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| Pricing | Free | Free |
| Capabilities | 6 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Provides a curated collection of multi-turn conversations structured to capture complex reasoning patterns, instruction-following behaviors, and dialogue coherence. The dataset is organized as conversation sequences with explicit reasoning chains embedded within turns, enabling models to learn step-by-step problem decomposition and justification patterns during fine-tuning. Data is hosted on Hugging Face Hub with streaming and local caching support via the datasets library.
Unique: Explicitly curates reasoning chains within multi-turn conversations rather than treating dialogue as flat text sequences, enabling models to learn structured problem-solving patterns. Focuses on 'steerability' — conversations designed to demonstrate how models should adapt behavior based on user intent shifts within a single dialogue thread.
vs alternatives: Differs from generic dialogue datasets (like DailyDialog) by prioritizing reasoning transparency and instruction-following over natural conversation realism, making it better suited for training steerable task-completion agents rather than open-domain chatbots.
Transforms raw multi-turn conversation data into structured instruction-response pairs optimized for supervised fine-tuning (SFT). The dataset encodes conversation context, speaker roles, and reasoning annotations into a format compatible with standard LLM training pipelines (e.g., Hugging Face Transformers, LLaMA-Factory). Handles variable-length contexts and supports both single-turn and multi-turn context windows.
Unique: Preserves reasoning chain annotations and multi-turn context during pair extraction, rather than flattening conversations into isolated Q&A pairs. Enables training on 'how to think' patterns, not just 'what to answer'.
vs alternatives: More sophisticated than simple dialogue-to-pairs conversion (like basic CSV extraction) because it maintains semantic relationships between turns and explicitly encodes reasoning steps, producing higher-quality instruction-tuned models.
Curates conversations across multiple domains and topic areas, with intentional variation in instruction phrasing, complexity, and specificity. The dataset includes examples where the same underlying task is expressed with different levels of detail, formality, and constraint specification, teaching models to handle instruction ambiguity and adapt to varied user communication styles. Topics span technical, creative, analytical, and interpersonal domains.
Unique: Intentionally includes instruction variants (same task, different phrasings) within the dataset to teach models to handle communication style variation, rather than assuming all instructions follow a single format or formality level.
vs alternatives: More comprehensive than single-style instruction datasets (like basic instruction-following benchmarks) because it explicitly teaches models to adapt to varied user communication patterns, improving real-world robustness.
Embeds explicit reasoning chains and step-by-step problem decomposition within conversation turns, allowing models to learn intermediate reasoning steps rather than just final answers. The dataset includes examples where models articulate their reasoning process, break down complex problems into sub-steps, and justify intermediate conclusions. This enables training of models that can produce interpretable, verifiable reasoning traces.
Unique: Explicitly annotates intermediate reasoning steps within conversation data, treating reasoning as a learnable component rather than an emergent behavior. Enables supervised training of reasoning quality, not just answer correctness.
vs alternatives: More structured than datasets that only include final answers (like basic Q&A datasets) because it provides explicit supervision for intermediate reasoning steps, enabling more reliable and verifiable model reasoning.
Includes conversation examples where model behavior adapts based on user intent shifts, constraint changes, or clarifications within a single dialogue thread. The dataset demonstrates how models should modify their approach, tone, or output format in response to evolving user requirements. This teaches models to be 'steerable' — responsive to mid-conversation instruction changes rather than locked into initial behavior patterns.
Unique: Explicitly includes examples of mid-conversation instruction changes and demonstrates expected model behavior adaptations, rather than treating conversations as static sequences. Teaches models to be responsive to evolving user intent within a single dialogue.
vs alternatives: More sophisticated than static instruction datasets because it includes dynamic instruction changes and demonstrates how models should adapt without losing context, enabling more interactive and user-responsive AI systems.
Applies curation and filtering to ensure conversation quality, coherence, and factual accuracy. The dataset excludes low-quality turns, incoherent exchanges, and factually incorrect information through manual review or automated quality metrics. This produces a higher-signal training set compared to raw web-scraped dialogue data, reducing noise and improving model training efficiency.
Unique: Applies explicit quality filtering and curation to dialogue data, rather than using raw web-scraped or crowd-sourced conversations. Prioritizes signal quality over dataset size, reducing training noise.
vs alternatives: More refined than raw dialogue datasets (like unfiltered Reddit or web conversations) because it applies quality standards and manual curation, producing cleaner training data that improves model coherence and factual accuracy.
Provides a standardized evaluation framework for code generation models that accepts generated code in 17 programming languages (C, C++, C#, Java, Kotlin, Go, Rust, Python, Ruby, PHP, JavaScript, Perl, Haskell, OCaml, Scala, D, Pascal) and validates correctness through actual execution against unit tests via the ExecEval Docker-based execution engine. Uses a centralized problem definition model with src_uid foreign keys linking generated code to shared problem descriptions and unittest_db.json, enabling consistent evaluation across language variants of the same problem.
Unique: Combines 25M training examples across 7,500 unique problems with an execution-based evaluation pipeline (ExecEval) that actually runs generated code in Docker containers against unit tests, rather than relying on static analysis or string matching. The src_uid linking system creates a normalized data model where problem descriptions and tests are stored once and referenced by all language variants, eliminating duplication and ensuring consistency.
vs alternatives: Larger scale (25M examples vs typical 10-100K) and true execution-based validation across more languages (17 vs 4-6) than HumanEval or CodeXGLUE, with explicit support for code translation and repair tasks beyond generation.
Implements a foreign key linking system where all task-specific datasets (program synthesis, code translation, APR, retrieval) reference shared problem definitions via src_uid identifiers. Problem descriptions and unit tests are stored once in centralized problem_descriptions.jsonl and unittest_db.json files, then linked by src_uid to avoid duplication. The Hugging Face datasets API automatically resolves these links during data loading, returning enriched DatasetDict objects with problem context pre-joined to task examples.
Unique: Uses a normalized relational data model (src_uid as foreign key) for a code benchmark, treating problem definitions as a separate entity layer rather than embedding them in each task dataset. This is more sophisticated than typical flat-file benchmark structures and enables consistent multi-task evaluation on identical problems.
xCodeEval scores higher at 67/100 vs Capybara at 60/100.
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vs alternatives: More efficient than duplicating problem descriptions across 7 task datasets (reduces storage by ~30-40%), and enables automatic link resolution via Hugging Face API unlike manual CSV joins in CodeXGLUE or HumanEval variants.
Provides a Python API for loading xCodeEval datasets from Hugging Face Hub (NTU-NLP-sg/xCodeEval) with automatic src_uid-based linking between task datasets and shared problem definitions. The datasets library handles data downloading, caching, and streaming, while the xCodeEval integration automatically joins task examples with problem_descriptions.jsonl and unittest_db.json using src_uid foreign keys. Returns DatasetDict objects with enriched examples ready for model training or evaluation.
Unique: Integrates xCodeEval with Hugging Face datasets library, providing automatic src_uid resolution and streaming support. Treats data loading as a first-class concern with built-in linking logic, rather than requiring manual JSON parsing.
vs alternatives: More convenient than manual Git LFS downloads because it handles caching and automatic linking, and integrates seamlessly with Hugging Face training pipelines vs custom data loaders.
Provides an alternative data access method using Git LFS for users who prefer direct file access or need selective dataset downloads. Supports cloning the repository with LFS disabled, then pulling specific task files or problem definitions on demand. Useful for custom processing pipelines or environments where Python/Hugging Face is not available, though requires manual src_uid linking to join task examples with problem definitions.
Unique: Provides Git LFS-based alternative to Hugging Face API, enabling direct file access and selective downloads. Requires manual src_uid linking but offers more control over data access patterns.
vs alternatives: More flexible than Hugging Face API for selective downloads and custom pipelines, but requires more manual work for src_uid linking and lacks automatic caching/streaming.
Implements a standardized three-phase evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) that applies consistently across all 7 tasks (program synthesis, code translation, APR, tag classification, code compilation, NL-code retrieval, code-code retrieval). Phase 1 generates or retrieves code, Phase 2 executes it via ExecEval or computes retrieval metrics, and Phase 3 aggregates results into pass@k, MRR, NDCG, or other task-specific metrics. Enables direct comparison of model performance across tasks.
Unique: Defines a unified three-phase evaluation pipeline that applies to all 7 tasks, treating generation, execution, and metric computation as separate concerns. Enables consistent evaluation methodology across diverse task types (generation, translation, retrieval, classification).
vs alternatives: More comprehensive than task-specific evaluation scripts because it provides a unified framework for all 7 tasks, and enables direct comparison of model performance across different task types.
Evaluates code generation models on the program synthesis task by accepting natural language problem descriptions and generating code solutions in any of 17 languages. The evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) runs generated code against unit tests via ExecEval, computing pass@k metrics (pass@1, pass@10, etc.) that measure the probability of finding a correct solution within k samples. Supports both single-solution and multi-sample evaluation modes for assessing model reliability.
Unique: Implements a three-phase evaluation pipeline (Generation → Execution → Metrics) with explicit pass@k computation that measures the probability of finding a correct solution within k attempts, rather than just binary pass/fail. Supports multi-sample evaluation across 17 languages with language-specific compiler configurations and timeout handling.
vs alternatives: More rigorous than HumanEval's simple pass@k because it handles language-specific compilation errors and timeouts explicitly, and scales to 25M training examples vs HumanEval's 164 problems.
Evaluates code translation models by accepting source code in one language and generated translations in a target language, then validating functional equivalence through execution against shared unit tests. The translation evaluation pipeline compiles and executes both source and translated code against the same unittest_db.json test cases, comparing outputs to detect translation errors. Supports all 17 language pairs (though not all pairs may have training data) and uses language-specific compiler mappings to handle syntax differences.
Unique: Validates code translation by executing both source and target code against identical unit tests and comparing outputs, ensuring functional equivalence rather than syntactic similarity. Uses language-specific compiler mappings to handle the complexity of 17 different compilation environments and their idiosyncrasies.
vs alternatives: More rigorous than BLEU-score-based translation metrics because it validates actual functional correctness through execution, and covers more language pairs (17 vs typical 2-4) with explicit compiler integration.
Evaluates program repair models by providing buggy code snippets and expecting corrected versions that pass unit tests. The APR evaluation pipeline executes repaired code against unittest_db.json test cases, measuring whether the repair successfully fixes the bug without introducing new failures. Supports repairs across all 17 languages and uses the same execution-based validation as program synthesis, enabling direct comparison of repair quality.
Unique: Treats program repair as an executable task where success is measured by unit test passage, rather than syntactic similarity to reference repairs. Integrates with the same ExecEval pipeline as program synthesis, enabling direct performance comparison between generation and repair models.
vs alternatives: More comprehensive than traditional APR benchmarks (Defects4J, QuixBugs) because it covers 17 languages and 7,500 problems vs 395 Java bugs, and uses consistent execution-based metrics across all repair types.
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