{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"codecontests","slug":"codecontests","name":"CodeContests","type":"dataset","url":"https://huggingface.co/datasets/deepmind/code_contests","page_url":"https://unfragile.ai/codecontests","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"codecontests__cap_0","uri":"capability://data.processing.analysis.competitive.programming.problem.corpus.with.multi.language.solutions","name":"competitive-programming-problem-corpus-with-multi-language-solutions","description":"Provides 13,328 curated competitive programming problems sourced from Codeforces, AtCoder, and other platforms, each with complete problem statements, reference solutions in multiple programming languages (C++, Python, Java, etc.), and comprehensive test case suites. The dataset is structured with metadata including problem difficulty calibration (median and 95th percentile solution metrics) and both public and hidden test cases, enabling direct evaluation of code generation models against real-world algorithmic challenges without synthetic problem generation.","intents":["Train code generation models on real competitive programming problems to improve algorithmic reasoning","Evaluate LLM code generation capabilities against standardized, difficulty-calibrated benchmarks","Build datasets for fine-tuning models specifically on algorithmic problem-solving vs general code completion","Benchmark code generation models using hidden test cases to measure generalization beyond public examples"],"best_for":["ML researchers training or evaluating code generation models (e.g., AlphaCode-style systems)","Teams building competitive programming assistants or AI tutoring systems","Researchers studying algorithmic reasoning in large language models","Organizations benchmarking code LLMs on standardized, difficulty-stratified problems"],"limitations":["Problems are primarily algorithmic/mathematical in nature — limited coverage of systems programming, web development, or domain-specific code","Solutions are reference implementations only; no coverage of alternative approaches or trade-offs for the same problem","Dataset is static and does not update with new competitive programming problems or platforms","Test cases are deterministic and may not cover edge cases or adversarial inputs beyond original problem setters' intent","Language coverage varies — not all problems have solutions in all major languages"],"requires":["HuggingFace Datasets library (datasets>=2.0.0) to load and stream the dataset","Python 3.7+ for dataset processing and integration","Sufficient disk space (~10-50GB depending on download format) or streaming capability for full dataset","Understanding of competitive programming problem formats and test case execution"],"input_types":["problem_statement (text/markdown with mathematical notation)","input_specification (text describing input format)","output_specification (text describing expected output format)","example_inputs (text/code snippets)","example_outputs (text/code snippets)"],"output_types":["reference_solutions (code in multiple languages: C++, Python, Java, etc.)","test_cases (structured input-output pairs)","difficulty_metrics (numeric: median runtime, 95th percentile runtime)","problem_metadata (tags, source platform, problem ID)"],"categories":["data-processing-analysis","model-training-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codecontests__cap_1","uri":"capability://code.generation.editing.multi.language.reference.solution.extraction","name":"multi-language-reference-solution-extraction","description":"Extracts and normalizes reference solutions across multiple programming languages (C++, Python, Java, JavaScript, Go, Rust, etc.) for each problem, with language-agnostic problem metadata and test case specifications. Solutions are parsed and validated against test cases to ensure correctness, enabling cross-language comparison of algorithmic approaches and language-specific implementation patterns for the same problem.","intents":["Compare how the same algorithm is implemented across different programming languages","Train multilingual code generation models with language-specific solution examples","Evaluate whether a code generation model produces correct solutions in languages it was trained less on","Extract language-agnostic algorithmic patterns by analyzing solutions across multiple implementations"],"best_for":["Multilingual code generation model developers training on language-diverse datasets","Researchers studying how algorithmic complexity translates across programming languages","Teams building polyglot code generation systems or language-agnostic code synthesis"],"limitations":["Not all problems have solutions in all languages — language coverage varies per problem","Solutions reflect competitive programming idioms and optimizations, not production code patterns","No explicit mapping of equivalent code segments across languages — requires manual or ML-based alignment","Language-specific libraries and built-in functions may not have direct equivalents across all languages"],"requires":["Language-specific compilers/interpreters to validate solutions (GCC/Clang for C++, Python 3.7+, Java 11+, etc.)","Test case execution environment supporting multiple languages","Parsing libraries for each language to extract and normalize code structure if needed"],"input_types":["problem_statement (language-agnostic text)","test_cases (language-agnostic input-output pairs)"],"output_types":["solutions (code in C++, Python, Java, JavaScript, Go, Rust, etc.)","solution_metadata (language, runtime, memory usage, submission timestamp)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codecontests__cap_2","uri":"capability://data.processing.analysis.public.and.hidden.test.case.stratification","name":"public-and-hidden-test-case-stratification","description":"Separates test cases into public (visible in problem statement) and hidden (used for final evaluation) categories, enabling evaluation of model generalization beyond memorization of example inputs/outputs. Hidden test cases are designed by problem setters to cover edge cases, boundary conditions, and adversarial inputs that public examples may not expose, allowing measurement of true algorithmic correctness vs. overfitting to visible examples.","intents":["Measure code generation model generalization by testing against unseen test cases","Identify whether models memorize public examples or learn underlying algorithmic patterns","Evaluate robustness to edge cases and boundary conditions not covered in problem statements","Benchmark models using the same evaluation methodology as competitive programming platforms"],"best_for":["Researchers evaluating code generation model generalization and robustness","Teams building competitive programming evaluation systems that require true correctness verification","Model developers needing realistic test coverage beyond public examples"],"limitations":["Hidden test cases are fixed and static — no adversarial or continuously-updated test suites","Test case design reflects original problem setters' intent, which may not cover all real-world edge cases","No explanation of why specific hidden test cases were chosen or what edge cases they target","Requires executing generated code against test cases — no static analysis alternative"],"requires":["Code execution environment with resource limits (timeout, memory) matching original platform specifications","Test case runner supporting multiple languages and handling I/O redirection","Ability to parse and execute test cases in structured format (JSON, CSV, or custom format)"],"input_types":["generated_code (code in any supported language)","test_cases (structured input-output pairs, separated into public and hidden)"],"output_types":["test_results (pass/fail per test case, execution time, memory usage)","correctness_metrics (percentage of public tests passed, percentage of hidden tests passed)","failure_details (which test case failed, expected vs actual output)"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codecontests__cap_3","uri":"capability://data.processing.analysis.difficulty.calibrated.problem.stratification","name":"difficulty-calibrated-problem-stratification","description":"Stratifies problems by difficulty using median and 95th percentile solution runtime metrics from real competitive programmers, enabling selection of problems at specific difficulty levels for targeted training or evaluation. Problems are tagged with difficulty ranges (easy, medium, hard, expert) derived from actual submission statistics rather than subjective classification, allowing researchers to study how model performance scales with problem complexity.","intents":["Select training data at specific difficulty levels to study curriculum learning effects","Evaluate model performance across difficulty spectrum to identify capability gaps","Build difficulty-stratified benchmarks for fair comparison across models","Study how algorithmic reasoning capability scales with problem complexity"],"best_for":["Researchers studying curriculum learning and difficulty-aware training for code generation","Teams building progressive coding tutors that adapt to learner skill level","Model developers benchmarking across difficulty tiers to identify capability gaps"],"limitations":["Difficulty metrics are based on runtime performance, not algorithmic complexity or conceptual difficulty — a problem may be easy algorithmically but hard to implement efficiently","Difficulty calibration is static and based on historical Codeforces/AtCoder data — may not reflect current platform difficulty or new problem types","No fine-grained difficulty sub-categories — only median/95th percentile metrics provided, not detailed breakdown of problem characteristics","Difficulty may not transfer across languages or implementation approaches"],"requires":["Understanding of competitive programming difficulty conventions and runtime-based metrics","Ability to parse and filter problems by difficulty metadata"],"input_types":["problem_metadata (difficulty metrics: median runtime, 95th percentile runtime)"],"output_types":["difficulty_stratified_subsets (problems grouped by difficulty tier)","difficulty_metrics (numeric: median runtime, 95th percentile runtime per problem)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codecontests__cap_4","uri":"capability://data.processing.analysis.problem.statement.parsing.and.normalization","name":"problem-statement-parsing-and-normalization","description":"Extracts and normalizes problem statements from multiple competitive programming platforms (Codeforces, AtCoder, etc.) into a unified format, including problem description, input/output specifications, constraints, and example inputs/outputs. Handles platform-specific formatting (HTML, Markdown, LaTeX mathematical notation) and converts to consistent structured representation, enabling uniform processing across problems from different sources.","intents":["Parse problem statements from multiple platforms into a unified format for model training","Extract structured problem metadata (constraints, input/output format) for code generation","Build problem understanding models that can reason about problem specifications","Create problem-to-code datasets with consistent problem representation"],"best_for":["Researchers building problem understanding models or code generation systems that reason about specifications","Teams aggregating problems from multiple competitive programming platforms","Model developers training on problem statements as input to code generation"],"limitations":["Problem statements contain natural language ambiguity and may have implicit assumptions not stated explicitly","Mathematical notation and constraints are not parsed into formal specifications — remain as text","Platform-specific formatting quirks may not be fully normalized, requiring manual cleanup","No semantic understanding of problem intent — only syntactic parsing of statement structure"],"requires":["HTML/Markdown parsing libraries for extracting problem text from platform pages","LaTeX or mathematical notation parser if formal constraint extraction is needed","Language understanding to handle natural language problem descriptions"],"input_types":["problem_statement (raw text from Codeforces, AtCoder, or other platforms in HTML/Markdown/text format)"],"output_types":["normalized_problem_statement (structured JSON/dict with problem description, input spec, output spec, constraints, examples)","problem_metadata (source platform, problem ID, title)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codecontests__cap_5","uri":"capability://automation.workflow.test.case.execution.and.validation.framework","name":"test-case-execution-and-validation-framework","description":"Provides infrastructure for executing generated code against test cases with resource limits (timeout, memory), capturing execution results (pass/fail, runtime, memory usage), and validating output correctness. Supports multiple programming languages and handles I/O redirection, standard output comparison, and floating-point tolerance for numerical problems, enabling automated evaluation of code generation model outputs.","intents":["Automatically evaluate code generation model outputs against test cases","Measure correctness, efficiency (runtime/memory), and robustness of generated code","Build evaluation pipelines that run generated code safely with resource limits","Compare model performance across languages and problem types"],"best_for":["Researchers evaluating code generation models on competitive programming problems","Teams building automated code evaluation systems with safety constraints","Model developers benchmarking code generation quality and efficiency"],"limitations":["Execution environment must be isolated and resource-limited to prevent malicious code from consuming system resources","Floating-point comparison requires tolerance thresholds that may not match problem setters' original tolerances","Some problems require interactive I/O or special handling (e.g., randomized problems) not supported by standard test case execution","Execution time varies by hardware — benchmark results are not portable across different machines"],"requires":["Sandboxed code execution environment (Docker, VM, or language-specific sandbox) to safely run untrusted code","Compilers/interpreters for all supported languages (GCC/Clang for C++, Python 3.7+, Java 11+, etc.)","Resource limit enforcement (timeout, memory limit) matching original platform specifications","Test case runner supporting multiple languages and I/O redirection"],"input_types":["generated_code (code in any supported language)","test_cases (structured input-output pairs with expected outputs)","execution_constraints (timeout, memory limit, language)"],"output_types":["test_results (pass/fail per test case, execution time, memory usage)","correctness_metrics (percentage of tests passed, number of failures)","failure_details (which test case failed, expected vs actual output, error messages)"],"categories":["automation-workflow","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codecontests__cap_6","uri":"capability://data.processing.analysis.source.platform.and.problem.metadata.tracking","name":"source-platform-and-problem-metadata-tracking","description":"Maintains metadata for each problem including source platform (Codeforces, AtCoder, etc.), problem ID, submission date, problem tags (algorithm type, data structure, etc.), and contest context. This enables filtering and analysis by platform, time period, or problem category, and allows tracing problems back to original sources for additional context or updates.","intents":["Filter problems by source platform or contest to study platform-specific problem characteristics","Analyze how problem types and difficulty have evolved over time","Categorize problems by algorithmic topic (dynamic programming, graph theory, etc.) for targeted training","Trace problems back to original sources for additional context or verification"],"best_for":["Researchers studying problem distribution across platforms or time periods","Teams building problem recommendation systems based on algorithmic topics","Model developers selecting training data by problem category or source"],"limitations":["Problem tags may be incomplete or inconsistent across platforms — no standardized tagging scheme","Metadata is static and does not update when problems are modified on original platforms","Some problems may be removed or made private on original platforms, breaking traceability","Contest context (contest name, date, rating) may not be available for all problems"],"requires":["Access to original problem metadata from Codeforces, AtCoder, and other platforms","Ability to parse and normalize metadata across platforms with different formats"],"input_types":["problem_metadata (source platform, problem ID, tags, contest context)"],"output_types":["filtered_problem_subsets (problems grouped by platform, time period, or category)","metadata_statistics (distribution of problems by platform, tag, difficulty, etc.)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codecontests__cap_7","uri":"capability://data.processing.analysis.large.scale.algorithmic.problem.distribution.analysis","name":"large-scale-algorithmic-problem-distribution-analysis","description":"Enables statistical analysis of the 13,328-problem corpus to understand problem distribution across algorithmic categories, difficulty levels, languages, and platforms. Provides aggregate statistics (e.g., percentage of problems requiring dynamic programming, distribution of problem difficulty, language coverage per problem) enabling researchers to characterize the dataset and identify coverage gaps.","intents":["Understand the distribution of algorithmic problem types in the dataset","Identify coverage gaps (e.g., underrepresented algorithm categories or languages)","Analyze how problem difficulty and type correlate across platforms","Characterize the dataset for research papers and benchmark documentation"],"best_for":["Researchers documenting dataset characteristics and coverage for publications","Teams analyzing whether dataset is representative of real competitive programming","Model developers understanding training data composition and potential biases"],"limitations":["Analysis is descriptive only — does not provide causal insights into why distributions are skewed","Problem categorization (algorithm type, data structure) may be incomplete or subjective","Statistical analysis does not account for problem interdependencies or learning prerequisites","Distribution may not be representative of real-world programming tasks outside competitive programming"],"requires":["Statistical analysis tools (Python with pandas/numpy, R, etc.)","Problem categorization/tagging data to enable distribution analysis"],"input_types":["problem_metadata (difficulty, category, language, platform)"],"output_types":["distribution_statistics (counts, percentages, histograms by category/difficulty/language/platform)","coverage_analysis (which categories/languages are well-represented, which are sparse)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codecontests__headline","uri":"capability://model.training.competitive.programming.dataset.for.ai.training","name":"competitive programming dataset for ai training","description":"A comprehensive dataset of competitive programming problems designed for training AI models like AlphaCode, featuring a wide range of problem difficulties and solutions in multiple programming languages.","intents":["best dataset for AI code generation","dataset for training competitive programming models","AI training data for algorithmic problem solving","competitive programming problems for machine learning","best resources for AI model evaluation"],"best_for":["AI model training","evaluating code generation algorithms"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["HuggingFace Datasets library (datasets>=2.0.0) to load and stream the dataset","Python 3.7+ for dataset processing and integration","Sufficient disk space (~10-50GB depending on download format) or streaming capability for full dataset","Understanding of competitive programming problem formats and test case execution","Language-specific compilers/interpreters to validate solutions (GCC/Clang for C++, Python 3.7+, Java 11+, etc.)","Test case execution environment supporting multiple languages","Parsing libraries for each language to extract and normalize code structure if needed","Code execution environment with resource limits (timeout, memory) matching original platform specifications","Test case runner supporting multiple languages and handling I/O redirection","Ability to parse and execute test cases in structured format (JSON, CSV, or custom format)"],"failure_modes":["Problems are primarily algorithmic/mathematical in nature — limited coverage of systems programming, web development, or domain-specific code","Solutions are reference implementations only; 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