MTEB
Multimodal toolbox for evaluating embeddings and retrieval systems
Installation | Usage | Leaderboard | Documentation | Citing
Installation
You can install mteb simply using pip or uv. For more on installation please see the documentation.
pip install mteb
For faster installation, you can also use uv:
uv add mteb
Example Usage
Below we present a simple use-case example. For more information, see the documentation.
import mteb
from sentence_transformers import SentenceTransformer
# Select model
model_name = "sentence-transformers/all-MiniLM-L6-v2"
model = mteb.get_model(
model_name
) # if the model is not implemented in MTEB it will be eq. to SentenceTransformer(model_name)
# Select tasks
tasks = mteb.get_tasks(tasks=["Banking77Classification.v2"])
# evaluate
results = mteb.evaluate(model, tasks=tasks)
You can also run it using the CLI:
mteb run \
-m sentence-transformers/all-MiniLM-L6-v2 \
-t "Banking77Classification.v2" \
--output-folder results
For more on how to use the CLI check out the related documentation.
Overview
| Overview | |
|---|---|
| 📈 Leaderboard | The interactive leaderboard of the benchmark |
| Get Started. | |
| 🏃 Get Started | Overview of how to use mteb |
| 🤖 Defining Models | How to use existing models and define custom ones |
| 📋 Selecting tasks | How to select tasks, benchmarks, splits etc. |
| 🏭 Running Evaluation | How to run the evaluations, including cache management, speeding up evaluations etc. |
| 📊 Loading Results | How to load and work with existing model results |
| Overview. | |
| 📋 Tasks | Overview of available tasks |
| 📐 Benchmarks | Overview of available benchmarks |
| 🤖 Models | Overview of available Models |
| Contributing | |
| 🤖 Adding a model | How to submit a model to MTEB and to the leaderboard |
| 👩💻 Adding a dataset | How to add a new task/dataset to MTEB |
| 👩💻 Adding a benchmark | How to add a new benchmark to MTEB and to the leaderboard |
| 🤝 Contributing | How to contribute to MTEB and set it up for development |
Citing
MTEB was introduced in "MTEB: Massive Text Embedding Benchmark", and heavily expanded in "MMTEB: Massive Multilingual Text Embedding Benchmark". When using mteb, we recommend that you cite both articles.
Bibtex Citation (click to unfold)
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022},
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
If you use any of the specific benchmarks, we also recommend that you cite the authors of both the benchmark and its tasks:
benchmark = mteb.get_benchmark("MTEB(eng, v2)")
benchmark.citation # get citation for a specific benchmark
# you can also create a table of the task for the appendix using:
benchmark.tasks.to_latex()
4 maintainers
Release files for mteb 2.21.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mteb-2.21.0.tar.gz | 4.3 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mteb-2.21.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 10.9 MB
Release files / mteb-2.21.0.tar.gz
| Download URL | mteb-2.21.0.tar.gz |
|---|---|
| Size | 4.3 MB |
| Tags | Source |
|
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| Size | 6.6 MB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
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