Audio-visual video embedding quality across retrieval, classification, clustering, pair classification, zero-shot classification, and video-centric...
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Massive Text Embeddings Benchmark
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MTEB is a Python framework for evaluating embeddings and retrieval systems for both text and image. MTEB covers more than 1000 languages and diverse tasks, from classics like classification and clustering to use-case specialized tasks such as legal, code, or healthcare retrieval.
You can get started using mteb.
| Overview | |
|---|---|
| π Leaderboard | The interactive leaderboard of the benchmark |
| Get Started. | |
| π Get Started | Overview of how to use mteb |
| π€ Defining Models | How to use existing model and define custom ones |
| π Selecting tasks | How to select tasks, benchmarks, splits etc. |
| π Running Evaluation | How to run the evaluations, including cache management, speeding up evaluations etc. |
| π Loading Results | How to load and work with existing model results |
| Overview. | |
| π Tasks | Overview of available tasks |
| π Benchmarks | Overview of available benchmarks |
| π€ Models | Overview of available Models |
| Contributing | |
| π€ Adding a model | How to submit a model to MTEB and to the leaderboard |
| π©βπ» Adding a dataset | How to add a new task/dataset to MTEB |
| π©βπ» Adding a benchmark | How to add a new benchmark to MTEB and to the leaderboard |
| π€ Contributing | How to contribute to MTEB and set it up for development |
spaces 6
pinned
Running on CPU Upgrade
7.52k
MTEB Leaderboard
π
Embedding Leaderboard
Running on CPU Upgrade
Leaderboard Backend
π
Paused
Agents
1
leaderboard-analytics-service
π
Analyze competition leaderboards and get visual insights
Running
Agents
40
MTEB Legacy Leaderboard
π₯
Explore and filter MTEB model benchmark results
Running
116
MTEB Arena
β
Display MTEB Arena interface
datasets 1,643
mteb/results
Viewer β’ Updated β’ 8.44M β’ 502k β’ 4
mteb/MultilingualNanoTouche2020Retrieval
Viewer β’ Updated β’ 73.9k β’ 112
mteb/MultilingualNanoSciFactRetrieval
Viewer β’ Updated β’ 33.3k β’ 72
mteb/MultilingualNanoSCIDOCSRetrieval
Viewer β’ Updated β’ 27.5k β’ 100
mteb/MultilingualNanoQuoraRetrieval
Viewer β’ Updated β’ 56.8k β’ 78
mteb/MultilingualNanoNQRetrieval
Viewer β’ Updated β’ 56.5k β’ 80
mteb/MultilingualNanoNFCorpusRetrieval
Viewer β’ Updated β’ 57.8k β’ 91
mteb/MultilingualNanoMSMARCORetrieval
Viewer β’ Updated β’ 56.6k β’ 74
mteb/MultilingualNanoHotpotQARetrieval
Viewer β’ Updated β’ 52.6k β’ 82
mteb/MultilingualNanoFiQA2018Retrieval
Viewer β’ Updated β’ 43.2k β’ 74