{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openassistant-conversations-oasst","slug":"openassistant-conversations-oasst","name":"OpenAssistant Conversations (OASST)","type":"dataset","url":"https://huggingface.co/datasets/OpenAssistant/oasst1","page_url":"https://unfragile.ai/openassistant-conversations-oasst","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"openassistant-conversations-oasst__cap_0","uri":"capability://data.processing.analysis.multi.turn.conversation.tree.extraction.with.branching.path.support","name":"multi-turn conversation tree extraction with branching path support","description":"Extracts complete conversation trees from 66,497 human-authored dialogues where each message can have multiple child responses, creating a directed acyclic graph (DAG) structure. The dataset preserves branching paths where volunteers provided alternative continuations at decision points, enabling training on diverse response distributions for the same context. This tree structure is serializable to JSON with parent-child message IDs, allowing downstream systems to reconstruct full conversation histories or sample specific branches for preference learning.","intents":["Train reward models that learn human preferences by comparing sibling branches (different responses to the same prompt)","Build conversation datasets where I can sample diverse multi-turn trajectories for RLHF without data duplication","Analyze conversation patterns and identify where human annotators diverged in their responses to the same context"],"best_for":["RLHF researchers building preference datasets from human feedback","Teams training dialogue models that need diverse response alternatives","Researchers studying human conversation branching patterns and decision points"],"limitations":["Tree structure requires custom parsing logic — no built-in graph database export, must reconstruct from message parent IDs","Branching depth varies significantly (some trees 1-2 turns, others 15+ turns), requiring careful sampling strategies to avoid bias toward shallow conversations","No explicit conversation intent labels — must infer task type (Q&A, creative writing, coding) from message content alone"],"requires":["Python 3.7+ with Hugging Face datasets library","Sufficient RAM to load full dataset in memory (~2-3GB uncompressed) or streaming mode for large-scale processing","Understanding of DAG structures and parent-child message relationships"],"input_types":["Hugging Face datasets API (streaming or download)","Parquet format export"],"output_types":["JSON conversation trees with message IDs and parent references","Flattened conversation pairs (prompt, response) for supervised fine-tuning","Preference triplets (prompt, preferred_response, dispreferred_response) for reward modeling"],"categories":["data-processing-analysis","conversation-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openassistant-conversations-oasst__cap_1","uri":"capability://data.processing.analysis.human.quality.rating.aggregation.with.inter.annotator.agreement.metrics","name":"human quality rating aggregation with inter-annotator agreement metrics","description":"Each message includes quality ratings from multiple human annotators (typically 3-5 raters per message) on dimensions like helpfulness, harmlessness, and honesty. The dataset provides aggregated scores (mean, median, or consensus) plus raw per-annotator ratings, enabling calculation of inter-rater reliability (Krippendorff's alpha, Fleiss' kappa) and identification of ambiguous examples. This multi-rater approach reduces individual bias and allows filtering by agreement threshold to create high-confidence training subsets.","intents":["Filter the dataset to only high-confidence examples where raters strongly agreed, improving training signal quality","Analyze which message types have low inter-rater agreement to identify ambiguous or controversial content","Weight training examples by rater agreement confidence rather than treating all examples equally"],"best_for":["Teams training reward models and wanting to weight examples by annotation confidence","Researchers studying annotation disagreement patterns in conversational AI","Practitioners building quality-filtered subsets for supervised fine-tuning"],"limitations":["Rater agreement varies by message type — coding/technical questions have higher agreement than subjective creative writing","No rater demographic or expertise metadata — cannot analyze if disagreement correlates with rater background","Aggregation method (mean vs median vs consensus) not fully specified in documentation, requiring empirical validation"],"requires":["Python 3.7+ with scipy/numpy for statistical calculations","Understanding of inter-rater reliability metrics (Krippendorff's alpha, Fleiss' kappa)","Familiarity with confidence weighting in machine learning"],"input_types":["Raw rating arrays from dataset (per-message, per-annotator)","Aggregated quality scores"],"output_types":["Filtered dataset subsets by agreement threshold","Inter-rater reliability statistics (alpha, kappa values)","Confidence weights for training examples","Disagreement analysis reports"],"categories":["data-processing-analysis","quality-assessment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openassistant-conversations-oasst__cap_2","uri":"capability://safety.moderation.toxicity.and.safety.annotation.with.multi.dimensional.labels","name":"toxicity and safety annotation with multi-dimensional labels","description":"Messages are annotated with toxicity scores and categorical safety labels (e.g., sexual content, violence, illegal activity, misinformation) applied by human annotators. The dataset exposes both binary flags (toxic/non-toxic) and continuous toxicity scores, plus detailed category breakdowns. This enables training safety classifiers, filtering harmful content, and analyzing the distribution of safety issues across conversation types and languages.","intents":["Filter out toxic or harmful messages to create a clean training dataset for instruction-following models","Train toxicity classifiers or safety guardrails using labeled examples across multiple safety dimensions","Analyze safety issue prevalence by language, conversation type, or user demographic"],"best_for":["Teams building safety-aligned language models and needing labeled toxic examples","Researchers studying toxicity patterns in multilingual conversational data","Practitioners implementing content moderation pipelines"],"limitations":["Toxicity labels are subjective and culturally dependent — definitions of 'harmful' vary across the 35 languages, potentially introducing bias","Annotation coverage uneven across languages — high-resource languages (English) have more thorough safety review than low-resource ones","No fine-grained toxicity severity levels — binary or coarse categorical labels may not capture nuanced harm gradations"],"requires":["Python 3.7+ with Hugging Face datasets library","Understanding of toxicity classification and safety alignment","Awareness of cultural and linguistic variation in harm definitions"],"input_types":["Message text in 35 languages","Annotator-provided toxicity scores and category labels"],"output_types":["Filtered dataset with toxic messages removed","Toxicity classification training data","Safety label distributions by language/conversation type","Confidence scores for safety predictions"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openassistant-conversations-oasst__cap_3","uri":"capability://data.processing.analysis.multilingual.conversation.dataset.with.35.language.support.and.cross.lingual.sampling","name":"multilingual conversation dataset with 35 language support and cross-lingual sampling","description":"Contains 161,443 messages across 35 languages with uneven distribution (English-dominant but includes low-resource languages like Swahili, Vietnamese, Polish). The dataset structure allows filtering by language code and sampling balanced subsets across languages. This enables training multilingual models, analyzing language-specific conversation patterns, and studying how human preferences vary across linguistic and cultural contexts.","intents":["Train multilingual instruction-following models using human-written examples in diverse languages","Create balanced language subsets for cross-lingual transfer learning or zero-shot evaluation","Analyze how conversation quality, safety issues, and human preferences differ across languages"],"best_for":["Teams building multilingual LLMs and needing diverse human feedback across languages","Researchers studying cross-lingual transfer in instruction-following","Practitioners developing language-specific safety guidelines"],"limitations":["Severe language imbalance — English dominates with ~60-70% of messages, while low-resource languages have <1% each, requiring careful sampling to avoid bias","Quality and annotation consistency varies by language — high-resource languages have more thorough human review","No explicit language-pair alignment — cannot directly compare translations or parallel examples across languages"],"requires":["Python 3.7+ with language detection libraries (e.g., langdetect) for validation","Understanding of multilingual dataset balancing and sampling strategies","Awareness of language-specific annotation quality variation"],"input_types":["Language-tagged messages (ISO 639-1 codes)","Conversation trees with language metadata"],"output_types":["Language-filtered subsets","Balanced multilingual samples","Language-specific statistics and quality metrics","Cross-lingual analysis reports"],"categories":["data-processing-analysis","multilingual-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openassistant-conversations-oasst__cap_4","uri":"capability://data.processing.analysis.preference.pair.generation.for.rlhf.training.via.sibling.response.comparison","name":"preference pair generation for rlhf training via sibling response comparison","description":"Automatically generates preference training pairs by comparing sibling responses (multiple continuations of the same prompt) using aggregated human quality ratings. For each prompt with N child responses, the system creates preference triplets (prompt, higher-rated_response, lower-rated_response) by ranking children by quality score. This avoids synthetic preference generation and grounds preference learning in actual human judgments, enabling direct training of reward models and DPO-style algorithms.","intents":["Generate preference pairs for reward model training without manual annotation of comparisons","Create DPO (Direct Preference Optimization) training data from natural conversation branching","Compare different response qualities to the same prompt using human ratings as ground truth"],"best_for":["RLHF practitioners training reward models from human feedback","Teams implementing DPO or other preference-based fine-tuning algorithms","Researchers studying how preference learning scales with dataset size"],"limitations":["Preference signal strength depends on rating difference — pairs with similar quality scores provide weak training signal","No explicit preference justification — only relative ratings, not explanations for why one response is better","Preference generation strategy (all pairs vs top-k vs threshold-based) not standardized, requiring custom implementation"],"requires":["Python 3.7+ with numpy for ranking and pair generation","Understanding of RLHF, reward modeling, and preference-based optimization","Familiarity with DPO or similar algorithms"],"input_types":["Conversation trees with quality ratings per message","Aggregated human quality scores"],"output_types":["Preference triplets (prompt, preferred, dispreferred)","Preference strength scores (rating difference magnitude)","Preference pair statistics and distribution analysis"],"categories":["data-processing-analysis","preference-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openassistant-conversations-oasst__cap_5","uri":"capability://data.processing.analysis.instruction.response.pair.extraction.for.supervised.fine.tuning","name":"instruction-response pair extraction for supervised fine-tuning","description":"Flattens conversation trees into instruction-response pairs by treating each user message as an instruction and the following assistant message as the response. Handles multi-turn context by optionally including conversation history or using only the immediate prompt-response pair. This enables straightforward supervised fine-tuning (SFT) of language models without requiring preference learning infrastructure, suitable for baseline model training or quick prototyping.","intents":["Create a simple instruction-following dataset for supervised fine-tuning without preference learning complexity","Extract (prompt, response) pairs while optionally preserving conversation context for multi-turn training","Build baseline models quickly without RLHF infrastructure"],"best_for":["Teams doing initial supervised fine-tuning before RLHF","Practitioners prototyping instruction-following models with limited resources","Researchers establishing SFT baselines for comparison"],"limitations":["Loses preference information — treats all responses equally regardless of quality ratings, potentially training on suboptimal examples","Flattening discards branching structure — cannot leverage multiple responses to the same prompt for diversity","Multi-turn context handling requires custom logic — no standardized approach for including conversation history"],"requires":["Python 3.7+ with basic data manipulation libraries","Understanding of supervised fine-tuning and instruction-following","Awareness of quality filtering importance when not using preference learning"],"input_types":["Conversation trees with message sequences","User and assistant message pairs"],"output_types":["Instruction-response pairs (flat format)","Multi-turn conversation sequences with context","Filtered pairs by quality threshold"],"categories":["data-processing-analysis","supervised-fine-tuning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openassistant-conversations-oasst__cap_6","uri":"capability://data.processing.analysis.conversation.metadata.and.filtering.by.task.type.and.domain","name":"conversation metadata and filtering by task type and domain","description":"Each conversation includes metadata tags or inferred categories (e.g., creative writing, coding, Q&A, general knowledge) enabling domain-specific filtering and analysis. While not explicitly documented as structured tags in the original dataset, the message content and conversation structure allow downstream systems to classify conversations by type. This enables creating domain-specific training subsets, analyzing model performance across task types, and studying how human preferences vary by domain.","intents":["Create domain-specific training subsets (e.g., coding-only, creative writing-only) for specialized model training","Analyze human preference patterns across different conversation types","Evaluate models on domain-specific benchmarks extracted from the dataset"],"best_for":["Teams training specialized models for specific domains (coding, creative writing, etc.)","Researchers analyzing domain-specific preference patterns","Practitioners building domain-specific safety guidelines"],"limitations":["No explicit task type labels — requires custom classification logic (keyword matching, LLM-based categorization, or manual annotation)","Domain distribution unknown and likely imbalanced — no guarantee of sufficient examples per domain","Cross-domain conversations not handled — some conversations span multiple domains, complicating filtering"],"requires":["Python 3.7+ with text classification libraries (e.g., sklearn, transformers)","Custom domain classification logic or pre-trained classifier","Understanding of domain-specific model training"],"input_types":["Conversation message content","Conversation trees and metadata"],"output_types":["Domain-classified conversation subsets","Domain-specific statistics and quality metrics","Domain preference analysis reports"],"categories":["data-processing-analysis","domain-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openassistant-conversations-oasst__cap_7","uri":"capability://data.processing.analysis.large.scale.human.written.dataset.with.volunteer.annotation.pipeline","name":"large-scale human-written dataset with volunteer annotation pipeline","description":"161,443 messages collected from 13,000+ volunteer annotators through a crowdsourced platform (Open Assistant project), not generated by LLMs or synthetic methods. The annotation pipeline includes message creation, quality rating, toxicity labeling, and ranking by multiple independent raters. This human-centric approach ensures authentic conversational patterns, diverse writing styles, and genuine human preferences, though with inherent quality variance across annotators.","intents":["Train models on authentic human-written conversations rather than LLM-generated or synthetic data","Study natural human conversation patterns and preference distributions","Avoid dataset bias from single-model generation (e.g., ChatGPT-only data)"],"best_for":["Researchers studying human-AI interaction and conversation patterns","Teams training models on diverse human writing styles","Practitioners building models resistant to single-model bias"],"limitations":["Quality variance across 13,000 annotators — no standardized writing quality or expertise level, introducing noise","Volunteer bias — annotators self-selected, potentially skewing demographics and perspectives","Annotation consistency issues — different raters may have different standards for quality, toxicity, and helpfulness","No rater expertise metadata — cannot distinguish expert from novice annotators"],"requires":["Python 3.7+ with Hugging Face datasets library","Understanding of crowdsourced data quality and annotation variance","Awareness of potential demographic bias in volunteer populations"],"input_types":["Human-written conversation messages","Annotator ratings and labels"],"output_types":["Full conversation dataset","Quality-filtered subsets","Annotator agreement statistics","Demographic analysis (if available)"],"categories":["data-processing-analysis","human-annotation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openassistant-conversations-oasst__headline","uri":"capability://model.training.human.generated.conversational.dataset.for.training.ai.models","name":"human-generated conversational dataset for training ai models","description":"A comprehensive dataset of human-generated conversations, ideal for training AI models in natural language understanding and reinforcement learning from human feedback (RLHF), featuring quality ratings and toxicity annotations.","intents":["best conversational dataset for AI training","dataset for RLHF research","largest human-written instruction dataset","training models with multi-turn conversations","human quality rated conversation data"],"best_for":["AI model training","RLHF research"],"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":["Python 3.7+ with Hugging Face datasets library","Sufficient RAM to load full dataset in memory (~2-3GB uncompressed) or streaming mode for large-scale processing","Understanding of DAG structures and parent-child message relationships","Python 3.7+ with scipy/numpy for statistical calculations","Understanding of inter-rater reliability metrics (Krippendorff's alpha, Fleiss' kappa)","Familiarity with confidence 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method (mean vs median vs consensus) not fully specified in documentation, requiring empirical validation","Toxicity labels are subjective and culturally dependent — definitions of 'harmful' vary across the 35 languages, potentially introducing bias","Annotation coverage uneven across languages — high-resource languages (English) have more thorough safety review than low-resource ones","No fine-grained toxicity severity levels — binary or coarse categorical labels may not capture nuanced harm gradations","Severe language imbalance — English dominates with ~60-70% of messages, while low-resource languages have <1% each, requiring careful sampling to avoid bias","builder identity is not verified yet","no observed match outcomes 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