diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index 861c2e6ba6..23f0a095ea 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -15,7 +15,7 @@ jobs:
 
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.9"
           cache: "pip"
 
       - name: Install dependencies
diff --git a/.github/workflows/mmteb.yml b/.github/workflows/mmteb.yml
index 6ae21152f2..522f0ab4af 100644
--- a/.github/workflows/mmteb.yml
+++ b/.github/workflows/mmteb.yml
@@ -16,7 +16,7 @@ jobs:
 
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.9"
           cache: "pip"
       
       - name: Install dependencies
@@ -38,7 +38,7 @@ jobs:
 
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.9"
           cache: "pip"
 
       - name: Install dependencies
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index e56a85ce99..1fdfff47a2 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -16,11 +16,11 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest] #, macos-latest, windows-latest]
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
         include:
           # Add Windows with Python 3.8 only to avoid tests taking too long
           - os: windows-latest
-            python-version: "3.8"
+            python-version: "3.9"
 
     steps:
       - uses: actions/checkout@v3
diff --git a/mteb/abstasks/AbsTask.py b/mteb/abstasks/AbsTask.py
index f7e606ec42..16f436ce03 100644
--- a/mteb/abstasks/AbsTask.py
+++ b/mteb/abstasks/AbsTask.py
@@ -3,7 +3,8 @@
 import logging
 import random
 from abc import ABC, abstractmethod
-from typing import Any, Dict, Sequence, TypedDict
+from collections.abc import Sequence
+from typing import Any, TypedDict
 
 import datasets
 import numpy as np
@@ -19,7 +20,7 @@
 
 logger = logging.getLogger(__name__)
 
-ScoresDict = Dict[str, Any]
+ScoresDict = dict[str, Any]
 # ^ e.g {'main_score': 0.5, 'hf_subset': 'en-de', 'languages': ['eng-Latn', 'deu-Latn']}
 
 
diff --git a/mteb/abstasks/AbsTaskClusteringFast.py b/mteb/abstasks/AbsTaskClusteringFast.py
index ad21c99f88..ba49b599dc 100644
--- a/mteb/abstasks/AbsTaskClusteringFast.py
+++ b/mteb/abstasks/AbsTaskClusteringFast.py
@@ -4,7 +4,7 @@
 import logging
 import random
 from collections import Counter, defaultdict
-from typing import Any, Dict
+from typing import Any
 
 import numpy as np
 import sklearn
@@ -21,7 +21,7 @@
 logger = logging.getLogger(__name__)
 
 
-MultilingualDataset = Dict[HFSubset, DatasetDict]
+MultilingualDataset = dict[HFSubset, DatasetDict]
 
 
 def evaluate_clustering_bootstrapped(
diff --git a/mteb/abstasks/AbsTaskInstructionRetrieval.py b/mteb/abstasks/AbsTaskInstructionRetrieval.py
index 1bcb36d78d..b30eb92945 100644
--- a/mteb/abstasks/AbsTaskInstructionRetrieval.py
+++ b/mteb/abstasks/AbsTaskInstructionRetrieval.py
@@ -248,12 +248,12 @@ class AbsTaskInstructionRetrieval(AbsTask):
         instruction: A relevant document will provide the projected or actual date of completion of the project, its estimated or actual total cost, or the estimated or ongoing electrical output of the finished project. Discussions of the social, political, or ecological impact of the project are not relevant.
 
     Child-classes must implement the following properties:
-    self.corpus = Dict[corpus_id, Dict[str, str]] #id => dict with document datas like title and text
-    self.queries = Dict[query_id, str] #id => query
-    self.relevant_docs = Dict[query_id, Dict[corpus_id, int]]
-    self.og_instructions = Dict[str, str] query => original instruction
-    self.changed_instructions = Dict[str, str] query => changed instruction
-    self.top_ranked = Dict[query_id, List[corpus_id]] #id => list of top ranked document ids
+    self.corpus = dict[corpus_id, dict[str, str]] #id => dict with document datas like title and text
+    self.queries = dict[query_id, str] #id => query
+    self.relevant_docs = dict[query_id, dict[corpus_id, int]]
+    self.og_instructions = dict[str, str] query => original instruction
+    self.changed_instructions = dict[str, str] query => changed instruction
+    self.top_ranked = dict[query_id, list[corpus_id]] #id => list of top ranked document ids
 
     See https://arxiv.org/abs/2403.15246 for more details
     """
diff --git a/mteb/abstasks/AbsTaskRetrieval.py b/mteb/abstasks/AbsTaskRetrieval.py
index a31aee761e..3445e1576a 100644
--- a/mteb/abstasks/AbsTaskRetrieval.py
+++ b/mteb/abstasks/AbsTaskRetrieval.py
@@ -219,8 +219,8 @@ class AbsTaskRetrieval(AbsTask):
         Semantically, it should contain dict[split_name, dict[sample_id, dict[str, str]]]
         E.g. {"test": {"document_one": {"_id": "d1", "title": "title", "text": "text"}}}
 
-    self.queries: dict[str, dict[str, Union[str, List[str]]]]
-        Semantically, it should contain dict[split_name, dict[sample_id, str]] or dict[split_name, dict[sample_id, List[str]]] for conversations
+    self.queries: dict[str, dict[str, Union[str, list[str]]]]
+        Semantically, it should contain dict[split_name, dict[sample_id, str]] or dict[split_name, dict[sample_id, list[str]]] for conversations
         E.g. {"test": {"q1": "query"}}
         or {"test": {"q1": ["turn1", "turn2", "turn3"]}}
 
diff --git a/mteb/abstasks/TaskMetadata.py b/mteb/abstasks/TaskMetadata.py
index c368022433..b130e30a9c 100644
--- a/mteb/abstasks/TaskMetadata.py
+++ b/mteb/abstasks/TaskMetadata.py
@@ -1,11 +1,12 @@
 from __future__ import annotations
 
 import logging
+from collections.abc import Mapping
 from datetime import date
-from typing import Any, Dict, List, Mapping, Union
+from typing import Annotated, Any, Union
 
 from pydantic import AnyUrl, BaseModel, BeforeValidator, TypeAdapter, field_validator
-from typing_extensions import Annotated, Literal
+from typing_extensions import Literal
 
 from ..languages import (
     ISO_LANGUAGE_SCRIPT,
@@ -114,7 +115,7 @@
 SPLIT_NAME = str
 HFSubset = str
 LANGUAGES = Union[
-    List[ISO_LANGUAGE_SCRIPT], Mapping[HFSubset, List[ISO_LANGUAGE_SCRIPT]]
+    list[ISO_LANGUAGE_SCRIPT], Mapping[HFSubset, list[ISO_LANGUAGE_SCRIPT]]
 ]
 
 PROGRAMMING_LANGS = [
@@ -162,7 +163,7 @@
 )
 
 METRIC_NAME = str
-METRIC_VALUE = Union[int, float, Dict[str, Any]]
+METRIC_VALUE = Union[int, float, dict[str, Any]]
 
 logger = logging.getLogger(__name__)
 
diff --git a/mteb/abstasks/stratification.py b/mteb/abstasks/stratification.py
index cb1bb91ac6..b44250aba9 100644
--- a/mteb/abstasks/stratification.py
+++ b/mteb/abstasks/stratification.py
@@ -113,7 +113,7 @@ def _get_most_desired_combination(samples_with_combination):
 
     Parameters
     ----------
-    samples_with_combination : Dict[Combination, List[int]], :code:`(n_combinations)`
+    samples_with_combination : dict[Combination, list[int]], :code:`(n_combinations)`
             map from each label combination present in y to list of sample indexes that have this combination assigned
 
     Returns:
@@ -155,7 +155,7 @@ class IterativeStratification(_BaseKFold):
     order : int, >= 1
         the order of label relationship to take into account when balancing sample distribution across labels
 
-    sample_distribution_per_fold : None or List[float], :code:`(n_splits)`
+    sample_distribution_per_fold : None or list[float], :code:`(n_splits)`
         desired percentage of samples in each of the folds, if None and equal distribution of samples per fold
         is assumed i.e. 1/n_splits for each fold. The value is held in :code:`self.percentage_per_fold`.
 
@@ -195,7 +195,7 @@ def __init__(
     def _prepare_stratification(self, y):
         """Prepares variables for performing stratification
 
-        For the purpose of clarity, the type Combination denotes List[int], :code:`(self.order)` and represents a
+        For the purpose of clarity, the type Combination denotes list[int], :code:`(self.order)` and represents a
         label combination of the order we want to preserve among folds in stratification. The total number of
         combinations present in :code:`(y)` will be denoted as :code:`(n_combinations)`.
 
@@ -208,7 +208,7 @@ def _prepare_stratification(self, y):
         self.desired_samples_per_fold: np.array[Float], :code:`(n_splits)`
             number of samples desired per fold
 
-        self.desired_samples_per_combination_per_fold: Dict[Combination, np.array[Float]], :code:`(n_combinations, n_splits)`
+        self.desired_samples_per_combination_per_fold: dict[Combination, np.array[Float]], :code:`(n_combinations, n_splits)`
             number of samples evidencing each combination desired per each fold
 
         Parameters
@@ -218,22 +218,22 @@ def _prepare_stratification(self, y):
 
         Returns:
         -------
-        rows : List[List[int]], :code:`(n_samples, n_labels)`
+        rows : list[list[int]], :code:`(n_samples, n_labels)`
             list of label indices assigned to each sample
 
-        rows_used : Dict[int, bool], :code:`(n_samples)`
+        rows_used : dict[int, bool], :code:`(n_samples)`
             boolean map from a given sample index to boolean value whether it has been already assigned to a fold or not
 
-        all_combinations :  List[Combination], :code:`(n_combinations)`
+        all_combinations :  list[Combination], :code:`(n_combinations)`
             list of all label combinations of order self.order present in y
 
-        per_row_combinations : List[Combination], :code:`(n_samples)`
+        per_row_combinations : list[Combination], :code:`(n_samples)`
             list of all label combinations of order self.order present in y per row
 
-        samples_with_combination : Dict[Combination, List[int]], :code:`(n_combinations)`
+        samples_with_combination : dict[Combination, list[int]], :code:`(n_combinations)`
             map from each label combination present in y to list of sample indexes that have this combination assigned
 
-        folds: List[List[int]] (n_splits)
+        folds: list[list[int]] (n_splits)
             list of lists to be populated with samples
 
         """
@@ -353,7 +353,7 @@ def _iter_test_indices(self, X, y=None, groups=None):
 
         Yields:
         ------
-        fold : List[int]
+        fold : list[int]
             indexes of test samples for a given fold, yielded for each of the folds
         """
         (
diff --git a/mteb/benchmarks/benchmarks.py b/mteb/benchmarks/benchmarks.py
index c40766045c..ae22c9f56c 100644
--- a/mteb/benchmarks/benchmarks.py
+++ b/mteb/benchmarks/benchmarks.py
@@ -1,10 +1,10 @@
 from __future__ import annotations
 
+from collections.abc import Sequence
 from dataclasses import dataclass
-from typing import Sequence
+from typing import Annotated
 
 from pydantic import AnyUrl, BeforeValidator, TypeAdapter
-from typing_extensions import Annotated
 
 from mteb.abstasks.AbsTask import AbsTask
 from mteb.overview import get_tasks
diff --git a/mteb/encoder_interface.py b/mteb/encoder_interface.py
index d4648dce44..26dbfd4e24 100644
--- a/mteb/encoder_interface.py
+++ b/mteb/encoder_interface.py
@@ -1,12 +1,13 @@
 from __future__ import annotations
 
+from collections.abc import Sequence
 from enum import Enum
-from typing import Any, Dict, List, Protocol, Sequence, Union, runtime_checkable
+from typing import Any, Protocol, Union, runtime_checkable
 
 import numpy as np
 import torch
 
-Corpus = Union[List[Dict[str, str]], Dict[str, List[str]]]
+Corpus = Union[list[dict[str, str]], dict[str, list[str]]]
 
 
 class PromptType(str, Enum):
diff --git a/mteb/evaluation/MTEB.py b/mteb/evaluation/MTEB.py
index 6851a57ca5..a60ac09021 100644
--- a/mteb/evaluation/MTEB.py
+++ b/mteb/evaluation/MTEB.py
@@ -4,12 +4,13 @@
 import logging
 import os
 import traceback
+from collections.abc import Iterable
 from copy import copy
 from datetime import datetime
 from itertools import chain
 from pathlib import Path
 from time import time
-from typing import Any, Iterable
+from typing import Any
 
 import datasets
 from sentence_transformers import SentenceTransformer
diff --git a/mteb/evaluation/evaluators/RerankingEvaluator.py b/mteb/evaluation/evaluators/RerankingEvaluator.py
index fe4a5fb0bf..a9f3603904 100644
--- a/mteb/evaluation/evaluators/RerankingEvaluator.py
+++ b/mteb/evaluation/evaluators/RerankingEvaluator.py
@@ -260,7 +260,7 @@ def _encode_candidates_individual(
             is_relevant = [True] * len(positive) + [False] * len(negative)
 
             if isinstance(query, str):
-                # .encoding interface requires List[str] as input
+                # .encoding interface requires list[str] as input
                 query = [query]
             query_emb = np.asarray(encode_queries_func(query, **self.encode_kwargs))
             docs_emb = np.asarray(encode_corpus_func(docs, **self.encode_kwargs))
@@ -345,7 +345,7 @@ def _encode_candidates_miracl_individual(
             docs = list(instance["candidates"])
 
             if isinstance(query, str):
-                # .encoding interface requires List[str] as input
+                # .encoding interface requires list[str] as input
                 query_emb = np.asarray(
                     encode_queries_func([query], **self.encode_kwargs)
                 )
@@ -545,8 +545,8 @@ def ap_score(is_relevant, pred_scores):
         """Computes AP score
 
         Args:
-            is_relevant (`List[bool]` of length `num_pos+num_neg`): True if the document is relevant
-            pred_scores (`List[float]` of length `num_pos+num_neg`): Predicted similarity scores
+            is_relevant (`list[bool]` of length `num_pos+num_neg`): True if the document is relevant
+            pred_scores (`list[float]` of length `num_pos+num_neg`): Predicted similarity scores
 
         Returns:
             ap_score (`float`): AP score
diff --git a/mteb/evaluation/evaluators/model_encode.py b/mteb/evaluation/evaluators/model_encode.py
index ef555ac045..8579d03bb8 100644
--- a/mteb/evaluation/evaluators/model_encode.py
+++ b/mteb/evaluation/evaluators/model_encode.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Sequence
+from collections.abc import Sequence
 
 import numpy as np
 import torch
diff --git a/mteb/evaluation/evaluators/utils.py b/mteb/evaluation/evaluators/utils.py
index 0cdfb6bd72..8c8850a3c6 100644
--- a/mteb/evaluation/evaluators/utils.py
+++ b/mteb/evaluation/evaluators/utils.py
@@ -281,13 +281,16 @@ def rank_score(x: dict[str, float]) -> float:
 def download(url: str, fname: str):
     resp = requests.get(url, stream=True)
     total = int(resp.headers.get("content-length", 0))
-    with open(fname, "wb") as file, tqdm.tqdm(
-        desc=fname,
-        total=total,
-        unit="iB",
-        unit_scale=True,
-        unit_divisor=1024,
-    ) as bar:
+    with (
+        open(fname, "wb") as file,
+        tqdm.tqdm(
+            desc=fname,
+            total=total,
+            unit="iB",
+            unit_scale=True,
+            unit_divisor=1024,
+        ) as bar,
+    ):
         for data in resp.iter_content(chunk_size=1024):
             size = file.write(data)
             bar.update(size)
diff --git a/mteb/load_results/load_results.py b/mteb/load_results/load_results.py
index 76c9d43828..aca6bd6835 100644
--- a/mteb/load_results/load_results.py
+++ b/mteb/load_results/load_results.py
@@ -5,8 +5,8 @@
 import os
 import subprocess
 from collections import defaultdict
+from collections.abc import Sequence
 from pathlib import Path
-from typing import Dict, List, Sequence
 
 from mteb.abstasks.AbsTask import AbsTask
 from mteb.load_results.mteb_results import MTEBResults
@@ -16,7 +16,7 @@
 MODEL_NAME = str
 REVISION = str
 
-RESULTS = Dict[MODEL_NAME, Dict[REVISION, List[MTEBResults]]]
+RESULTS = dict[MODEL_NAME, dict[REVISION, list[MTEBResults]]]
 
 
 def download_of_results(
diff --git a/mteb/model_meta.py b/mteb/model_meta.py
index fbb93c5f8a..d6fcf49905 100644
--- a/mteb/model_meta.py
+++ b/mteb/model_meta.py
@@ -2,11 +2,10 @@
 
 from datetime import date
 from functools import partial
-from typing import Any, Callable, Literal
+from typing import Annotated, Any, Callable, Literal
 
 from pydantic import BaseModel, BeforeValidator, TypeAdapter
 from sentence_transformers import SentenceTransformer
-from typing_extensions import Annotated
 
 from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode
 
diff --git a/mteb/task_aggregation.py b/mteb/task_aggregation.py
index 899b6ae553..57fb542bb7 100644
--- a/mteb/task_aggregation.py
+++ b/mteb/task_aggregation.py
@@ -2,7 +2,6 @@
 
 import logging
 from collections import defaultdict
-from typing import Dict
 
 import numpy as np
 
@@ -12,7 +11,7 @@
 
 logger = logging.getLogger(__name__)
 
-AGGREGATION = Dict[MODEL_NAME, Dict[REVISION, Dict[str, float]]]
+AGGREGATION = dict[MODEL_NAME, dict[REVISION, dict[str, float]]]
 
 
 def mean(results: RESULTS) -> AGGREGATION:
diff --git a/mteb/task_selection.py b/mteb/task_selection.py
index d5a499c415..935e5157ec 100644
--- a/mteb/task_selection.py
+++ b/mteb/task_selection.py
@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Any, Callable, List
+from typing import Any, Callable
 
 import pandas as pd
 from scipy.stats import pearsonr, spearmanr
@@ -14,7 +14,7 @@
 
 MODEL_NAME = str
 REVISION = str
-METRIC = Callable[[List[float], List[float]], float]
+METRIC = Callable[[list[float], list[float]], float]
 
 
 def spearman(x: list[float], y: list[float]) -> float:
diff --git a/mteb/tasks/Clustering/nob/snl_clustering.py b/mteb/tasks/Clustering/nob/snl_clustering.py
index 5f21dd0dbe..0acb6d52b1 100644
--- a/mteb/tasks/Clustering/nob/snl_clustering.py
+++ b/mteb/tasks/Clustering/nob/snl_clustering.py
@@ -1,8 +1,9 @@
 from __future__ import annotations
 
 import random
+from collections.abc import Iterable
 from itertools import islice
-from typing import Iterable, TypeVar
+from typing import TypeVar
 
 import datasets
 
diff --git a/mteb/tasks/Clustering/nob/vg_clustering.py b/mteb/tasks/Clustering/nob/vg_clustering.py
index 769f69da1a..6c6c692fb7 100644
--- a/mteb/tasks/Clustering/nob/vg_clustering.py
+++ b/mteb/tasks/Clustering/nob/vg_clustering.py
@@ -1,8 +1,9 @@
 from __future__ import annotations
 
 import random
+from collections.abc import Iterable
 from itertools import islice
-from typing import Iterable, TypeVar
+from typing import TypeVar
 
 import datasets
 
diff --git a/mteb/tasks/Retrieval/dan/DanFeverRetrieval.py b/mteb/tasks/Retrieval/dan/DanFeverRetrieval.py
index f255963114..2468463a13 100644
--- a/mteb/tasks/Retrieval/dan/DanFeverRetrieval.py
+++ b/mteb/tasks/Retrieval/dan/DanFeverRetrieval.py
@@ -70,9 +70,9 @@ def load_data(self, **kwargs):
     def dataset_transform(self) -> None:
         """And transform to a retrieval datset, which have the following attributes
 
-        self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document data like title and text
-        self.queries = Dict[query_id, str] #id => query
-        self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+        self.corpus = dict[doc_id, dict[str, str]] #id => dict with document data like title and text
+        self.queries = dict[query_id, str] #id => query
+        self.relevant_docs = dict[query_id, dict[[doc_id, score]]
         """
         self.corpus = {}
         self.relevant_docs = {}
@@ -182,9 +182,9 @@ def load_data(self, **kwargs):
     def dataset_transform(self) -> None:
         """And transform to a retrieval datset, which have the following attributes
 
-        self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document data like title and text
-        self.queries = Dict[query_id, str] #id => query
-        self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+        self.corpus = dict[doc_id, dict[str, str]] #id => dict with document data like title and text
+        self.queries = dict[query_id, str] #id => query
+        self.relevant_docs = dict[query_id, dict[[doc_id, score]]
         """
         self.corpus = {}
         self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/dan/TV2Nordretrieval.py b/mteb/tasks/Retrieval/dan/TV2Nordretrieval.py
index 2cf8f824f1..0f81e28618 100644
--- a/mteb/tasks/Retrieval/dan/TV2Nordretrieval.py
+++ b/mteb/tasks/Retrieval/dan/TV2Nordretrieval.py
@@ -81,9 +81,9 @@ def load_data(self, **kwargs):
     def dataset_transform(self) -> None:
         """And transform to a retrieval datset, which have the following attributes
 
-        self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
-        self.queries = Dict[query_id, str] #id => query
-        self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+        self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+        self.queries = dict[query_id, str] #id => query
+        self.relevant_docs = dict[query_id, dict[[doc_id, score]]
         """
         self.corpus = {}
         self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/dan/TwitterHjerneRetrieval.py b/mteb/tasks/Retrieval/dan/TwitterHjerneRetrieval.py
index b29b526dde..85e9d1b8aa 100644
--- a/mteb/tasks/Retrieval/dan/TwitterHjerneRetrieval.py
+++ b/mteb/tasks/Retrieval/dan/TwitterHjerneRetrieval.py
@@ -60,9 +60,9 @@ def load_data(self, **kwargs):
     def dataset_transform(self) -> None:
         """And transform to a retrieval datset, which have the following attributes
 
-        self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
-        self.queries = Dict[query_id, str] #id => query
-        self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+        self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+        self.queries = dict[query_id, str] #id => query
+        self.relevant_docs = dict[query_id, dict[[doc_id, score]]
         """
         self.corpus = {}
         self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/nob/norquad.py b/mteb/tasks/Retrieval/nob/norquad.py
index dc45e6914f..ce11b4b710 100644
--- a/mteb/tasks/Retrieval/nob/norquad.py
+++ b/mteb/tasks/Retrieval/nob/norquad.py
@@ -71,9 +71,9 @@ def load_data(self, **kwargs):
     def dataset_transform(self) -> None:
         """And transform to a retrieval datset, which have the following attributes
 
-        self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
-        self.queries = Dict[query_id, str] #id => query
-        self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+        self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+        self.queries = dict[query_id, str] #id => query
+        self.relevant_docs = dict[query_id, dict[[doc_id, score]]
         """
         self.corpus = {}
         self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/nob/snl_retrieval.py b/mteb/tasks/Retrieval/nob/snl_retrieval.py
index df37b0fb90..3c8045fe6d 100644
--- a/mteb/tasks/Retrieval/nob/snl_retrieval.py
+++ b/mteb/tasks/Retrieval/nob/snl_retrieval.py
@@ -59,9 +59,9 @@ def load_data(self, **kwargs):
     def dataset_transform(self) -> None:
         """And transform to a retrieval datset, which have the following attributes
 
-        self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
-        self.queries = Dict[query_id, str] #id => query
-        self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+        self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+        self.queries = dict[query_id, str] #id => query
+        self.relevant_docs = dict[query_id, dict[[doc_id, score]]
         """
         self.corpus = {}
         self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/swe/SweFaqRetrieval.py b/mteb/tasks/Retrieval/swe/SweFaqRetrieval.py
index 1f496b62eb..f01cb25db8 100644
--- a/mteb/tasks/Retrieval/swe/SweFaqRetrieval.py
+++ b/mteb/tasks/Retrieval/swe/SweFaqRetrieval.py
@@ -62,9 +62,9 @@ def load_data(self, **kwargs):
     def dataset_transform(self) -> None:
         """And transform to a retrieval datset, which have the following attributes
 
-        self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
-        self.queries = Dict[query_id, str] #id => query
-        self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+        self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+        self.queries = dict[query_id, str] #id => query
+        self.relevant_docs = dict[query_id, dict[[doc_id, score]]
         """
         self.corpus = {}
         self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/swe/SwednRetrieval.py b/mteb/tasks/Retrieval/swe/SwednRetrieval.py
index 0f579ce499..381961542c 100644
--- a/mteb/tasks/Retrieval/swe/SwednRetrieval.py
+++ b/mteb/tasks/Retrieval/swe/SwednRetrieval.py
@@ -61,9 +61,9 @@ def load_data(self, **kwargs):
     def dataset_transform(self) -> None:
         """And transform to a retrieval datset, which have the following attributes
 
-        self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
-        self.queries = Dict[query_id, str] #id => query
-        self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+        self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+        self.queries = dict[query_id, str] #id => query
+        self.relevant_docs = dict[query_id, dict[[doc_id, score]]
         """
         self.corpus = {}
         self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/tur/TurHistQuad.py b/mteb/tasks/Retrieval/tur/TurHistQuad.py
index 8dc489a3fd..d896e36fa0 100644
--- a/mteb/tasks/Retrieval/tur/TurHistQuad.py
+++ b/mteb/tasks/Retrieval/tur/TurHistQuad.py
@@ -58,9 +58,9 @@ class TurHistQuadRetrieval(AbsTaskRetrieval):
     def load_data(self, **kwargs) -> None:
         """And transform to a retrieval datset, which have the following attributes
 
-        self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
-        self.queries = Dict[query_id, str] #id => query
-        self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+        self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+        self.queries = dict[query_id, str] #id => query
+        self.relevant_docs = dict[query_id, dict[[doc_id, score]]
         """
         if self.data_loaded:
             return
diff --git a/pyproject.toml b/pyproject.toml
index 74297fa6d6..65638d0281 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,7 @@ classifiers = [
     "Operating System :: OS Independent",
     "Programming Language :: Python",
 ]
-requires-python = ">=3.8"
+requires-python = ">=3.9"
 dependencies = [
     "datasets>=2.19.0",
     "numpy>=1.0.0,<2.0.0", # note: https://github.com/huggingface/datasets/issues/6980
@@ -89,7 +89,7 @@ exclude = ["tests", "results"]
 
 [tool.ruff]
 
-target-version = "py38"
+target-version = "py39"
 
 
 [tool.ruff.lint]
diff --git a/scripts/running_model/check_results.py b/scripts/running_model/check_results.py
index a4b166e0c8..2d69b7acdf 100644
--- a/scripts/running_model/check_results.py
+++ b/scripts/running_model/check_results.py
@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from typing import Iterable
+from collections.abc import Iterable
 
 import pandas as pd
 
diff --git a/scripts/running_model/create_slurm_jobs.py b/scripts/running_model/create_slurm_jobs.py
index 177775144e..606630d9e5 100644
--- a/scripts/running_model/create_slurm_jobs.py
+++ b/scripts/running_model/create_slurm_jobs.py
@@ -3,8 +3,8 @@
 from __future__ import annotations
 
 import subprocess
+from collections.abc import Iterable
 from pathlib import Path
-from typing import Iterable
 
 import mteb