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https://github.com/embeddings-benchmark/mteb

MTEB: Massive Text Embedding Benchmark
https://github.com/embeddings-benchmark/mteb

benchmark bitext-mining clustering information-retrieval multilingual-nlp neural-search reranking retrieval sbert semantic-search sentence-transformers sgpt sts text-classification text-embedding

Last synced: 24 days ago
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MTEB: Massive Text Embedding Benchmark

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Massive Text Embedding Benchmark





    

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## Installation



```bash

pip install mteb

```



## Example Usage



* Using a Python script:



```python

import mteb

from sentence_transformers import SentenceTransformer



# Define the sentence-transformers model name

model_name = "average_word_embeddings_komninos"

# or directly from huggingface:

# model_name = "sentence-transformers/all-MiniLM-L6-v2"



model = SentenceTransformer(model_name)

tasks = mteb.get_tasks(tasks=["Banking77Classification"])

evaluation = mteb.MTEB(tasks=tasks)

results = evaluation.run(model, output_folder=f"results/{model_name}")

```



* Using CLI



```bash

mteb available_tasks



mteb run -m sentence-transformers/all-MiniLM-L6-v2 \

    -t Banking77Classification  \

    --verbosity 3



# if nothing is specified default to saving the results in the results/{model_name} folder

```



* Using multiple GPUs in parallel can be done by just having a custom encode function that distributes the inputs to multiple GPUs like e.g. [here](https://github.com/microsoft/unilm/blob/b60c741f746877293bb85eed6806736fc8fa0ffd/e5/mteb_eval.py#L60) or [here](https://github.com/ContextualAI/gritlm/blob/09d8630f0c95ac6a456354bcb6f964d7b9b6a609/gritlm/gritlm.py#L75).



## Usage Documentation

Click on each section below to see the details.




 



    Task selection 



### Task selection



Tasks can be selected by providing the list of datasets, but also



* by their task (e.g. "Clustering" or "Classification")



```python

tasks = mteb.get_tasks(task_types=["Clustering", "Retrieval"]) # Only select clustering and retrieval tasks

```



* by their categories e.g. "s2s" (sentence to sentence) or "p2p" (paragraph to paragraph)



```python

tasks = mteb.get_tasks(categories=["s2s", "p2p"]) # Only select sentence2sentence and paragraph2paragraph datasets

```



* by their languages



```python

tasks = mteb.get_tasks(languages=["eng", "deu"]) # Only select datasets which contain "eng" or "deu" (iso 639-3 codes)

```



You can also specify which languages to load for multilingual/cross-lingual tasks like below:



```python

import mteb



tasks = [

    mteb.get_task("AmazonReviewsClassification", languages = ["eng", "fra"]),

    mteb.get_task("BUCCBitextMining", languages = ["deu"]), # all subsets containing "deu"

]



# or you can select specific huggingface subsets like this:

from mteb.tasks import AmazonReviewsClassification, BUCCBitextMining



evaluation = mteb.MTEB(tasks=[

        AmazonReviewsClassification(hf_subsets=["en", "fr"]) # Only load "en" and "fr" subsets of Amazon Reviews

        BUCCBitextMining(hf_subsets=["de-en"]), # Only load "de-en" subset of BUCC

])

# for an example of a HF subset see "Subset" in the dataset viewer at: https://huggingface.co/datasets/mteb/bucc-bitext-mining

```



    Running a benchmark 



### Running a Benchmark



`mteb` comes with a set of predefined benchmarks. These can be fetched using `get_benchmark` and run in a similar fashion to other sets of tasks. 

For instance to select the 56 English datasets that form the "Overall MTEB English leaderboard":



```python

import mteb

benchmark = mteb.get_benchmark("MTEB(eng)")

evaluation = mteb.MTEB(tasks=benchmark)

```



The benchmark specified not only a list of tasks, but also what splits and language to run on. To get an overview of all available benchmarks simply run:



```python

import mteb

benchmarks = mteb.get_benchmarks()

```



Generally we use the naming scheme for benchmarks `MTEB(*)`, where the "*" denotes the target of the benchmark. In the case of a language, we use the three-letter language code. For large groups of languages, we use the group notation, e.g., `MTEB(Scandinavian)` for Scandinavian languages. External benchmarks implemented in MTEB like `CoIR` use their original name. When using a benchmark from MTEB please cite `mteb` along with the citations of the benchmark which you can access using:



```python

benchmark.citation

```



    Passing in `encode` arguments 



### Passing in `encode` arguments



To pass in arguments to the model's `encode` function, you can use the encode keyword arguments (`encode_kwargs`):



```python

evaluation.run(model, encode_kwargs={"batch_size": 32}

```



    Selecting evaluation split 



### Selecting evaluation split

You can evaluate only on `test` splits of all tasks by doing the following:



```python

evaluation.run(model, eval_splits=["test"])

```



Note that the public leaderboard uses the test splits for all datasets except MSMARCO, where the "dev" split is used.



    Using a custom model 



### Using a custom model



Models should implement the following interface, implementing an `encode` function taking as inputs a list of sentences, and returning a list of embeddings (embeddings can be `np.array`, `torch.tensor`, etc.). For inspiration, you can look at the [mteb/mtebscripts repo](https://github.com/embeddings-benchmark/mtebscripts) used for running diverse models via SLURM scripts for the paper.



```python

class MyModel():

    def encode(

        self, sentences: list[str], **kwargs: Any

    ) -> torch.Tensor | np.ndarray:

        """Encodes the given sentences using the encoder.



        Args:

            sentences: The sentences to encode.

            **kwargs: Additional arguments to pass to the encoder.



        Returns:

            The encoded sentences.

        """

        pass



model = MyModel()

tasks = mteb.get_task("Banking77Classification")

evaluation = MTEB(tasks=tasks)

evaluation.run(model)

```



If you'd like to use different encoding functions for query and corpus when evaluating on Retrieval or Reranking tasks, you can add separate methods for `encode_queries` and `encode_corpus`. If these methods exist, they will be automatically used for those tasks. You can refer to the `DRESModel` at `mteb/evaluation/evaluators/RetrievalEvaluator.py` for an example of these functions.



```python

class MyModel():

    def encode_queries(self, queries: list[str], **kwargs) -> list[np.ndarray] | list[torch.Tensor]:

        """

        Returns a list of embeddings for the given sentences.

        Args:

            queries: List of sentences to encode



        Returns:

            List of embeddings for the given sentences

        """

        pass



    def encode_corpus(self, corpus: list[str] | list[dict[str, str]], **kwargs) -> list[np.ndarray] | list[torch.Tensor]:

        """

        Returns a list of embeddings for the given sentences.

        Args:

            corpus: List of sentences to encode

                or list of dictionaries with keys "title" and "text"



        Returns:

            List of embeddings for the given sentences

        """

        pass

```



    Evaluating on a custom dataset 



### Evaluating on a custom dataset



To evaluate on a custom task, you can run the following code on your custom task. See [how to add a new task](docs/adding_a_dataset.md), for how to create a new task in MTEB.



```python

from mteb import MTEB

from mteb.abstasks.AbsTaskReranking import AbsTaskReranking

from sentence_transformers import SentenceTransformer



class MyCustomTask(AbsTaskReranking):

    ...



model = SentenceTransformer("average_word_embeddings_komninos")

evaluation = MTEB(tasks=[MyCustomTask()])

evaluation.run(model)

```



    Using a cross encoder for reranking



### Using a cross encoder for reranking



To use a cross encoder for reranking, you can directly use a CrossEncoder from SentenceTransformers. The following code shows a two-stage run with the second stage reading results saved from the first stage. 



```python

from mteb import MTEB

import mteb

from sentence_transformers import CrossEncoder, SentenceTransformer



cross_encoder = CrossEncoder("cross-encoder/ms-marco-TinyBERT-L-2-v2")

dual_encoder = SentenceTransformer("all-MiniLM-L6-v2")



tasks = mteb.get_tasks(tasks=["NFCorpus"], languages=["eng"])



subset = "default" # subset name used in the NFCorpus dataset

eval_splits = ["test"]



evaluation = MTEB(tasks=tasks)

evaluation.run(

    dual_encoder,

    eval_splits=eval_splits,

    save_predictions=True,

    output_folder="results/stage1",

)

evaluation.run(

    cross_encoder,

    eval_splits=eval_splits,

    top_k=5,

    save_predictions=True,

    output_folder="results/stage2",

    previous_results=f"results/stage1/NFCorpus_{subset}_predictions.json",

)

```



    Saving retrieval task predictions 



### Saving retrieval task predictions



To save the predictions from a retrieval task, add the `--save_predictions` flag in the CLI or set `save_predictions=True` in the run method. The filename will be in the "{task_name}_{subset}_predictions.json" format.



Python:

```python

from mteb import MTEB

import mteb

from sentence_transformers import SentenceTransformer



model = SentenceTransformer("all-MiniLM-L6-v2")



tasks = mteb.get_tasks(tasks=["NFCorpus"], languages=["eng"])



evaluation = MTEB(tasks=tasks)

evaluation.run(

    model,

    eval_splits=["test"],

    save_predictions=True,

    output_folder="results",

)

```



CLI:

```bash

mteb run -t NFCorpus -m all-MiniLM-L6-v2 --output_folder results --save_predictions

```



   Fetching result from the results repository 



### Fetching results from the results repository



Multiple models have already been run on tasks available within MTEB. These results are available results [repository](https://github.com/embeddings-benchmark/results).



To make the results more easily accessible, we have designed custom functionality for retrieving from the repository. For instance, if you are selecting the best model for your French and English retrieval task on legal documents you could fetch the relevant tasks and create a dataframe of the results using the following code:



```python

import mteb

from mteb.task_selection import results_to_dataframe



tasks = mteb.get_tasks(

    task_types=["Retrieval"], languages=["eng", "fra"], domains=["Legal"]

)



model_names = [

    "GritLM/GritLM-7B",

    "intfloat/multilingual-e5-small",

    "intfloat/multilingual-e5-base",

    "intfloat/multilingual-e5-large",

]

models = [mteb.get_model_meta(name) for name in model_names]



results = mteb.load_results(models=models, tasks=tasks)



df = results_to_dataframe(results)

```




 



## Documentation



| Documentation                  |                        |

| ------------------------------ | ---------------------- |

| 📋 [Tasks] | Overview of available tasks |

| 📐 [Benchmarks] | Overview of available benchmarks |

| 📈 [Leaderboard] | The interactive leaderboard of the benchmark |

| 🤖 [Adding a model] | Information related to how to submit a model to the leaderboard |

| 👩‍🔬 [Reproducible workflows] | Information related to how to reproduce and create reproducible workflows with MTEB |

| 👩‍💻 [Adding a dataset] | How to add a new task/dataset to MTEB | 

| 👩‍💻 [Adding a leaderboard tab] | How to add a new leaderboard tab to MTEB | 

| 🤝 [Contributing] | How to contribute to MTEB and set it up for development |

| 🌐 [MMTEB] | An open-source effort to extend MTEB to cover a broad set of languages |  



[Tasks]: docs/tasks.md

[Benchmarks]: docs/benchmarks.md

[Contributing]: CONTRIBUTING.md

[Adding a model]: docs/adding_a_model.md

[Adding a dataset]: docs/adding_a_dataset.md

[Adding a leaderboard tab]: docs/adding_a_leaderboard_tab.md

[Leaderboard]: https://huggingface.co/spaces/mteb/leaderboard

[MMTEB]: docs/mmteb/readme.md

[Reproducible workflows]: docs/reproducible_workflow.md



## Citing



MTEB was introduced in "[MTEB: Massive Text Embedding Benchmark](https://arxiv.org/abs/2210.07316)", feel free to cite:



```bibtex

@article{muennighoff2022mteb,

  doi = {10.48550/ARXIV.2210.07316},

  url = {https://arxiv.org/abs/2210.07316},

  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},

  title = {MTEB: Massive Text Embedding Benchmark},

  publisher = {arXiv},

  journal={arXiv preprint arXiv:2210.07316},  

  year = {2022}

}

```



You may also want to read and cite the amazing work that has extended MTEB & integrated new datasets:

- Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff. "[C-Pack: Packaged Resources To Advance General Chinese Embedding](https://arxiv.org/abs/2309.07597)" arXiv 2023

- Michael Günther, Jackmin Ong, Isabelle Mohr, Alaeddine Abdessalem, Tanguy Abel, Mohammad Kalim Akram, Susana Guzman, Georgios Mastrapas, Saba Sturua, Bo Wang, Maximilian Werk, Nan Wang, Han Xiao. "[Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents](https://arxiv.org/abs/2310.19923)" arXiv 2023

- Silvan Wehrli, Bert Arnrich, Christopher Irrgang. "[German Text Embedding Clustering Benchmark](https://arxiv.org/abs/2401.02709)" arXiv 2024

- Orion Weller, Benjamin Chang, Sean MacAvaney, Kyle Lo, Arman Cohan, Benjamin Van Durme, Dawn Lawrie, Luca Soldaini. "[FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions](https://arxiv.org/abs/2403.15246)" arXiv 2024

- Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, Sujian Li. "[LongEmbed: Extending Embedding Models for Long Context Retrieval](https://arxiv.org/abs/2404.12096)" arXiv 2024

- Kenneth Enevoldsen, Márton Kardos, Niklas Muennighoff, Kristoffer Laigaard Nielbo. "[The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding](https://arxiv.org/abs/2406.02396)" arXiv 2024



For works that have used MTEB for benchmarking, you can find them on the [leaderboard](https://huggingface.co/spaces/mteb/leaderboard).