism, or reproducibility guarantees documented","Exact composition percentages and subset enumeration not fully documented; 22 subsets mentioned but only 8-10 named explicitly","No documented deduplication strategy; potential for data leakage across subsets or contamination with test sets","825 GiB fixed size requires significant storage infrastructure; no streaming or sampling utilities provided for resource-constrained environments"],"requires":["zstandard decompression tool (zstd) for decompressing jsonlines files","minimum 1 TB disk storage for full dataset plus working space for decompression","familiarity with jsonlines format and standard LLM training pipelines (PyTorch, TensorFlow, or equivalent)","Python 3.7+ for parsing and preprocessing jsonlines data"],"input_types":["pre-collected, pre-curated text from 22 sources (no user input required)"],"output_types":["jsonlines format (one JSON object per line, typically containing 'text' field)","raw text suitable for tokenization and language model training"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"the-pile__cap_1","uri":"capability://data.processing.analysis.cross.domain.model.evaluation.via.pile.bpb.metric","name":"cross-domain model evaluation via pile bpb metric","description":"Provides a standardized evaluation metric (Pile Bits Per Byte, or BPB) that measures language model perplexity across the full 22-subset corpus, enabling comparison of model generalization across diverse text domains. The metric is computed by evaluating a trained model on held-out portions of each subset and aggregating results, producing a single scalar score where lower values indicate better cross-domain performance. This approach surfaces domain-specific weaknesses that single-domain metrics would miss.","intents":["I need a standardized benchmark to compare my language model's generalization across multiple text domains against published baselines (GPT-3, GPT-2)","I want to identify which text domains my model performs poorly on and prioritize data collection or fine-tuning accordingly","I need to verify that my model hasn't overfit to a single domain and can handle diverse text types"],"best_for":["model developers and researchers comparing pretraining approaches and dataset compositions","teams evaluating whether a model trained on their custom dataset generalizes as well as Pile-trained baselines","benchmark leaderboard maintainers seeking a standardized, reproducible evaluation metric"],"limitations":["Leaderboard contains only 2 published entries (GPT-3, GPT-2) with asterisks indicating 'potential test-set overlap', severely limiting comparative value","Metric assumes models were trained on diverse domains; zero-shot evaluation caveat states 'not all components of the Pile were present in training data' for some models, making comparisons unreliable","No documented methodology for computing BPB across subsets (e.g., weighted average, macro average, per-subset reporting); aggregation approach unclear","No per-subset breakdown provided in leaderboard; users cannot diagnose domain-specific weaknesses","Evaluation code referenced but not detailed in documentation; reproducibility of metric computation uncertain"],"requires":["trained language model compatible with standard evaluation frameworks (PyTorch, TensorFlow, or equivalent)","access to held-out evaluation splits of the Pile (not documented whether these are provided or must be created by users)","knowledge of bits-per-byte metric computation and language model evaluation best practices"],"input_types":["trained language model (checkpoint or weights)","evaluation split of the Pile dataset (jsonlines format)"],"output_types":["scalar BPB score (bits per byte, lower is better)","optionally: per-subset BPB scores for domain-specific analysis"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"the-pile__cap_10","uri":"capability://data.processing.analysis.model.agnostic.training.data.format.and.integration","name":"model-agnostic training data format and integration","description":"Provides training data in a model-agnostic jsonlines format that integrates with standard ML frameworks (PyTorch, TensorFlow, Hugging Face) without requiring custom preprocessing or format conversion. The jsonlines + zstandard approach enables seamless integration with existing dataloaders, tokenizers, and training pipelines, reducing friction for researchers adopting the dataset. No custom APIs or proprietary tools are required — standard open-source libraries suffice.","intents":["Integrate large-scale pretraining data into existing ML training pipelines without custom preprocessing","Use Pile with standard frameworks (PyTorch DataLoader, Hugging Face Datasets) without format conversion","Stream training data efficiently from disk during model training without memory overhead"],"best_for":["ML engineers building training pipelines with PyTorch, TensorFlow, or Hugging Face","Teams seeking to minimize data engineering overhead when adopting large-scale pretraining datasets","Researchers using standard ML frameworks who want to avoid custom data loading code"],"limitations":["Jsonlines format requires sequential parsing — no random access or efficient sampling without full scan","Metadata structure within JSON objects not standardized — different components may have different schemas","No documented guidance on distributed data loading across multiple GPUs or nodes","Zstandard decompression adds latency (~50-100ms per file) — cumulative impact on training throughput not documented","No built-in support for data augmentation, filtering, or sampling strategies — requires custom code"],"requires":["Standard ML framework (PyTorch, TensorFlow, or Hugging Face Datasets)","jsonlines parser (built into most frameworks)","zstandard decompression library (zstandard-python, etc.)","Tokenizer compatible with target model architecture"],"input_types":["Zstandard-compressed jsonlines files"],"output_types":["Tokenized training batches suitable for model training"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"the-pile__cap_2","uri":"capability://data.processing.analysis.jsonlines.formatted.text.corpus.with.zstandard.compression","name":"jsonlines-formatted text corpus with zstandard compression","description":"Encodes the 825 GiB corpus as jsonlines (one JSON object per line, typically with a 'text' field containing raw text) and compresses with zstandard (zstd), a modern compression algorithm offering faster decompression and better compression ratios than gzip. This format choice enables streaming decompression and line-by-line parsing without loading the entire dataset into memory, critical for training pipelines on resource-constrained hardware. The jsonlines structure allows metadata (e.g., source subset, document ID) to be stored alongside text.","intents":["I need to decompress and stream the Pile dataset into my training pipeline without allocating 825 GiB of RAM upfront","I want to parse individual documents from the Pile while preserving metadata about their source (e.g., which subset, document ID)","I need to integrate the Pile into my existing PyTorch DataLoader or TensorFlow tf.data pipeline with minimal custom code"],"best_for":["machine learning engineers building training pipelines in PyTorch, TensorFlow, or JAX","researchers working on resource-constrained hardware (e.g., single GPU, limited RAM) who need streaming data loading","data engineers integrating the Pile into ETL pipelines or data lakes"],"limitations":["zstandard decompression is not built into Python standard library; requires external zstd tool or Python library (zstandard package)","jsonlines format requires line-by-line parsing; no built-in indexing or random access by document ID","No documented schema for JSON objects; users must infer structure (e.g., 'text' field name) from examples","Compression ratio and decompression speed not documented; users cannot predict I/O bottlenecks or storage requirements","No streaming API or sampling utilities provided; users must implement custom data loading logic"],"requires":["zstandard decompression tool (zstd CLI) or Python zstandard library (pip install zstandard)","Python 3.7+ with json module for parsing jsonlines","familiarity with streaming data loading patterns (e.g., generators, iterators) for efficient memory usage"],"input_types":["zstandard-compressed jsonlines files (binary format)"],"output_types":["jsonlines (one JSON object per line, decompressed)","parsed Python dictionaries with 'text' field and optional metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"the-pile__cap_3","uri":"capability://data.processing.analysis.subset.level.source.attribution.and.composition.transparency","name":"subset-level source attribution and composition transparency","description":"Explicitly enumerates the 22 constituent subsets of the Pile (academic papers from PubMed and ArXiv, books from Books3 and Gutenberg, code from GitHub, web text from OpenWebText2 and Pile-CC, specialized sources like USPTO patents, Ubuntu IRC, and Stack Exchange) and provides source attribution for each document. This transparency enables users to understand the composition of their training data, audit for potential biases or contamination, and selectively exclude subsets if needed. However, exact composition percentages and subset enumeration are not fully documented.","intents":["I need to understand what sources are in the Pile and whether they align with my model's intended use case (e.g., code-heavy vs. web-heavy)","I want to audit the Pile for potential data contamination or bias from specific sources (e.g., is Stack Exchange overrepresented?)","I need to exclude certain subsets (e.g., code, patents) from my training run due to licensing or domain constraints"],"best_for":["model developers and researchers concerned with data provenance and potential biases in pretraining","teams with specific licensing requirements (e.g., cannot use code from GitHub due to GPL constraints)","auditors and compliance teams evaluating training data for regulatory or ethical concerns"],"limitations":["Exact composition percentages of the 22 subsets not documented; users cannot determine whether code or web text dominates","Only 8-10 subset names explicitly mentioned (PubMed, ArXiv, Books3, Gutenberg, GitHub, OpenWebText2, Pile-CC, USPTO, Ubuntu IRC, Stack Exchange); remaining 12+ subsets unnamed","No per-document source attribution provided; users cannot filter or exclude specific subsets without re-downloading and re-processing the entire corpus","Data collection dates and temporal bias not documented; unclear whether subsets are from 2019, 2020, or mixed years","No documented deduplication or overlap detection across subsets; potential for data leakage or test-set contamination"],"requires":["access to Pile documentation or paper (Gao et al., 2020, arXiv:2101.00027) for full subset enumeration","understanding of licensing implications for each subset (e.g., GPL for GitHub code, copyright for Books3)"],"input_types":["Pile dataset documentation or paper"],"output_types":["list of 22 subsets with source attribution","optionally: per-document source labels (if provided in jsonlines metadata)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"the-pile__cap_4","uri":"capability://data.processing.analysis.academic.and.specialized.text.domain.coverage","name":"academic and specialized text domain coverage","description":"Includes curated subsets of academic papers (PubMed, ArXiv), specialized technical sources (USPTO patents, Stack Exchange), and code repositories (GitHub), providing dense coverage of high-signal, domain-specific text that is underrepresented in web-only corpora. These subsets are integrated into the broader corpus at a fixed ratio, ensuring that models trained on the Pile develop specialized knowledge in these domains without requiring separate fine-tuning. The inclusion of academic papers and code is particularly valuable for training models intended for scientific or technical applications.","intents":["I need a pretraining dataset that includes substantial academic papers and code so my model can handle scientific and technical tasks without additional fine-tuning","I want to train a model that understands patent language, technical documentation, and specialized terminology from domains like medicine and computer science","I need to evaluate whether my model generalizes well to academic and technical text, not just web text"],"best_for":["researchers training models for scientific, technical, or code-related applications (e.g., code generation, scientific writing)","teams building domain-specific language models that require strong performance on academic papers or technical documentation","benchmark developers evaluating model performance on specialized text types"],"limitations":["Exact composition percentages for academic and specialized subsets not documented; unclear whether code or academic papers dominate","Academic papers subset limited to PubMed (biomedical) and ArXiv (physics, CS, math); other fields (law, economics, social sciences) may be underrepresented","Code subset limited to GitHub; no documentation of programming language distribution (e.g., Python vs. Java vs. C++) or code quality filtering","Stack Exchange subset may introduce question-answer format bias; unclear whether answers are included or only questions","No documented filtering for low-quality academic papers or code (e.g., papers with low citation counts, code with low GitHub stars)"],"requires":["understanding of domain-specific text characteristics and evaluation metrics (e.g., scientific accuracy, code correctness)","familiarity with academic paper and code formats (e.g., LaTeX, markdown, programming syntax)"],"input_types":["academic papers (PubMed, ArXiv in PDF or text format)","code repositories (GitHub in source code format)","specialized text (patents, Stack Exchange posts)"],"output_types":["jsonlines format with academic papers, code, and specialized text"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"the-pile__cap_5","uri":"capability://data.processing.analysis.books.and.long.form.text.corpus.inclusion","name":"books and long-form text corpus inclusion","description":"Incorporates two book-focused subsets (Books3 and Gutenberg) providing long-form, narrative text with complex linguistic structures, enabling models to develop strong performance on coherent, multi-paragraph generation and understanding of narrative arcs. Books represent a fundamentally different text distribution than web text (longer documents, more complex grammar, narrative structure) and are valuable for training models intended for creative writing, summarization, or long-context understanding. The inclusion of both contemporary books (Books3) and public-domain classics (Gutenberg) provides temporal and stylistic diversity.","intents":["I need a pretraining dataset that includes long-form, narrative text so my model can generate coherent multi-paragraph text and understand complex linguistic structures","I want to train a model that performs well on book-related tasks (e.g., summarization, continuation, literary analysis) without requiring separate fine-tuning","I need to evaluate whether my model generalizes well to long-form text and narrative structures, not just short web snippets"],"best_for":["researchers training models for creative writing, summarization, or long-context understanding","teams building models intended for literary analysis or book-related applications","benchmark developers evaluating model performance on long-form text"],"limitations":["Exact composition percentages for Books3 and Gutenberg not documented; unclear which subset dominates","Books3 composition and licensing unclear; potential copyright concerns for contemporary books not addressed in documentation","Gutenberg subset limited to public-domain works (pre-1923 in US), introducing temporal bias toward older writing styles and vocabulary","No documented filtering for book quality, length, or language complexity; potential inclusion of low-quality or poorly OCR'd texts","Document boundaries and chapter/section structure not documented; unclear whether books are split into chunks or kept as single documents"],"requires":["understanding of long-form text characteristics and evaluation metrics (e.g., coherence, narrative structure)","familiarity with book formats and potential OCR artifacts (e.g., scanning errors, formatting inconsistencies)"],"input_types":["books from Books3 and Gutenberg in text format"],"output_types":["jsonlines format with long-form narrative text"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"the-pile__cap_6","uri":"capability://data.processing.analysis.web.scale.text.corpus.with.deduplication.and.quality.filtering","name":"web-scale text corpus with deduplication and quality filtering","description":"Combines two web-derived subsets (OpenWebText2 and Pile-CC) providing broad coverage of diverse web text while applying quality filtering and deduplication to reduce noise compared to raw Common Crawl. OpenWebText2 is derived from URLs shared on Reddit (a proxy for human-curated quality), while Pile-CC is a filtered subset of Common Crawl. Together, these subsets provide web-scale coverage without the extreme noise and duplication of raw web scrapes, balancing breadth with quality.","intents":["I need a pretraining dataset that includes diverse web text (news, blogs, forums, etc.) without the extreme noise of raw Common Crawl","I want to train a model that generalizes well to web-scale text distributions and can handle diverse writing styles and topics","I need to evaluate whether my model performs well on web-derived text while maintaining quality standards"],"best_for":["researchers training general-purpose language models intended for broad web-scale applications","teams building models that need to handle diverse writing styles and topics","benchmark developers evaluating model performance on web-derived text"],"limitations":["Exact composition percentages for OpenWebText2 and Pile-CC not documented; unclear which subset dominates","OpenWebText2 filtering methodology not documented; unclear what quality criteria are applied beyond Reddit URL curation","Pile-CC filtering methodology not documented; unclear what deduplication or quality filtering is applied compared to raw Common Crawl","No documented handling of non-English text, spam, or low-quality content; potential for inclusion of machine-generated or low-signal text","Temporal bias not documented; unclear whether web text is from 2019, 2020, or mixed years"],"requires":["understanding of web text characteristics and potential biases (e.g., Reddit bias toward tech and gaming communities)","familiarity with Common Crawl and web scraping artifacts (e.g., HTML markup, encoding issues)"],"input_types":["web text from OpenWebText2 (Reddit-derived) and Pile-CC (Common Crawl-derived)"],"output_types":["jsonlines format with web-derived text"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"the-pile__cap_7","uri":"capability://data.processing.analysis.static.dataset.versioning.and.reproducibility","name":"static dataset versioning and reproducibility","description":"Provides a fixed, immutable 825 GiB snapshot of the Pile corpus, enabling reproducible model training and evaluation across teams and time periods. The static nature ensures that models trained on the Pile in 2021 can be compared directly with models trained in 2024 without worrying about dataset drift or updates. However, no explicit versioning scheme, release notes, or update mechanism is documented, limiting transparency about potential corrections or improvements.","intents":["I need to train a model on a fixed dataset that won't change, so I can reproduce my results and compare with other teams' models","I want to publish a model trained on the Pile and ensure that other researchers can replicate my results using the same dataset","I need to establish a baseline for model evaluation that remains stable over time"],"best_for":["researchers publishing models and requiring reproducibility guarantees","teams comparing model performance across different training runs and time periods","benchmark maintainers establishing stable evaluation sets"],"limitations":["No explicit versioning scheme documented (e.g., v1.0, v1.1); unclear whether the Pile has been updated or corrected since initial release","No release notes or changelog documenting potential corrections, deduplication, or filtering improvements","No documented mechanism for reporting and fixing data quality issues (e.g., corrupted files, encoding errors)","Static nature means the Pile cannot be updated to reflect new data or correct errors; users are locked into a potentially outdated dataset","No documented guarantees about long-term availability or archival; dataset could be taken offline or moved without notice"],"requires":["understanding of reproducibility requirements and best practices for model training","ability to store and manage 825 GiB of data long-term"],"input_types":["none (static dataset)"],"output_types":["fixed, immutable 825 GiB corpus"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"the-pile__cap_8","uri":"capability://data.processing.analysis.citation.and.attribution.framework.for.multi.source.datasets","name":"citation and attribution framework for multi-source datasets","description":"Provides formal citation guidance (Gao et al., 2020, arXiv:2101.00027) for the Pile itself and requires attribution to individual component datasets, establishing a precedent for proper data provenance documentation in large pretraining corpora. This framework enables researchers to trace the lineage of their training data and acknowledge the original sources and curators. However, no machine-readable citation metadata or automated attribution tools are provided.","intents":["I need to properly cite the Pile and its component datasets in my research paper or model card","I want to understand the original sources of the data in the Pile and acknowledge the curators and researchers who created each subset","I need to provide attribution to individual datasets when publishing a model trained on the Pile"],"best_for":["researchers publishing models or papers using the Pile","teams building model cards or documentation that require proper data attribution","data curators and archivists tracking data lineage and provenance"],"limitations":["Citation guidance provided only for the Pile itself (Gao et al., 2020); no guidance for citing individual component datasets","No machine-readable citation metadata (e.g., BibTeX, RIS, JSON-LD) provided; users must manually format citations","No automated attribution tools or scripts to generate citations for models trained on the Pile","No documented licensing terms for the Pile or individual subsets; unclear whether commercial use is permitted","No standardized model card template or documentation requirements for models trained on the Pile"],"requires":["familiarity with academic citation formats (BibTeX, APA, Chicago, etc.)","access to the Pile paper (Gao et al., 2020, arXiv:2101.00027) for full citation details"],"input_types":["Pile paper and documentation"],"output_types":["citation in BibTeX or other academic format","attribution statements for model cards and documentation"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"the-pile__cap_9","uri":"capability://automation.workflow.public.reproducibility.and.open.source.model.training","name":"public reproducibility and open-source model training","description":"Enables reproducible, open-source language model training by providing a publicly-available, freely-downloadable dataset used to train GPT-NeoX, Pythia, and other open models. The dataset is released under an open license (exact license terms not specified in artifact), allowing researchers and organizations to train models with full transparency and reproducibility. The Pile has influenced the design of subsequent open datasets, establishing a standard for open-source LLM training data.","intents":["Train language models with full reproducibility and transparency, without proprietary data restrictions","Build open-source LLMs that can be audited, modified, and distributed freely","Establish a shared benchmark for open-source LLM development and evaluation"],"best_for":["Researchers and organizations committed to open-source AI development","Teams building models for academic publication with reproducibility requirements","Communities seeking to democratize LLM training without proprietary data dependencies"],"limitations":["License terms for Pile and individual component datasets not fully documented — potential legal ambiguity","No commercial support or SLA — dataset availability depends on The Eye archive service","No versioning or update strategy — fixed snapshot from 2020 may be outdated","Reproducibility limited by undocumented preprocessing and composition — exact replication difficult","No managed service or API — requires manual download, decompression, and local processing"],"requires":["Commitment to open-source development and public model release","Understanding of open licensing and attribution requirements","Infrastructure for large-scale model training (GPUs, distributed systems, etc.)"],"input_types":["Pile dataset (825 GiB jsonlines corpus)"],"output_types":["Trained language model (weights, architecture, evaluation results)","Published model and training code (for reproducibility)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"the-pile__headline","uri":"capability://data.processing.analysis.large.scale.english.text.dataset.for.training.language.models","name":"large-scale english text dataset for training language models","description":"The Pile is a comprehensive 825 GiB dataset designed for training large language models, featuring diverse high-quality text sources including academic papers, books, and code repositories, making it ideal for researchers and developers in NLP.","intents":["best dataset for training language models","large text dataset for NLP","open-source dataset for machine learning","high-quality text data for AI training","comprehensive dataset for language model evaluation"],"best_for":["NLP researchers","machine learning developers"],"limitations":["may have data overlap","not exhaustive for niche topics"],"requires":["basic understanding of NLP","ability to handle large datasets"],"input_types":["text data"],"output_types":["trained language models"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["zstandard decompression tool (zstd) for decompressing jsonlines files","minimum 1 TB disk storage for full dataset plus working space for decompression","familiarity with jsonlines format and standard LLM training pipelines (PyTorch, TensorFlow, or equivalent)","Python 3.7+ for parsing and preprocessing jsonlines data","trained language model compatible with standard evaluation frameworks (PyTorch, TensorFlow, or equivalent)","access to held-out evaluation splits of the Pile (not documented whether these are provided or must be created by users)","knowledge of bits-per-byte metric computation and language model evaluation best practices","Standard ML framework (PyTorch, TensorFlow, or Hugging Face Datasets)","jsonlines parser (built into most frameworks)","zstandard decompression library (zstandard-python, etc.)"],"failure_modes":["English-only; no multilingual coverage or non-English language support","Static snapshot with no versioning, update mechanism, or reproducibility guarantees documented","Exact composition percentages and subset enumeration not fully documented; 22 subsets mentioned but only 8-10 named explicitly","No documented deduplication strategy; potential for data leakage across subsets or contamination with test sets","825 GiB fixed size requires significant storage infrastructure; no streaming or sampling utilities provided for resource-constrained environments","Leaderboard contains only 2 published entries (GPT-3, GPT-2) with asterisks indicating 'potential test-set overlap', severely limiting comparative value","Metric assumes models were trained on diverse domains; zero-shot evaluation caveat states 'not all components of the Pile were present in training data' for some models, making comparisons unreliable","No documented methodology for computing BPB across subsets (e.g., weighted average, macro average, per-subset reporting); aggregation approach unclear","No per-subset breakdown provided in leaderboard; users cannot diagnose domain-specific weaknesses","Evaluation code referenced but not detailed in documentation; reproducibility of metric computation uncertain","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"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":"2026-05-24T12:16:28.696Z","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=the-pile","compare_url":"https://unfragile.ai/compare?artifact=the-pile"}},"signature":"eswt5ugO8F48tfaZwlJvfxY8jE+umKU/tIhiwjuRrLgwNQV4JqNiaojrmqklkNJbzpincdkyE84oKcptpuLCDA==","signedAt":"2026-06-23T11:46:29.037Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/the-pile","artifact":"https://unfragile.ai/the-pile","verify":"https://unfragile.ai/api/v1/verify?slug=the-pile","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"}}