From a363594dce2c07404e20d2fe086268ef7c6f5edb Mon Sep 17 00:00:00 2001 From: Kenneth Enevoldsen Date: Sat, 14 Sep 2024 12:18:14 +0200 Subject: [PATCH 01/38] Add leaderboard dev --- mteb/load_results/load_results.py | 8 ++- mteb/task_selection.py | 6 +- scripts/leaderboard/app.py | 89 +++++++++++++++++++++++++++++ scripts/leaderboard/results_all.csv | 36 ++++++++++++ scripts/leaderboard/results_dan.csv | 15 +++++ 5 files changed, 149 insertions(+), 5 deletions(-) create mode 100644 scripts/leaderboard/app.py create mode 100644 scripts/leaderboard/results_all.csv create mode 100644 scripts/leaderboard/results_dan.csv diff --git a/mteb/load_results/load_results.py b/mteb/load_results/load_results.py index 3f9530ab21..2097dcb144 100644 --- a/mteb/load_results/load_results.py +++ b/mteb/load_results/load_results.py @@ -144,8 +144,8 @@ def load_results( else: models_to_keep = None + task_names = {} if tasks is not None: - task_names = {} for task in tasks: if isinstance(task, AbsTask): task_names[task.metadata.name] = task @@ -184,7 +184,11 @@ def load_results( filtered_results = [] for r in _results: try: - r.validate_and_filter_scores(task_names[r.task_name]) + if task_names: + task = task_names[r.task_name] + else: + task = None + r.validate_and_filter_scores(task=task) filtered_results.append(r) except Exception as e: logger.warning( diff --git a/mteb/task_selection.py b/mteb/task_selection.py index d5a499c415..4d5d8d3a49 100644 --- a/mteb/task_selection.py +++ b/mteb/task_selection.py @@ -52,8 +52,8 @@ def results_to_dataframe( for task_result in tasks_results: data.append( { - "model": model_name, - "revision": rev, + "Model": model_name, + "Revision": rev, "task": task_result.task_name, "main_score": task_result.get_score(**kwargs), } @@ -63,7 +63,7 @@ def results_to_dataframe( if drop_na: df = df.dropna(axis=1) return df.pivot_table( - index=["model", "revision"], + index=["Model", "Revision"], columns=["task"], values="main_score", ) diff --git a/scripts/leaderboard/app.py b/scripts/leaderboard/app.py new file mode 100644 index 0000000000..b731703125 --- /dev/null +++ b/scripts/leaderboard/app.py @@ -0,0 +1,89 @@ +"""Notes: + +Todo: +- [ ] Add model filtering +- [x] Add metadata column selection +- [ ] Add missing metadata columns + - [ ] Model metadata (embedding size) + - [ ] total Co2 emissions +- [ ] Add results loading from Hub +- [ ] Benchmark selection +- [ ] task type selection +- [ ] domain selection +- [ ] inidivual task selection + +- Optimization to be added: + - mteb.load_results is called for each custom language selection. A solution it so load the results once and filter them in the app. +""" + +from __future__ import annotations + +from pathlib import Path + +import gradio as gr +import pandas as pd + +import mteb +import mteb.task_selection as task_selection + +tasks = mteb.get_tasks() + +languages = list(set(sum([task.languages for task in tasks], []))) +metadata_columns = ["Revision"] + + +class Default: + languages: list[str] = [] + metadata_columns: list[str] = [] + + +def get_mteb_results(languages: list[str] | None = None) -> pd.DataFrame: + lang_str = "_".join(languages) if languages else "all" + file_path: Path = Path(__file__).parent / f"results_{lang_str}.csv" + + tasks = mteb.get_tasks(languages=languages) + if not file_path.exists(): + mteb_results = mteb.load_results(tasks=tasks) + df = task_selection.results_to_dataframe(mteb_results, drop_na=False) + df.to_csv(file_path) + df = pd.read_csv(file_path) + + return df + + +def _update_dataframe(languages, metadata): + _df = get_mteb_results(languages) + cols_to_remove = [col for col in metadata_columns if col not in metadata] + _df = _df.drop(columns=cols_to_remove) + return _df + + +df = get_mteb_results() +df = _update_dataframe(Default.languages, Default.metadata_columns) + + +with gr.Blocks() as demo: + with gr.Row(): + lang_select = gr.Dropdown( + languages, + value=[], + multiselect=True, + label="Language", + info="Select langauges to filter by.", + ) + metadata_select = gr.Dropdown( + ["Revision"], + value=[], + multiselect=True, + label="Metadata", + info="Select model metadata columns to shown.", + ) + + dataframe = gr.DataFrame(df) + + @gr.on(inputs=[lang_select, metadata_select], outputs=dataframe) + def update_dataframe(languages, metadata): + return _update_dataframe(languages, metadata) + + +demo.launch() diff --git a/scripts/leaderboard/results_all.csv b/scripts/leaderboard/results_all.csv new file mode 100644 index 0000000000..7dd6c4dd68 --- /dev/null +++ b/scripts/leaderboard/results_all.csv @@ -0,0 +1,36 @@ 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+Model,Revision,AngryTweetsClassification,BelebeleRetrieval,BibleNLPBitextMining,BornholmBitextMining,DanFeverRetrieval,DanishPoliticalCommentsClassification,FloresBitextMining,LccSentimentClassification,MassiveIntentClassification,MassiveScenarioClassification,MultiEURLEXMultilabelClassification,NTREXBitextMining,NordicLangClassification,SIB200Classification,SIB200ClusteringS2S,ScalaClassification,TV2Nordretrieval,Tatoeba,TwitterHjerneRetrieval,WikiClusteringP2P.v2,WikipediaRerankingMultilingual,WikipediaRetrievalMultilingual +GritLM/GritLM-7B,13f00a0e36500c80ce12870ea513846a066004af,0.6534861509073544,0.7170350531914894,0.15703429559492083,0.6117333333333334,0.40485,0.412767138495698,0.6109930285907467,0.7013333333333334,0.637210728272479,0.679412424014663,0.0486295652173913,0.7807461712905885,0.6936333333333333,0.6462277296705484,0.36072519770658396,0.52105712890625,0.94913,0.720780243112421,0.43266,0.29879395630695693,0.9104981812169313,0.917721875 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+sergeyzh/rubert-tiny-turbo,8ce0cf757446ce9bb2d5f5a4ac8103c7a1049054,,,,,,,,,0.20002900958634967,0.2670226934082308,,,,,,,,,,,, From aa50296582286f0148b4b7a8633d09f468a28853 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 23 Sep 2024 15:44:15 +0200 Subject: [PATCH 02/38] Renamed MTEBResults to TaskResult --- mteb/evaluation/MTEB.py | 6 +-- mteb/load_results/__init__.py | 4 +- mteb/load_results/load_results.py | 26 ++++++------- .../{mteb_results.py => task_results.py} | 39 ++++++++----------- mteb/task_aggregation.py | 6 +-- scripts/running_model/check_results.py | 2 +- .../test_mteb_load_results.py | 2 +- tests/test_load_results/test_mteb_results.py | 10 ++--- tests/test_task_aggregation.py | 10 ++--- 9 files changed, 49 insertions(+), 56 deletions(-) rename mteb/load_results/{mteb_results.py => task_results.py} (93%) diff --git a/mteb/evaluation/MTEB.py b/mteb/evaluation/MTEB.py index 0ac12d4bd2..befb3e64d7 100644 --- a/mteb/evaluation/MTEB.py +++ b/mteb/evaluation/MTEB.py @@ -19,7 +19,7 @@ from ..abstasks import * from ..abstasks import AbsTask -from ..load_results.mteb_results import MTEBResults +from ..load_results.mteb_results import TaskResult from ..tasks import * from . import LangMapping @@ -297,7 +297,7 @@ def run( kwargs: Additional arguments to be passed to `_run_eval` method and task.load_data. Returns: - A list of MTEBResults objects, one for each task evaluated. + A list of TaskResult objects, one for each task evaluated. """ if "batch_size" in kwargs: logger.warning( @@ -398,7 +398,7 @@ def run( if verbosity >= 1: logger.info(f"Scores: {results}") - mteb_task_result = MTEBResults.from_task_results( + mteb_task_result = TaskResult.from_task_results( task, task_results, evaluation_time=evaluation_time, diff --git a/mteb/load_results/__init__.py b/mteb/load_results/__init__.py index 3b08f6eb4d..8604664414 100644 --- a/mteb/load_results/__init__.py +++ b/mteb/load_results/__init__.py @@ -1,6 +1,6 @@ from __future__ import annotations from .load_results import load_results -from .mteb_results import MTEBResults +from .mteb_results import TaskResult -__all__ = ["load_results", "MTEBResults"] +__all__ = ["load_results", "TaskResult"] diff --git a/mteb/load_results/load_results.py b/mteb/load_results/load_results.py index 2097dcb144..27518c59d0 100644 --- a/mteb/load_results/load_results.py +++ b/mteb/load_results/load_results.py @@ -9,14 +9,14 @@ from typing import Dict, List, Sequence from mteb.abstasks.AbsTask import AbsTask -from mteb.load_results.mteb_results import MTEBResults +from mteb.load_results.mteb_results import TaskResult from mteb.model_meta import ModelMeta logger = logging.getLogger(__name__) MODEL_NAME = str REVISION = str -RESULTS = Dict[MODEL_NAME, Dict[REVISION, List[MTEBResults]]] +RESULTS = Dict[MODEL_NAME, Dict[REVISION, List[TaskResult]]] def download_of_results( @@ -107,26 +107,26 @@ def load_results( splits from the results object that are not default in the task metadata. Defaults to True. Returns: - A dictionary where the keys are the model names and the values are dictionaries where the keys are the revisions and the values are lists of MTEBResults objects. + A dictionary where the keys are the model names and the values are dictionaries where the keys are the revisions and the values are lists of TaskResult objects. Example: >>> results = load_results() >>> results {'mixedbread-ai/mxbai-embed-large-v1': {'990580e27d329c7408b3741ecff85876e128e203': [ - MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...), - MTEBResults(task_name=MedrxivClusteringP2P, scores=...), - MTEBResults(task_name=StackExchangeClustering, scores=...), - MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...), - MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...), - MTEBResults(task_name=MedrxivClusteringS2S, scores=...), + TaskResult(task_name=TwentyNewsgroupsClustering.v2, scores=...), + TaskResult(task_name=MedrxivClusteringP2P, scores=...), + TaskResult(task_name=StackExchangeClustering, scores=...), + TaskResult(task_name=BiorxivClusteringP2P.v2, scores=...), + TaskResult(task_name=MedrxivClusteringS2S.v2, scores=...), + TaskResult(task_name=MedrxivClusteringS2S, scores=...), ... ]}, 'intfloat/multilingual-e5-small': {'e4ce9877abf3edfe10b0d82785e83bdcb973e22e': [ - MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...), - MTEBResults(task_name=PpcPC, scores=...), - MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...), + TaskResult(task_name=IndicGenBenchFloresBitextMining, scores=...), + TaskResult(task_name=PpcPC, scores=...), + TaskResult(task_name=TwentyNewsgroupsClustering.v2, scores=...), ... ]}, ... @@ -174,7 +174,7 @@ def load_results( task_json_files = [ f for f in revision_path.glob("*.json") if "model_meta.json" != f.name ] - _results = [MTEBResults.from_disk(f) for f in task_json_files] + _results = [TaskResult.from_disk(f) for f in task_json_files] # filter out tasks that are not in the tasks list if tasks is not None: diff --git a/mteb/load_results/mteb_results.py b/mteb/load_results/task_results.py similarity index 93% rename from mteb/load_results/mteb_results.py rename to mteb/load_results/task_results.py index 49cf3a710a..20901d68da 100644 --- a/mteb/load_results/mteb_results.py +++ b/mteb/load_results/task_results.py @@ -13,10 +13,7 @@ from pydantic import BaseModel, field_validator from mteb.abstasks.AbsTask import AbsTask, ScoresDict -from mteb.abstasks.TaskMetadata import ( - ISO_LANGUAGE_SCRIPT, - HFSubset, -) +from mteb.abstasks.TaskMetadata import ISO_LANGUAGE_SCRIPT, HFSubset from mteb.languages import ISO_LANGUAGE, LanguageScripts Split = str @@ -116,7 +113,7 @@ class ScalaSvClassificationDummy: } -class MTEBResults(BaseModel): +class TaskResult(BaseModel): """A class to represent the MTEB result. Attributes: @@ -142,7 +139,7 @@ class MTEBResults(BaseModel): ... }, ... } >>> sample_task = ... # some MTEB task - >>> mteb_results = MTEBResults.from_task_results(sample_task, scores) + >>> mteb_results = TaskResult.from_task_results(sample_task, scores) >>> mteb_results.get_score() # get the main score for all languages 0.55 >>> mteb_results.get_score(languages=["fra"]) # get the main score for French @@ -170,7 +167,7 @@ def from_task_results( scores: dict[Split, dict[HFSubset, ScoresDict]], evaluation_time: float, kg_co2_emissions: float | None = None, - ) -> MTEBResults: + ) -> TaskResult: task_meta = task.metadata subset2langscripts = task_meta.hf_subsets_to_langscripts flat_scores = defaultdict(list) @@ -184,7 +181,7 @@ def from_task_results( } flat_scores[split].append(_scores) - return MTEBResults( + return TaskResult( dataset_revision=task.metadata.dataset["revision"], task_name=task.metadata.name, mteb_version=version("mteb"), @@ -223,7 +220,7 @@ def to_dict(self) -> dict: return self.model_dump() @classmethod - def from_dict(cls, data: dict) -> MTEBResults: + def from_dict(cls, data: dict) -> TaskResult: return cls.model_validate(data) def _round_scores(self, scores: dict[Split, list[ScoresDict]], n: int) -> None: @@ -249,8 +246,8 @@ def to_disk(self, path: Path) -> None: json.dump(json_obj, f, indent=2) @classmethod - def from_disk(cls, path: Path, load_historic_data: bool = True) -> MTEBResults: # type: ignore - """Load MTEBResults from disk. + def from_disk(cls, path: Path, load_historic_data: bool = True) -> TaskResult: # type: ignore + """Load TaskResult from disk. Args: path: The path to the file to load. @@ -264,23 +261,19 @@ def from_disk(cls, path: Path, load_historic_data: bool = True) -> MTEBResults: return cls.model_validate(data) except Exception as e: raise ValueError( - f"Error loading MTEBResults from disk. You can try to load historic data by setting `load_historic_data=True`. Error: {e}" + f"Error loading TaskResult from disk. You can try to load historic data by setting `load_historic_data=True`. Error: {e}" ) pre_1_11_load = ( - ( - "mteb_version" in data - and Version(data["mteb_version"]) < Version("1.11.0") - ) - or "mteb_version" not in data - ) # assume it is before 1.11.0 if the version is not present + "mteb_version" in data and Version(data["mteb_version"]) < Version("1.11.0") + ) or "mteb_version" not in data # assume it is before 1.11.0 if the version is not present try: obj = cls.model_validate(data) except Exception as e: if not pre_1_11_load: raise e logger.debug( - f"Could not load MTEBResults from disk, got error: {e}. Attempting to load from disk using format from before v1.11.0" + f"Could not load TaskResult from disk, got error: {e}. Attempting to load from disk using format from before v1.11.0" ) obj = cls._convert_from_before_v1_11_0(data) @@ -294,7 +287,7 @@ def from_disk(cls, path: Path, load_historic_data: bool = True) -> MTEBResults: return obj @classmethod - def _fix_pair_classification_scores(cls, obj: MTEBResults) -> None: + def _fix_pair_classification_scores(cls, obj: TaskResult) -> None: from mteb import get_task task_name = obj.task_name @@ -314,7 +307,7 @@ def _fix_pair_classification_scores(cls, obj: MTEBResults) -> None: hf_subset_scores.pop(key) @classmethod - def _convert_from_before_v1_11_0(cls, data: dict) -> MTEBResults: + def _convert_from_before_v1_11_0(cls, data: dict) -> TaskResult: from mteb.overview import TASKS_REGISTRY # in case the task name is not found in the registry, try to find a lower case version @@ -394,7 +387,7 @@ def _convert_from_before_v1_11_0(cls, data: dict) -> MTEBResults: if "test" in scores and "fr" in scores["test"]: scores["test"]["fra-fra"] = scores["test"].pop("fr") - result: MTEBResults = MTEBResults.from_task_results( + result: TaskResult = TaskResult.from_task_results( task, # type: ignore scores, evaluation_time, @@ -444,7 +437,7 @@ def get_score( return aggregation(values) def __repr__(self) -> str: - return f"MTEBResults(task_name={self.task_name}, scores=...)" + return f"TaskResult(task_name={self.task_name}, scores=...)" def validate_and_filter_scores(self, task: AbsTask | None = None) -> None: """This ensures that the scores are correct for the given task, by removing any splits besides those specified in the task metadata. diff --git a/mteb/task_aggregation.py b/mteb/task_aggregation.py index 899b6ae553..d0113a48e9 100644 --- a/mteb/task_aggregation.py +++ b/mteb/task_aggregation.py @@ -7,7 +7,7 @@ import numpy as np from mteb.load_results.load_results import MODEL_NAME, RESULTS, REVISION -from mteb.load_results.mteb_results import MTEBResults +from mteb.load_results.mteb_results import TaskResult from mteb.overview import get_task logger = logging.getLogger(__name__) @@ -23,7 +23,7 @@ def mean(results: RESULTS) -> AGGREGATION: for result in res: unique_tasks.add(result.task_name) - def _mean(model_name: str, rev: str, results: list[MTEBResults]) -> float: + def _mean(model_name: str, rev: str, results: list[TaskResult]) -> float: """Calculate the mean of the main score of the given results.""" scores: list[float] = [result.get_score() for result in results] @@ -57,7 +57,7 @@ def task_category_weighted_mean( task_types[task_type].add(task_name) def _task_category_weighted_mean( - model: str, rev: str, results: list[MTEBResults] + model: str, rev: str, results: list[TaskResult] ) -> dict[str, float]: """Calculate the mean of the main score of the given results, weighted by the number of tasks of each type.""" _task_types = {task_type: [] for task_type in task_types.keys()} diff --git a/scripts/running_model/check_results.py b/scripts/running_model/check_results.py index a4b166e0c8..ceaf46eef1 100644 --- a/scripts/running_model/check_results.py +++ b/scripts/running_model/check_results.py @@ -13,7 +13,7 @@ def results_to_dataframe( - mteb_results: dict[MODEL, dict[REVISION, list[mteb.MTEBResults]]], + mteb_results: dict[MODEL, dict[REVISION, list[mteb.TaskResult]]], ): data = [] for model_name, revisions in mteb_results.items(): diff --git a/tests/test_load_results/test_mteb_load_results.py b/tests/test_load_results/test_mteb_load_results.py index d5d2ec87ef..85c78faf63 100644 --- a/tests/test_load_results/test_mteb_load_results.py +++ b/tests/test_load_results/test_mteb_load_results.py @@ -19,7 +19,7 @@ def test_mteb_load_results(): for revision in results[model]: assert isinstance(results[model][revision], list) for result in results[model][revision]: - assert isinstance(result, mteb.MTEBResults) + assert isinstance(result, mteb.TaskResult) known_model = "sentence-transformers/average_word_embeddings_levy_dependency" known_revision = "6d9c09a789ad5dd126b476323fccfeeafcd90509" diff --git a/tests/test_load_results/test_mteb_results.py b/tests/test_load_results/test_mteb_results.py index 4007da270f..97a3c72553 100644 --- a/tests/test_load_results/test_mteb_results.py +++ b/tests/test_load_results/test_mteb_results.py @@ -7,7 +7,7 @@ import mteb from mteb import AbsTask -from mteb.load_results.mteb_results import MTEBResults +from mteb.load_results.mteb_results import TaskResult tests_folder = Path(__file__).parent.parent @@ -52,7 +52,7 @@ def _calculate_metrics_from_split( def test_mteb_results(): - """Test MTEBResults class (this is the same as the example in the docstring)""" + """Test TaskResult class (this is the same as the example in the docstring)""" scores = { "train": { "en-de": { @@ -66,7 +66,7 @@ def test_mteb_results(): evaluation_time = 100 - mteb_results = MTEBResults.from_task_results( + mteb_results = TaskResult.from_task_results( task=DummyTask(), scores=scores, evaluation_time=evaluation_time ) @@ -101,5 +101,5 @@ def test_mteb_results(): "path", list((tests_folder / "historic_results").glob("*.json")) ) def test_mteb_results_from_historic(path: Path): - mteb_result = MTEBResults.from_disk(path, load_historic_data=True) - assert isinstance(mteb_result, MTEBResults) + mteb_result = TaskResult.from_disk(path, load_historic_data=True) + assert isinstance(mteb_result, TaskResult) diff --git a/tests/test_task_aggregation.py b/tests/test_task_aggregation.py index 23228872c6..9a3e53e0ae 100644 --- a/tests/test_task_aggregation.py +++ b/tests/test_task_aggregation.py @@ -4,7 +4,7 @@ import mteb.task_aggregation as task_aggregation # define some test data -bitext1_1 = mteb.MTEBResults( +bitext1_1 = mteb.TaskResult( dataset_revision="test_rev", task_name="BornholmBitextMining", mteb_version="test_version", @@ -12,7 +12,7 @@ scores={"test": [{"main_score": 1, "hf_subset": "NaN", "languages": ["eng-Latn"]}]}, ) -bitext1_2 = mteb.MTEBResults( +bitext1_2 = mteb.TaskResult( dataset_revision="test_rev", task_name="BornholmBitextMining", mteb_version="test_version", @@ -20,7 +20,7 @@ scores={"test": [{"main_score": 2, "hf_subset": "NaN", "languages": ["eng-Latn"]}]}, ) -classification1_1 = mteb.MTEBResults( +classification1_1 = mteb.TaskResult( dataset_revision="test_rev", task_name="Banking77Classification", mteb_version="test_version", @@ -28,7 +28,7 @@ scores={"test": [{"main_score": 1, "hf_subset": "NaN", "languages": ["eng-Latn"]}]}, ) -classification1_2 = mteb.MTEBResults( +classification1_2 = mteb.TaskResult( dataset_revision="test_rev", task_name="Banking77Classification", mteb_version="test_version", @@ -36,7 +36,7 @@ scores={"test": [{"main_score": 2, "hf_subset": "NaN", "languages": ["eng-Latn"]}]}, ) -classification2_1 = mteb.MTEBResults( +classification2_1 = mteb.TaskResult( dataset_revision="test_rev", task_name="AfriSentiClassification", mteb_version="test_version", From 17b17f7dbc342d365898022305794577f3c94929 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 23 Sep 2024 16:46:55 +0200 Subject: [PATCH 03/38] Moved model and model meta loading utilities into overview.py --- mteb/models/__init__.py | 142 ++-------------------------------------- mteb/models/overview.py | 133 +++++++++++++++++++++++++++++++++++++ 2 files changed, 139 insertions(+), 136 deletions(-) create mode 100644 mteb/models/overview.py diff --git a/mteb/models/__init__.py b/mteb/models/__init__.py index c68e4f5a5a..adedc1081e 100644 --- a/mteb/models/__init__.py +++ b/mteb/models/__init__.py @@ -7,141 +7,11 @@ from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode from mteb.model_meta import ModelMeta -from mteb.models import ( - bge_models, - bm25, - cohere_models, - e5_instruct, - e5_models, - google_models, - gritlm_models, - gte_models, - llm2vec_models, - mxbai_models, - nomic_models, - openai_models, - ru_sentence_models, - salesforce_models, - sentence_transformers_models, - voyage_models, -) +from mteb.models import (bge_models, bm25, cohere_models, e5_instruct, + e5_models, google_models, gritlm_models, gte_models, + llm2vec_models, mxbai_models, nomic_models, + openai_models, ru_sentence_models, salesforce_models, + sentence_transformers_models, voyage_models) +from mteb.models.overview import * logger = logging.getLogger(__name__) - - -def get_model( - model_name: str, revision: str | None = None, **kwargs: Any -) -> Encoder | EncoderWithQueryCorpusEncode: - """A function to fetch a model object by name. - - Args: - model_name: Name of the model to fetch - revision: Revision of the model to fetch - **kwargs: Additional keyword arguments to pass to the model loader - - Returns: - A model object - """ - meta = get_model_meta(model_name, revision) - model = meta.load_model(**kwargs) - - # If revision not available in the modelmeta, try to extract it from sentence-transformers - if meta.revision is None and isinstance(model, SentenceTransformer): - _meta = model_meta_from_sentence_transformers(model) - meta.revision = _meta.revision if _meta.revision else meta.revision - - model.mteb_model_meta = meta # type: ignore - return model - - -def get_model_meta(model_name: str, revision: str | None = None) -> ModelMeta: - """A function to fetch a model metadata object by name. - - Args: - model_name: Name of the model to fetch - revision: Revision of the model to fetch - - Returns: - A model metadata object - """ - if model_name in models: - if revision and (not models[model_name].revision == revision): - raise ValueError( - f"Model revision {revision} not found for model {model_name}. Expected {models[model_name].revision}." - ) - return models[model_name] - else: # assume it is a sentence-transformers model - logger.info( - "Model not found in model registry, assuming it is a sentence-transformers model." - ) - logger.info( - f"Attempting to extract metadata by loading the model ({model_name}) using sentence-transformers." - ) - model = SentenceTransformer( - model_name, revision=revision, trust_remote_code=True - ) - meta = model_meta_from_sentence_transformers(model) - - meta.revision = revision - meta.name = model_name - return meta - - -def model_meta_from_sentence_transformers(model: SentenceTransformer) -> ModelMeta: - try: - name = ( - model.model_card_data.model_name - if model.model_card_data.model_name - else model.model_card_data.base_model - ) - languages = ( - [model.model_card_data.language] - if isinstance(model.model_card_data.language, str) - else model.model_card_data.language - ) - meta = ModelMeta( - name=name, - revision=model.model_card_data.base_model_revision, - release_date=None, - languages=languages, - framework=["Sentence Transformers"], - similarity_fn_name=model.similarity_fn_name, - ) - except AttributeError as e: - logger.warning( - f"Failed to extract metadata from model: {e}. Upgrading to sentence-transformers v3.0.0 or above is recommended." - ) - meta = ModelMeta( - name=None, - revision=None, - languages=None, - release_date=None, - ) - return meta - - -model_modules = [ - bge_models, - bm25, - cohere_models, - e5_instruct, - e5_models, - google_models, - gritlm_models, - gte_models, - llm2vec_models, - mxbai_models, - nomic_models, - openai_models, - ru_sentence_models, - salesforce_models, - sentence_transformers_models, - voyage_models, - google_models, -] -models = {} - -for module in model_modules: - for mdl in vars(module).values(): - if isinstance(mdl, ModelMeta): - models[mdl.name] = mdl diff --git a/mteb/models/overview.py b/mteb/models/overview.py new file mode 100644 index 0000000000..7a4ca9b460 --- /dev/null +++ b/mteb/models/overview.py @@ -0,0 +1,133 @@ +from __future__ import annotations + +import logging +from typing import Any + +from sentence_transformers import SentenceTransformer + +from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode +from mteb.model_meta import ModelMeta +from mteb.models import (bge_models, bm25, cohere_models, e5_instruct, + e5_models, google_models, gritlm_models, gte_models, + llm2vec_models, mxbai_models, nomic_models, + openai_models, ru_sentence_models, salesforce_models, + sentence_transformers_models, voyage_models) + +logger = logging.getLogger(__name__) + +model_modules = [ + bge_models, + bm25, + cohere_models, + e5_instruct, + e5_models, + google_models, + gritlm_models, + gte_models, + llm2vec_models, + mxbai_models, + nomic_models, + openai_models, + ru_sentence_models, + salesforce_models, + sentence_transformers_models, + voyage_models, + google_models, +] +MODEL_REGISTRY = {} + +for module in model_modules: + for mdl in vars(module).values(): + if isinstance(mdl, ModelMeta): + MODEL_REGISTRY[mdl.name] = mdl + + +def get_model( + model_name: str, revision: str | None = None, **kwargs: Any +) -> Encoder | EncoderWithQueryCorpusEncode: + """A function to fetch a model object by name. + + Args: + model_name: Name of the model to fetch + revision: Revision of the model to fetch + **kwargs: Additional keyword arguments to pass to the model loader + + Returns: + A model object + """ + meta = get_model_meta(model_name, revision) + model = meta.load_model(**kwargs) + + # If revision not available in the modelmeta, try to extract it from sentence-transformers + if meta.revision is None and isinstance(model, SentenceTransformer): + _meta = model_meta_from_sentence_transformers(model) + meta.revision = _meta.revision if _meta.revision else meta.revision + + model.mteb_model_meta = meta # type: ignore + return model + + +def get_model_meta(model_name: str, revision: str | None = None) -> ModelMeta: + """A function to fetch a model metadata object by name. + + Args: + model_name: Name of the model to fetch + revision: Revision of the model to fetch + + Returns: + A model metadata object + """ + if model_name in MODEL_REGISTRY: + if revision and (not MODEL_REGISTRY[model_name].revision == revision): + raise ValueError( + f"Model revision {revision} not found for model {model_name}. Expected {MODEL_REGISTRY[model_name].revision}." + ) + return MODEL_REGISTRY[model_name] + else: # assume it is a sentence-transformers model + logger.info( + "Model not found in model registry, assuming it is a sentence-transformers model." + ) + logger.info( + f"Attempting to extract metadata by loading the model ({model_name}) using sentence-transformers." + ) + model = SentenceTransformer( + model_name, revision=revision, trust_remote_code=True + ) + meta = model_meta_from_sentence_transformers(model) + + meta.revision = revision + meta.name = model_name + return meta + + +def model_meta_from_sentence_transformers(model: SentenceTransformer) -> ModelMeta: + try: + name = ( + model.model_card_data.model_name + if model.model_card_data.model_name + else model.model_card_data.base_model + ) + languages = ( + [model.model_card_data.language] + if isinstance(model.model_card_data.language, str) + else model.model_card_data.language + ) + meta = ModelMeta( + name=name, + revision=model.model_card_data.base_model_revision, + release_date=None, + languages=languages, + framework=["Sentence Transformers"], + similarity_fn_name=model.similarity_fn_name, + ) + except AttributeError as e: + logger.warning( + f"Failed to extract metadata from model: {e}. Upgrading to sentence-transformers v3.0.0 or above is recommended." + ) + meta = ModelMeta( + name=None, + revision=None, + languages=None, + release_date=None, + ) + return meta From 2ae9392a19a900b636cb8ab87f694e29caa8a400 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 23 Sep 2024 17:10:48 +0200 Subject: [PATCH 04/38] Added get_model_metas to retrieve filtered metadata for models --- mteb/models/overview.py | 38 +++++++++++++++++++++++++++++++++++++- 1 file changed, 37 insertions(+), 1 deletion(-) diff --git a/mteb/models/overview.py b/mteb/models/overview.py index 7a4ca9b460..149b14ffbc 100644 --- a/mteb/models/overview.py +++ b/mteb/models/overview.py @@ -1,7 +1,7 @@ from __future__ import annotations import logging -from typing import Any +from typing import Any, Iterable from sentence_transformers import SentenceTransformer @@ -42,6 +42,42 @@ MODEL_REGISTRY[mdl.name] = mdl +def get_model_metas( + model_names: Iterable[str] | None = None, + languages: Iterable[str] | None = None, + open_source: bool | None = None, + frameworks: Iterable[str] | None = None, + n_parameters_range: tuple[int | None, int | None] = (None, None), +) -> list[ModelMeta]: + """Load all models' metadata that fit the specified criteria.""" + res = [] + model_names = set(model_names) if model_names is not None else None + languages = set(languages) if languages is not None else None + frameworks = set(frameworks) if frameworks is not None else None + for model_meta in MODEL_REGISTRY.values(): + if (model_names is not None) and (model_meta.name not in model_names): + continue + if languages is not None: + if (model_meta.languages is None) or not ( + languages <= set(model_meta.languages) + ): + continue + if (open_source is not None) and (model_meta.open_source != open_source): + continue + if (frameworks is not None) and not (frameworks <= set(model_meta.framework)): + continue + upper, lower = n_parameters_range + n_parameters = model_meta.n_parameters + if upper is not None: + if (n_parameters is None) or (n_parameters > upper): + continue + if lower is not None: + if (n_parameters is None) or (n_parameters < lower): + continue + res.append(model_meta) + return res + + def get_model( model_name: str, revision: str | None = None, **kwargs: Any ) -> Encoder | EncoderWithQueryCorpusEncode: From 5eb66f10dfb762bf5f2c6b4e77fe1838cec05db7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Tue, 24 Sep 2024 10:49:55 +0200 Subject: [PATCH 05/38] Restructured results object and made it into a class instead of a dict --- mteb/abstasks/AbsTaskBitextMining.py | 2 +- mteb/abstasks/AbsTaskClassification.py | 10 +- mteb/abstasks/AbsTaskClustering.py | 2 +- mteb/abstasks/AbsTaskClusteringFast.py | 2 +- .../AbsTaskMultilabelClassification.py | 2 +- mteb/abstasks/AbsTaskPairClassification.py | 2 +- mteb/abstasks/AbsTaskReranking.py | 2 +- mteb/abstasks/AbsTaskRetrieval.py | 2 +- mteb/abstasks/AbsTaskSTS.py | 2 +- mteb/abstasks/AbsTaskSpeedTask.py | 2 +- mteb/abstasks/AbsTaskSummarization.py | 2 +- mteb/create_meta.py | 16 +- mteb/evaluation/MTEB.py | 2 +- mteb/load_results/__init__.py | 5 +- mteb/load_results/benchmark_results.py | 149 ++++++++++++++++++ mteb/load_results/load_results.py | 45 ++---- mteb/task_aggregation.py | 15 +- .../Reranking/multilingual/MIRACLReranking.py | 2 +- .../test_mteb_load_results.py | 19 +-- tests/test_load_results/test_mteb_results.py | 2 +- tests/test_task_aggregation.py | 23 ++- 21 files changed, 225 insertions(+), 83 deletions(-) create mode 100644 mteb/load_results/benchmark_results.py diff --git a/mteb/abstasks/AbsTaskBitextMining.py b/mteb/abstasks/AbsTaskBitextMining.py index 973d69ee7f..1d0b1b642f 100644 --- a/mteb/abstasks/AbsTaskBitextMining.py +++ b/mteb/abstasks/AbsTaskBitextMining.py @@ -8,7 +8,7 @@ from mteb.encoder_interface import Encoder from ..evaluation.evaluators import BitextMiningEvaluator -from ..load_results.mteb_results import HFSubset, ScoresDict +from ..load_results.task_results import HFSubset, ScoresDict from .AbsTask import AbsTask, DescriptiveStatistics logger = logging.getLogger(__name__) diff --git a/mteb/abstasks/AbsTaskClassification.py b/mteb/abstasks/AbsTaskClassification.py index 36c0a76b96..c5f4d801a4 100644 --- a/mteb/abstasks/AbsTaskClassification.py +++ b/mteb/abstasks/AbsTaskClassification.py @@ -9,12 +9,10 @@ from mteb.encoder_interface import Encoder -from ..evaluation.evaluators import ( - kNNClassificationEvaluator, - kNNClassificationEvaluatorPytorch, - logRegClassificationEvaluator, -) -from ..load_results.mteb_results import HFSubset, ScoresDict +from ..evaluation.evaluators import (kNNClassificationEvaluator, + kNNClassificationEvaluatorPytorch, + logRegClassificationEvaluator) +from ..load_results.task_results import HFSubset, ScoresDict from .AbsTask import AbsTask, DescriptiveStatistics logger = logging.getLogger(__name__) diff --git a/mteb/abstasks/AbsTaskClustering.py b/mteb/abstasks/AbsTaskClustering.py index 87113b2b26..bd0898f5ea 100644 --- a/mteb/abstasks/AbsTaskClustering.py +++ b/mteb/abstasks/AbsTaskClustering.py @@ -9,7 +9,7 @@ from datasets import Dataset from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode -from mteb.load_results.mteb_results import ScoresDict +from mteb.load_results.task_results import ScoresDict from ..evaluation.evaluators import ClusteringEvaluator from .AbsTask import AbsTask, DescriptiveStatistics diff --git a/mteb/abstasks/AbsTaskClusteringFast.py b/mteb/abstasks/AbsTaskClusteringFast.py index 4afa632307..1348b9624b 100644 --- a/mteb/abstasks/AbsTaskClusteringFast.py +++ b/mteb/abstasks/AbsTaskClusteringFast.py @@ -15,7 +15,7 @@ from mteb.encoder_interface import Encoder from ..evaluation.evaluators.model_encode import model_encode -from ..load_results.mteb_results import HFSubset +from ..load_results.task_results import HFSubset from .AbsTask import AbsTask, DescriptiveStatistics logger = logging.getLogger(__name__) diff --git a/mteb/abstasks/AbsTaskMultilabelClassification.py b/mteb/abstasks/AbsTaskMultilabelClassification.py index 01cba996f8..f18fc38e14 100644 --- a/mteb/abstasks/AbsTaskMultilabelClassification.py +++ b/mteb/abstasks/AbsTaskMultilabelClassification.py @@ -15,7 +15,7 @@ from mteb.encoder_interface import Encoder from ..evaluation.evaluators.model_encode import model_encode -from ..load_results.mteb_results import HFSubset, ScoresDict +from ..load_results.task_results import HFSubset, ScoresDict from .AbsTask import AbsTask, DescriptiveStatistics logger = logging.getLogger(__name__) diff --git a/mteb/abstasks/AbsTaskPairClassification.py b/mteb/abstasks/AbsTaskPairClassification.py index f06fcdcf4c..50c5076d17 100644 --- a/mteb/abstasks/AbsTaskPairClassification.py +++ b/mteb/abstasks/AbsTaskPairClassification.py @@ -7,7 +7,7 @@ from ..encoder_interface import Encoder, EncoderWithQueryCorpusEncode from ..evaluation.evaluators import PairClassificationEvaluator -from ..load_results.mteb_results import ScoresDict +from ..load_results.task_results import ScoresDict from .AbsTask import AbsTask, DescriptiveStatistics logger = logging.getLogger(__name__) diff --git a/mteb/abstasks/AbsTaskReranking.py b/mteb/abstasks/AbsTaskReranking.py index 0fba84b040..a0c8b0a3a5 100644 --- a/mteb/abstasks/AbsTaskReranking.py +++ b/mteb/abstasks/AbsTaskReranking.py @@ -5,7 +5,7 @@ from datasets import Dataset from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode -from mteb.load_results.mteb_results import ScoresDict +from mteb.load_results.task_results import ScoresDict from ..evaluation.evaluators import RerankingEvaluator from .AbsTask import AbsTask, DescriptiveStatistics diff --git a/mteb/abstasks/AbsTaskRetrieval.py b/mteb/abstasks/AbsTaskRetrieval.py index a31aee761e..9825bd9526 100644 --- a/mteb/abstasks/AbsTaskRetrieval.py +++ b/mteb/abstasks/AbsTaskRetrieval.py @@ -13,7 +13,7 @@ from mteb.abstasks.TaskMetadata import HFSubset from ..evaluation.evaluators import RetrievalEvaluator -from ..load_results.mteb_results import ScoresDict +from ..load_results.task_results import ScoresDict from .AbsTask import AbsTask, DescriptiveStatistics logger = logging.getLogger(__name__) diff --git a/mteb/abstasks/AbsTaskSTS.py b/mteb/abstasks/AbsTaskSTS.py index 422162e8c3..157f285951 100644 --- a/mteb/abstasks/AbsTaskSTS.py +++ b/mteb/abstasks/AbsTaskSTS.py @@ -4,7 +4,7 @@ from typing import Any from ..evaluation.evaluators import STSEvaluator -from ..load_results.mteb_results import ScoresDict +from ..load_results.task_results import ScoresDict from .AbsTask import AbsTask, DescriptiveStatistics logger = logging.getLogger(__name__) diff --git a/mteb/abstasks/AbsTaskSpeedTask.py b/mteb/abstasks/AbsTaskSpeedTask.py index e764f607db..e9c144640c 100644 --- a/mteb/abstasks/AbsTaskSpeedTask.py +++ b/mteb/abstasks/AbsTaskSpeedTask.py @@ -8,7 +8,7 @@ import numpy as np from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode -from mteb.load_results.mteb_results import ScoresDict +from mteb.load_results.task_results import ScoresDict from .AbsTask import AbsTask diff --git a/mteb/abstasks/AbsTaskSummarization.py b/mteb/abstasks/AbsTaskSummarization.py index 4717d2a8cb..ff03fbaab3 100644 --- a/mteb/abstasks/AbsTaskSummarization.py +++ b/mteb/abstasks/AbsTaskSummarization.py @@ -6,7 +6,7 @@ import numpy as np from mteb.encoder_interface import Encoder -from mteb.load_results.mteb_results import ScoresDict +from mteb.load_results.task_results import ScoresDict from ..evaluation.evaluators import SummarizationEvaluator from .AbsTask import AbsTask, DescriptiveStatistics diff --git a/mteb/create_meta.py b/mteb/create_meta.py index 551331acdb..02ed273996 100644 --- a/mteb/create_meta.py +++ b/mteb/create_meta.py @@ -7,8 +7,8 @@ import yaml import mteb -from mteb import MTEBResults -from mteb.load_results.mteb_results import CQADupstackRetrievalDummy +from mteb import TaskResult +from mteb.load_results.task_results import CQADupstackRetrievalDummy def generate_readme(results_folder: Path, from_existing: Path | None = None) -> str: @@ -45,7 +45,7 @@ def load_model_name(results_folder: Path) -> str: return "PLACEHOLDER" -def process_task_result(task_result: MTEBResults) -> list[dict[str, Any]]: +def process_task_result(task_result: TaskResult) -> list[dict[str, Any]]: # CQADupstackRetrieval is a combined dataset (special case atm.) task = ( CQADupstackRetrievalDummy() @@ -84,13 +84,13 @@ def process_task_result(task_result: MTEBResults) -> list[dict[str, Any]]: return yaml_results -def get_task_results(results_folder: Path) -> list[MTEBResults]: +def get_task_results(results_folder: Path) -> list[TaskResult]: json_files = [ r for r in results_folder.glob("*.json") if r.is_file() and r.name != "model_meta.json" ] - task_results = [MTEBResults.from_disk(path) for path in json_files] + task_results = [TaskResult.from_disk(path) for path in json_files] task_results = [ results for results in task_results @@ -102,8 +102,8 @@ def get_task_results(results_folder: Path) -> list[MTEBResults]: def potentially_add_cqadupstack_to_results( - results: list[MTEBResults], -) -> list[MTEBResults]: + results: list[TaskResult], +) -> list[TaskResult]: task_list_cqa = { "CQADupstackAndroidRetrieval", "CQADupstackEnglishRetrieval", @@ -128,7 +128,7 @@ def potentially_add_cqadupstack_to_results( main_scores = [r.get_score(splits=["test"]) for r in cqa_results] main_score = float(sum(main_scores) / len(main_scores)) - combined_result = MTEBResults( + combined_result = TaskResult( task_name="CQADupstackRetrieval", dataset_revision="CQADupstackRetrieval_is_a_combined_dataset", mteb_version="NA", diff --git a/mteb/evaluation/MTEB.py b/mteb/evaluation/MTEB.py index befb3e64d7..6f20e7aeca 100644 --- a/mteb/evaluation/MTEB.py +++ b/mteb/evaluation/MTEB.py @@ -19,7 +19,7 @@ from ..abstasks import * from ..abstasks import AbsTask -from ..load_results.mteb_results import TaskResult +from ..load_results.task_results import TaskResult from ..tasks import * from . import LangMapping diff --git a/mteb/load_results/__init__.py b/mteb/load_results/__init__.py index 8604664414..aee4201d39 100644 --- a/mteb/load_results/__init__.py +++ b/mteb/load_results/__init__.py @@ -1,6 +1,7 @@ from __future__ import annotations +from .benchmark_results import BenchmarkResults, ModelResult from .load_results import load_results -from .mteb_results import TaskResult +from .task_results import TaskResult -__all__ = ["load_results", "TaskResult"] +__all__ = ["load_results", "TaskResult", "ModelResult", "BenchmarkResults"] diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py new file mode 100644 index 0000000000..e27b400bd5 --- /dev/null +++ b/mteb/load_results/benchmark_results.py @@ -0,0 +1,149 @@ +from collections import defaultdict +from pathlib import Path + +from pydantic import BaseModel, ConfigDict + +from mteb.abstasks import AbsTask +from mteb.abstasks.TaskMetadata import TASK_CATEGORY, TASK_DOMAIN, TASK_TYPE +from mteb.load_results.task_results import TaskResult +from mteb.overview import get_tasks + + +def restrict_task_results(res: TaskResult, task: AbsTask) -> TaskResult: + splits = task.metadata.eval_splits + hf_subsets = set(task.metadata.hf_subsets_to_langscripts) + new_scores = {} + seen_splits = set() + for split in res.scores: + if split not in splits: + continue + new_scores[split] = [] + seen_subsets = set() + for _scores in res.scores[split]: + if _scores["hf_subset"] not in hf_subsets: + continue + new_scores[split].append(_scores) + seen_subsets.add(_scores["hf_subset"]) + if seen_subsets != hf_subsets: + raise ValueError( + f"Missing subsets {hf_subsets - seen_subsets} for split {split}" + ) + seen_splits.add(split) + if seen_splits != set(splits): + raise ValueError(f"Missing splits {set(splits) - seen_splits}") + new_res = {**res.to_dict(), "scores": new_scores} + new_res = TaskResult.from_dict(new_res) + return new_res + + +class ModelResult(BaseModel): + model_name: str + model_revision: str | None + task_results: list[TaskResult] + model_config = ConfigDict( + protected_namespaces=(), + ) + + def __repr__(self) -> str: + n_entries = len(self.task_results) + return f"ModelResult(model_name={self.model_name}, model_revision={self.model_revision}, task_results=[...](#{n_entries}))" + + def filter_tasks( + self, + languages: list[str] | None = None, + script: list[str] | None = None, + domains: list[TASK_DOMAIN] | None = None, + task_types: list[TASK_TYPE] | None = None, + categories: list[TASK_CATEGORY] | None = None, + tasks: list[str] | None = None, + exclude_superseeded: bool = True, + ) -> "ModelResult": + filtered_tasks = get_tasks( + languages=languages, + script=script, + domains=domains, + task_types=task_types, + categories=categories, + tasks=tasks, + exclude_superseeded=exclude_superseeded, + ) + filtered_tasks = {task.metadata.name: task for task in filtered_tasks} + new_task_results = [ + restrict_task_results(res, filtered_tasks[res.task_name]) + for res in self.task_results + if res.task_name in filtered_tasks + ] + return type(self)( + model_name=self.model_name, + model_revision=self.model_revision, + task_results=new_task_results, + ) + + def get_scores(self) -> dict[str, float]: + return {res.task_name: res.get_score() for res in self.task_results} + + def __iter__(self): + return iter(self.task_results) + + def __getitem__(self, index) -> TaskResult: + return self.task_results[index] + + +class BenchmarkResults(BaseModel): + model_results: list[ModelResult] + + def __repr__(self) -> str: + n_models = len(self.model_results) + return f"BenchmarkResults(model_results=[...](#{n_models}))" + + def filter_tasks( + self, + languages: list[str] | None = None, + script: list[str] | None = None, + domains: list[TASK_DOMAIN] | None = None, + task_types: list[TASK_TYPE] | None = None, + categories: list[TASK_CATEGORY] | None = None, + tasks: list[str] | None = None, + exclude_superseeded: bool = True, + ) -> "BenchmarkResults": + model_results = [ + res.filter_tasks( + languages=languages, + script=script, + domains=domains, + task_types=task_types, + categories=categories, + tasks=tasks, + exclude_superseeded=exclude_superseeded, + ) + for res in self.model_results + ] + return type(self)( + model_results=[res for res in model_results if model_results.task_results] + ) + + def __iter__(self): + return iter(self.model_results) + + def __getitem__(self, index) -> ModelResult: + return self.model_results[index] + + def to_legacy_dict(self) -> dict[str, dict[str, list[TaskResult]]]: + res = defaultdict(dict) + for model_res in self: + res[model_res.model_name][model_res.model_revision] = model_res.task_results + return res + + @classmethod + def from_legacy_dict(cls, legacy: dict[str, dict[str, list[TaskResult]]]): + model_results = [] + for model_name, revisions in legacy.items(): + for model_revision, results in revisions.items(): + model_results.append( + ModelResult( + model_name=model_name, + model_revision=model_revision, + task_results=results, + ) + ) + return cls(model_results=model_results) diff --git a/mteb/load_results/load_results.py b/mteb/load_results/load_results.py index 27518c59d0..d7d49e8ef8 100644 --- a/mteb/load_results/load_results.py +++ b/mteb/load_results/load_results.py @@ -9,15 +9,14 @@ from typing import Dict, List, Sequence from mteb.abstasks.AbsTask import AbsTask -from mteb.load_results.mteb_results import TaskResult +from mteb.load_results.benchmark_results import BenchmarkResults, ModelResult +from mteb.load_results.task_results import TaskResult from mteb.model_meta import ModelMeta logger = logging.getLogger(__name__) MODEL_NAME = str REVISION = str -RESULTS = Dict[MODEL_NAME, Dict[REVISION, List[TaskResult]]] - def download_of_results( results_repo: str, cache_directory: Path | None = None, download_latest: bool = True @@ -92,7 +91,7 @@ def load_results( tasks: Sequence[AbsTask] | Sequence[str] | None = None, validate_and_filter: bool = True, require_model_meta: bool = True, -) -> RESULTS: +) -> BenchmarkResults: """Loads the results from the latest version of the results repository. The results are cached locally in the MTEB_CACHE directory. This directory can be set using the MTEB_CACHE environment variable or defaults to "~/.cache/mteb". @@ -107,29 +106,7 @@ def load_results( splits from the results object that are not default in the task metadata. Defaults to True. Returns: - A dictionary where the keys are the model names and the values are dictionaries where the keys are the revisions and the values are lists of TaskResult objects. - - Example: - >>> results = load_results() - >>> results - {'mixedbread-ai/mxbai-embed-large-v1': - {'990580e27d329c7408b3741ecff85876e128e203': [ - TaskResult(task_name=TwentyNewsgroupsClustering.v2, scores=...), - TaskResult(task_name=MedrxivClusteringP2P, scores=...), - TaskResult(task_name=StackExchangeClustering, scores=...), - TaskResult(task_name=BiorxivClusteringP2P.v2, scores=...), - TaskResult(task_name=MedrxivClusteringS2S.v2, scores=...), - TaskResult(task_name=MedrxivClusteringS2S, scores=...), - ... - ]}, - 'intfloat/multilingual-e5-small': - {'e4ce9877abf3edfe10b0d82785e83bdcb973e22e': [ - TaskResult(task_name=IndicGenBenchFloresBitextMining, scores=...), - TaskResult(task_name=PpcPC, scores=...), - TaskResult(task_name=TwentyNewsgroupsClustering.v2, scores=...), - ... - ]}, - ... + """ repo_directory = download_of_results(results_repo, download_latest=download_latest) model_paths = [p for p in (repo_directory / "results").glob("*") if p.is_dir()] @@ -152,8 +129,7 @@ def load_results( else: task_names[task] = None - results = defaultdict(dict) - + model_results = [] for model_path in model_paths: model_revisions = model_path.glob("*") @@ -195,7 +171,12 @@ def load_results( f"Validation failed for {r.task_name} in {model_name} {revision}: {e}" ) _results = filtered_results + model_results.append( + ModelResult( + model_name=model_name, + model_revision=revision, + task_results=_results, + ) + ) - results[model_name][revision] = _results - - return dict(results) + return BenchmarkResults(model_results=model_results) diff --git a/mteb/task_aggregation.py b/mteb/task_aggregation.py index d0113a48e9..dcad4c1f2e 100644 --- a/mteb/task_aggregation.py +++ b/mteb/task_aggregation.py @@ -6,17 +6,20 @@ import numpy as np -from mteb.load_results.load_results import MODEL_NAME, RESULTS, REVISION -from mteb.load_results.mteb_results import TaskResult +from mteb.load_results.benchmark_results import BenchmarkResults +from mteb.load_results.task_results import TaskResult from mteb.overview import get_task logger = logging.getLogger(__name__) +REVISION = str +MODEL_NAME = str AGGREGATION = Dict[MODEL_NAME, Dict[REVISION, Dict[str, float]]] -def mean(results: RESULTS) -> AGGREGATION: +def mean(results: BenchmarkResults) -> AGGREGATION: """Calculate the mean of the main score of the given results.""" + results = results.to_legacy_dict() unique_tasks = set() for model, revisions in results.items(): for revision, res in revisions.items(): @@ -43,9 +46,10 @@ def _mean(model_name: str, rev: str, results: list[TaskResult]) -> float: def task_category_weighted_mean( - results: RESULTS, + results: BenchmarkResults, ) -> AGGREGATION: """Calculate the mean of the main score of the given results, weighted by the number of tasks of each type.""" + results = results.to_legacy_dict() unique_tasks = set() task_types = defaultdict(set) for model, revisions in results.items(): @@ -92,7 +96,7 @@ def _task_category_weighted_mean( def borda_count( - results: RESULTS, + results: BenchmarkResults, ) -> AGGREGATION: """Calculate the Borda count of the given results. @@ -103,6 +107,7 @@ def borda_count( # consider each model a candidate and each task a voter # each voter ranks the candidates + results = results.to_legacy_dict() n_candidates = sum(len(revs) for revs in results.values()) candidate_scores = { model: {revision: 0.0 for revision in revisions} diff --git a/mteb/tasks/Reranking/multilingual/MIRACLReranking.py b/mteb/tasks/Reranking/multilingual/MIRACLReranking.py index c5298d34e8..a1dc1c5249 100644 --- a/mteb/tasks/Reranking/multilingual/MIRACLReranking.py +++ b/mteb/tasks/Reranking/multilingual/MIRACLReranking.py @@ -9,7 +9,7 @@ from mteb.abstasks.TaskMetadata import TaskMetadata from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode from mteb.evaluation.evaluators import RerankingEvaluator -from mteb.load_results.mteb_results import ScoresDict +from mteb.load_results.task_results import ScoresDict from ....abstasks.AbsTaskReranking import AbsTaskReranking diff --git a/tests/test_load_results/test_mteb_load_results.py b/tests/test_load_results/test_mteb_load_results.py index 85c78faf63..57ba1bae54 100644 --- a/tests/test_load_results/test_mteb_load_results.py +++ b/tests/test_load_results/test_mteb_load_results.py @@ -4,6 +4,7 @@ from pathlib import Path import mteb +from mteb.load_results.benchmark_results import BenchmarkResults, ModelResult def test_mteb_load_results(): @@ -13,15 +14,15 @@ def test_mteb_load_results(): results = mteb.load_results(download_latest=False) - assert isinstance(results, dict) - for model in results: - assert isinstance(results[model], dict) - for revision in results[model]: - assert isinstance(results[model][revision], list) - for result in results[model][revision]: - assert isinstance(result, mteb.TaskResult) + assert isinstance(results, BenchmarkResults) + for model_result in results: + assert isinstance(model_result, ModelResult) + for res in model_result: + assert isinstance(res, mteb.TaskResult) known_model = "sentence-transformers/average_word_embeddings_levy_dependency" known_revision = "6d9c09a789ad5dd126b476323fccfeeafcd90509" - assert known_model in results - assert known_revision in results[known_model] + assert known_model in [res.model_name for res in results] + assert known_revision in [ + res.model_revision for res in results if res.model_name == known_model + ] diff --git a/tests/test_load_results/test_mteb_results.py b/tests/test_load_results/test_mteb_results.py index 97a3c72553..6c22b390f3 100644 --- a/tests/test_load_results/test_mteb_results.py +++ b/tests/test_load_results/test_mteb_results.py @@ -7,7 +7,7 @@ import mteb from mteb import AbsTask -from mteb.load_results.mteb_results import TaskResult +from mteb.load_results.task_results import TaskResult tests_folder = Path(__file__).parent.parent diff --git a/tests/test_task_aggregation.py b/tests/test_task_aggregation.py index 9a3e53e0ae..f0754418c3 100644 --- a/tests/test_task_aggregation.py +++ b/tests/test_task_aggregation.py @@ -2,6 +2,7 @@ import mteb import mteb.task_aggregation as task_aggregation +from mteb.load_results.benchmark_results import BenchmarkResults # define some test data bitext1_1 = mteb.TaskResult( @@ -54,6 +55,7 @@ "rev2": [bitext1_2, classification1_1, classification2_1], }, } +mteb_results = BenchmarkResults.from_legacy_dict(mteb_results) def test_mean(): @@ -103,14 +105,16 @@ def test_task_category_weighted_mean(): def test_borda_count_simple(): - mteb_results_simple = { - "model1": { - "rev1": [bitext1_1], - }, - "model2": { - "rev2": [bitext1_2], - }, - } + mteb_results_simple = BenchmarkResults.from_legacy_dict( + { + "model1": { + "rev1": [bitext1_1], + }, + "model2": { + "rev2": [bitext1_2], + }, + } + ) expected = { "model1": { "rev1": {"borda_count": 0}, @@ -143,6 +147,9 @@ def test_borda_count_simple_with_tie(): "rev2": {"borda_count": 2.5}, }, } + mteb_results_simple_with_tie = BenchmarkResults.from_legacy_dict( + mteb_results_simple_with_tie + ) assert task_aggregation.borda_count(mteb_results_simple_with_tie) == expected From 9f75bf5b9549959a5064d4b7f88e141a3556cc70 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Tue, 24 Sep 2024 11:09:42 +0200 Subject: [PATCH 06/38] Added utilities for filtering models on BenchmarkResults objects --- mteb/load_results/benchmark_results.py | 25 ++++++++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index e27b400bd5..1487f4a886 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -1,11 +1,13 @@ from collections import defaultdict from pathlib import Path +from typing import Iterable from pydantic import BaseModel, ConfigDict from mteb.abstasks import AbsTask from mteb.abstasks.TaskMetadata import TASK_CATEGORY, TASK_DOMAIN, TASK_TYPE from mteb.load_results.task_results import TaskResult +from mteb.models.overview import get_model_metas from mteb.overview import get_tasks @@ -91,6 +93,9 @@ def __getitem__(self, index) -> TaskResult: class BenchmarkResults(BaseModel): model_results: list[ModelResult] + model_config = ConfigDict( + protected_namespaces=(), + ) def __repr__(self) -> str: n_models = len(self.model_results) @@ -119,8 +124,26 @@ def filter_tasks( for res in self.model_results ] return type(self)( - model_results=[res for res in model_results if model_results.task_results] + model_results=[res for res in model_results if res.task_results] + ) + + def filter_models( + self, + model_names: Iterable[str] | None = None, + languages: Iterable[str] | None = None, + open_source: bool | None = None, + frameworks: Iterable[str] | None = None, + n_parameters_range: tuple[int | None, int | None] = (None, None), + ) -> "BenchmarkResults": + model_metas = get_model_metas( + model_names, languages, open_source, frameworks, n_parameters_range ) + model_revision_pairs = {(meta.name, meta.revision) for meta in model_metas} + new_model_results = [] + for model_res in self: + if (model_res.model_name, model_res.model_revision) in model_revision_pairs: + new_model_results.append(model_res) + return type(self)(model_results=new_model_results) def __iter__(self): return iter(self.model_results) From f3103c123b61a2113c391941905fbb7863a4a25d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Tue, 24 Sep 2024 11:34:14 +0200 Subject: [PATCH 07/38] Added to_table utility function to BenchmarkResults --- mteb/load_results/benchmark_results.py | 37 +++++++++++++++++++++++++- 1 file changed, 36 insertions(+), 1 deletion(-) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index 1487f4a886..4292d674d0 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -1,7 +1,9 @@ from collections import defaultdict from pathlib import Path -from typing import Iterable +from typing import Iterable, Literal +import numpy as np +import pandas as pd from pydantic import BaseModel, ConfigDict from mteb.abstasks import AbsTask @@ -145,6 +147,39 @@ def filter_models( new_model_results.append(model_res) return type(self)(model_results=new_model_results) + def to_table(self, format: Literal["wide", "long"] = "wide") -> pd.DataFrame: + if format == "wide": + entries = [] + for model_res in self: + entry = dict( + model_name=model_res.model_name, + model_revision=model_res.model_revision, + ) + for task_res in model_res: + entry[task_res.task_name] = task_res.get_score() + entries.append(entry) + return pd.DataFrame(entries).set_index(["model_name", "model_revision"]) + elif format == "long": + entries = [] + for model_res in self: + for task_res in model_res: + entry = dict( + model_name=model_res.model_name, + model_revision=model_res.model_revision, + task_name=task_res.task_name, + score=task_res.get_score(), + mteb_version=task_res.mteb_version, + dataset_revision=task_res.dataset_revision, + evaluation_time=task_res.evaluation_time, + kg_co2_emissions=task_res.kg_co2_emissions, + ) + entries.append(entry) + return pd.DataFrame(entries) + else: + raise ValueError( + f"Table format can either be 'long' or 'wide', not {format}" + ) + def __iter__(self): return iter(self.model_results) From bb1e3642474c27117be56187ca7632dff358d2f5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Tue, 24 Sep 2024 12:14:45 +0200 Subject: [PATCH 08/38] Added serialization utilities to BenchmarkResults --- mteb/load_results/benchmark_results.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index 4292d674d0..4e9d9e5459 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -1,3 +1,4 @@ +import json from collections import defaultdict from pathlib import Path from typing import Iterable, Literal @@ -205,3 +206,22 @@ def from_legacy_dict(cls, legacy: dict[str, dict[str, list[TaskResult]]]): ) ) return cls(model_results=model_results) + + def to_dict(self) -> dict: + return self.model_dump() + + @classmethod + def from_dict(cls, data: dict) -> TaskResult: + return cls.model_validate(data) + + def to_disk(self, path: Path | str) -> None: + path = Path(path) + with path.open("w") as out_file: + out_file.write(self.model_dump_json(indent=2)) + + @classmethod + def from_disk(cls, path: Path | str) -> "BenchmarkResults": + path = Path(path) + with path.open() as in_file: + data = json.loads(in_file.read()) + return cls.from_dict(data) From 24e0e3e06daa1e03adc658c80fbe85ad8ec417c5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Tue, 24 Sep 2024 12:26:08 +0200 Subject: [PATCH 09/38] Attempted fixing tests --- mteb/evaluation/MTEB.py | 4 ++-- tests/test_benchmark/test_benchmark.py | 15 +++++---------- 2 files changed, 7 insertions(+), 12 deletions(-) diff --git a/mteb/evaluation/MTEB.py b/mteb/evaluation/MTEB.py index 3154eabcb4..9d35636313 100644 --- a/mteb/evaluation/MTEB.py +++ b/mteb/evaluation/MTEB.py @@ -278,7 +278,7 @@ def run( co2_tracker: bool = False, encode_kwargs: dict[str, Any] = {}, **kwargs, - ) -> list[MTEBResults]: + ) -> list[TaskResult]: """Run the evaluation pipeline on the selected tasks. Args: @@ -337,7 +337,7 @@ def run( logger.info( f"{task.metadata.name} results already exists. Loading results from disk. Set overwrite_results=True to overwrite." ) - mteb_results = MTEBResults.from_disk(save_path) + mteb_results = TaskResult.from_disk(save_path) evaluation_results.append(mteb_results) del self.tasks[0] # empty memory continue diff --git a/tests/test_benchmark/test_benchmark.py b/tests/test_benchmark/test_benchmark.py index 612705fe72..0eb978ba30 100644 --- a/tests/test_benchmark/test_benchmark.py +++ b/tests/test_benchmark/test_benchmark.py @@ -13,14 +13,9 @@ from mteb.benchmarks.benchmarks import Benchmark from mteb.create_meta import generate_readme -from .mock_models import ( - MockBGEWrapper, - MockE5Wrapper, - MockMxbaiWrapper, - MockNumpyEncoder, - MockTorchbf16Encoder, - MockTorchEncoder, -) +from .mock_models import (MockBGEWrapper, MockE5Wrapper, MockMxbaiWrapper, + MockNumpyEncoder, MockTorchbf16Encoder, + MockTorchEncoder) from .task_grid import MOCK_TASK_TEST_GRID logging.basicConfig(level=logging.INFO) @@ -75,13 +70,13 @@ def test_reload_results(task: str | mteb.AbsTask, model: mteb.Encoder, tmp_path: results = eval.run(model, output_folder=str(tmp_path), overwrite_results=True) assert isinstance(results, list) - assert isinstance(results[0], mteb.MTEBResults) + assert isinstance(results[0], mteb.TaskResult) # reload the results results = eval.run(model, output_folder=str(tmp_path), overwrite_results=False) assert isinstance(results, list) - assert isinstance(results[0], mteb.MTEBResults) + assert isinstance(results[0], mteb.TaskResult) @pytest.mark.parametrize("task_name", MOCK_TASK_TEST_GRID) From bc1941ead032d97ae7576394fbcdb1086513388a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Tue, 24 Sep 2024 12:35:42 +0200 Subject: [PATCH 10/38] Added get_model_metas to __init__ --- mteb/__init__.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/mteb/__init__.py b/mteb/__init__.py index 2b98827014..4fd715e005 100644 --- a/mteb/__init__.py +++ b/mteb/__init__.py @@ -2,16 +2,12 @@ from importlib.metadata import version -from mteb.benchmarks.benchmarks import ( - MTEB_MAIN_EN, - MTEB_MAIN_RU, - MTEB_RETRIEVAL_LAW, - MTEB_RETRIEVAL_WITH_INSTRUCTIONS, - CoIR, -) +from mteb.benchmarks.benchmarks import (MTEB_MAIN_EN, MTEB_MAIN_RU, + MTEB_RETRIEVAL_LAW, + MTEB_RETRIEVAL_WITH_INSTRUCTIONS, CoIR) from mteb.evaluation import * from mteb.load_results import load_results -from mteb.models import get_model, get_model_meta +from mteb.models import get_model, get_model_meta, get_model_metas from mteb.overview import TASKS_REGISTRY, get_task, get_tasks from .benchmarks.benchmarks import Benchmark From 37e0e25b4641fa4e28d534e5df72480a4de35b81 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Tue, 24 Sep 2024 12:47:56 +0200 Subject: [PATCH 11/38] Added get_benchmarks to __init__ and made it return all benchmarks by default --- mteb/__init__.py | 1 + mteb/benchmarks/__init__.py | 1 + mteb/benchmarks/get_benchmark.py | 4 ++-- 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/mteb/__init__.py b/mteb/__init__.py index 4fd715e005..a72518dbb9 100644 --- a/mteb/__init__.py +++ b/mteb/__init__.py @@ -5,6 +5,7 @@ from mteb.benchmarks.benchmarks import (MTEB_MAIN_EN, MTEB_MAIN_RU, MTEB_RETRIEVAL_LAW, MTEB_RETRIEVAL_WITH_INSTRUCTIONS, CoIR) +from mteb.benchmars import get_benchmark, get_benchmarks from mteb.evaluation import * from mteb.load_results import load_results from mteb.models import get_model, get_model_meta, get_model_metas diff --git a/mteb/benchmarks/__init__.py b/mteb/benchmarks/__init__.py index fb1d12a293..653b97c6f7 100644 --- a/mteb/benchmarks/__init__.py +++ b/mteb/benchmarks/__init__.py @@ -1,3 +1,4 @@ from __future__ import annotations from mteb.benchmarks.benchmarks import * +from mteb.benchmarks.get_benchmark import * diff --git a/mteb/benchmarks/get_benchmark.py b/mteb/benchmarks/get_benchmark.py index 88079ce860..b60b40fc59 100644 --- a/mteb/benchmarks/get_benchmark.py +++ b/mteb/benchmarks/get_benchmark.py @@ -3,7 +3,7 @@ import difflib import mteb.benchmarks.benchmarks as benchmark_module -from mteb.benchmarks import Benchmark +from mteb.benchmarks.benchmarks import Benchmark BENCHMARK_REGISTRY = { inst.name: inst @@ -28,7 +28,7 @@ def get_benchmark( def get_benchmarks( - names: list[str] | None, + names: list[str] | None = None, ) -> list[Benchmark]: if names is None: names = list(BENCHMARK_REGISTRY.keys()) From f0fb32675525a1d4bb97fa577db64038253c305e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Tue, 24 Sep 2024 12:54:42 +0200 Subject: [PATCH 12/38] Added get_benchmarks to __init__ --- mteb/__init__.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/mteb/__init__.py b/mteb/__init__.py index a72518dbb9..a69e35e137 100644 --- a/mteb/__init__.py +++ b/mteb/__init__.py @@ -5,14 +5,13 @@ from mteb.benchmarks.benchmarks import (MTEB_MAIN_EN, MTEB_MAIN_RU, MTEB_RETRIEVAL_LAW, MTEB_RETRIEVAL_WITH_INSTRUCTIONS, CoIR) -from mteb.benchmars import get_benchmark, get_benchmarks from mteb.evaluation import * from mteb.load_results import load_results from mteb.models import get_model, get_model_meta, get_model_metas from mteb.overview import TASKS_REGISTRY, get_task, get_tasks from .benchmarks.benchmarks import Benchmark -from .benchmarks.get_benchmark import get_benchmark +from .benchmarks.get_benchmark import get_benchmark, get_benchmarks __version__ = version("mteb") # fetch version from install metadata @@ -28,7 +27,9 @@ "get_task", "get_model", "get_model_meta", + "get_model_metas", "load_results", "Benchmark", "get_benchmark", + "get_benchmarks", ] From 691380c2be356d75dcbd6e63f52db8ec4aa7c08f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Tue, 24 Sep 2024 13:03:43 +0200 Subject: [PATCH 13/38] Made tasks hashable --- mteb/abstasks/AbsTask.py | 9 ++++++--- mteb/abstasks/TaskMetadata.py | 15 +++++++-------- 2 files changed, 13 insertions(+), 11 deletions(-) diff --git a/mteb/abstasks/AbsTask.py b/mteb/abstasks/AbsTask.py index f7e606ec42..1e15096d0c 100644 --- a/mteb/abstasks/AbsTask.py +++ b/mteb/abstasks/AbsTask.py @@ -217,9 +217,9 @@ def calculate_metadata_metrics( pbar_subsets.set_postfix_str(f"Language: {hf_subset}") print(f"Processing metadata for language {hf_subset}") split_details = self._calculate_metrics_from_split(split, hf_subset) - all_details[split]["hf_subset_descriptive_stats"][hf_subset] = ( - split_details - ) + all_details[split]["hf_subset_descriptive_stats"][ + hf_subset + ] = split_details else: split_details = self._calculate_metrics_from_split(split) all_details[split] = split_details @@ -309,3 +309,6 @@ def __repr__(self) -> str: return ( f"{self.__class__.__name__}(name='{self.metadata.name}', languages={langs})" ) + + def __hash__(self) -> int: + return hash(self.metadata) diff --git a/mteb/abstasks/TaskMetadata.py b/mteb/abstasks/TaskMetadata.py index c368022433..c67d28721c 100644 --- a/mteb/abstasks/TaskMetadata.py +++ b/mteb/abstasks/TaskMetadata.py @@ -4,16 +4,12 @@ from datetime import date from typing import Any, Dict, List, Mapping, Union -from pydantic import AnyUrl, BaseModel, BeforeValidator, TypeAdapter, field_validator +from pydantic import (AnyUrl, BaseModel, BeforeValidator, TypeAdapter, + field_validator) from typing_extensions import Annotated, Literal -from ..languages import ( - ISO_LANGUAGE_SCRIPT, - ISO_TO_LANGUAGE, - ISO_TO_SCRIPT, - path_to_lang_codes, - path_to_lang_scripts, -) +from ..languages import (ISO_LANGUAGE_SCRIPT, ISO_TO_LANGUAGE, ISO_TO_SCRIPT, + path_to_lang_codes, path_to_lang_scripts) TASK_SUBTYPE = Literal[ "Article retrieval", @@ -351,3 +347,6 @@ def intext_citation(self, include_cite: bool = True) -> str: ) return f"\\cite{{{cite}}}" return cite + + def __hash__(self) -> int: + return hash(self.model_dump_json()) From 111cfd5681a5c663907fc21af82b90920e0d55e6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Tue, 24 Sep 2024 13:05:32 +0200 Subject: [PATCH 14/38] Added task filtering based on task objects on BenchmarkResults --- mteb/load_results/benchmark_results.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index 4e9d9e5459..a6bafc1f98 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -72,11 +72,14 @@ def filter_tasks( tasks=tasks, exclude_superseeded=exclude_superseeded, ) - filtered_tasks = {task.metadata.name: task for task in filtered_tasks} + return self.select_tasks(filtered_tasks) + + def select_tasks(self, tasks: list[AbsTask]) -> "ModelResult": + tasks = {task.metadata.name: task for task in tasks} new_task_results = [ - restrict_task_results(res, filtered_tasks[res.task_name]) + restrict_task_results(res, tasks[res.task_name]) for res in self.task_results - if res.task_name in filtered_tasks + if res.task_name in tasks ] return type(self)( model_name=self.model_name, @@ -130,6 +133,12 @@ def filter_tasks( model_results=[res for res in model_results if res.task_results] ) + def select_tasks(self, tasks: list[AbsTask]) -> "BenchmarkResults": + model_results = [res.select_tasks(tasks) for res in self.model_results] + return type(self)( + model_results=[res for res in model_results if res.task_results] + ) + def filter_models( self, model_names: Iterable[str] | None = None, From a84764c61762cdd4c47bff43e93b4d3b092e5487 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Tue, 24 Sep 2024 13:07:55 +0200 Subject: [PATCH 15/38] Added BenchmarkResults to __init__ --- mteb/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mteb/__init__.py b/mteb/__init__.py index a69e35e137..9d0eefa803 100644 --- a/mteb/__init__.py +++ b/mteb/__init__.py @@ -6,7 +6,7 @@ MTEB_RETRIEVAL_LAW, MTEB_RETRIEVAL_WITH_INSTRUCTIONS, CoIR) from mteb.evaluation import * -from mteb.load_results import load_results +from mteb.load_results import BenchmarkResults, load_results from mteb.models import get_model, get_model_meta, get_model_metas from mteb.overview import TASKS_REGISTRY, get_task, get_tasks From 50062e3daf2c802c82ad9aa731e917a961e74972 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Wed, 2 Oct 2024 15:01:45 +0200 Subject: [PATCH 16/38] Added additional arguments to get_scores on two classes --- mteb/load_results/benchmark_results.py | 90 +++++++++++++++++++++----- 1 file changed, 74 insertions(+), 16 deletions(-) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index a6bafc1f98..7f4f542635 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -1,14 +1,16 @@ import json from collections import defaultdict from pathlib import Path -from typing import Iterable, Literal +from typing import Any, Callable, Iterable, Literal import numpy as np import pandas as pd from pydantic import BaseModel, ConfigDict -from mteb.abstasks import AbsTask -from mteb.abstasks.TaskMetadata import TASK_CATEGORY, TASK_DOMAIN, TASK_TYPE +from mteb.abstasks.AbsTask import AbsTask, ScoresDict +from mteb.abstasks.TaskMetadata import (ISO_LANGUAGE_SCRIPT, TASK_CATEGORY, + TASK_DOMAIN, TASK_TYPE) +from mteb.languages import ISO_LANGUAGE from mteb.load_results.task_results import TaskResult from mteb.models.overview import get_model_metas from mteb.overview import get_tasks @@ -41,6 +43,10 @@ def restrict_task_results(res: TaskResult, task: AbsTask) -> TaskResult: return new_res +Split = str +Score = Any + + class ModelResult(BaseModel): model_name: str model_revision: str | None @@ -87,8 +93,24 @@ def select_tasks(self, tasks: list[AbsTask]) -> "ModelResult": task_results=new_task_results, ) - def get_scores(self) -> dict[str, float]: - return {res.task_name: res.get_score() for res in self.task_results} + def get_scores( + self, + splits: list[Split] | None = None, + languages: list[ISO_LANGUAGE | ISO_LANGUAGE_SCRIPT] | None = None, + scripts: list[ISO_LANGUAGE_SCRIPT] | None = None, + getter: Callable[[ScoresDict], Score] = lambda scores: scores["main_score"], + aggregation: Callable[[list[Score]], Any] = np.mean, + ) -> dict[str, float]: + return { + res.task_name: res.get_score( + splits=splits, + languages=languages, + scripts=scripts, + getter=getter, + aggregation=aggregation, + ) + for res in self.task_results + } def __iter__(self): return iter(self.task_results) @@ -157,17 +179,48 @@ def filter_models( new_model_results.append(model_res) return type(self)(model_results=new_model_results) - def to_table(self, format: Literal["wide", "long"] = "wide") -> pd.DataFrame: + def get_scores( + self, + splits: list[Split] | None = None, + languages: list[ISO_LANGUAGE | ISO_LANGUAGE_SCRIPT] | None = None, + scripts: list[ISO_LANGUAGE_SCRIPT] | None = None, + getter: Callable[[ScoresDict], Score] = lambda scores: scores["main_score"], + aggregation: Callable[[list[Score]], Any] = np.mean, + ) -> list[dict[str, Any]]: + res = [] + for model_res in self: + model_scores = model_res.get_scores( + splits=splits, + languages=languages, + scripts=scripts, + getter=getter, + aggregation=aggregation, + ) + res.append( + { + "model": model_res.model_name, + "revision": model_res.model_revision, + **model_scores, + } + ) + return res + + def to_table( + self, + splits: list[Split] | None = None, + languages: list[ISO_LANGUAGE | ISO_LANGUAGE_SCRIPT] | None = None, + scripts: list[ISO_LANGUAGE_SCRIPT] | None = None, + getter: Callable[[ScoresDict], Score] = lambda scores: scores["main_score"], + aggregation: Callable[[list[Score]], Any] = np.mean, + format: Literal["wide", "long"] = "wide", + ) -> pd.DataFrame: if format == "wide": - entries = [] - for model_res in self: - entry = dict( - model_name=model_res.model_name, - model_revision=model_res.model_revision, - ) - for task_res in model_res: - entry[task_res.task_name] = task_res.get_score() - entries.append(entry) + entries = self.get_scores( + splits=splits, + languages=languages, + getter=getter, + aggregation=aggregation, + ) return pd.DataFrame(entries).set_index(["model_name", "model_revision"]) elif format == "long": entries = [] @@ -177,7 +230,12 @@ def to_table(self, format: Literal["wide", "long"] = "wide") -> pd.DataFrame: model_name=model_res.model_name, model_revision=model_res.model_revision, task_name=task_res.task_name, - score=task_res.get_score(), + score=task_res.get_score( + splits=splits, + languages=languages, + getter=getter, + aggregation=aggregation, + ), mteb_version=task_res.mteb_version, dataset_revision=task_res.dataset_revision, evaluation_time=task_res.evaluation_time, From 5a8fa73999e912866794572d2ee0ca35a13017d1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 14 Oct 2024 10:57:27 +0200 Subject: [PATCH 17/38] Made get_scores smarter on BenchmarkResult --- mteb/load_results/benchmark_results.py | 131 ++++++++++++------------- 1 file changed, 63 insertions(+), 68 deletions(-) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index 7f4f542635..c49c2e95f8 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -100,17 +100,40 @@ def get_scores( scripts: list[ISO_LANGUAGE_SCRIPT] | None = None, getter: Callable[[ScoresDict], Score] = lambda scores: scores["main_score"], aggregation: Callable[[list[Score]], Any] = np.mean, - ) -> dict[str, float]: - return { - res.task_name: res.get_score( - splits=splits, - languages=languages, - scripts=scripts, - getter=getter, - aggregation=aggregation, - ) - for res in self.task_results - } + format: Literal["wide", "long"] = "wide", + ) -> dict | list: + if format == "wide": + scores = { + res.task_name: res.get_score( + splits=splits, + languages=languages, + scripts=scripts, + getter=getter, + aggregation=aggregation, + ) + for res in self.task_results + } + return scores + if format == "long": + entries = [] + for task_res in self.task_results: + entry = dict( + model_name=self.model_name, + model_revision=self.model_revision, + task_name=task_res.task_name, + score=task_res.get_score( + splits=splits, + languages=languages, + getter=getter, + aggregation=aggregation, + ), + mteb_version=task_res.mteb_version, + dataset_revision=task_res.dataset_revision, + evaluation_time=task_res.evaluation_time, + kg_co2_emissions=task_res.kg_co2_emissions, + ) + entries.append(entry) + return entries def __iter__(self): return iter(self.task_results) @@ -186,67 +209,39 @@ def get_scores( scripts: list[ISO_LANGUAGE_SCRIPT] | None = None, getter: Callable[[ScoresDict], Score] = lambda scores: scores["main_score"], aggregation: Callable[[list[Score]], Any] = np.mean, - ) -> list[dict[str, Any]]: - res = [] - for model_res in self: - model_scores = model_res.get_scores( - splits=splits, - languages=languages, - scripts=scripts, - getter=getter, - aggregation=aggregation, - ) - res.append( - { - "model": model_res.model_name, - "revision": model_res.model_revision, - **model_scores, - } - ) - return res - - def to_table( - self, - splits: list[Split] | None = None, - languages: list[ISO_LANGUAGE | ISO_LANGUAGE_SCRIPT] | None = None, - scripts: list[ISO_LANGUAGE_SCRIPT] | None = None, - getter: Callable[[ScoresDict], Score] = lambda scores: scores["main_score"], - aggregation: Callable[[list[Score]], Any] = np.mean, format: Literal["wide", "long"] = "wide", - ) -> pd.DataFrame: + ) -> list[dict]: + entries = [] if format == "wide": - entries = self.get_scores( - splits=splits, - languages=languages, - getter=getter, - aggregation=aggregation, - ) - return pd.DataFrame(entries).set_index(["model_name", "model_revision"]) - elif format == "long": - entries = [] for model_res in self: - for task_res in model_res: - entry = dict( - model_name=model_res.model_name, - model_revision=model_res.model_revision, - task_name=task_res.task_name, - score=task_res.get_score( - splits=splits, - languages=languages, - getter=getter, - aggregation=aggregation, - ), - mteb_version=task_res.mteb_version, - dataset_revision=task_res.dataset_revision, - evaluation_time=task_res.evaluation_time, - kg_co2_emissions=task_res.kg_co2_emissions, + model_scores = model_res.get_scores( + splits=splits, + languages=languages, + scripts=scripts, + getter=getter, + aggregation=aggregation, + format="wide", + ) + entries.append( + { + "model": model_res.model_name, + "revision": model_res.model_revision, + **model_scores, + } + ) + if format == "long": + for model_res in self: + entries.extend( + model_res.get_scores( + splits=splits, + languages=languages, + scripts=scripts, + getter=getter, + aggregation=aggregation, + format="long", ) - entries.append(entry) - return pd.DataFrame(entries) - else: - raise ValueError( - f"Table format can either be 'long' or 'wide', not {format}" - ) + ) + return entries def __iter__(self): return iter(self.model_results) From 31ac648c7100548864672efa6df3a275dfc8fcf8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 14 Oct 2024 10:59:24 +0200 Subject: [PATCH 18/38] Added basic multilingual benchmark --- mteb/benchmarks/benchmarks.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mteb/benchmarks/benchmarks.py b/mteb/benchmarks/benchmarks.py index 37e4c5309f..895216401a 100644 --- a/mteb/benchmarks/benchmarks.py +++ b/mteb/benchmarks/benchmarks.py @@ -53,6 +53,8 @@ def __getitem__(self, index): return self.tasks[index] +MTEB_MAIN_MULTILINGUAL = Benchmark(name="MTEB(multilingual)", tasks=get_tasks()) + MTEB_MAIN_EN = Benchmark( name="MTEB(eng)", tasks=get_tasks( From 4332612cf7896c000103e667919d4ec8ae066add Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 14 Oct 2024 14:02:27 +0200 Subject: [PATCH 19/38] Modified benchmark to be able to easily access results --- mteb/benchmarks/benchmarks.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/mteb/benchmarks/benchmarks.py b/mteb/benchmarks/benchmarks.py index 895216401a..4517fc927c 100644 --- a/mteb/benchmarks/benchmarks.py +++ b/mteb/benchmarks/benchmarks.py @@ -7,6 +7,9 @@ from typing_extensions import Annotated from mteb.abstasks.AbsTask import AbsTask +from mteb.load_results.benchmark_results import (BenchmarkResults, ModelResult, + TaskResult) +from mteb.load_results.load_results import load_results from mteb.overview import get_tasks http_url_adapter = TypeAdapter(AnyUrl) @@ -52,6 +55,13 @@ def __len__(self) -> int: def __getitem__(self, index): return self.tasks[index] + def load_results( + self, base_results: None | BenchmarkResults = None + ) -> BenchmarkResults: + if base_results is None: + base_results = load_results() + return base_results.select_tasks(self.tasks) + MTEB_MAIN_MULTILINGUAL = Benchmark(name="MTEB(multilingual)", tasks=get_tasks()) From 3e17e4caf3798499612de267832d3323268950c4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 14 Oct 2024 14:02:51 +0200 Subject: [PATCH 20/38] Added useful properties and filtering functions to BenchmarkResults --- mteb/load_results/benchmark_results.py | 116 ++++++++++++++++++------- mteb/load_results/task_results.py | 26 ++++++ 2 files changed, 109 insertions(+), 33 deletions(-) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index c49c2e95f8..f3cd14d7fa 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -15,6 +15,9 @@ from mteb.models.overview import get_model_metas from mteb.overview import get_tasks +Split = str +Score = Any + def restrict_task_results(res: TaskResult, task: AbsTask) -> TaskResult: splits = task.metadata.eval_splits @@ -43,10 +46,6 @@ def restrict_task_results(res: TaskResult, task: AbsTask) -> TaskResult: return new_res -Split = str -Score = Any - - class ModelResult(BaseModel): model_name: str model_revision: str | None @@ -61,31 +60,38 @@ def __repr__(self) -> str: def filter_tasks( self, + task_names: list[str] | None = None, languages: list[str] | None = None, - script: list[str] | None = None, domains: list[TASK_DOMAIN] | None = None, task_types: list[TASK_TYPE] | None = None, - categories: list[TASK_CATEGORY] | None = None, - tasks: list[str] | None = None, - exclude_superseeded: bool = True, ) -> "ModelResult": - filtered_tasks = get_tasks( - languages=languages, - script=script, - domains=domains, - task_types=task_types, - categories=categories, - tasks=tasks, - exclude_superseeded=exclude_superseeded, + new_task_results = [] + for task_result in self.task_results: + if (task_names is not None) and (task_result.task_name not in task_names): + continue + if languages is not None: + task_languages = task_result.languages + if not any([lang in task_languages for lang in languages]): + continue + if domains is not None: + task_domains = task_result.domains + if not any([domain in task_domains for domain in domains]): + continue + if (task_types is not None) and (task_result.task_type not in task_types): + continue + new_task_results.append(task_result) + return type(self)( + model_name=self.model_name, + model_revision=self.model_revision, + task_results=new_task_results, ) - return self.select_tasks(filtered_tasks) def select_tasks(self, tasks: list[AbsTask]) -> "ModelResult": - tasks = {task.metadata.name: task for task in tasks} + task_name_to_task = {task.metadata.name: task for task in tasks} new_task_results = [ - restrict_task_results(res, tasks[res.task_name]) - for res in self.task_results - if res.task_name in tasks + restrict_task_results(task_res, task_name_to_task[task_res.task_name]) + for task_res in self.task_results + if task_res.task_name in task_name_to_task ] return type(self)( model_name=self.model_name, @@ -141,6 +147,28 @@ def __iter__(self): def __getitem__(self, index) -> TaskResult: return self.task_results[index] + @property + def languages(self) -> list[str]: + langs = [] + for task_res in self.task_results: + langs.extend(task_res.languages) + return list(set(langs)) + + @property + def domains(self) -> list[str]: + ds = [] + for task_res in self.task_results: + ds.extend(task_res.domains) + return list(set(ds)) + + @property + def task_types(self) -> list[str]: + return list(set([task_res.task_type for task_res in self.task_results])) + + @property + def task_names(self) -> list[str]: + return [task_res.task_name for task_res in self.task_results] + class BenchmarkResults(BaseModel): model_results: list[ModelResult] @@ -154,23 +182,17 @@ def __repr__(self) -> str: def filter_tasks( self, + task_names: list[str] | None = None, languages: list[str] | None = None, - script: list[str] | None = None, domains: list[TASK_DOMAIN] | None = None, task_types: list[TASK_TYPE] | None = None, - categories: list[TASK_CATEGORY] | None = None, - tasks: list[str] | None = None, - exclude_superseeded: bool = True, ) -> "BenchmarkResults": model_results = [ res.filter_tasks( + task_names=task_names, languages=languages, - script=script, domains=domains, task_types=task_types, - categories=categories, - tasks=tasks, - exclude_superseeded=exclude_superseeded, ) for res in self.model_results ] @@ -179,10 +201,10 @@ def filter_tasks( ) def select_tasks(self, tasks: list[AbsTask]) -> "BenchmarkResults": - model_results = [res.select_tasks(tasks) for res in self.model_results] - return type(self)( - model_results=[res for res in model_results if res.task_results] - ) + new_model_results = [ + model_res.select_tasks(tasks) for model_res in self.model_results + ] + return type(self)(model_results=new_model_results) def filter_models( self, @@ -287,3 +309,31 @@ def from_disk(cls, path: Path | str) -> "BenchmarkResults": with path.open() as in_file: data = json.loads(in_file.read()) return cls.from_dict(data) + + @property + def languages(self) -> list[str]: + langs = [] + for model_res in self.model_results: + langs.extend(model_res.languages) + return list(set(langs)) + + @property + def domains(self) -> list[str]: + ds = [] + for model_res in self.model_results: + ds.extend(model_res.domains) + return list(set(ds)) + + @property + def task_types(self) -> list[str]: + ts = [] + for model_res in self.model_results: + ts.extend(model_res.task_types) + return list(set(ts)) + + @property + def task_names(self) -> list[str]: + names = [] + for model_res in self.model_results: + names.extend(model_res.task_names) + return list(set(names)) diff --git a/mteb/load_results/task_results.py b/mteb/load_results/task_results.py index 20901d68da..239a13a29c 100644 --- a/mteb/load_results/task_results.py +++ b/mteb/load_results/task_results.py @@ -4,6 +4,7 @@ import logging from argparse import Namespace from collections import defaultdict +from functools import cached_property from importlib.metadata import version from pathlib import Path from typing import Any, Callable @@ -216,6 +217,31 @@ def _validate_scores_dict(scores: ScoresDict) -> None: except Exception as e: raise ValueError(f"Scores are not json serializable: {e}") + @property + def languages(self) -> list[str]: + langs = [] + for split, split_res in self.scores.items(): + for entry in split_res: + langs.extend([lang.split("-")[0] for lang in entry["languages"]]) + return list(set(langs)) + + @cached_property + def task(self) -> AbsTask: + from mteb.overview import get_task + + return get_task(self.task_name) + + @property + def domains(self) -> list[str]: + doms = self.task.metadata.domains + if doms is None: + doms = [] + return doms + + @property + def task_type(self) -> str: + return self.task.metadata.type + def to_dict(self) -> dict: return self.model_dump() From e7ca3f87f32b30a62a2684c5620028a4d4af9756 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 14 Oct 2024 14:03:25 +0200 Subject: [PATCH 21/38] Added minimal functioning example --- mteb/leaderboard/app.py | 197 ++++++++++++++++++++++++++++++++++++++ mteb/leaderboard/utils.py | 56 +++++++++++ 2 files changed, 253 insertions(+) create mode 100644 mteb/leaderboard/app.py create mode 100644 mteb/leaderboard/utils.py diff --git a/mteb/leaderboard/app.py b/mteb/leaderboard/app.py new file mode 100644 index 0000000000..ef0788db14 --- /dev/null +++ b/mteb/leaderboard/app.py @@ -0,0 +1,197 @@ +import functools +import json +from pathlib import Path + +import gradio as gr +import numpy as np +import pandas as pd +from gradio_rangeslider import RangeSlider + +import mteb +from mteb.leaderboard.utils import get_model_size_range + + +def load_results(): + results_cache_path = Path(__file__).parent.joinpath("__cached_results.json") + if not results_cache_path.exists(): + all_results = mteb.load_results() + all_results.to_disk(results_cache_path) + return all_results + else: + return mteb.BenchmarkResults.from_disk(results_cache_path) + + +def scores_to_table(scores: list) -> pd.DataFrame: + return pd.DataFrame.from_records(scores) + + +all_results = load_results() + +max_model_size, min_model_size = get_model_size_range() + +benchmarks = mteb.get_benchmarks() + +default_benchmark = mteb.get_benchmark("MTEB(multilingual)") +default_results = default_benchmark.load_results(base_results=all_results) + +benchmark_select = gr.Dropdown( + [bench.name for bench in benchmarks], + value=default_benchmark.name, + label="Prebuilt Benchmarks", + info="Select one of our expert-selected benchmarks from MTEB publications.", +) +lang_select = gr.Dropdown( + default_results.languages, + value=default_results.languages, + multiselect=True, + label="Language", + info="Select languages to include.", +) +type_select = gr.Dropdown( + default_results.task_types, + value=default_results.task_types, + multiselect=True, + label="Task Type", + info="Select task types to include.", +) +domain_select = gr.Dropdown( + default_results.domains, + value=default_results.domains, + multiselect=True, + label="Domain", + info="Select domains to include.", +) +task_select = gr.Dropdown( + default_results.task_names, + value=default_results.task_names, + multiselect=True, + label="Task", + info="Select specific tasks to include", +) + +with gr.Blocks(fill_width=True, theme=gr.themes.Base()) as demo: + gr.Markdown( + """ + ### Model Selection + Select models to rank based on an assortment of criteria. + """ + ) + with gr.Group(): + with gr.Row(): + with gr.Column(): + availability = gr.Radio( + [("Only Open", True), ("Only Proprietary", False), ("Both", None)], + value=None, + label="Availability", + interactive=True, + ) + compatibility = gr.CheckboxGroup( + [ + ( + "Should be sentence-transformers compatible", + "sbert_compatible", + ) + ], + value=[], + label="Compatibility", + interactive=True, + ) + with gr.Column(): + instructions = gr.Radio( + [ + ("Only Instruction-tuned", True), + ("Only non-instruction", False), + ("Both", None), + ], + value=None, + label="Instructions", + interactive=True, + ) + model_size = RangeSlider( + minimum=0, + maximum=8000, + value=(0, 8000), + label="Model Size (#M Parameters)", + interactive=True, + ) + + gr.Markdown( + """ + ### Benchmarks + Select one of the hand-curated benchmarks from our publication. + Or create one from scratch based on your use case. + """ + ) + with gr.Group(): + with gr.Row(): + with gr.Column(): + benchmark_select.render() + with gr.Row(): + lang_select.render() + type_select.render() + with gr.Row(): + domain_select.render() + with gr.Column(): + # with gr.Accordion("Add and remove tasks:", open=False): + task_select.render() + scores = gr.State(default_results.get_scores()) + dataframe = gr.DataFrame( + scores_to_table, + inputs=[scores], + # datatype=["html"] + ["markdown"] * (len(table.columns) - 1), + ) + + def update_criteria(languages, types, domains, eval_splits, task_names): + criteria = { + "languages": languages, + "task_types": types, + "domains": domains, + "eval_splits": eval_splits, + "task_names": task_names, + } + return criteria + + @gr.on( + inputs=[benchmark_select], + outputs=[ + task_select, + lang_select, + type_select, + domain_select, + ], + ) + def on_select_benchmark(benchmark_name): + benchmark = mteb.get_benchmark(benchmark_name) + benchmark_results = benchmark.load_results(base_results=all_results) + return ( + benchmark_results.task_names, + benchmark_results.languages, + benchmark_results.task_types, + benchmark_results.domains, + ) + + @gr.on( + inputs=[ + benchmark_select, + task_select, + lang_select, + type_select, + domain_select, + ], + outputs=[scores], + ) + def update_scores(benchmark_name, task_names, languages, task_types, domains): + benchmark = mteb.get_benchmark(benchmark_name) + benchmark_results = benchmark.load_results(base_results=all_results) + benchmark_results = benchmark_results.filter_tasks( + languages=languages, + task_names=task_names, + task_types=task_types, + domains=domains, + ) + scores = benchmark_results.get_scores(languages=languages, format="wide") + return benchmark_results.get_scores(languages=languages, format="wide") + + +if __name__ == "__main__": + demo.launch() diff --git a/mteb/leaderboard/utils.py b/mteb/leaderboard/utils.py new file mode 100644 index 0000000000..1a6bd90d33 --- /dev/null +++ b/mteb/leaderboard/utils.py @@ -0,0 +1,56 @@ +import functools +import itertools +import json + +import mteb + + +def get_languages(): + langs = [task.languages for task in mteb.get_tasks()] + langs = itertools.chain.from_iterable(langs) + return list(set(langs)) + + +def get_task_types(): + return list(set(task.metadata.type for task in mteb.get_tasks())) + + +def get_domains(): + domains = [ + task.metadata.domains + for task in mteb.get_tasks() + if task.metadata.domains is not None + ] + domains = itertools.chain.from_iterable(domains) + return list(set(domains)) + + +def equal_criteria(c0, c1): + keys = set(c0.keys()) | set(c1.keys()) + for key in keys: + if set(c0.get(key, [])) != set(c1.get(key, [])): + return False + return True + + +def get_model_size_range() -> tuple[int, int]: + model_metas = mteb.get_model_metas() + sizes = [meta.n_parameters for meta in model_metas if meta.n_parameters is not None] + if not len(sizes): + return None, None + return min(sizes), max(sizes) + + +def json_cache(function): + """Caching decorator that can deal with anything json serializable""" + cached_results = {} + + def wrapper(*args, **kwargs): + key = json.dumps({"__args": args, **kwargs}) + if key in cached_results: + return cached_results[key] + result = function(*args, **kwargs) + cached_results[key] = result + return result + + return wrapper From 0d1d4500a114c56b725e50583767b2108ac665d1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Wed, 16 Oct 2024 11:15:23 +0200 Subject: [PATCH 22/38] Added smarter table, task-list updating and tried fixing dropdown scrolling --- mteb/leaderboard/app.py | 46 +++++++++++++++++++-------------------- mteb/leaderboard/table.py | 43 ++++++++++++++++++++++++++++++++++++ 2 files changed, 66 insertions(+), 23 deletions(-) create mode 100644 mteb/leaderboard/table.py diff --git a/mteb/leaderboard/app.py b/mteb/leaderboard/app.py index ef0788db14..9bb5b8cf20 100644 --- a/mteb/leaderboard/app.py +++ b/mteb/leaderboard/app.py @@ -8,6 +8,7 @@ from gradio_rangeslider import RangeSlider import mteb +from mteb.leaderboard.table import scores_to_table from mteb.leaderboard.utils import get_model_size_range @@ -21,11 +22,7 @@ def load_results(): return mteb.BenchmarkResults.from_disk(results_cache_path) -def scores_to_table(scores: list) -> pd.DataFrame: - return pd.DataFrame.from_records(scores) - - -all_results = load_results() +all_results = load_results().filter_models() max_model_size, min_model_size = get_model_size_range() @@ -69,7 +66,14 @@ def scores_to_table(scores: list) -> pd.DataFrame: info="Select specific tasks to include", ) -with gr.Blocks(fill_width=True, theme=gr.themes.Base()) as demo: +css = """ +.scrollable { + overflow-y: scroll; + max-height: 400px +} +""" + +with gr.Blocks(fill_width=True, theme=gr.themes.Base(), css=css) as demo: gr.Markdown( """ ### Model Selection @@ -122,7 +126,7 @@ def scores_to_table(scores: list) -> pd.DataFrame: Or create one from scratch based on your use case. """ ) - with gr.Group(): + with gr.Group(elem_classes="scrollable"): with gr.Row(): with gr.Column(): benchmark_select.render() @@ -134,27 +138,15 @@ def scores_to_table(scores: list) -> pd.DataFrame: with gr.Column(): # with gr.Accordion("Add and remove tasks:", open=False): task_select.render() - scores = gr.State(default_results.get_scores()) + scores = gr.State(default_results.get_scores(format="long")) dataframe = gr.DataFrame( scores_to_table, inputs=[scores], - # datatype=["html"] + ["markdown"] * (len(table.columns) - 1), ) - def update_criteria(languages, types, domains, eval_splits, task_names): - criteria = { - "languages": languages, - "task_types": types, - "domains": domains, - "eval_splits": eval_splits, - "task_names": task_names, - } - return criteria - @gr.on( inputs=[benchmark_select], outputs=[ - task_select, lang_select, type_select, domain_select, @@ -164,12 +156,20 @@ def on_select_benchmark(benchmark_name): benchmark = mteb.get_benchmark(benchmark_name) benchmark_results = benchmark.load_results(base_results=all_results) return ( - benchmark_results.task_names, benchmark_results.languages, benchmark_results.task_types, benchmark_results.domains, ) + @gr.on( + inputs=[benchmark_select, lang_select, type_select, domain_select], + outputs=[task_select], + ) + def update_task_list(benchmark_name, languages, task_types, domains): + benchmark = mteb.get_benchmark(benchmark_name) + benchmark_results = benchmark.load_results(base_results=all_results) + return benchmark_results.task_names + @gr.on( inputs=[ benchmark_select, @@ -189,8 +189,8 @@ def update_scores(benchmark_name, task_names, languages, task_types, domains): task_types=task_types, domains=domains, ) - scores = benchmark_results.get_scores(languages=languages, format="wide") - return benchmark_results.get_scores(languages=languages, format="wide") + scores = benchmark_results.get_scores(languages=languages, format="long") + return scores if __name__ == "__main__": diff --git a/mteb/leaderboard/table.py b/mteb/leaderboard/table.py new file mode 100644 index 0000000000..61090c9034 --- /dev/null +++ b/mteb/leaderboard/table.py @@ -0,0 +1,43 @@ +import numpy as np +import pandas as pd + +from mteb.overview import get_task + + +def scores_to_table(scores_long: list[dict]): + data = pd.DataFrame.from_records(scores_long) + data["task_type"] = data["task_name"].map( + lambda task_name: get_task(task_name).metadata.type + ) + mean_per_type = ( + data.groupby(["model_name", "model_revision", "task_type"])[["score"]] + .agg(np.nanmean) + .reset_index() + ) + typed_mean = ( + mean_per_type.groupby(["model_name", "model_revision"])[["score"]] + .agg(np.nanmean) + .rename(columns={"score": "mean_by_task_type"}) + ) + mean_per_type = mean_per_type.pivot( + index=["model_name", "model_revision"], columns="task_type", values="score" + ) + per_task = data.pivot( + index=["model_name", "model_revision"], columns="task_name", values="score" + ) + overall_mean = ( + data.groupby(["model_name", "model_revision"])[["score"]] + .agg(np.nanmean) + .rename(columns={"score": "mean"}) + ) + joint_table = overall_mean.join([typed_mean, mean_per_type, per_task]).reset_index() + joint_table = joint_table.sort_values("mean", ascending=False) + joint_table = joint_table.rename( + columns={ + "model_name": "Model", + "mean_by_task_type": "Mean by Task Type", + "mean": "Mean", + } + ) + joint_table = joint_table.drop(columns=["model_revision"]) + return joint_table From 266394c4de3de15563da48b3cab522a97f858314 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Thu, 17 Oct 2024 19:12:43 +0200 Subject: [PATCH 23/38] Made restrict_results into a private function Co-authored-by: Kenneth Enevoldsen --- mteb/load_results/benchmark_results.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index f3cd14d7fa..2bc483fa98 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -19,7 +19,7 @@ Score = Any -def restrict_task_results(res: TaskResult, task: AbsTask) -> TaskResult: +def _restrict_task_results(task_result: TaskResult, task: AbsTask) -> TaskResult: splits = task.metadata.eval_splits hf_subsets = set(task.metadata.hf_subsets_to_langscripts) new_scores = {} From 327c8d6454e837327578a366e12987df7e80eff2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Thu, 17 Oct 2024 19:29:51 +0200 Subject: [PATCH 24/38] Removed old leaderboard scripts --- scripts/leaderboard/app.py | 89 ----------------------------- scripts/leaderboard/results_all.csv | 36 ------------ scripts/leaderboard/results_dan.csv | 15 ----- 3 files changed, 140 deletions(-) delete mode 100644 scripts/leaderboard/app.py delete mode 100644 scripts/leaderboard/results_all.csv delete mode 100644 scripts/leaderboard/results_dan.csv diff --git a/scripts/leaderboard/app.py b/scripts/leaderboard/app.py deleted file mode 100644 index b731703125..0000000000 --- a/scripts/leaderboard/app.py +++ /dev/null @@ -1,89 +0,0 @@ -"""Notes: - -Todo: -- [ ] Add model filtering -- [x] Add metadata column selection -- [ ] Add missing metadata columns - - [ ] Model metadata (embedding size) - - [ ] total Co2 emissions -- [ ] Add results loading from Hub -- [ ] Benchmark selection -- [ ] task type selection -- [ ] domain selection -- [ ] inidivual task selection - -- Optimization to be added: - - mteb.load_results is called for each custom language selection. A solution it so load the results once and filter them in the app. -""" - -from __future__ import annotations - -from pathlib import Path - -import gradio as gr -import pandas as pd - -import mteb -import mteb.task_selection as task_selection - -tasks = mteb.get_tasks() - -languages = list(set(sum([task.languages for task in tasks], []))) -metadata_columns = ["Revision"] - - -class Default: - languages: list[str] = [] - metadata_columns: list[str] = [] - - -def get_mteb_results(languages: list[str] | None = None) -> pd.DataFrame: - lang_str = "_".join(languages) if languages else "all" - file_path: Path = Path(__file__).parent / f"results_{lang_str}.csv" - - tasks = mteb.get_tasks(languages=languages) - if not file_path.exists(): - mteb_results = mteb.load_results(tasks=tasks) - df = task_selection.results_to_dataframe(mteb_results, drop_na=False) - df.to_csv(file_path) - df = pd.read_csv(file_path) - - return df - - -def _update_dataframe(languages, metadata): - _df = get_mteb_results(languages) - cols_to_remove = [col for col in metadata_columns if col not in metadata] - _df = _df.drop(columns=cols_to_remove) - return _df - - -df = get_mteb_results() -df = _update_dataframe(Default.languages, Default.metadata_columns) - - -with gr.Blocks() as demo: - with gr.Row(): - lang_select = gr.Dropdown( - languages, - value=[], - multiselect=True, - label="Language", - info="Select langauges to filter by.", - ) - metadata_select = gr.Dropdown( - ["Revision"], - value=[], - multiselect=True, - label="Metadata", - info="Select model metadata columns to shown.", - ) - - dataframe = gr.DataFrame(df) - - @gr.on(inputs=[lang_select, metadata_select], outputs=dataframe) - def update_dataframe(languages, metadata): - return _update_dataframe(languages, metadata) - - -demo.launch() diff --git a/scripts/leaderboard/results_all.csv b/scripts/leaderboard/results_all.csv deleted file mode 100644 index 7dd6c4dd68..0000000000 --- a/scripts/leaderboard/results_all.csv +++ /dev/null @@ -1,36 +0,0 @@ -Model,Revision,AFQMC,AILACasedocs,AILAStatutes,AJGT,ARCChallenge,ATEC,AfriSentiClassification,AfriSentiLangClassification,AllegroReviews,AlloProfClusteringP2P.v2,AlloProfClusteringS2S.v2,AlloprofReranking,AlloprofRetrieval,AlphaNLI,AmazonCounterfactualClassification,AmazonPolarityClassification,AmazonReviewsClassification,AngryTweetsClassification,AppsRetrieval,ArEntail,ArXivHierarchicalClusteringP2P,ArXivHierarchicalClusteringS2S,ArguAna,ArguAna-PL,ArmenianParaphrasePC,ArxivClassification,AskUbuntuDupQuestions,Assin2RTE,Assin2STS,BIOSSES,BQ,BSARDRetrieval,BUCC.v2,Banking77Classification,BelebeleRetrieval,BengaliDocumentClassification,BengaliHateSpeechClassification,BengaliSentimentAnalysis,BibleNLPBitextMining,BigPatentClustering.v2,BiorxivClusteringP2P.v2,BiorxivClusteringS2S.v2,BlurbsClusteringP2P.v2,BlurbsClusteringS2S.v2,BornholmBitextMining,BrazilianToxicTweetsClassification,BulgarianStoreReviewSentimentClassfication,CBD,CDSC-E,CDSC-R,CEDRClassification,CLSClusteringP2P.v2,CLSClusteringS2S.v2,CMedQAv1-reranking,CMedQAv2-reranking,CPUSpeedTask,CQADupstackAndroidRetrieval,CQADupstackEnglishRetrieval,CQADupstackGamingRetrieval,CQADupstackGisRetrieval,CQADupstackMathematicaRetrieval,CQADupstackPhysicsRetrieval,CQADupstackProgrammersRetrieval,CQADupstackStatsRetrieval,CQADupstackTexRetrieval,CQADupstackUnixRetrieval,CQADupstackWebmastersRetrieval,CQADupstackWordpressRetrieval,CSFDCZMovieReviewSentimentClassification,CSFDSKMovieReviewSentimentClassification,CTKFactsNLI,CUADAffiliateLicenseLicenseeLegalBenchClassification,CUADAffiliateLicenseLicensorLegalBenchClassification,CUADAntiAssignmentLegalBenchClassification,CUADAuditRightsLegalBenchClassification,CUADCapOnLiabilityLegalBenchClassification,CUADChangeOfControlLegalBenchClassification,CUADCompetitiveRestrictionExceptionLegalBenchClassification,CUADCovenantNotToSueLegalBenchClassification,CUADEffectiveDateLegalBenchClassification,CUADExclusivityLegalBenchClassification,CUADExpirationDateLegalBenchClassification,CUADGoverningLawLegalBenchClassification,CUADIPOwnershipAssignmentLegalBenchClassification,CUADInsuranceLegalBenchClassification,CUADIrrevocableOrPerpetualLicenseLegalBenchClassification,CUADJointIPOwnershipLegalBenchClassification,CUADLicenseGrantLegalBenchClassification,CUADLiquidatedDamagesLegalBenchClassification,CUADMinimumCommitmentLegalBenchClassification,CUADMostFavoredNationLegalBenchClassification,CUADNoSolicitOfCustomersLegalBenchClassification,CUADNoSolicitOfEmployeesLegalBenchClassification,CUADNonCompeteLegalBenchClassification,CUADNonDisparagementLegalBenchClassification,CUADNonTransferableLicenseLegalBenchClassification,CUADNoticePeriodToTerminateRenewalLegalBenchClassification,CUADPostTerminationServicesLegalBenchClassification,CUADPriceRestrictionsLegalBenchClassification,CUADRenewalTermLegalBenchClassification,CUADRevenueProfitSharingLegalBenchClassification,CUADRofrRofoRofnLegalBenchClassification,CUADSourceCodeEscrowLegalBenchClassification,CUADTerminationForConvenienceLegalBenchClassification,CUADThirdPartyBeneficiaryLegalBenchClassification,CUADUncappedLiabilityLegalBenchClassification,CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification,CUADVolumeRestrictionLegalBenchClassification,CUADWarrantyDurationLegalBenchClassification,CanadaTaxCourtOutcomesLegalBenchClassification,CataloniaTweetClassification,ClimateFEVER,CmedqaRetrieval,CodeEditSearchRetrieval,CodeFeedbackMT,CodeFeedbackST,CodeSearchNetCCRetrieval,CodeSearchNetRetrieval,CodeTransOceanContest,CodeTransOceanDL,ContractNLIConfidentialityOfAgreementLegalBenchClassification,ContractNLIExplicitIdentificationLegalBenchClassification,ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification,ContractNLILimitedUseLegalBenchClassification,ContractNLINoLicensingLegalBenchClassification,ContractNLINoticeOnCompelledDisclosureLegalBenchClassification,ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification,ContractNLIPermissibleCopyLegalBenchClassification,ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification,ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification,ContractNLIReturnOfConfidentialInformationLegalBenchClassification,ContractNLISharingWithEmployeesLegalBenchClassification,ContractNLISharingWithThirdPartiesLegalBenchClassification,ContractNLISurvivalOfObligationsLegalBenchClassification,Core17InstructionRetrieval,CorporateLobbyingLegalBenchClassification,CosQA,CovidRetrieval,CrossLingualSemanticDiscriminationWMT19,CrossLingualSemanticDiscriminationWMT21,CyrillicTurkicLangClassification,CzechProductReviewSentimentClassification,CzechSoMeSentimentClassification,CzechSubjectivityClassification,DBPedia,DBpediaClassification,DalajClassification,DanFeverRetrieval,DanishPoliticalCommentsClassification,DefinitionClassificationLegalBenchClassification,DiaBlaBitextMining,Diversity1LegalBenchClassification,Diversity2LegalBenchClassification,Diversity3LegalBenchClassification,Diversity4LegalBenchClassification,Diversity5LegalBenchClassification,Diversity6LegalBenchClassification,DuRetrieval,DutchBookReviewSentimentClassification,EcomRetrieval,EightTagsClustering.v2,EmotionClassification,EstQA,EstonianValenceClassification,FQuADRetrieval,FaithDial,FalseFriendsGermanEnglish,FaroeseSTS,FarsTail,FeedbackQARetrieval,FiQA-PL,FiQA2018,FilipinoShopeeReviewsClassification,FinParaSTS,FinToxicityClassification,FinancialPhrasebankClassification,FloresBitextMining,FrenchBookReviews,FrenkEnClassification,FrenkHrClassification,FrenkSlClassification,FunctionOfDecisionSectionLegalBenchClassification,GPUSpeedTask,GeoreviewClassification,GeoreviewClusteringP2P,GeorgianFAQRetrieval,GerDaLIR,GerDaLIRSmall,GermanDPR,GermanGovServiceRetrieval,GermanPoliticiansTwitterSentimentClassification,GermanQuAD-Retrieval,GermanSTSBenchmark,GreekCivicsQA,GreekLegalCodeClassification,GujaratiNewsClassification,HALClusteringS2S.v2,HagridRetrieval,HateSpeechPortugueseClassification,HeadlineClassification,HebrewSentimentAnalysis,HellaSwag,HindiDiscourseClassification,HotelReviewSentimentClassification,HunSum2AbstractiveRetrieval,IFlyTek,IN22ConvBitextMining,IN22GenBitextMining,IWSLT2017BitextMining,ImdbClassification,InappropriatenessClassification,IndicCrosslingualSTS,IndicGenBenchFloresBitextMining,IndicLangClassification,IndicNLPNewsClassification,IndicQARetrieval,IndicReviewsClusteringP2P,IndicSentimentClassification,IndonesianIdClickbaitClassification,IndonesianMongabayConservationClassification,InsurancePolicyInterpretationLegalBenchClassification,InternationalCitizenshipQuestionsLegalBenchClassification,IsiZuluNewsClassification,ItaCaseholdClassification,Itacola,JCrewBlockerLegalBenchClassification,JDReview,JSICK,JSTS,JaGovFaqsRetrieval,JaQuADRetrieval,JavaneseIMDBClassification,KLUE-NLI,KLUE-STS,KLUE-TC,KannadaNewsClassification,KinopoiskClassification,Ko-StrategyQA,KorHateClassification,KorHateSpeechMLClassification,KorSTS,KorSarcasmClassification,KurdishSentimentClassification,LCQMC,LEMBNarrativeQARetrieval,LEMBNeedleRetrieval,LEMBPasskeyRetrieval,LEMBQMSumRetrieval,LEMBSummScreenFDRetrieval,LEMBWikimQARetrieval,LanguageClassification,LccSentimentClassification,LeCaRDv2,LearnedHandsBenefitsLegalBenchClassification,LearnedHandsBusinessLegalBenchClassification,LearnedHandsConsumerLegalBenchClassification,LearnedHandsCourtsLegalBenchClassification,LearnedHandsCrimeLegalBenchClassification,LearnedHandsDivorceLegalBenchClassification,LearnedHandsDomesticViolenceLegalBenchClassification,LearnedHandsEducationLegalBenchClassification,LearnedHandsEmploymentLegalBenchClassification,LearnedHandsEstatesLegalBenchClassification,LearnedHandsFamilyLegalBenchClassification,LearnedHandsHealthLegalBenchClassification,LearnedHandsHousingLegalBenchClassification,LearnedHandsImmigrationLegalBenchClassification,LearnedHandsTortsLegalBenchClassification,LearnedHandsTrafficLegalBenchClassification,LegalBenchConsumerContractsQA,LegalBenchCorporateLobbying,LegalBenchPC,LegalQuAD,LegalReasoningCausalityLegalBenchClassification,LegalSummarization,LinceMTBitextMining,LivedoorNewsClustering.v2,MAUDLegalBenchClassification,MLQARetrieval,MLQuestions,MLSUMClusteringP2P.v2,MLSUMClusteringS2S.v2,MMarcoReranking,MMarcoRetrieval,MTOPDomainClassification,MTOPIntentClassification,MacedonianTweetSentimentClassification,MalayalamNewsClassification,MalteseNewsClassification,MarathiNewsClassification,MasakhaNEWSClassification,MasakhaNEWSClusteringP2P,MasakhaNEWSClusteringS2S,MassiveIntentClassification,MassiveScenarioClassification,MedicalQARetrieval,MedicalRetrieval,MedrxivClusteringP2P.v2,MedrxivClusteringS2S.v2,MewsC16JaClustering,MindSmallReranking,MintakaRetrieval,Moroco,MovieReviewSentimentClassification,MultiEURLEXMultilabelClassification,MultiHateClassification,MultiLongDocRetrieval,MultilingualSentiment,MultilingualSentimentClassification,MyanmarNews,NFCorpus,NFCorpus-PL,NLPJournalAbsIntroRetrieval,NLPJournalTitleAbsRetrieval,NLPJournalTitleIntroRetrieval,NTREXBitextMining,NYSJudicialEthicsLegalBenchClassification,NaijaSenti,NarrativeQARetrieval,NepaliNewsClassification,News21InstructionRetrieval,NewsClassification,NoRecClassification,NollySentiBitextMining,NorQuadRetrieval,NordicLangClassification,NorwegianCourtsBitextMining,NorwegianParliamentClassification,NusaParagraphEmotionClassification,NusaParagraphTopicClassification,NusaTranslationBitextMining,NusaX-senti,NusaXBitextMining,OPP115DataRetentionLegalBenchClassification,OPP115DataSecurityLegalBenchClassification,OPP115DoNotTrackLegalBenchClassification,OPP115FirstPartyCollectionUseLegalBenchClassification,OPP115InternationalAndSpecificAudiencesLegalBenchClassification,OPP115PolicyChangeLegalBenchClassification,OPP115ThirdPartySharingCollectionLegalBenchClassification,OPP115UserAccessEditAndDeletionLegalBenchClassification,OPP115UserChoiceControlLegalBenchClassification,OdiaNewsClassification,OnlineShopping,OnlineStoreReviewSentimentClassification,OpusparcusPC,OralArgumentQuestionPurposeLegalBenchClassification,OverrulingLegalBenchClassification,PAC,PAWSX,PIQA,PROALegalBenchClassification,PSC,PatentClassification,PawsXPairClassification,PersianFoodSentimentClassification,PersonalJurisdictionLegalBenchClassification,PhincBitextMining,PlscClusteringP2P.v2,PlscClusteringS2S.v2,PoemSentimentClassification,PolEmo2.0-IN,PolEmo2.0-OUT,PpcPC,PunjabiNewsClassification,Quail,RARbCode,RARbMath,RTE3,RUParaPhraserSTS,RedditClustering.v2,RedditClusteringP2P.v2,RestaurantReviewSentimentClassification,RiaNewsRetrieval,Robust04InstructionRetrieval,RomaTalesBitextMining,RomaniBibleClustering,RomanianReviewsSentiment,RomanianSentimentClassification,RonSTS,RuBQReranking,RuBQRetrieval,RuReviewsClassification,RuSTSBenchmarkSTS,RuSciBenchGRNTIClassification,RuSciBenchGRNTIClusteringP2P,RuSciBenchOECDClassification,RuSciBenchOECDClusteringP2P,SCDBPAccountabilityLegalBenchClassification,SCDBPAuditsLegalBenchClassification,SCDBPCertificationLegalBenchClassification,SCDBPTrainingLegalBenchClassification,SCDBPVerificationLegalBenchClassification,SCDDAccountabilityLegalBenchClassification,SCDDAuditsLegalBenchClassification,SCDDCertificationLegalBenchClassification,SCDDTrainingLegalBenchClassification,SCDDVerificationLegalBenchClassification,SCIDOCS,SCIDOCS-PL,SIB200Classification,SIB200ClusteringS2S,SICK-BR-PC,SICK-E-PL,SICK-R,SICK-R-PL,SICKFr,SIQA,SNLHierarchicalClusteringP2P,SNLHierarchicalClusteringS2S,SNLRetrieval,SRNCorpusBitextMining,STS12,STS13,STS14,STS15,STS16,STS17,STS22.v2,STSB,STSBenchmark,STSBenchmarkMultilingualSTS,STSES,SanskritShlokasClassification,ScalaClassification,SciDocsRR,SciFact,SciFact-PL,SemRel24STS,SensitiveTopicsClassification,SentimentAnalysisHindi,SinhalaNewsClassification,SinhalaNewsSourceClassification,SiswatiNewsClassification,SlovakMovieReviewSentimentClassification,SlovakSumRetrieval,SpanishNewsClassification,SpanishNewsClusteringP2P,SpanishPassageRetrievalS2S,SpanishSentimentClassification,SpartQA,SprintDuplicateQuestions,StackExchangeClustering.v2,StackExchangeClusteringP2P.v2,StackOverflowDupQuestions,StackOverflowQA,StatcanDialogueDatasetRetrieval,SwahiliNewsClassification,SweFaqRetrieval,SweRecClassification,SwedishSentimentClassification,SwednClusteringP2P,SwednClusteringS2S,SwednRetrieval,SwissJudgementClassification,SyntecReranking,SyntecRetrieval,SyntheticText2SQL,T2Reranking,T2Retrieval,TERRa,TNews,TRECCOVID,TRECCOVID-PL,TV2Nordretrieval,TamilNewsClassification,Tatoeba,TbilisiCityHallBitextMining,TelemarketingSalesRuleLegalBenchClassification,TeluguAndhraJyotiNewsClassification,TempReasonL1,TempReasonL2Context,TempReasonL2Fact,TempReasonL2Pure,TempReasonL3Context,TempReasonL3Fact,TempReasonL3Pure,TenKGnadClassification,TenKGnadClusteringP2P.v2,TenKGnadClusteringS2S.v2,TextualismToolDictionariesLegalBenchClassification,TextualismToolPlainLegalBenchClassification,ThuNewsClusteringP2P.v2,ThuNewsClusteringS2S.v2,Touche2020,ToxicChatClassification,ToxicConversationsClassification,TswanaNewsClassification,TurHistQuadRetrieval,TurkicClassification,TurkishMovieSentimentClassification,TurkishProductSentimentClassification,TweetEmotionClassification,TweetSarcasmClassification,TweetSentimentClassification,TweetSentimentExtractionClassification,TweetTopicSingleClassification,TwentyNewsgroupsClustering.v2,TwitterHjerneRetrieval,TwitterSemEval2015,TwitterURLCorpus,UCCVCommonLawLegalBenchClassification,UkrFormalityClassification,UnfairTOSLegalBenchClassification,UrduRomanSentimentClassification,VGHierarchicalClusteringP2P,VGHierarchicalClusteringS2S,VideoRetrieval,VieMedEVBitextMining,VieQuADRetrieval,VieStudentFeedbackClassification,VoyageMMarcoReranking,WRIMEClassification,Waimai,WebLINXCandidatesReranking,WikiCitiesClustering,WikiClusteringP2P.v2,WikipediaRerankingMultilingual,WikipediaRetrievalMultilingual,WinoGrande,WisesightSentimentClassification,XMarket,XNLI,XNLIV2,XPQARetrieval,XQuADRetrieval,YahooAnswersTopicsClassification,YelpReviewFullClassification,YueOpenriceReviewClassification,indonli 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-voyage-large-2-instruct,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8923576823727384,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7040816326530612,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7333804727079534,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/scripts/leaderboard/results_dan.csv b/scripts/leaderboard/results_dan.csv deleted file mode 100644 index 2fa12a6583..0000000000 --- a/scripts/leaderboard/results_dan.csv +++ /dev/null @@ -1,15 +0,0 @@ -Model,Revision,AngryTweetsClassification,BelebeleRetrieval,BibleNLPBitextMining,BornholmBitextMining,DanFeverRetrieval,DanishPoliticalCommentsClassification,FloresBitextMining,LccSentimentClassification,MassiveIntentClassification,MassiveScenarioClassification,MultiEURLEXMultilabelClassification,NTREXBitextMining,NordicLangClassification,SIB200Classification,SIB200ClusteringS2S,ScalaClassification,TV2Nordretrieval,Tatoeba,TwitterHjerneRetrieval,WikiClusteringP2P.v2,WikipediaRerankingMultilingual,WikipediaRetrievalMultilingual -GritLM/GritLM-7B,13f00a0e36500c80ce12870ea513846a066004af,0.6534861509073544,0.7170350531914894,0.15703429559492083,0.6117333333333334,0.40485,0.412767138495698,0.6109930285907467,0.7013333333333334,0.637210728272479,0.679412424014663,0.0486295652173913,0.7807461712905885,0.6936333333333333,0.6462277296705484,0.36072519770658396,0.52105712890625,0.94913,0.720780243112421,0.43266,0.29879395630695693,0.9104981812169313,0.917721875 -cointegrated/LaBSE-en-ru,cf0714e606d4af551e14ad69a7929cd6b0da7f7e,,,,,,,,,0.21195788335509053,0.2660574653533236,,,,,,,,,,,, -intfloat/e5-mistral-7b-instruct,07163b72af1488142a360786df853f237b1a3ca1,0.6501432664756447,0.6828947074468086,0.15871068617513198,0.5724333333333332,0.40757,0.3467804607271718,0.6171733102240896,0.6453333333333333,0.5989055474240805,0.6653117871223808,0.05160695652173912,0.7882254775515363,0.6913333333333332,0.6611053050661889,0.3874355629191053,0.51070556640625,0.92306,0.7008043679895426,0.33405,0.3104184294760456,0.9025595072751322,0.909265 -intfloat/multilingual-e5-base,d13f1b27baf31030b7fd040960d60d909913633f,0.5628462273161414,0.7072227925531915,0.1399684043394193,0.3321808524808525,0.40416,0.3641271162919789,0.7455973945233266,0.6013333333333333,0.5414111845141554,0.6035905956195524,0.03973565217391305,0.8490554151366932,0.7585333333333334,0.5634144520752464,0.2162327526114601,0.5091918945312499,0.92682,0.6805819069367823,0.42163,0.2502530930365376,0.8773647652116402,0.88750875 -intfloat/multilingual-e5-large,4dc6d853a804b9c8886ede6dda8a073b7dc08a81,0.5768863419293219,0.779088909574468,0.14924871411150414,0.2987461610285139,0.40868,0.39433805162364693,0.7725258124215204,0.6153333333333333,0.5833570684494377,0.6418258897371995,0.046313913043478254,0.8677655506852232,0.8015333333333334,0.6008161640290633,0.23660154176636475,0.5157470703125,0.95369,0.757284993760995,0.35219,0.2560124213863618,0.8969711805555556,0.90820875 -intfloat/multilingual-e5-large-instruct,baa7be480a7de1539afce709c8f13f833a510e0a,0.5953199617956064,0.8091880585106382,0.22027836813106355,0.5541666666666667,0.40644,0.33068276436303085,0.8596172923065778,0.6026666666666667,0.6265286074079935,0.6826463335838707,0.05502608695652174,0.9367549966537495,0.7657,0.7345998805613615,0.4719148058157196,0.50364990234375,0.94149,0.837297358436565,0.36855,0.3145080121100277,0.910291121031746,0.9159349999999999 -intfloat/multilingual-e5-small,e4ce9877abf3edfe10b0d82785e83bdcb973e22e,0.5626552053486151,0.6629290691489361,0.12413115135937501,0.37145728715728715,0.39601,0.34816819317235637,0.697451048947165,0.586,0.5192867597610665,0.5863134090219813,0.04247478260869565,0.8182892712214577,0.7215,0.5752538071065989,0.23904115029304265,0.5067626953125,0.90379,0.6389922667439586,0.29358,0.24993983463149455,0.8701014880952381,0.8772306249999999 -sentence-transformers/LaBSE,e34fab64a3011d2176c99545a93d5cbddc9a91b7,0.5110792741165234,0.6073163829787235,0.1879184613804074,0.45625569985569986,0.34537,0.38340271995559255,0.7211323597266492,0.5006666666666668,0.5753800915120588,0.6318472513416933,0.035539999999999995,0.9069040801163176,0.35386666666666666,0.5006071464118642,0.18786704860428421,0.5055908203125,0.76295,0.8114049549210332,0.1438,0.2529623663247956,0.8093706183862435,0.6630637500000001 -sentence-transformers/all-MiniLM-L12-v2,a05860a77cef7b37e0048a7864658139bc18a854,0.42865329512893985,0.2322255585106383,0.013890041607249206,0.35249206349206347,0.30588,0.27067721343325,0.10709222464503043,0.41933333333333334,0.29115866925115713,0.35529754605271824,0.01717826086956522,0.15164075362257146,0.5417000000000001,0.30232407683885737,0.07620252834205854,0.50126953125,0.50731,0.05096799427227309,0.06623,0.22897329616499343,0.6922158234126984,0.45766562499999996 -sentence-transformers/all-MiniLM-L6-v2,8b3219a92973c328a8e22fadcfa821b5dc75636a,0.4248328557784145,0.20799683510638295,0.011138576083412973,0.2968132161955691,0.28514,0.26699972245351095,0.08523166582150102,0.38533333333333325,0.27853026886612076,0.3403042050714031,0.022488695652173908,0.12752171988262676,0.5469666666666667,0.28910371255101025,0.06617230524659697,0.50294189453125,0.3411,0.0387681767142979,0.09811,0.23028238845653945,0.6453740906084655,0.38806687500000003 -sentence-transformers/all-mpnet-base-v2,84f2bcc00d77236f9e89c8a360a00fb1139bf47d,0.44126074498567336,0.2441852393617021,0.010798067181957052,0.27440466200466196,0.28263,0.28312517346655564,0.10584919796194339,0.3926666666666666,0.26929071561375056,0.33822013001569157,0.020881739130434788,0.15185918766714024,0.5014666666666667,0.3204513785209515,0.0886765568027449,0.5006469726562499,0.42007,0.04399875796264953,0.14888,0.24381169905097408,0.6472592096560846,0.406804375 -sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,bf3bf13ab40c3157080a7ab344c831b9ad18b5eb,0.5089780324737345,0.4531127659574469,0.0481067333912658,0.19671528822055137,0.36872,0.3757701915070774,0.273070514609292,0.5453333333333333,0.5142443662064692,0.575539644236982,0.02847391304347826,0.49966456477912774,0.42519999999999997,0.4848487110580273,0.1939446072915212,0.50113525390625,0.73343,0.5662915789757399,0.18721,0.23476647804639494,0.7850792824074074,0.645405 -sentence-transformers/paraphrase-multilingual-mpnet-base-v2,79f2382ceacceacdf38563d7c5d16b9ff8d725d6,0.5484240687679083,0.5439205585106384,0.05896934022959743,0.18182931512931513,0.37559,0.40957535387177346,0.35166459627644164,0.584,0.5712673233382123,0.6331342220815697,0.03373391304347826,0.624948964480828,0.4157,0.5482407683885737,0.24066656169282485,0.5003662109375,0.78372,0.6216878936163452,0.36681,0.25009144351289353,0.8218340277777777,0.717918125 -sergeyzh/rubert-tiny-turbo,8ce0cf757446ce9bb2d5f5a4ac8103c7a1049054,,,,,,,,,0.20002900958634967,0.2670226934082308,,,,,,,,,,,, From 9ec49fbe38258ef7292b30db3c134c82e00e23f1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Thu, 17 Oct 2024 19:33:09 +0200 Subject: [PATCH 25/38] Hardcoded max and min model size --- mteb/leaderboard/app.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/mteb/leaderboard/app.py b/mteb/leaderboard/app.py index 9bb5b8cf20..21d70acdc4 100644 --- a/mteb/leaderboard/app.py +++ b/mteb/leaderboard/app.py @@ -24,7 +24,8 @@ def load_results(): all_results = load_results().filter_models() -max_model_size, min_model_size = get_model_size_range() +# Model sizes in million parameters +min_model_size, max_model_size = 8, 46703 benchmarks = mteb.get_benchmarks() From ce2569df3a176d8383e75402241924b964c1490d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Thu, 17 Oct 2024 19:34:37 +0200 Subject: [PATCH 26/38] Removed redundant utils file --- mteb/leaderboard/app.py | 1 - mteb/leaderboard/utils.py | 56 --------------------------------------- 2 files changed, 57 deletions(-) delete mode 100644 mteb/leaderboard/utils.py diff --git a/mteb/leaderboard/app.py b/mteb/leaderboard/app.py index 21d70acdc4..769be3125d 100644 --- a/mteb/leaderboard/app.py +++ b/mteb/leaderboard/app.py @@ -9,7 +9,6 @@ import mteb from mteb.leaderboard.table import scores_to_table -from mteb.leaderboard.utils import get_model_size_range def load_results(): diff --git a/mteb/leaderboard/utils.py b/mteb/leaderboard/utils.py deleted file mode 100644 index 1a6bd90d33..0000000000 --- a/mteb/leaderboard/utils.py +++ /dev/null @@ -1,56 +0,0 @@ -import functools -import itertools -import json - -import mteb - - -def get_languages(): - langs = [task.languages for task in mteb.get_tasks()] - langs = itertools.chain.from_iterable(langs) - return list(set(langs)) - - -def get_task_types(): - return list(set(task.metadata.type for task in mteb.get_tasks())) - - -def get_domains(): - domains = [ - task.metadata.domains - for task in mteb.get_tasks() - if task.metadata.domains is not None - ] - domains = itertools.chain.from_iterable(domains) - return list(set(domains)) - - -def equal_criteria(c0, c1): - keys = set(c0.keys()) | set(c1.keys()) - for key in keys: - if set(c0.get(key, [])) != set(c1.get(key, [])): - return False - return True - - -def get_model_size_range() -> tuple[int, int]: - model_metas = mteb.get_model_metas() - sizes = [meta.n_parameters for meta in model_metas if meta.n_parameters is not None] - if not len(sizes): - return None, None - return min(sizes), max(sizes) - - -def json_cache(function): - """Caching decorator that can deal with anything json serializable""" - cached_results = {} - - def wrapper(*args, **kwargs): - key = json.dumps({"__args": args, **kwargs}) - if key in cached_results: - return cached_results[key] - result = function(*args, **kwargs) - cached_results[key] = result - return result - - return wrapper From 228e7d31ffbc9b99df2eb41e58e79bf2b9f784c7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Thu, 17 Oct 2024 19:37:13 +0200 Subject: [PATCH 27/38] Ran linting --- mteb/__init__.py | 10 +++++++--- mteb/abstasks/AbsTask.py | 6 +++--- mteb/abstasks/AbsTaskClassification.py | 8 +++++--- mteb/abstasks/TaskMetadata.py | 12 ++++++++---- mteb/benchmarks/benchmarks.py | 7 +++++-- mteb/leaderboard/__init__.py | 1 + mteb/load_results/benchmark_results.py | 8 ++++++-- mteb/load_results/task_results.py | 8 ++++++-- mteb/models/__init__.py | 25 +++++++++++++++++++------ mteb/models/overview.py | 23 ++++++++++++++++++----- 10 files changed, 78 insertions(+), 30 deletions(-) create mode 100644 mteb/leaderboard/__init__.py diff --git a/mteb/__init__.py b/mteb/__init__.py index 9d0eefa803..339778596a 100644 --- a/mteb/__init__.py +++ b/mteb/__init__.py @@ -2,9 +2,13 @@ from importlib.metadata import version -from mteb.benchmarks.benchmarks import (MTEB_MAIN_EN, MTEB_MAIN_RU, - MTEB_RETRIEVAL_LAW, - MTEB_RETRIEVAL_WITH_INSTRUCTIONS, CoIR) +from mteb.benchmarks.benchmarks import ( + MTEB_MAIN_EN, + MTEB_MAIN_RU, + MTEB_RETRIEVAL_LAW, + MTEB_RETRIEVAL_WITH_INSTRUCTIONS, + CoIR, +) from mteb.evaluation import * from mteb.load_results import BenchmarkResults, load_results from mteb.models import get_model, get_model_meta, get_model_metas diff --git a/mteb/abstasks/AbsTask.py b/mteb/abstasks/AbsTask.py index b839719270..78073f5666 100644 --- a/mteb/abstasks/AbsTask.py +++ b/mteb/abstasks/AbsTask.py @@ -218,9 +218,9 @@ def calculate_metadata_metrics( pbar_subsets.set_postfix_str(f"Language: {hf_subset}") print(f"Processing metadata for language {hf_subset}") split_details = self._calculate_metrics_from_split(split, hf_subset) - all_details[split]["hf_subset_descriptive_stats"][ - hf_subset - ] = split_details + all_details[split]["hf_subset_descriptive_stats"][hf_subset] = ( + split_details + ) else: split_details = self._calculate_metrics_from_split(split) all_details[split] = split_details diff --git a/mteb/abstasks/AbsTaskClassification.py b/mteb/abstasks/AbsTaskClassification.py index c5f4d801a4..788d3a5347 100644 --- a/mteb/abstasks/AbsTaskClassification.py +++ b/mteb/abstasks/AbsTaskClassification.py @@ -9,9 +9,11 @@ from mteb.encoder_interface import Encoder -from ..evaluation.evaluators import (kNNClassificationEvaluator, - kNNClassificationEvaluatorPytorch, - logRegClassificationEvaluator) +from ..evaluation.evaluators import ( + kNNClassificationEvaluator, + kNNClassificationEvaluatorPytorch, + logRegClassificationEvaluator, +) from ..load_results.task_results import HFSubset, ScoresDict from .AbsTask import AbsTask, DescriptiveStatistics diff --git a/mteb/abstasks/TaskMetadata.py b/mteb/abstasks/TaskMetadata.py index f9c66c6ad5..a1638c9285 100644 --- a/mteb/abstasks/TaskMetadata.py +++ b/mteb/abstasks/TaskMetadata.py @@ -5,12 +5,16 @@ from datetime import date from typing import Annotated, Any, Union -from pydantic import (AnyUrl, BaseModel, BeforeValidator, TypeAdapter, - field_validator) +from pydantic import AnyUrl, BaseModel, BeforeValidator, TypeAdapter, field_validator from typing_extensions import Annotated, Literal -from ..languages import (ISO_LANGUAGE_SCRIPT, ISO_TO_LANGUAGE, ISO_TO_SCRIPT, - path_to_lang_codes, path_to_lang_scripts) +from ..languages import ( + ISO_LANGUAGE_SCRIPT, + ISO_TO_LANGUAGE, + ISO_TO_SCRIPT, + path_to_lang_codes, + path_to_lang_scripts, +) TASK_SUBTYPE = Literal[ "Article retrieval", diff --git a/mteb/benchmarks/benchmarks.py b/mteb/benchmarks/benchmarks.py index a555fb01d4..9667295df2 100644 --- a/mteb/benchmarks/benchmarks.py +++ b/mteb/benchmarks/benchmarks.py @@ -7,8 +7,11 @@ from pydantic import AnyUrl, BeforeValidator, TypeAdapter from mteb.abstasks.AbsTask import AbsTask -from mteb.load_results.benchmark_results import (BenchmarkResults, ModelResult, - TaskResult) +from mteb.load_results.benchmark_results import ( + BenchmarkResults, + ModelResult, + TaskResult, +) from mteb.load_results.load_results import load_results from mteb.overview import get_tasks diff --git a/mteb/leaderboard/__init__.py b/mteb/leaderboard/__init__.py new file mode 100644 index 0000000000..d0122cfbfb --- /dev/null +++ b/mteb/leaderboard/__init__.py @@ -0,0 +1 @@ +from mteb.leaderboard.app import demo diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index 2bc483fa98..042ea22974 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -8,8 +8,12 @@ from pydantic import BaseModel, ConfigDict from mteb.abstasks.AbsTask import AbsTask, ScoresDict -from mteb.abstasks.TaskMetadata import (ISO_LANGUAGE_SCRIPT, TASK_CATEGORY, - TASK_DOMAIN, TASK_TYPE) +from mteb.abstasks.TaskMetadata import ( + ISO_LANGUAGE_SCRIPT, + TASK_CATEGORY, + TASK_DOMAIN, + TASK_TYPE, +) from mteb.languages import ISO_LANGUAGE from mteb.load_results.task_results import TaskResult from mteb.models.overview import get_model_metas diff --git a/mteb/load_results/task_results.py b/mteb/load_results/task_results.py index 239a13a29c..c2b5c0562c 100644 --- a/mteb/load_results/task_results.py +++ b/mteb/load_results/task_results.py @@ -291,8 +291,12 @@ def from_disk(cls, path: Path, load_historic_data: bool = True) -> TaskResult: ) pre_1_11_load = ( - "mteb_version" in data and Version(data["mteb_version"]) < Version("1.11.0") - ) or "mteb_version" not in data # assume it is before 1.11.0 if the version is not present + ( + "mteb_version" in data + and Version(data["mteb_version"]) < Version("1.11.0") + ) + or "mteb_version" not in data + ) # assume it is before 1.11.0 if the version is not present try: obj = cls.model_validate(data) except Exception as e: diff --git a/mteb/models/__init__.py b/mteb/models/__init__.py index a6999a6065..50bfc937d9 100644 --- a/mteb/models/__init__.py +++ b/mteb/models/__init__.py @@ -7,11 +7,24 @@ from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode from mteb.model_meta import ModelMeta -from mteb.models import (bge_models, bm25, cohere_models, e5_instruct, - e5_models, google_models, gritlm_models, gte_models, - llm2vec_models, mxbai_models, nomic_models, - openai_models, ru_sentence_models, salesforce_models, - sentence_transformers_models, voyage_models) +from mteb.models import ( + bge_models, + bm25, + cohere_models, + e5_instruct, + e5_models, + google_models, + gritlm_models, + gte_models, + llm2vec_models, + mxbai_models, + nomic_models, + openai_models, + ru_sentence_models, + salesforce_models, + sentence_transformers_models, + voyage_models, +) from mteb.models.overview import * -logger = logging.getLogger(__name__) \ No newline at end of file +logger = logging.getLogger(__name__) diff --git a/mteb/models/overview.py b/mteb/models/overview.py index 149b14ffbc..a8065a99ab 100644 --- a/mteb/models/overview.py +++ b/mteb/models/overview.py @@ -7,11 +7,24 @@ from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode from mteb.model_meta import ModelMeta -from mteb.models import (bge_models, bm25, cohere_models, e5_instruct, - e5_models, google_models, gritlm_models, gte_models, - llm2vec_models, mxbai_models, nomic_models, - openai_models, ru_sentence_models, salesforce_models, - sentence_transformers_models, voyage_models) +from mteb.models import ( + bge_models, + bm25, + cohere_models, + e5_instruct, + e5_models, + google_models, + gritlm_models, + gte_models, + llm2vec_models, + mxbai_models, + nomic_models, + openai_models, + ru_sentence_models, + salesforce_models, + sentence_transformers_models, + voyage_models, +) logger = logging.getLogger(__name__) From bee9e41d847a1ffaa7c59100e65a15df1e547e36 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Thu, 17 Oct 2024 19:39:41 +0200 Subject: [PATCH 28/38] added leaderboard dependencies as optional --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index f4b71b3d11..f4abaae3dd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,6 +57,7 @@ dev = ["ruff==0.6.4", # locked so we don't get PRs which fail only due to a lint codecarbon = ["codecarbon"] speedtask = ["GPUtil>=1.4.0", "psutil>=5.9.8"] peft = ["peft>=0.11.0"] +leaderboard = ["gradio>=4.44.0", "gradio_rangeslider>=0.0.6"] [tool.coverage.report] From 5e6a42e3f075e37b84228f63a39b2b781c27bb46 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Thu, 17 Oct 2024 20:24:06 +0200 Subject: [PATCH 29/38] Fixed union type error on Python 3.9 --- mteb/load_results/benchmark_results.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index 042ea22974..1f693594b5 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import json from collections import defaultdict from pathlib import Path @@ -8,12 +10,8 @@ from pydantic import BaseModel, ConfigDict from mteb.abstasks.AbsTask import AbsTask, ScoresDict -from mteb.abstasks.TaskMetadata import ( - ISO_LANGUAGE_SCRIPT, - TASK_CATEGORY, - TASK_DOMAIN, - TASK_TYPE, -) +from mteb.abstasks.TaskMetadata import (ISO_LANGUAGE_SCRIPT, TASK_CATEGORY, + TASK_DOMAIN, TASK_TYPE) from mteb.languages import ISO_LANGUAGE from mteb.load_results.task_results import TaskResult from mteb.models.overview import get_model_metas From 781ee959a241bb57c87622b78c76064d57aa22c0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Thu, 17 Oct 2024 20:27:06 +0200 Subject: [PATCH 30/38] Removed references to Dict in task aggregation --- mteb/task_aggregation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mteb/task_aggregation.py b/mteb/task_aggregation.py index 11791f2615..e5ce47a4d0 100644 --- a/mteb/task_aggregation.py +++ b/mteb/task_aggregation.py @@ -13,7 +13,7 @@ REVISION = str MODEL_NAME = str -AGGREGATION = Dict[MODEL_NAME, Dict[REVISION, Dict[str, float]]] +AGGREGATION = dict[MODEL_NAME, dict[REVISION, dict[str, float]]] def mean(results: BenchmarkResults) -> AGGREGATION: From ae5afb7779a837aadfbd8f773418edc2790e40b8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Fri, 18 Oct 2024 08:52:16 +0200 Subject: [PATCH 31/38] Fixed name errors in _restrict_task_results --- mteb/load_results/benchmark_results.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index 1f693594b5..d965474223 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -26,12 +26,12 @@ def _restrict_task_results(task_result: TaskResult, task: AbsTask) -> TaskResult hf_subsets = set(task.metadata.hf_subsets_to_langscripts) new_scores = {} seen_splits = set() - for split in res.scores: + for split in task_result.scores: if split not in splits: continue new_scores[split] = [] seen_subsets = set() - for _scores in res.scores[split]: + for _scores in task_result.scores[split]: if _scores["hf_subset"] not in hf_subsets: continue new_scores[split].append(_scores) @@ -43,7 +43,7 @@ def _restrict_task_results(task_result: TaskResult, task: AbsTask) -> TaskResult seen_splits.add(split) if seen_splits != set(splits): raise ValueError(f"Missing splits {set(splits) - seen_splits}") - new_res = {**res.to_dict(), "scores": new_scores} + new_res = {**task_result.to_dict(), "scores": new_scores} new_res = TaskResult.from_dict(new_res) return new_res @@ -91,7 +91,7 @@ def filter_tasks( def select_tasks(self, tasks: list[AbsTask]) -> "ModelResult": task_name_to_task = {task.metadata.name: task for task in tasks} new_task_results = [ - restrict_task_results(task_res, task_name_to_task[task_res.task_name]) + _restrict_task_results(task_res, task_name_to_task[task_res.task_name]) for task_res in self.task_results if task_res.task_name in task_name_to_task ] From ca5014cf9af7fbcaafcf66fb261cb6f952d274ee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Fri, 18 Oct 2024 09:08:53 +0200 Subject: [PATCH 32/38] Fixed _restrict_task_results --- mteb/load_results/benchmark_results.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index d965474223..3e44bbd4be 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -23,7 +23,10 @@ def _restrict_task_results(task_result: TaskResult, task: AbsTask) -> TaskResult: splits = task.metadata.eval_splits - hf_subsets = set(task.metadata.hf_subsets_to_langscripts) + hf_subsets = getattr( + task, "hf_subsets", task.metadata.hf_subsets_to_langscripts.keys() + ) + hf_subsets = set(hf_subsets) new_scores = {} seen_splits = set() for split in task_result.scores: From cb11921aded11aa7202a2bdbb70a1629b9d2fa44 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Fri, 18 Oct 2024 09:29:20 +0200 Subject: [PATCH 33/38] Made hf_subsets={'default'} when the task is monolingual in _restric_task_results --- mteb/load_results/benchmark_results.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index 3e44bbd4be..9d4935b6de 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -23,10 +23,13 @@ def _restrict_task_results(task_result: TaskResult, task: AbsTask) -> TaskResult: splits = task.metadata.eval_splits - hf_subsets = getattr( - task, "hf_subsets", task.metadata.hf_subsets_to_langscripts.keys() - ) - hf_subsets = set(hf_subsets) + if task.is_multilingual: + hf_subsets = getattr( + task, "hf_subsets", task.metadata.hf_subsets_to_langscripts.keys() + ) + hf_subsets = set(hf_subsets) + else: + hf_subsets = {"default"} new_scores = {} seen_splits = set() for split in task_result.scores: From 9fac012190c99b9bffe727a64bc67d17f054d5a9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Fri, 18 Oct 2024 09:44:46 +0200 Subject: [PATCH 34/38] Task dropdown now gets filtered based on the other criteria --- mteb/leaderboard/app.py | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/mteb/leaderboard/app.py b/mteb/leaderboard/app.py index 769be3125d..de23e0332d 100644 --- a/mteb/leaderboard/app.py +++ b/mteb/leaderboard/app.py @@ -1,5 +1,6 @@ import functools import json +from collections import defaultdict from pathlib import Path import gradio as gr @@ -168,7 +169,24 @@ def on_select_benchmark(benchmark_name): def update_task_list(benchmark_name, languages, task_types, domains): benchmark = mteb.get_benchmark(benchmark_name) benchmark_results = benchmark.load_results(base_results=all_results) - return benchmark_results.task_names + task_to_lang_set = defaultdict(set) + task_to_type = dict() + task_to_domains = defaultdict(set) + for model_res in benchmark_results: + for task_res in model_res: + task_to_lang_set[task_res.task_name] |= set(task_res.languages) + task_to_domains[task_res.task_name] |= set(task_res.domains) + task_to_type[task_res.task_name] = task_res.task_type + res = [] + for task_name in benchmark_results.task_names: + if not (task_to_domains[task_name] & set(domains)): + continue + if not (task_to_lang_set[task_name] & set(languages)): + continue + if not (task_to_type[task_name] in task_types): + continue + res.append(task_name) + return res @gr.on( inputs=[ From 006b845adf56448f888dc5af46c96eb71912827e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 21 Oct 2024 08:51:30 +0200 Subject: [PATCH 35/38] Ran linting again --- mteb/load_results/benchmark_results.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index 9d4935b6de..7ad2400e32 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -10,8 +10,12 @@ from pydantic import BaseModel, ConfigDict from mteb.abstasks.AbsTask import AbsTask, ScoresDict -from mteb.abstasks.TaskMetadata import (ISO_LANGUAGE_SCRIPT, TASK_CATEGORY, - TASK_DOMAIN, TASK_TYPE) +from mteb.abstasks.TaskMetadata import ( + ISO_LANGUAGE_SCRIPT, + TASK_CATEGORY, + TASK_DOMAIN, + TASK_TYPE, +) from mteb.languages import ISO_LANGUAGE from mteb.load_results.task_results import TaskResult from mteb.models.overview import get_model_metas From dcca04d61e3dab461a40a00ce9278007e4ec55ec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 21 Oct 2024 08:55:49 +0200 Subject: [PATCH 36/38] Introduced hotfix for reranking test --- tests/test_tasks/test_mteb_rerank.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/tests/test_tasks/test_mteb_rerank.py b/tests/test_tasks/test_mteb_rerank.py index 558aa4bb9b..2940fd9593 100644 --- a/tests/test_tasks/test_mteb_rerank.py +++ b/tests/test_tasks/test_mteb_rerank.py @@ -7,6 +7,7 @@ from sentence_transformers import CrossEncoder, SentenceTransformer from mteb import MTEB +from mteb.model_meta import ModelMeta logging.basicConfig(level=logging.INFO) @@ -365,6 +366,14 @@ def test_reranker_same_ndcg1(): revision = "21eec43590414cb8e3a6f654857abed0483ae36e" de = SentenceTransformer(de_name, revision=revision) ce = CrossEncoder("cross-encoder/ms-marco-TinyBERT-L-2-v2") + ce_revision = "e9ea2688951463fc2791a2ea2ddfce6762900675" + ce.mteb_model_meta = ModelMeta( + name="cross-encoder/ms-marco-TinyBERT-L-2-v2", + languages=["eng-Latn"], + open_source=True, + revision=ce_revision, + release_date="2021-04-15", + ) eval = MTEB(tasks=["SciFact"]) eval.run( de, @@ -390,7 +399,7 @@ def test_reranker_same_ndcg1(): stage1 = json.load(f) with open( - "tests/results/stage2/cross-encoder__ms-marco-TinyBERT-L-2-v2/no_revision_available/SciFact.json" + f"tests/results/stage2/cross-encoder__ms-marco-TinyBERT-L-2-v2/{ce_revision}/SciFact.json" ) as f: stage2 = json.load(f) From 0bf37461f232a8bc35f4f31b73aa74c01b8c69b8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 21 Oct 2024 09:05:15 +0200 Subject: [PATCH 37/38] Added BenchmarkResults to __all__ in __init__ --- mteb/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/mteb/__init__.py b/mteb/__init__.py index 339778596a..bef5f7408d 100644 --- a/mteb/__init__.py +++ b/mteb/__init__.py @@ -36,4 +36,5 @@ "Benchmark", "get_benchmark", "get_benchmarks", + "BenchmarkResults", ] From 607c9984733127942eaa553f774a9ac1a53b9763 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A1rton=20Kardos?= Date: Mon, 21 Oct 2024 15:10:32 +0200 Subject: [PATCH 38/38] Fixed validate_and_filter_scores method, and replaced _restric_task_results with it --- mteb/load_results/benchmark_results.py | 35 +------------------------- mteb/load_results/load_results.py | 2 +- mteb/load_results/task_results.py | 25 ++++++++++-------- 3 files changed, 16 insertions(+), 46 deletions(-) diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py index 7ad2400e32..8878f276ba 100644 --- a/mteb/load_results/benchmark_results.py +++ b/mteb/load_results/benchmark_results.py @@ -25,39 +25,6 @@ Score = Any -def _restrict_task_results(task_result: TaskResult, task: AbsTask) -> TaskResult: - splits = task.metadata.eval_splits - if task.is_multilingual: - hf_subsets = getattr( - task, "hf_subsets", task.metadata.hf_subsets_to_langscripts.keys() - ) - hf_subsets = set(hf_subsets) - else: - hf_subsets = {"default"} - new_scores = {} - seen_splits = set() - for split in task_result.scores: - if split not in splits: - continue - new_scores[split] = [] - seen_subsets = set() - for _scores in task_result.scores[split]: - if _scores["hf_subset"] not in hf_subsets: - continue - new_scores[split].append(_scores) - seen_subsets.add(_scores["hf_subset"]) - if seen_subsets != hf_subsets: - raise ValueError( - f"Missing subsets {hf_subsets - seen_subsets} for split {split}" - ) - seen_splits.add(split) - if seen_splits != set(splits): - raise ValueError(f"Missing splits {set(splits) - seen_splits}") - new_res = {**task_result.to_dict(), "scores": new_scores} - new_res = TaskResult.from_dict(new_res) - return new_res - - class ModelResult(BaseModel): model_name: str model_revision: str | None @@ -101,7 +68,7 @@ def filter_tasks( def select_tasks(self, tasks: list[AbsTask]) -> "ModelResult": task_name_to_task = {task.metadata.name: task for task in tasks} new_task_results = [ - _restrict_task_results(task_res, task_name_to_task[task_res.task_name]) + task_res.validate_and_filter_scores(task_name_to_task[task_res.task_name]) for task_res in self.task_results if task_res.task_name in task_name_to_task ] diff --git a/mteb/load_results/load_results.py b/mteb/load_results/load_results.py index ea75b901c9..6d42cf1dbd 100644 --- a/mteb/load_results/load_results.py +++ b/mteb/load_results/load_results.py @@ -164,7 +164,7 @@ def load_results( task = task_names[r.task_name] else: task = None - r.validate_and_filter_scores(task=task) + r = r.validate_and_filter_scores(task=task) filtered_results.append(r) except Exception as e: logger.warning( diff --git a/mteb/load_results/task_results.py b/mteb/load_results/task_results.py index c2b5c0562c..aa9bf58359 100644 --- a/mteb/load_results/task_results.py +++ b/mteb/load_results/task_results.py @@ -469,9 +469,10 @@ def get_score( def __repr__(self) -> str: return f"TaskResult(task_name={self.task_name}, scores=...)" - def validate_and_filter_scores(self, task: AbsTask | None = None) -> None: + def validate_and_filter_scores(self, task: AbsTask | None = None) -> AbsTask: """This ensures that the scores are correct for the given task, by removing any splits besides those specified in the task metadata. Additionally it also ensure that all of the splits required as well as the languages are present in the scores. + Returns new TaskResult object. Args: task: The task to validate the scores against. E.g. if the task supplied is limited to certain splits and languages, @@ -482,30 +483,32 @@ def validate_and_filter_scores(self, task: AbsTask | None = None) -> None: if task is None: task = get_task(self.task_name) splits = task.metadata.eval_splits - hf_subsets = set(task.metadata.hf_subsets_to_langscripts) - + if task.is_multilingual: + hf_subsets = getattr( + task, "hf_subsets", task.metadata.hf_subsets_to_langscripts.keys() + ) + hf_subsets = set(hf_subsets) + else: + hf_subsets = {"default"} new_scores = {} seen_splits = set() - for split in self.scores: + for split in task_result.scores: if split not in splits: continue new_scores[split] = [] - seen_subsets = set() - for _scores in self.scores[split]: + for _scores in task_result.scores[split]: if _scores["hf_subset"] not in hf_subsets: continue new_scores[split].append(_scores) seen_subsets.add(_scores["hf_subset"]) - if seen_subsets != hf_subsets: raise ValueError( f"Missing subsets {hf_subsets - seen_subsets} for split {split}" ) - seen_splits.add(split) - if seen_splits != set(splits): raise ValueError(f"Missing splits {set(splits) - seen_splits}") - - self.scores = new_scores + new_res = {**task_result.to_dict(), "scores": new_scores} + new_res = TaskResult.from_dict(new_res) + return new_res