results
DatasetFreeDataset by mteb. 10,39,913 downloads.
Capabilities5 decomposed
mteb benchmark result aggregation and versioning
Medium confidenceAggregates evaluation results from the Massive Text Embedding Benchmark (MTEB) across multiple model architectures, embedding dimensions, and task categories (retrieval, clustering, semantic similarity, reranking, classification, etc.). 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.
Centralizes MTEB evaluation results in a versioned, publicly-accessible HuggingFace dataset with 1M+ result records, enabling reproducible model comparisons without requiring local benchmark execution. Implements a standardized schema across 50+ embedding models and 50+ task variants, with automatic updates as new models are evaluated.
Eliminates the need to run MTEB locally (which requires 48+ GPU hours) by providing pre-computed results; more comprehensive than individual model cards because it enables cross-model comparison at scale
multi-dimensional embedding model filtering and ranking
Medium confidenceEnables 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'.
Provides a unified tabular interface for comparing 50+ embedding models across 50+ tasks with standardized metrics, eliminating the need to aggregate results from individual model cards or papers. Implements a denormalized schema optimized for filtering and ranking queries rather than a normalized relational structure.
More comprehensive and queryable than individual HuggingFace model cards; faster than running MTEB locally; more standardized than academic papers which use inconsistent evaluation protocols
time-series tracking of embedding model performance evolution
Medium confidenceMaintains 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.
Preserves historical MTEB evaluation results across multiple dataset versions on HuggingFace Hub, enabling reproducible time-series analysis of embedding model performance without requiring users to maintain their own version archives. Implements automatic versioning aligned with MTEB release cycles.
Eliminates the need to manually archive MTEB results; more reliable than relying on academic papers for historical performance data; enables programmatic trend analysis vs manual leaderboard screenshots
cross-lingual embedding model performance comparison
Medium confidenceDisaggregates 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'.
Provides language-stratified evaluation results for 50+ embedding models across 100+ language-task combinations, enabling direct comparison of monolingual vs multilingual model performance without requiring separate evaluation runs. Implements a language-indexed schema optimized for cross-lingual analysis.
More comprehensive than individual model cards which rarely provide language-specific performance breakdowns; eliminates the need to run MTEB in multiple languages locally
standardized metric normalization and comparison across task types
Medium confidenceNormalizes 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.
Provides a unified schema for comparing embedding models across heterogeneous task types with different metric definitions, enabling meta-analysis of model generalization without requiring users to manually normalize metrics. Implements task-aware metric aggregation.
More systematic than manual leaderboard inspection; enables programmatic cross-task analysis vs task-specific leaderboards that prevent direct comparison
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with results, ranked by overlap. Discovered automatically through the match graph.
leaderboard
leaderboard — AI demo on HuggingFace
MTEB
Embedding model benchmark — 8 tasks, 112 languages, the standard for comparing embeddings.
bge-large-en-v1.5
feature-extraction model by undefined. 1,17,45,865 downloads.
nomic-embed-text-v1
sentence-similarity model by undefined. 55,53,124 downloads.
jina-embeddings-v3
feature-extraction model by undefined. 24,51,907 downloads.
mxbai-embed-large-v1
feature-extraction model by undefined. 43,12,964 downloads.
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
- ✓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
Known 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
- ⚠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
Requirements
Input / Output
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results — a dataset on HuggingFace with 10,39,913 downloads
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