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Implements a versioned dataset structure on HuggingFace Hub that tracks model performance over time, allowing researchers to query historical leaderboard snapshots and compare embedding model capabilities across standardized evaluation protocols.","intents":["Compare embedding model performance across standardized benchmarks without running evaluations locally","Track how embedding models improve over time and identify performance regressions","Build meta-analyses of which model architectures excel at specific task categories","Reproduce published MTEB leaderboard rankings and validate model selection decisions"],"best_for":["ML researchers evaluating embedding model quality","Teams selecting embedding models for production RAG/semantic search systems","Benchmark maintainers tracking model ecosystem evolution","Academic papers requiring standardized embedding evaluation baselines"],"limitations":["Results reflect only MTEB task coverage — does not include domain-specific embedding evaluations or proprietary benchmarks","Evaluation results are point-in-time snapshots; model inference may differ if weights or quantization methods change","No built-in filtering or querying interface — requires manual dataset loading and pandas/polars manipulation to extract specific model comparisons","Results depend on MTEB maintainers' evaluation infrastructure; no guarantee of hardware consistency across evaluation runs"],"requires":["HuggingFace Datasets library (datasets>=2.0.0)","Python 3.8+","Internet access to HuggingFace Hub or local dataset cache","Sufficient disk space for full dataset (~500MB+ uncompressed)"],"input_types":["model_name (string identifier)","task_name (string: 'retrieval', 'clustering', 'semantic_similarity', etc.)","language (string: 'en', 'zh', etc.)"],"output_types":["structured data (pandas DataFrame with columns: model_name, task, metric, score, timestamp)","JSON (individual result records with metadata)","leaderboard rankings (sorted by metric score)"],"categories":["data-processing-analysis","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-mteb--results__cap_1","uri":"capability://data.processing.analysis.multi.dimensional.embedding.model.filtering.and.ranking","name":"multi-dimensional embedding model filtering and ranking","description":"Enables filtering and ranking of embedding models across multiple dimensions: task category (retrieval, clustering, semantic similarity), language support (monolingual vs multilingual), model size (parameter count), inference latency, and metric type (NDCG, MAP, accuracy). Implements a tabular schema where each row represents a model's performance on a specific task, allowing users to construct complex queries like 'find the fastest multilingual retrieval model with NDCG@10 > 0.5'.","intents":["Find the best embedding model for a specific task (e.g., dense retrieval, clustering) with known performance thresholds","Identify models that balance performance and inference speed for latency-constrained deployments","Compare multilingual vs monolingual model performance to decide on localization strategy","Build automated model selection pipelines that choose models based on task requirements and hardware constraints"],"best_for":["ML engineers building production recommendation or semantic search systems","AutoML systems that need to select embedding models programmatically","Researchers conducting meta-analyses of embedding model design patterns","Teams with strict latency or memory budgets evaluating model trade-offs"],"limitations":["Filtering requires manual dataset loading and query construction — no built-in query API or SQL interface","Results do not include inference latency or memory footprint; users must cross-reference with model cards","Task coverage is limited to MTEB's 56 tasks; domain-specific tasks (e.g., legal document retrieval, medical similarity) are not represented","Model metadata (parameter count, architecture type) is sparse and must be inferred from model names or external sources"],"requires":["Python 3.8+","pandas or polars for efficient filtering","HuggingFace Datasets library","Basic SQL or pandas query knowledge"],"input_types":["filter criteria (task_name, language, metric_threshold)","sort key (metric name, model name)"],"output_types":["ranked list of models (DataFrame with scores)","model metadata (name, architecture, parameter count)","performance metrics (NDCG, MAP, accuracy, F1)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-mteb--results__cap_2","uri":"capability://data.processing.analysis.time.series.tracking.of.embedding.model.performance.evolution","name":"time-series tracking of embedding model performance evolution","description":"Maintains historical snapshots of model evaluation results, enabling researchers to track how embedding model performance changes over time as new models are released and existing models are re-evaluated with improved hardware or evaluation protocols. Implements a versioned dataset structure where each version corresponds to a MTEB release, preserving the ability to reproduce historical leaderboard states and analyze performance trends.","intents":["Analyze how embedding model performance has improved year-over-year to understand model scaling trends","Identify when a model's performance regressed due to evaluation methodology changes or hardware differences","Build time-series forecasts of embedding model capability improvements","Reproduce published results from a specific MTEB release date for academic citations"],"best_for":["Researchers studying embedding model scaling laws and capability trends","Teams making long-term model selection decisions based on historical performance trajectories","Benchmark maintainers validating evaluation methodology consistency","Academic papers requiring reproducible historical baselines"],"limitations":["Version history is limited to MTEB release dates; does not capture intra-release model updates or re-evaluations","Evaluation methodology may change between MTEB versions, making direct time-series comparisons unreliable","No built-in time-series analysis tools — requires manual version loading and comparison","Older MTEB versions may have evaluated fewer models, limiting historical comparison scope"],"requires":["HuggingFace Datasets library with revision parameter support","Python 3.8+","Knowledge of MTEB release dates and version tags"],"input_types":["model_name (string)","task_name (string)","date_range or version_list (MTEB release identifiers)"],"output_types":["time-series data (model performance over MTEB versions)","trend analysis (performance delta, improvement rate)","historical snapshots (full leaderboard at specific date)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-mteb--results__cap_3","uri":"capability://data.processing.analysis.cross.lingual.embedding.model.performance.comparison","name":"cross-lingual embedding model performance comparison","description":"Disaggregates embedding model evaluation results by language, enabling researchers to compare monolingual vs multilingual model performance and identify language-specific performance gaps. Implements a language-stratified schema where results are indexed by language code (en, zh, fr, etc.), allowing queries like 'find models with >0.5 NDCG@10 on English retrieval AND >0.4 on Chinese retrieval'.","intents":["Evaluate whether a multilingual model meets performance requirements across all target languages","Identify which languages have the largest performance gaps compared to English baselines","Select language-specific models vs multilingual models based on performance-cost trade-offs","Build language-aware RAG systems that choose embedding models per language"],"best_for":["Teams building multilingual search or recommendation systems","Researchers studying cross-lingual transfer in embedding models","Companies localizing products to non-English markets","NLP practitioners optimizing for specific language pairs"],"limitations":["Language coverage is limited to languages included in MTEB; many low-resource languages are not evaluated","Results reflect MTEB's language-specific task datasets, which may not represent real-world language distributions","No built-in language-specific filtering — requires manual dataset loading and language-based grouping","Performance gaps may reflect dataset quality differences rather than true model capability differences"],"requires":["HuggingFace Datasets library","Python 3.8+","Knowledge of ISO 639-1 language codes"],"input_types":["language_code (string: 'en', 'zh', 'fr', etc.)","task_name (string)","model_name (string)"],"output_types":["language-stratified performance metrics","cross-lingual comparison tables","language-specific model rankings"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-mteb--results__cap_4","uri":"capability://data.processing.analysis.standardized.metric.normalization.and.comparison.across.task.types","name":"standardized metric normalization and comparison across task types","description":"Normalizes evaluation metrics across different task types (retrieval uses NDCG, clustering uses V-measure, classification uses accuracy) into a unified comparison framework, enabling researchers to identify which models excel across diverse task categories. Implements metric-specific normalization functions that map heterogeneous metrics (0-1 scales, different optimization directions) into comparable performance scores.","intents":["Identify general-purpose embedding models that perform well across retrieval, clustering, and classification tasks","Compare task-specific models to understand specialization trade-offs","Build ensemble models that combine task-specific embeddings with performance guarantees","Analyze which model architectures generalize best across diverse downstream tasks"],"best_for":["Researchers studying embedding model generalization and transfer learning","Teams building multi-task embedding systems","AutoML systems that need to select embeddings for unknown downstream tasks","Academic papers analyzing embedding model design patterns"],"limitations":["Metric normalization is lossy — normalizing NDCG and accuracy to a common scale obscures task-specific nuances","Different tasks have different optimal metric thresholds; a 0.7 NDCG@10 may not be equivalent to 0.7 accuracy","No built-in metric weighting — users must manually assign importance to different task types","Normalization approach is not documented in the dataset; users must infer methodology from results"],"requires":["HuggingFace Datasets library","Python 3.8+","Understanding of MTEB metric definitions and scales"],"input_types":["model_name (string)","task_type (string: 'retrieval', 'clustering', 'classification', etc.)"],"output_types":["normalized performance scores (0-1 scale)","task-type comparison tables","generalization rankings (average performance across task types)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"low","permissions":["HuggingFace Datasets library (datasets>=2.0.0)","Python 3.8+","Internet access to HuggingFace Hub or local dataset cache","Sufficient disk space for full dataset (~500MB+ uncompressed)","pandas or polars for efficient filtering","HuggingFace Datasets library","Basic SQL or pandas query knowledge","HuggingFace Datasets library with revision parameter support","Knowledge of MTEB release dates and version tags","Knowledge of ISO 639-1 language codes"],"failure_modes":["Results reflect only MTEB task coverage — does not include domain-specific embedding evaluations or proprietary benchmarks","Evaluation results are point-in-time snapshots; model inference may differ if weights or quantization methods change","No built-in filtering or querying interface — requires manual dataset loading and pandas/polars manipulation to extract specific model comparisons","Results depend on MTEB maintainers' evaluation infrastructure; no guarantee of hardware consistency across evaluation runs","Filtering requires manual dataset loading and query construction — no built-in query API or SQL interface","Results do not include inference latency or memory footprint; users must cross-reference with model cards","Task coverage is limited to MTEB's 56 tasks; domain-specific tasks (e.g., legal document retrieval, medical similarity) are not represented","Model metadata (parameter count, architecture type) is sparse and must be inferred from model names or external sources","Version history is limited to MTEB release dates; does not capture intra-release model updates or re-evaluations","Evaluation methodology may change between MTEB versions, making direct time-series comparisons unreliable","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.33,"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":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:22:48.064Z","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=mteb--results","compare_url":"https://unfragile.ai/compare?artifact=mteb--results"}},"signature":"Wz5XbTqMdzfqZC0TK+AQH5niBdNeK5vM79szYGV8toKDX5I72RmuQea2zGuh6CSOhJEnhIeGPVqduJVT4J5AAw==","signedAt":"2026-06-21T20:03:57.032Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mteb--results","artifact":"https://unfragile.ai/mteb--results","verify":"https://unfragile.ai/api/v1/verify?slug=mteb--results","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"}}