| # Welcome to MMTEB! 👋 |
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| The Massive Multilingual Text Embedding Benchmark (MMTEB) is a community-led extension of [MTEB](https://arxiv.org/abs/2210.07316) to cover embedding tasks for a massive number of languages. |
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| ## Background |
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| The Massive Text Embedding Benchmark (MTEB) is intended to evaluate the quality of document embeddings. When it was initially introduced, the benchmark consisted of 8 embedding tasks and 58 different datasets. Since then, MTEB has been subject to multiple community contributions as well as benchmark extensions over specific languages such as [SEB](https://openreview.net/pdf/f5f1953a9c798ec61bb050e62bc7a94037fd4fab.pdf), [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) and [MTEB-French](https://github.com/Lyon-NLP/mteb-french). However, we want even wider coverage and thus announce the community-led extension of MTEB, where we seek to expand coverage of MTEB to as many languages as possible. |
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| ## Contributing to MMTEB |
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| Everyone can join and contribute to this initiative from: |
| - 10th of April 2024 to 15th of May 2024 for adding new datasets |
| - 15th of May to 30th of May for running models |
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| Win some SWAG, and become a co-author of our upcoming paper. We aim to publish the results of our findings at a top conference such as EMNLP, NeurIPS, etc. We have identified four ways to contribute: |
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| ### 🗃️ 1: Contribute a new dataset |
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| For this segment, you open a PR in the MTEB repository where you create an implementation (subclass) of a task using a new language dataset uploaded to huggingface. Read more about how to add a dataset [here](../adding_a_dataset.md) and check out [one of the previous additions](https://github.com/embeddings-benchmark/mteb/pull/247) for an example. |
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| ### 🖥️ 2: Contribute a new task |
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| MTEB currently consists of 8 embedding tasks including tasks such as STS, retrieval, reranking, and more. If you feel like there is a category of tasks that is not yet covered, we would welcome contributions of these as well. |
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| ### 🔍 3: Contribute new scores |
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| Once we have the datasets, we want to evaluate models on them. We welcome evaluation scores for models, which will be added to the leaderboard. |
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| ### 🔓 4: Review PRs |
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| We welcome reviews of PRs adding new datasets. If you wish to review PRs of a specific language feel free to contact members of the MTEB team. |
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| ## Authorship |
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| We follow a similar approach as in the [SeaCrowd Project](https://github.com/SEACrowd#contributing-to-seacrowd) and use a point-based system to determine co-authorships. |
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| To be considered a co-author, at least 10 contribution points are required. The position of contributors in the author list is determined by the score they acquire, higher scores will appear first. |
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| To monitor how many points you have obtained, the contribution point tracking is now live at [this sheet](points.md) and we recommend updating the score along with your PR. Past contributions also count. |
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| Everyone with sufficient points will also be added to the MTEB GitHub and Huggingface repository as a contributor. |
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| # Contribution point guideline |
| The contribution points are computed using the following table: |
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| > **Note**: The purpose of the point system is not to barrier collaboration, but to reward contributions. We might adjust the point requirement lower to accommodate more co-authorship if needed. |
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| | Contribution type | Demand | Points | Description | |
| | ------------------- | ------------------- | ------- | ----------------------------------------------------------------------------------------------------------------- | |
| | New dataset | As many as possible | 2+bonus | The first dataset for a language x task gains 4 bonus points. If the number of new languages is >= 12 then points for that PR for a new dataset are capped at 50 (12 * 4 + 2 = 48 + 2 = 50).| |
| | New task | If relevant | 10 | Task 2. | |
| | Dataset annotations | On demand | 1 | Adding missing dataset annotations to existing datasets. | |
| | Bug fixes | On demand | 1-10 | Points depends the effect of code changes. If you want to find issues related to the MMTEB you can find them [here](https://github.com/embeddings-benchmark/mteb/milestone/1), issues marked with "help-wanted" or "good-first-issue" are great places to start. | |
| | Running Models | On demand | 1 | Task 3. | |
| | Review PR | On demand | 2 | Task 4. | |
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| For the purpose of counting points, a language is defined by its [ISO 639-3](https://en.wikipedia.org/wiki/ISO_639-3) code, however, we encourage dialects or written language variants. All programming languages are considered one language. |
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| Team submissions are free to distribute points among the members as they like. |
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| ## Communication Channels |
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| We will communicate via this GitHub repository. Please feel free to open issues or discussions and `Watch` the repository to be notified of any changes. |
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| # Acknowledgments |
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| We thank [Contextual AI](https://contextual.ai/) for sponsoring the compute for this project. |
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