MTEB is a massive benchmark for measuring the performance of text embedding models on diverse embedding tasks.
The 🥇 leaderboard provides a holistic view of the best text embedding models out there on a variety of tasks.
The 📝 paper gives background on the tasks and datasets in MTEB and analyzes leaderboard results!
The 💻 Github repo contains the code for benchmarking and submitting any model of your choice to the leaderboard.
Why Text Embeddings?
Text Embeddings are vector representations of text that encode semantic information. As machines require numerical inputs to perform computations, text embeddings are a crucial component of many downstream NLP applications. For example, Google uses text embeddings to power their search engine. Text Embeddings can also be used for finding patterns in large amount of text via clustering or as inputs to text classification models, such as in our recent SetFit work. The quality of text embeddings, however, is highly dependent on the embedding model used. MTEB is designed to help you find the best embedding model out there for a variety of tasks!
MTEB
🐋 Massive: MTEB includes 56 datasets across 8 tasks and currently summarizes >2000 results on the leaderboard.
🌎 Multilingual: MTEB contains up to 112 different languages! We have benchmarked several multilingual models on Bitext Mining, Classification, and STS.
🦚 Extensible: Be it new tasks, datasets, metrics, or leaderboard additions, any contribution is very welcome. Check out the GitHub repository to submit to the leaderboard or solve open issues. We hope you join us on the journey of finding the best text embedding model!
Overview of tasks and datasets in MTEB. Multilingual datasets are marked with a purple shade.
Models
For the initial benchmarking of MTEB, we focused on models claiming state-of-the-art results and popular models on the Hub. This led to a high representation of transformers. 🤖
Models by average English MTEB score (y) vs speed (x) vs embedding size (circle size).
We grouped models into the following three attributes to simplify finding the best model for your task:
🏎 Maximum speed Models like Glove offer high speed, but suffer from a lack of context awareness resulting in low average MTEB scores.
⚖️ Speed and performance Slightly slower, but significantly stronger, all-mpnet-base-v2 or all-MiniLM-L6-v2 provide a good balance between speed and performance.
💪 Maximum performance Multi-billion parameter models like ST5-XXL, GTR-XXL or SGPT-5.8B-msmarco dominate on MTEB. They tend to also produce bigger embeddings like SGPT-5.8B-msmarco which produces 4096 dimensional embeddings requiring more storage!
Model performance varies a lot depending on the task and dataset, so we recommend checking the various tabs of the leaderboard before deciding which model to use!
Benchmark your model
Using the MTEB library, you can benchmark any model that produces embeddings and add its results to the public leaderboard. Let’s run through a quick example!
First, install the library:
pip install mteb
Next, benchmark a model on a dataset, for example komninos word embeddings on Banking77.
from mteb import MTEB
from sentence_transformers import SentenceTransformer
model_name = "average_word_embeddings_komninos"
model = SentenceTransformer(model_name)
evaluation = MTEB(tasks=["Banking77Classification"])
results = evaluation.run(model, output_folder=f"results/{model_name}")
This should produce a results/average_word_embeddings_komninos/Banking77Classification.json file!
Now you can submit the results to the leaderboard by adding it to the metadata of the README.md of any model on the Hub.
Run our automatic script to generate the metadata:
python mteb_meta.py results/average_word_embeddings_komninos
The script will produce a mteb_metadata.md file that looks like this:
“`sh
tags:
– mteb
model-index:
– name: average_word_embeddings_komninos
results:
– task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
– type: accuracy
value: 66.76623376623377
– type: f1
value: 66.59096432882667
Now add the metadata to the top of a `README.md` of any model on the Hub, like this [SGPT-5.8B-msmarco](https://huggingface.co/Muennighoff/SGPT-5.8B-weightedmean-msmarco-specb-bitfit/blob/main/README.md) model, and it will show up on the [leaderboard](https://huggingface.co/spaces/mteb/leaderboard) after refreshing!
## Next steps
Go out there and benchmark any model you like! Let us know if you have questions or feedback by opening an issue on our [GitHub repo](https://github.com/embeddings-benchmark/mteb) or the [leaderboard community tab](https://huggingface.co/spaces/mteb/leaderboard/discussions) 🤗
Happy embedding!
## Credits
Huge thanks to the following who contributed to the article or to the MTEB codebase (listed in alphabetical order): Steven Liu, Loïc Magne, Nils Reimers and Nouamane Tazi.
