Skip to content

Latest commit

 

History

History
51 lines (32 loc) · 2.71 KB

reproducible_workflow.md

File metadata and controls

51 lines (32 loc) · 2.71 KB
Raw

Reproducible Workflows

This section introduces how MTEB uses reproducible workflows. The main goal is to make the results reproducible and transparent. The workflow is based on the following principles:

  1. Version control: Both code and data are versioned using git revision IDs.
  2. Model Repository: MTEB includes a model registry of models run on MTEB since June 2024. These implementations are stored in mteb/models/. This is to ensure that the model run is transparent, documented, and reproducible. Note that models which are simply loaded and run using SentenceTransformers are not documented, as referring only to the revision ID of the model is sufficient to reproduce the results.
  3. Result Reproducibility: Results within MTEB are expected to be reproducible up to the 3rd decimal point.

Using a reproducible workflow:

import mteb

model_name = "intfloat/multilingual-e5-small"
revision = "4dc6d853a804b9c8886ede6dda8a073b7dc08a81"

model = mteb.get_model(model_name, revision_id=revision) # load model using registry implementation if available, otherwise use SentenceTransformers

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

evaluation = mteb.MTEB(tasks=tasks)
results = evaluation.run(model)

This workflow should produce the same results as the original run. The results are by default stored in results/{model_name}/{revision_id}/{task_name}.json. This approach is equivalent to using the CLI.

Adding a model to the model registry

To add a model to the model registry, the following steps should be followed:

  1. Add a ModelMeta

Add a ModelMeta object to mteb/models/*. This object among other things contains: - model_name: The name of the model, e.g. "sentence-transformers/all-MiniLM-L6-v2". - revision: The revision id of the model - languages: The list of languages the model is trained on. - ...

You may additionally want to specify parameters like whether the model is open-source, framework, etc.

  1. If your model is not compatible with SentenceTransformer

Additionally specify the loader in the ModelMeta object. This is a function that loads the model and returns a mteb compatible Encoder model. For the Encoder class, see mteb/encoder_interface.py.

  1. Submit a pull request

Submit a pull request with the new model. The model will be reviewed and added to the model repository. Please include the checklist in the pull request:

  • I have filled out the ModelMeta object to the extent possible
  • I have ensured that my model can be loaded using mteb.get_model(model_name, revision_id) and mteb.get_model_meta(model_name, revision_id)
  • I have tested the implementation works for a representative set of tasks.