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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Dolma aggregates 3 trillion tokens from 7 heterogeneous sources (Common Crawl, The Stack, peS2o, Project Gutenberg, Wikipedia, Wikibooks, C4) with fully documented filtering criteria, deduplication methods, and mixing ratios. The composition system enables researchers to understand exactly which data proportions and quality thresholds were applied, making training runs reproducible across different teams and hardware configurations. Data is segmented into pretraining, mid-training, and post-training pools to support staged model development.
Unique: Dolma's distinguishing feature is comprehensive documentation of data curation decisions (exact filtering rules, deduplication methods via Duplodocus, mixing ratios) released alongside trained models (OLMo 7B, 32B), enabling full reproducibility. Most pretraining datasets (C4, The Pile, ROOTS) document composition at a high level but not the specific algorithmic rules applied. Dolma's integration with OlmoTrace enables tracing model outputs back to source training documents, providing data provenance that most datasets lack.
vs alternatives: Dolma provides greater transparency and reproducibility than C4 or The Pile through documented filtering rules and deduplication specifications, while offering more diverse source coverage (code + academic + literary) than web-only datasets like C4, though it is smaller than ROOTS (1.6T vs 3T tokens) and less frequently updated than continuously-refreshed web crawl datasets.
Dolma implements source-specific filtering pipelines using documented rules applied through tools like Datamap-rs (large-scale data cleaning) and Duplodocus (fuzzy deduplication). Each of the 7 sources undergoes tailored quality filtering appropriate to its characteristics: web crawl data is filtered for language and content quality, code is filtered for license and syntax validity, academic papers are filtered by venue quality, and literary text is filtered for encoding and completeness. Filtering rules are explicitly documented to enable researchers to understand and potentially modify quality thresholds.
Unique: Dolma's filtering approach is distinguished by source-specific quality criteria (e.g., academic papers filtered by venue quality, code filtered by license validity) rather than uniform filtering across all data. The integration of Duplodocus for fuzzy deduplication (vs. exact-match deduplication) is more sophisticated than simple hash-based approaches, enabling detection of near-duplicate content across sources. Documentation of exact filtering rules is rare in published datasets.
vs alternatives: Dolma's documented, source-specific filtering is more transparent than C4's undisclosed filtering rules, and more sophisticated than The Pile's simple language detection, though it requires external tools (Datamap-rs, Duplodocus) rather than providing integrated filtering infrastructure like some commercial training platforms.
Dolma's post-training data pool is designed for use with Open Instruct, Allen AI's instruction tuning framework, enabling seamless transition from pretraining to instruction tuning. The post-training pool contains instruction-formatted data (format unspecified) optimized for alignment and capability refinement. Integration with Open Instruct provides data loading, instruction formatting, and training orchestration for the post-training phase. This integration enables researchers to implement the full training pipeline (pretraining → continued pretraining → instruction tuning) using coordinated Dolma and Open Instruct components.
Unique: Dolma's post-training data pool with Open Instruct integration provides a coordinated instruction tuning solution that is rare in open-source ecosystems. Most datasets provide pretraining data only; Dolma's inclusion of post-training data and integration with Open Instruct enables end-to-end training without external instruction data curation. The simultaneous release of Dolma, OlmoCore, and Open Instruct provides a complete, reproducible training pipeline.
vs alternatives: Dolma's integrated post-training pipeline is more complete than datasets providing pretraining data only, though it is less flexible than using generic instruction datasets (e.g., Alpaca, ShareGPT) that support multiple training frameworks.
Dolma provides three distinct data pools optimized for different training stages: a pretraining pool for initial model training on diverse, general-purpose text; a mid-training pool for continued pretraining with potentially different source ratios or quality thresholds; and a post-training pool for instruction tuning and alignment. This segmentation enables researchers to apply different data compositions at different training phases without managing separate datasets, and allows for staged training strategies where model behavior is refined through targeted data exposure.
Unique: Dolma's segmentation into three explicit training phases (pretraining, mid-training, post-training) with separate downloadable pools is uncommon in published datasets. Most datasets provide a single corpus; Dolma's phase-specific segmentation enables researchers to implement sophisticated multi-stage training strategies without custom data partitioning. The integration with Open Instruct for post-training suggests end-to-end training pipeline support.
vs alternatives: Dolma's staged data segmentation is more structured than generic datasets like C4 or The Pile, which provide single corpora; it is comparable to commercial training platforms that offer phase-specific data curation, but with full transparency and reproducibility.
Dolma integrates with the OlmoTrace tool, which enables researchers to trace model outputs and behaviors back to the specific source documents in the training dataset that contributed to those outputs. This capability works by maintaining mappings between training data and model internals, allowing queries like 'which documents influenced this model's response?' or 'what is the source distribution of training data for this capability?'. Traceability is implemented through document-level tracking during preprocessing and training, enabling post-hoc analysis of model behavior in terms of training data composition.
Unique: OlmoTrace's document-level provenance tracing from model outputs back to training data is a rare capability in open-source LLM ecosystems. Most models provide no tracing mechanism; some provide source-level statistics but not output-specific tracing. Dolma's integration of traceability at the dataset level (maintaining document identifiers through preprocessing) enables this capability without post-hoc model modification.
vs alternatives: Dolma's provenance tracing via OlmoTrace provides transparency unavailable in most open models (which provide no tracing) and exceeds the source-level statistics provided by some datasets like C4, though it is less detailed than commercial model cards that sometimes include data attribution.
Dolma incorporates The Stack, a large-scale source code dataset, with code-specific filtering and quality control. Code data is filtered for license compliance (removing GPL and other restrictive licenses), syntax validity, and repository quality. The Stack integration provides access to diverse programming languages and coding patterns without requiring separate code dataset curation. Code is deduplicated using the same Duplodocus fuzzy deduplication as other sources, enabling detection of near-duplicate code across repositories.
Unique: Dolma's integration of The Stack with explicit license filtering (removing GPL) is distinctive because it enables commercial use of code-trained models while maintaining open-source compliance. Most code datasets (e.g., CodeParrot, GitHub Copilot training data) do not document license filtering or provide GPL-free variants. The combination of license filtering with fuzzy deduplication across code repositories is more sophisticated than simple exact-match deduplication.
vs alternatives: Dolma's code data provides license-compliant code training without GPL restrictions, making it suitable for commercial models, whereas The Pile and other generic datasets either include GPL code or lack code data entirely. However, it is smaller and less frequently updated than GitHub's full code index.
Dolma incorporates peS2o, a large-scale academic paper dataset, with venue-based quality filtering that prioritizes papers from high-impact conferences and journals. Academic papers are filtered by publication venue quality (e.g., top-tier conferences, high-impact journals) rather than citation count or other metrics, ensuring training data includes rigorous, peer-reviewed research. Paper text is extracted from PDFs and structured metadata, enabling models to learn from scientific writing and domain-specific knowledge. Academic data is deduplicated using the same fuzzy deduplication as other sources.
Unique: Dolma's use of venue-based quality filtering for academic papers (rather than citation count or other metrics) is distinctive because it prioritizes peer-review rigor over popularity, potentially reducing bias toward highly-cited but potentially flawed work. Integration of peS2o with explicit venue quality criteria is rare in published datasets; most datasets either exclude academic content or include it without quality filtering.
vs alternatives: Dolma's academic data provides peer-reviewed, venue-filtered content that exceeds generic datasets like C4 or The Pile in academic quality, though it is smaller and less frequently updated than full academic paper indices like arXiv or PubMed.
Dolma integrates web text from both Common Crawl (raw web crawl) and C4 (pre-filtered web text), with documented filtering rules for language detection, content quality, and toxicity. Web data undergoes source-specific filtering appropriate to its characteristics: Common Crawl data is filtered more aggressively due to lower baseline quality, while C4 data benefits from existing filtering. All web data is deduplicated using Duplodocus fuzzy deduplication to remove near-duplicate content across domains. The combination of two web sources with different filtering approaches provides diversity while maintaining quality standards.
Unique: Dolma's use of two complementary web sources (Common Crawl and C4) with source-specific filtering is distinctive because it balances raw coverage (Common Crawl) with pre-filtered quality (C4), providing diversity while maintaining standards. Most datasets use either raw crawls or pre-filtered sources, but not both. The documented filtering rules (though not detailed in available materials) enable reproducibility that most web datasets lack.
vs alternatives: Dolma's dual-source web data provides greater transparency and reproducibility than C4 alone, while offering broader coverage than C4-only datasets, though it is smaller and less frequently updated than continuously-refreshed web crawl datasets.
+3 more capabilities
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 Dolma 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.
+5 more capabilities