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.gitattributes

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+testbed/pyocd__pyOCD/test/data/NXP.MK64F12_DFP.11.0.0.pack filter=lfs diff=lfs merge=lfs -text
+testbed/pydicom__pydicom/pydicom/data/test_files/liver.dcm filter=lfs diff=lfs merge=lfs -text
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+testbed/pydicom__pydicom/pydicom/data/test_files/color3d_jpeg_baseline.dcm filter=lfs diff=lfs merge=lfs -text
+testbed/pydicom__pydicom/pydicom/data/test_files/SC_rgb_16bit_2frame.dcm filter=lfs diff=lfs merge=lfs -text
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+testbed/pydicom__pydicom/pydicom/data/test_files/OBXXXX1A_expb.dcm filter=lfs diff=lfs merge=lfs -text
+testbed/pydicom__pydicom/pydicom/data/test_files/JPEG-LL.dcm filter=lfs diff=lfs merge=lfs -text
+testbed/pydicom__pydicom/pydicom/data/test_files/OBXXXX1A_expb_2frame.dcm filter=lfs diff=lfs merge=lfs -text
+testbed/pydicom__pydicom/pydicom/data/test_files/OBXXXX1A_2frame.dcm filter=lfs diff=lfs merge=lfs -text
+testbed/pydicom__pydicom/pydicom/data/test_files/OT-PAL-8-face.dcm filter=lfs diff=lfs merge=lfs -text
+testbed/pydicom__pydicom/pydicom/data/test_files/SC_rgb_32bit.dcm filter=lfs diff=lfs merge=lfs -text
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testbed/embeddings-benchmark__mteb/.gitignore

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+.DS_Store
+.idea/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# error logs
+error_logs.txt
+
+# tests
+tests/results
+tmp.py

testbed/embeddings-benchmark__mteb/CONTRIBUTING.md

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+## Contributing to MTEB
+We welcome contributions such as new datasets to MTEB! Please see detailed see the related [issue](https://github.com/embeddings-benchmark/mteb/issues/360) for more information. 
+
+Once you have decided on your contribution, this document describes how to set up the repository for development.
+
+### Development Installation
+If you want to submit a dataset or on other ways contribute to MTEB, you can install the package in development mode:
+
+```bash
+git clone https://github.com/embeddings-benchmark/mteb
+cd mteb
+
+# create your virtual environment and activate it
+make install
+```
+
+### Running Tests
+To run the tests, you can use the following command:
+
+```bash
+make test
+```
+
+This is also run by the CI pipeline, so you can be sure that your changes do not break the package. We recommend running the tests in the lowest version of python supported by the package (see the pyproject.toml) to ensure compatibility.
+
+### Running linting
+To run the linting before a PR you can use the following command:
+
+```bash
+make lint
+```
+
+This command is equivalent to the command run during CI. It will check for code style and formatting issues.
+
+## Semantic Versioning and Releases
+MTEB follows [semantic versioning](https://semver.org/). This means that the version number of the package is composed of three numbers: `MAJOR.MINOR.PATCH`. This allow us to use existing tools to automatically manage the versioning of the package. For maintainers (and contributors), this means that commits with the following prefixes will automatically trigger a version bump:
+
+- `fix:` for patches
+- `feat:` for minor versions
+- `breaking:` for major versions
+
+Any commit with one of these prefixes will trigger a version bump upon merging to the main branch as long as tests pass. A version bump will then trigger a new release on PyPI as well as a new release on GitHub.
+
+Other prefixes will not trigger a version bump. For example, `docs:`, `chore:`, `refactor:`, etc., however they will structure the commit history and the changelog. You can find more information about this in the [python-semantic-release documentation](https://python-semantic-release.readthedocs.io/en/latest/). If you do not intend to trigger a version bump you're not required to follow this convention when contributing to MTEB.

testbed/embeddings-benchmark__mteb/LICENSE

@@ -0,0 +1,201 @@
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testbed/embeddings-benchmark__mteb/Makefile

@@ -0,0 +1,28 @@
+install:
+	@echo "--- 🚀 Installing project dependencies ---"
+	pip install -e ".[dev]"
+
+install-for-tests:
+	@echo "--- 🚀 Installing project dependencies for test ---"
+	@echo "This ensures that the project is not installed in editable mode"
+	pip install ".[dev]"
+
+lint:
+	@echo "--- 🧹 Running linters ---"
+	ruff format . 			# running ruff formatting
+	ruff check . --fix  	# running ruff linting
+
+lint-check:
+	@echo "--- 🧹 Check is project is linted ---"
+	# Required for CI to work, otherwise it will just pass
+	ruff format . --check						    # running ruff formatting
+	ruff check **/*.py 						        # running ruff linting
+
+test:
+	@echo "--- 🧪 Running tests ---"
+	pytest -n auto --durations=5
+
+pr:
+	@echo "--- 🚀 Running requirements for a PR ---"
+	make lint
+	make test

testbed/embeddings-benchmark__mteb/README.md

@@ -0,0 +1,259 @@
+<h1 align="center">Massive Text Embedding Benchmark</h1>
+
+<p align="center">
+    <a href="https://github.com/embeddings-benchmark/mteb/releases">
+        <img alt="GitHub release" src="https://img.shields.io/github/release/embeddings-benchmark/mteb.svg">
+    </a>
+    <a href="https://arxiv.org/abs/2210.07316">
+        <img alt="GitHub release" src="https://img.shields.io/badge/arXiv-2305.14251-b31b1b.svg">
+    </a>
+    <a href="https://github.com/embeddings-benchmark/mteb/blob/master/LICENSE">
+        <img alt="License" src="https://img.shields.io/github/license/embeddings-benchmark/mteb.svg?color=green">
+    </a>
+    <a href="https://pepy.tech/project/mteb">
+        <img alt="Downloads" src="https://static.pepy.tech/personalized-badge/mteb?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads">
+    </a>
+</p>
+
+<h4 align="center">
+    <p>
+        <a href="#installation">Installation</a> |
+        <a href="#usage">Usage</a> |
+        <a href="https://huggingface.co/spaces/mteb/leaderboard">Leaderboard</a> |
+        <a href="#documentation">Documentation</a> |
+        <a href="#citing">Citing</a>
+    <p>
+</h4>
+
+<h3 align="center">
+    <a href="https://huggingface.co/spaces/mteb/leaderboard"><img style="float: middle; padding: 10px 10px 10px 10px;" width="60" height="55" src="./docs/images/hf_logo.png" /></a>
+</h3>
+
+
+## Installation
+
+```bash
+pip install mteb
+```
+
+## Usage
+
+* Using a python script (see [scripts/run_mteb_english.py](https://github.com/embeddings-benchmark/mteb/blob/main/scripts/run_mteb_english.py) and [mteb/mtebscripts](https://github.com/embeddings-benchmark/mtebscripts) for more):
+
+```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 -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).
+
+<br /> 
+
+<details>
+  <summary> Advanced Usage (click to unfold) </summary>
+
+
+## Advanced Usage
+
+
+### Dataset selection
+
+Datasets 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
+```
+
+There are also presets available for certain task collections, e.g. to select the 56 English datasets that form the "Overall MTEB English leaderboard":
+
+```python
+from mteb import MTEB_MAIN_EN
+evaluation = mteb.MTEB(tasks=MTEB_MAIN_EN, task_langs=["en"])
+```
+
+
+### 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
+
+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
+
+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)
+```
+
+</details>
+
+<br /> 
+
+## Documentation
+
+| Documentation                          |                        |
+| ------------------------------ | ---------------------- |
+| 📋 [Tasks] | Overview of available tasks |
+| 📈 [Leaderboard] | The interactive leaderboard of the benchmark |
+| 🤖 [Adding a model] | Information related to how to submit a model to the leaderboard |
+| 👩‍💻 [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
+[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
+
+## 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
+
+For works that have used MTEB for benchmarking, you can find them on the [leaderboard](https://huggingface.co/spaces/mteb/leaderboard).

testbed/embeddings-benchmark__mteb/pyproject.toml

@@ -0,0 +1,101 @@
+[build-system]
+requires = ["setuptools>=42", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "mteb"
+version = "1.12.5"
+description = "Massive Text Embedding Benchmark"
+readme = "README.md"
+authors = [
+    { name = "MTEB Contributors", email = "niklas@huggingface.co" },
+    { email = "nouamane@huggingface.co" },
+    { email = "info@nils-reimers.de" },
+]
+license = { file = "LICENSE" }
+keywords = ["deep learning", "text embeddings", "benchmark"]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Environment :: Console",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Information Technology",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python",
+]
+requires-python = ">=3.8"
+dependencies = [
+    "datasets>=2.19.0",
+    "jsonlines",
+    "numpy",
+    "requests>=2.26.0",
+    "scikit_learn>=1.0.2",
+    "scipy",
+    "sentence_transformers>=2.2.0",
+    "torch",
+    "tqdm",
+    "rich",
+    "pytrec-eval-terrier>=0.5.6",
+    "pydantic>=2.0.0",
+    "typing_extensions",
+    "eval_type_backport",
+    "polars>=0.20.22",
+]
+
+
+[project.urls]
+homepage = "https://github.com/embeddings-benchmark/mteb"
+"Huggingface Organization" = "https://huggingface.co/mteb"
+"Source Code" = "https://github.com/embeddings-benchmark/mteb"
+
+[project.scripts]
+mteb = "mteb.cmd:main"
+
+[project.optional-dependencies]
+dev = ["ruff>=0.0.254", "pytest", "pytest-xdist"]
+codecarbon = ["codecarbon"]
+
+
+[tool.setuptools.packages.find]
+exclude = ["tests", "results"]
+
+[tool.setuptools.package-data]
+"*" = ["*.json"]
+
+[tool.ruff]
+target-version = "py38"
+
+[tool.ruff.lint]
+select = ["F", "I", "E", "D"]
+ignore = ["E501",   # line too long 
+        "E741",     # ambiguous variable name
+        "F403",     # undefined import
+        "D100",     # Missing docstring in public module
+        "D101",     # Missing docstring in public class
+        "D102",     # Missing docstring in public method
+        "D103",     # Missing docstring in public function
+        "D105",     # Missing docstring in magic method
+        "D104",     # Missing docstring in public package
+        "D107",     # Missing docstring in __init__
+        "D205",     # 1 blank line required between summary line and description
+        "D415",     # First line should end with a period
+]
+ignore-init-module-imports = true
+
+[tool.ruff.lint.pydocstyle]
+convention = "google"
+
+[tool.ruff.lint.flake8-annotations]
+mypy-init-return = true
+suppress-none-returning = true
+
+[tool.semantic_release]
+branch = "main"
+version_toml = ["pyproject.toml:project.version"]
+build_command = "python -m pip install build; python -m build"
+tag_format = "{version}"
+
+[tool.semantic_release.commit_parser_options]
+major_types = ["breaking"]
+minor_types = ["feat"]
+patch_types = ["fix", "perf"]

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/CUADPriceRestrictionsLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "CUADPriceRestrictionsLegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
+    "accuracy": 0.9130434782608695,
+    "accuracy_stderr": 0.0,
+    "ap": 0.8518518518518519,
+    "ap_stderr": 0.0,
+    "evaluation_time": 12.05,
+    "f1": 0.9123809523809522,
+    "f1_stderr": 1.1102230246251565e-16,
+    "main_score": 0.9130434782608695
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/CUADThirdPartyBeneficiaryLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "CUADThirdPartyBeneficiaryLegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
+    "accuracy": 0.8529411764705882,
+    "accuracy_stderr": 0.0,
+    "ap": 0.7823529411764706,
+    "ap_stderr": 0.0,
+    "evaluation_time": 27.29,
+    "f1": 0.8517872711421098,
+    "f1_stderr": 0.0,
+    "main_score": 0.8529411764705882
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/CUADUncappedLiabilityLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "CUADUncappedLiabilityLegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
+    "accuracy": 0.7687074829931972,
+    "accuracy_stderr": 0.0,
+    "ap": 0.6861290857806537,
+    "ap_stderr": 0.0,
+    "evaluation_time": 44.73,
+    "f1": 0.7593412942989215,
+    "f1_stderr": 0.0,
+    "main_score": 0.7687074829931972
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/CUADVolumeRestrictionLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "CUADVolumeRestrictionLegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
+    "accuracy": 0.7795031055900621,
+    "accuracy_stderr": 0.0,
+    "ap": 0.765527950310559,
+    "ap_stderr": 0.0,
+    "evaluation_time": 49.74,
+    "f1": 0.7712953792903374,
+    "f1_stderr": 0.0,
+    "main_score": 0.7795031055900621
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/CanadaTaxCourtOutcomesLegalBenchClassification.json

@@ -0,0 +1,13 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "CanadaTaxCourtOutcomesLegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
+    "accuracy": 0.7745901639344261,
+    "accuracy_stderr": 1.1102230246251565e-16,
+    "evaluation_time": 31.73,
+    "f1": 0.6713839797060511,
+    "f1_stderr": 0.0,
+    "main_score": 0.7745901639344261
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/CodeEditSearchRetrieval.json

@@ -0,0 +1,489 @@
+{
+  "dataset_revision": "4e51c66e0939303f6928472f13ad0848b2a3f4c0",
+  "mteb_dataset_name": "CodeEditSearchRetrieval",
+  "mteb_version": "1.7.41",
+  "train": {
+    "c": {
+      "map_at_1": 0.186,
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+      "mrr_at_20": 0.23964,
+      "mrr_at_3": 0.218,
+      "mrr_at_5": 0.22605,
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+      "recall_at_1": 0.186,
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+      "recall_at_1000": 1.0,
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+      "recall_at_5": 0.295
+    },
+    "c++": {
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testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
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+    "main_score": 0.37410071942446044
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/ContractNLILimitedUseLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "ContractNLILimitedUseLegalBenchClassification",
+  "mteb_version": "1.7.7",
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+    "main_score": 0.5625
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/ContractNLINoLicensingLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "ContractNLINoLicensingLegalBenchClassification",
+  "mteb_version": "1.7.7",
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+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/ContractNLINoticeOnCompelledDisclosureLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "ContractNLINoticeOnCompelledDisclosureLegalBenchClassification",
+  "mteb_version": "1.7.7",
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+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
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+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/ContractNLIPermissibleCopyLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "ContractNLIPermissibleCopyLegalBenchClassification",
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+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification",
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+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/ContractNLIReturnOfConfidentialInformationLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "ContractNLIReturnOfConfidentialInformationLegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
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+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/ContractNLISharingWithEmployeesLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "ContractNLISharingWithEmployeesLegalBenchClassification",
+  "mteb_version": "1.7.7",
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+    "main_score": 0.711764705882353
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/ContractNLISharingWithThirdPartiesLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "ContractNLISharingWithThirdPartiesLegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
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+    "main_score": 0.5666666666666667
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+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/ContractNLISurvivalOfObligationsLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "ContractNLISurvivalOfObligationsLegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
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+    "f1": 0.6114649681528662,
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+    "main_score": 0.6114649681528662
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/CorporateLobbyingLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "CorporateLobbyingLegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
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+    "main_score": 0.7591836734693878
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/CrossLingualSemanticDiscriminationWMT19.json

@@ -0,0 +1,82 @@
+{
+  "dataset_revision": "9627fbdb39b827ee5c066011ebe1e947cdb137bd",
+  "mteb_dataset_name": "CrossLingualSemanticDiscriminationWMT19",
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+      "recall_at_3": 0.9776,
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+      "mrr_at_1000": 0.92609,
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+      "precision_at_1000": 0.001,
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+      "recall_at_1000": 1.0,
+      "recall_at_20": 0.99661,
+      "recall_at_3": 0.97624,
+      "recall_at_5": 0.98914
+    }
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/CrossLingualSemanticDiscriminationWMT21.json

@@ -0,0 +1,82 @@
+{
+  "dataset_revision": "9627fbdb39b827ee5c066011ebe1e947cdb137bd",
+  "mteb_dataset_name": "CrossLingualSemanticDiscriminationWMT21",
+  "mteb_version": "1.8.0",
+  "test": {
+    "deu-fra": {
+      "map_at_1": 0.89586,
+      "map_at_10": 0.93992,
+      "map_at_100": 0.93992,
+      "map_at_1000": 0.93992,
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+      "mrr_at_1000": 0.93992,
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+      "mrr_at_3": 0.93673,
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+      "ndcg_at_1000": 0.95513,
+      "ndcg_at_20": 0.95513,
+      "ndcg_at_3": 0.94903,
+      "ndcg_at_5": 0.95323,
+      "precision_at_1": 0.89586,
+      "precision_at_10": 0.1,
+      "precision_at_100": 0.01,
+      "precision_at_1000": 0.001,
+      "precision_at_20": 0.05,
+      "precision_at_3": 0.32811,
+      "precision_at_5": 0.19888,
+      "recall_at_1": 0.89586,
+      "recall_at_10": 1.0,
+      "recall_at_100": 1.0,
+      "recall_at_1000": 1.0,
+      "recall_at_20": 1.0,
+      "recall_at_3": 0.98432,
+      "recall_at_5": 0.9944
+    },
+    "evaluation_time": 181.66,
+    "fra-deu": {
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+      "map_at_10": 0.93212,
+      "map_at_100": 0.93212,
+      "map_at_1000": 0.93212,
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+      "mrr_at_1000": 0.93212,
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+      "recall_at_1000": 1.0,
+      "recall_at_20": 1.0,
+      "recall_at_3": 0.97872,
+      "recall_at_5": 0.99664
+    }
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/CyrillicTurkicLangClassification.json

@@ -0,0 +1,13 @@
+{
+  "dataset_revision": "e42d330f33d65b7b72dfd408883daf1661f06f18",
+  "mteb_dataset_name": "CyrillicTurkicLangClassification",
+  "mteb_version": "1.7.27",
+  "test": {
+    "accuracy": 0.323828125,
+    "accuracy_stderr": 0.011146937995905885,
+    "evaluation_time": 12.49,
+    "f1": 0.3246119961767252,
+    "f1_stderr": 0.01256954046884907,
+    "main_score": 0.323828125
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/CzechProductReviewSentimentClassification.json

@@ -0,0 +1,13 @@
+{
+  "dataset_revision": "2e6fedf42c9c104e83dfd95c3a453721e683e244",
+  "mteb_dataset_name": "CzechProductReviewSentimentClassification",
+  "mteb_version": "1.8.3",
+  "test": {
+    "accuracy": 0.513427734375,
+    "accuracy_stderr": 0.017551316905810053,
+    "evaluation_time": 16.69,
+    "f1": 0.5089843914012989,
+    "f1_stderr": 0.017227192977086348,
+    "main_score": 0.513427734375
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/CzechSoMeSentimentClassification.json

@@ -0,0 +1,13 @@
+{
+  "dataset_revision": "6ced1d87a030915822b087bf539e6d5c658f1988",
+  "mteb_dataset_name": "CzechSoMeSentimentClassification",
+  "mteb_version": "1.8.3",
+  "test": {
+    "accuracy": 0.5833,
+    "accuracy_stderr": 0.024220033030530733,
+    "evaluation_time": 16.46,
+    "f1": 0.5796407099118327,
+    "f1_stderr": 0.0223418109125969,
+    "main_score": 0.5833
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/CzechSubjectivityClassification.json

@@ -0,0 +1,25 @@
+{
+  "dataset_revision": "e387ddf167f3eba99936cff89909ed6264f17e1f",
+  "mteb_dataset_name": "CzechSubjectivityClassification",
+  "mteb_version": "1.6.12",
+  "test": {
+    "accuracy": 0.7359500000000001,
+    "accuracy_stderr": 0.05993306683292621,
+    "ap": 0.6846848037480958,
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+    "evaluation_time": 3.73,
+    "f1": 0.7336733883205542,
+    "f1_stderr": 0.06045262849081143,
+    "main_score": 0.7359500000000001
+  },
+  "validation": {
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+    "accuracy_stderr": 0.05099960784162953,
+    "ap": 0.684491904824965,
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+    "evaluation_time": 5.44,
+    "f1": 0.7346713249823063,
+    "f1_stderr": 0.052259333936750106,
+    "main_score": 0.7372000000000001
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/DBpediaClassification.json

@@ -0,0 +1,13 @@
+{
+  "dataset_revision": "9abd46cf7fc8b4c64290f26993c540b92aa145ac",
+  "mteb_dataset_name": "DBpediaClassification",
+  "mteb_version": "1.6.36",
+  "test": {
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+    "accuracy_stderr": 0.013677541375994482,
+    "evaluation_time": 34.75,
+    "f1": 0.8482267879321842,
+    "f1_stderr": 0.01348333759139074,
+    "main_score": 0.850830078125
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/DanFEVER.json

@@ -0,0 +1,38 @@
+{
+  "dataset_revision": "5d01e3f6a661d48e127ab5d7e3aaa0dc8331438a",
+  "mteb_dataset_name": "DanFEVER",
+  "mteb_version": "1.2.1.dev0",
+  "train": {
+    "evaluation_time": 21.94,
+    "map_at_1": 0.27656,
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+    "map_at_5": 0.33752,
+    "mrr_at_1": 0.27664,
+    "mrr_at_10": 0.34049,
+    "mrr_at_100": 0.34222,
+    "mrr_at_1000": 0.34224,
+    "mrr_at_3": 0.33273,
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+    "ndcg_at_10": 0.36542,
+    "ndcg_at_100": 0.37339,
+    "ndcg_at_1000": 0.37403,
+    "ndcg_at_3": 0.34942,
+    "ndcg_at_5": 0.35841,
+    "precision_at_1": 0.27664,
+    "precision_at_10": 0.04417,
+    "precision_at_100": 0.00478,
+    "precision_at_1000": 0.00048,
+    "precision_at_3": 0.1327,
+    "precision_at_5": 0.08398,
+    "recall_at_1": 0.27656,
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+    "recall_at_100": 0.47748,
+    "recall_at_1000": 0.4825,
+    "recall_at_3": 0.39793,
+    "recall_at_5": 0.41966
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/DefinitionClassificationLegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "DefinitionClassificationLegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
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+    "accuracy_stderr": 0.0,
+    "ap": 0.7668456788707598,
+    "ap_stderr": 1.1102230246251565e-16,
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+    "f1": 0.8039589442815249,
+    "f1_stderr": 0.0,
+    "main_score": 0.8040388930441287
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/DiaBlaBitextMining.json

@@ -0,0 +1,22 @@
+{
+  "dataset_revision": "5345895c56a601afe1a98519ce3199be60a27dba",
+  "mteb_dataset_name": "DiaBlaBitextMining",
+  "mteb_version": "1.2.1.dev0",
+  "test": {
+    "en-fr": {
+      "accuracy": 0.8107167710508003,
+      "f1": 0.7772438859983376,
+      "main_score": 0.7772438859983376,
+      "precision": 0.7637658654697831,
+      "recall": 0.8107167710508003
+    },
+    "evaluation_time": 24.84,
+    "fr-en": {
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+      "f1": 0.7772438859983376,
+      "main_score": 0.7772438859983376,
+      "precision": 0.7637658654697831,
+      "recall": 0.8107167710508003
+    }
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/Diversity1LegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "Diversity1LegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
+    "accuracy": 0.7633333333333334,
+    "accuracy_stderr": 1.1102230246251565e-16,
+    "ap": 0.23666666666666666,
+    "ap_stderr": 0.0,
+    "evaluation_time": 14.4,
+    "f1": 0.43289224952741023,
+    "f1_stderr": 0.0,
+    "main_score": 0.7633333333333334
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/Diversity2LegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "Diversity2LegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
+    "accuracy": 0.7466666666666668,
+    "accuracy_stderr": 1.1102230246251565e-16,
+    "ap": 0.25333333333333335,
+    "ap_stderr": 0.0,
+    "evaluation_time": 17.29,
+    "f1": 0.42748091603053434,
+    "f1_stderr": 5.551115123125783e-17,
+    "main_score": 0.7466666666666668
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/Diversity3LegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "Diversity3LegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
+    "accuracy": 0.6133333333333333,
+    "accuracy_stderr": 0.0,
+    "ap": 0.6283273152081563,
+    "ap_stderr": 0.0,
+    "evaluation_time": 16.97,
+    "f1": 0.575091575091575,
+    "f1_stderr": 1.1102230246251565e-16,
+    "main_score": 0.6133333333333333
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/Diversity4LegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "Diversity4LegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
+    "accuracy": 0.5333333333333333,
+    "accuracy_stderr": 0.0,
+    "ap": 0.5371412907268172,
+    "ap_stderr": 0.0,
+    "evaluation_time": 16.31,
+    "f1": 0.43338190059899623,
+    "f1_stderr": 5.551115123125783e-17,
+    "main_score": 0.5333333333333333
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/Diversity5LegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "Diversity5LegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
+    "accuracy": 0.5600000000000002,
+    "accuracy_stderr": 1.1102230246251565e-16,
+    "ap": 0.5799574588769143,
+    "ap_stderr": 0.0,
+    "evaluation_time": 21.45,
+    "f1": 0.4794679005205322,
+    "f1_stderr": 5.551115123125783e-17,
+    "main_score": 0.5600000000000002
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/Diversity6LegalBenchClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "12ca3b695563788fead87a982ad1a068284413f4",
+  "mteb_dataset_name": "Diversity6LegalBenchClassification",
+  "mteb_version": "1.7.7",
+  "test": {
+    "accuracy": 0.5333333333333333,
+    "accuracy_stderr": 0.0,
+    "ap": 0.5498023360287512,
+    "ap_stderr": 0.0,
+    "evaluation_time": 30.58,
+    "f1": 0.5330012453300125,
+    "f1_stderr": 1.1102230246251565e-16,
+    "main_score": 0.5333333333333333
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/DutchBookReviewSentimentClassification.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": "3f756ab4572e071eb53e887ab629f19fa747d39e",
+  "mteb_dataset_name": "DutchBookReviewSentimentClassification",
+  "mteb_version": "1.6.10",
+  "test": {
+    "accuracy": 0.5443794964028777,
+    "accuracy_stderr": 0.02015878375346909,
+    "ap": 0.5244085371991012,
+    "ap_stderr": 0.011173856228832468,
+    "evaluation_time": 117.63,
+    "f1": 0.5247460568799662,
+    "f1_stderr": 0.027208677500913513,
+    "main_score": 0.5443794964028777
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/EightTagsClustering.v2.json

@@ -0,0 +1,33 @@
+{
+  "dataset_revision": "78b962b130c6690659c65abf67bf1c2f030606b6",
+  "evaluation_time": 2.30400013923645,
+  "kg_co2_emissions": null,
+  "mteb_version": "1.8.3",
+  "scores": {
+    "test": [
+      {
+        "hf_subset": "default",
+        "languages": [
+          "pol-Latn"
+        ],
+        "main_score": 0.235457755936313,
+        "v_measure": 0.235457755936313,
+        "v_measures": {
+          "Level 0": [
+            0.2725069596851991,
+            0.23018117329755852,
+            0.2214043574291382,
+            0.23777464961133232,
+            0.22133178236025697,
+            0.18582865012594377,
+            0.20959447123162897,
+            0.2731351291863508,
+            0.24049320047618436,
+            0.26232718595953725
+          ]
+        }
+      }
+    ]
+  },
+  "task_name": "EightTagsClustering.v2"
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/EstQA.json

@@ -0,0 +1,43 @@
+{
+  "dataset_revision": "99d6f921d9dd4d09116a6312deceb22c16529cfb",
+  "mteb_dataset_name": "EstQA",
+  "mteb_version": "1.6.10",
+  "test": {
+    "evaluation_time": 8.91,
+    "map_at_1": 0.5141,
+    "map_at_10": 0.63549,
+    "map_at_100": 0.64132,
+    "map_at_1000": 0.64133,
+    "map_at_20": 0.64013,
+    "map_at_3": 0.60448,
+    "map_at_5": 0.62454,
+    "mrr_at_1": 0.5141,
+    "mrr_at_10": 0.63549,
+    "mrr_at_100": 0.64132,
+    "mrr_at_1000": 0.64133,
+    "mrr_at_20": 0.64013,
+    "mrr_at_3": 0.60448,
+    "mrr_at_5": 0.62454,
+    "ndcg_at_1": 0.5141,
+    "ndcg_at_10": 0.6966,
+    "ndcg_at_100": 0.72148,
+    "ndcg_at_1000": 0.72173,
+    "ndcg_at_20": 0.71287,
+    "ndcg_at_3": 0.63407,
+    "ndcg_at_5": 0.6699,
+    "precision_at_1": 0.5141,
+    "precision_at_10": 0.08889,
+    "precision_at_100": 0.00998,
+    "precision_at_1000": 0.001,
+    "precision_at_20": 0.0476,
+    "precision_at_3": 0.23991,
+    "precision_at_5": 0.16119,
+    "recall_at_1": 0.5141,
+    "recall_at_10": 0.88889,
+    "recall_at_100": 0.99834,
+    "recall_at_1000": 1.0,
+    "recall_at_20": 0.95191,
+    "recall_at_3": 0.71973,
+    "recall_at_5": 0.80597
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/EstonianValenceClassification.json

@@ -0,0 +1,13 @@
+{
+  "dataset_revision": "9157397f05a127b3ac93b93dd88abf1bdf710c22",
+  "mteb_dataset_name": "EstonianValenceClassification",
+  "mteb_version": "1.6.10",
+  "test": {
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+    "main_score": 0.3875305623471883
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/FQuADRetrieval.json

@@ -0,0 +1,81 @@
+{
+  "dataset_revision": "5384ce827bbc2156d46e6fcba83d75f8e6e1b4a6",
+  "mteb_dataset_name": "FQuADRetrieval",
+  "mteb_version": "1.6.36",
+  "test": {
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+    "mrr_at_1000": 0.52721,
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+    "mrr_at_5": 0.50446,
+    "ndcg_at_1": 0.405,
+    "ndcg_at_10": 0.58013,
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+    "ndcg_at_1000": 0.62584,
+    "ndcg_at_20": 0.6012,
+    "ndcg_at_3": 0.51473,
+    "ndcg_at_5": 0.54583,
+    "precision_at_1": 0.405,
+    "precision_at_10": 0.07775,
+    "precision_at_100": 0.0097,
+    "precision_at_1000": 0.001,
+    "precision_at_20": 0.04313,
+    "precision_at_3": 0.19833,
+    "precision_at_5": 0.134,
+    "recall_at_1": 0.405,
+    "recall_at_10": 0.7775,
+    "recall_at_100": 0.97,
+    "recall_at_1000": 1.0,
+    "recall_at_20": 0.8625,
+    "recall_at_3": 0.595,
+    "recall_at_5": 0.67
+  },
+  "validation": {
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+    "map_at_100": 0.66348,
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+    "map_at_5": 0.65017,
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+    "precision_at_20": 0.048,
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+    "precision_at_5": 0.168,
+    "recall_at_1": 0.52,
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+    "recall_at_100": 1.0,
+    "recall_at_1000": 1.0,
+    "recall_at_20": 0.96,
+    "recall_at_3": 0.78,
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+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/FaroeseSTS.json

@@ -0,0 +1,20 @@
+{
+  "dataset_revision": "8cb36efa69428b3dc290e1125995a999963163c5",
+  "mteb_dataset_name": "FaroeseSTS",
+  "mteb_version": "1.7.40",
+  "train": {
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+      "spearman": 0.4729473496697959
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+    "evaluation_time": 0.72,
+    "manhattan": {
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+      "spearman": 0.41850333234409315
+    }
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/FarsTail.json

@@ -0,0 +1,49 @@
+{
+  "dataset_revision": "7335288588f14e5a687d97fc979194c2abe6f4e7",
+  "mteb_dataset_name": "FarsTail",
+  "mteb_version": "1.10.1",
+  "test": {
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+      "precision": 0.5510204081632653,
+      "recall": 0.884393063583815
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+    "dot": {
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+      "precision": 0.5053554040895814,
+      "recall": 1.0
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+      "precision": 0.5494117647058824,
+      "recall": 0.8998073217726397
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+    "evaluation_time": 7.06,
+    "manhattan": {
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+      "precision": 0.5574572127139364,
+      "recall": 0.8786127167630058
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+    "max": {
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+      "ap": 0.6484016684922852,
+      "f1": 0.6822498173849526
+    }
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/FilipinoHateSpeechClassification.json

@@ -0,0 +1,25 @@
+{
+  "dataset_revision": "1994e9bb7f3ec07518e3f0d9e870cb293e234686",
+  "mteb_dataset_name": "FilipinoHateSpeechClassification",
+  "mteb_version": "1.7.16",
+  "test": {
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+    "ap": 0.5039842907879104,
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+    "f1": 0.5536573846297469,
+    "f1_stderr": 0.038059572332273875,
+    "main_score": 0.560986328125
+  },
+  "validation": {
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+    "evaluation_time": 27.67,
+    "f1": 0.5582113131675175,
+    "f1_stderr": 0.04244987733223218,
+    "main_score": 0.565478515625
+  }
+}

testbed/embeddings-benchmark__mteb/results/sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2/FilipinoShopeeReviewsClassification.json

@@ -0,0 +1,21 @@
+{
+  "dataset_revision": "d096f402fdc76886458c0cfb5dedc829bea2b935",
+  "mteb_dataset_name": "FilipinoShopeeReviewsClassification",
+  "mteb_version": "1.7.58",
+  "test": {
+    "accuracy": 0.2609375,
+    "accuracy_stderr": 0.01915307710392323,
+    "evaluation_time": 4.56,
+    "f1": 0.2573114474927469,
+    "f1_stderr": 0.01897645749493735,
+    "main_score": 0.2609375
+  },
+  "validation": {
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+    "accuracy_stderr": 0.022346216879227603,
+    "evaluation_time": 6.21,
+    "f1": 0.2560173894318752,
+    "f1_stderr": 0.021947674046389195,
+    "main_score": 0.26005859375
+  }
+}