diff --git a/.gitignore b/.gitignore
index 4bf5d365..52ed372d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,163 @@
 *.DS_Store
-.env*
-__pycache__
 ChromaDB
-models
-.vscode
\ No newline at end of file
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env*
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
\ No newline at end of file
diff --git a/.python-version b/.python-version
deleted file mode 100644
index c8cfe395..00000000
--- a/.python-version
+++ /dev/null
@@ -1 +0,0 @@
-3.10
diff --git a/README.md b/README.md
index a85817af..03e6d2cf 100644
--- a/README.md
+++ b/README.md
@@ -1,93 +1,70 @@
-# MTEB Scripts
+# Scripts to run the French MTEB benchmark
 
-This repository contains scripts used for [MTEB](https://github.com/embeddings-benchmark/mteb) benchmarking. Some scripts rely on a results folder, which can be obtained via `git clone https://huggingface.co/datasets/mteb/results`.
+This folder contains the scripts used to generate the French tab results on the [MTEB](https://github.com/embeddings-benchmark/mteb) benchmark.
 
-<!-- TOC -->
+Below are instructions to run the main scripts.
 
-- [MTEB Scripts](#mteb-scripts)
-    - [Benchmark](#benchmark)
-    - [Env Setup](#env-setup)
-    - [Model setup](#model-setup)
-        - [Download](#download)
-        - [Load](#load)
+## Benchmark
 
-<!-- /TOC -->
+### Running on host using venv
 
-## Benchmark
+* Navigate to the repository root folder
+* Create your virtual env:
 
-Basic with Internet
-```python
-from mteb import MTEB
-from sentence_transformers import SentenceTransformer
-model_path = "/gpfswork/rech/six/commun/models/Muennighoff_SGPT-125M-weightedmean-nli-bitfit"
-model_name = model_path.split("/")[-1].split("_")[-1]
-model = SentenceTransformer(model_path)
-evaluation = MTEB(tasks=["Banking77Classification"])
-evaluation.run(model, output_folder=f"results/{model_name}")
+```bash
+python3 -m venv .venv
 ```
-
-No Internet Access (Download data first)
-```python
-import os
-os.environ["HF_DATASETS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_CACHE"]="/gpfswork/rech/six/commun/models"
-os.environ["HF_DATASETS_CACHE"]="/gpfswork/rech/six/commun/datasets"
-os.environ["HF_MODULES_CACHE"]="/gpfswork/rech/six/commun/modules"
-os.environ["HF_METRICS_CACHE"]="/gpfswork/rech/six/commun/metrics"
-from mteb import MTEB
-from sentence_transformers import SentenceTransformer
-model_path = "/gpfswork/rech/six/commun/models/Muennighoff_SGPT-125M-weightedmean-nli-bitfit"
-model_name = model_path.split("/")[-1].split("_")[-1]
-model = SentenceTransformer(model_path)
-evaluation = MTEB(tasks=["Banking77Classification"])
-evaluation.run(model, output_folder=f"results/{model_name}")
+* Activate it and install the requirements:
+```bash
+source .venv/bin/activate
+pip install -r requirements.txt
 ```
-
-
-## Env Setup
-
+* Run the benchmark:
 ```bash
-export CONDA_ENVS_PATH=$six_ALL_CCFRWORK/conda
-
-conda create -y -n hf-prod python=3.8
-conda activate hf-prod
+cd script_mteb_french
+python run_benchmark.py
+```
 
-# pt-1.10.1 / cuda 11.3
-conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
+By default the benchmark runs on sentence_transformer models but you can specify the type with the argument `--model_type`:
+```bash
+# default ['sentence_transformer']
+python run_benchmark.py
+# choosing other type ['voyage_ai']
+python run_benchmark.py --model_type voyage_ai
+# running on two types ['voyage_ai', 'sentence_transformer']
+python run_benchmark.py --model_type voyage_ai sentence_transformer
+```
 
-# Custom fork that uses offline datasets
-!pip install --upgrade git+https://github.com/Muennighoff/mteb.git@offlineaccess
-!pip install --upgrade git+https://github.com/Muennighoff/sentence-transformers.git@sgpt_poolings
-# If you want to run BEIR tasks
-!pip install --upgrade git+https://github.com/beir-cellar/beir.git
+You can also run the benchmark on one model only by specifying `--model_name`:
+```bash
+# default ['sentence_transformer'] -> all models of this type
+python run_benchmark.py
+# running on one model 'camembert-base'
+python run_benchmark.py --model_type sentence_transformer --model_name "xlm-roberta-base"
 ```
+Note that the `model_name` should be included in models of specified `model_type`.
 
-## Model setup
+You can run the benchmark on one task type in ["all", "classification", "clustering", "reranking", "retrieval", "pair_classification", "sts", "summarization", "bitextmining"], default is set to "all" and will run all tasks :
+```bash
+# running 'sentence_transformer' models on 'classification' task
+python run_benchmark.py --model_type sentence_transformer --task_type classification
+```
 
-### Download
+## Running using Docker
 
-```python
-import os
-import sentence_transformers
-os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/gpfswork/rech/six/commun/models"
-sentence_transformers_cache_dir = os.getenv("SENTENCE_TRANSFORMERS_HOME")
-model_repo="sentence-transformers/allenai-specter"
-revision="29f9f45ff2a85fe9dfe8ce2cef3d8ec4e65c5f37"
-model_path = os.path.join(sentence_transformers_cache_dir, model_repo.replace("/", "_"))
-model_path_tmp = sentence_transformers.util.snapshot_download(
-    repo_id=model_repo,
-    revision=revision,
-    cache_dir=sentence_transformers_cache_dir,
-    library_name="sentence-transformers",
-    library_version=sentence_transformers.__version__,
-    ignore_files=["flax_model.msgpack", "rust_model.ot", "tf_model.h5",],
-)
-os.rename(model_path_tmp, model_path)
+* Navigate to the repository root folder
+* Build the docker image:
+```bash
+docker build -t mtebscripts_image .
+```
+* Run the benchmark in the container as follows:
+```
+docker run -v $(pwd):/mtebscripts mtebscripts_image sh -c "cd script_mteb_french && python run_benchmark.py"
 ```
+If you want to use the gpu, make sure to add the `--gpus` option to your run command, or `--runtime=nvidia` if you are using an older version of docker.
 
-### Load
+Note: Because the volume is shared between the host and the container, the results will be available in the host at the end.
 
-```python
-model = SentenceTransformer("/gpfswork/rech/six/commun/models/Muennighoff_SGPT-125M-weightedmean-nli-bitfit")
-```
+## Models' characteristics
+
+Additionnaly, you can find a script `get_model_specs.py` to compute models' characteristics (size, number of params, embeddings dimension). You can run it similarly to the benchmark by substituting `run_benchmark.py` with `get_model_specs.py`.
diff --git a/README_orig.md b/README_orig.md
new file mode 100644
index 00000000..a85817af
--- /dev/null
+++ b/README_orig.md
@@ -0,0 +1,93 @@
+# MTEB Scripts
+
+This repository contains scripts used for [MTEB](https://github.com/embeddings-benchmark/mteb) benchmarking. Some scripts rely on a results folder, which can be obtained via `git clone https://huggingface.co/datasets/mteb/results`.
+
+<!-- TOC -->
+
+- [MTEB Scripts](#mteb-scripts)
+    - [Benchmark](#benchmark)
+    - [Env Setup](#env-setup)
+    - [Model setup](#model-setup)
+        - [Download](#download)
+        - [Load](#load)
+
+<!-- /TOC -->
+
+## Benchmark
+
+Basic with Internet
+```python
+from mteb import MTEB
+from sentence_transformers import SentenceTransformer
+model_path = "/gpfswork/rech/six/commun/models/Muennighoff_SGPT-125M-weightedmean-nli-bitfit"
+model_name = model_path.split("/")[-1].split("_")[-1]
+model = SentenceTransformer(model_path)
+evaluation = MTEB(tasks=["Banking77Classification"])
+evaluation.run(model, output_folder=f"results/{model_name}")
+```
+
+No Internet Access (Download data first)
+```python
+import os
+os.environ["HF_DATASETS_OFFLINE"]="1" # 1 for offline
+os.environ["TRANSFORMERS_OFFLINE"]="1" # 1 for offline
+os.environ["TRANSFORMERS_CACHE"]="/gpfswork/rech/six/commun/models"
+os.environ["HF_DATASETS_CACHE"]="/gpfswork/rech/six/commun/datasets"
+os.environ["HF_MODULES_CACHE"]="/gpfswork/rech/six/commun/modules"
+os.environ["HF_METRICS_CACHE"]="/gpfswork/rech/six/commun/metrics"
+from mteb import MTEB
+from sentence_transformers import SentenceTransformer
+model_path = "/gpfswork/rech/six/commun/models/Muennighoff_SGPT-125M-weightedmean-nli-bitfit"
+model_name = model_path.split("/")[-1].split("_")[-1]
+model = SentenceTransformer(model_path)
+evaluation = MTEB(tasks=["Banking77Classification"])
+evaluation.run(model, output_folder=f"results/{model_name}")
+```
+
+
+## Env Setup
+
+```bash
+export CONDA_ENVS_PATH=$six_ALL_CCFRWORK/conda
+
+conda create -y -n hf-prod python=3.8
+conda activate hf-prod
+
+# pt-1.10.1 / cuda 11.3
+conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
+
+# Custom fork that uses offline datasets
+!pip install --upgrade git+https://github.com/Muennighoff/mteb.git@offlineaccess
+!pip install --upgrade git+https://github.com/Muennighoff/sentence-transformers.git@sgpt_poolings
+# If you want to run BEIR tasks
+!pip install --upgrade git+https://github.com/beir-cellar/beir.git
+```
+
+## Model setup
+
+### Download
+
+```python
+import os
+import sentence_transformers
+os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/gpfswork/rech/six/commun/models"
+sentence_transformers_cache_dir = os.getenv("SENTENCE_TRANSFORMERS_HOME")
+model_repo="sentence-transformers/allenai-specter"
+revision="29f9f45ff2a85fe9dfe8ce2cef3d8ec4e65c5f37"
+model_path = os.path.join(sentence_transformers_cache_dir, model_repo.replace("/", "_"))
+model_path_tmp = sentence_transformers.util.snapshot_download(
+    repo_id=model_repo,
+    revision=revision,
+    cache_dir=sentence_transformers_cache_dir,
+    library_name="sentence-transformers",
+    library_version=sentence_transformers.__version__,
+    ignore_files=["flax_model.msgpack", "rust_model.ot", "tf_model.h5",],
+)
+os.rename(model_path_tmp, model_path)
+```
+
+### Load
+
+```python
+model = SentenceTransformer("/gpfswork/rech/six/commun/models/Muennighoff_SGPT-125M-weightedmean-nli-bitfit")
+```
diff --git a/script_mteb_french/results_analysis/models_characteristics.csv b/assets/models_characteristics.csv
similarity index 100%
rename from script_mteb_french/results_analysis/models_characteristics.csv
rename to assets/models_characteristics.csv
diff --git a/download_tasks.py b/download_tasks.py
deleted file mode 100644
index 0fc34286..00000000
--- a/download_tasks.py
+++ /dev/null
@@ -1,79 +0,0 @@
-"""Downloads all MTEB tasks"""
-
-TASK_LIST = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-    "ArguAna",
-    "ClimateFEVER",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-    "SummEval",
-]
-
-import os
-os.environ["HF_DATASETS_OFFLINE"]="0" # 1 for offline
-os.environ["TRANSFORMERS_OFFLINE"]="0" # 1 for offline
-os.environ["TRANSFORMERS_CACHE"]="/gpfswork/rech/six/commun/models"
-os.environ["HF_DATASETS_CACHE"]="/gpfswork/rech/six/commun/datasets"
-os.environ["HF_MODULES_CACHE"]="/gpfswork/rech/six/commun/modules"
-os.environ["HF_METRICS_CACHE"]="/gpfswork/rech/six/commun/metrics"
-
-from mteb import MTEB
-evaluation = MTEB(tasks=TASK_LIST, task_langs=["en"])
-
-for task in evaluation.tasks:
-    task.load_data()
-
-    # Alternatively clone to desired place
-    #path = "/gpfsscratch/rech/six/commun/commun/experiments/muennighoff/" + task.description["hf_hub_name"]
-    #from git import Repo
-    #Repo.clone_from("https://huggingface.co/datasets/" + self.description["hf_hub_name"], path)
-    #self.dataset = datasets.load_dataset(path, revision=self.description.get("revision", None))
diff --git a/script_mteb_french/src/AbstractEmbeddingFunction.py b/embedders/AbstractEmbeddingFunction.py
similarity index 100%
rename from script_mteb_french/src/AbstractEmbeddingFunction.py
rename to embedders/AbstractEmbeddingFunction.py
diff --git a/script_mteb_french/src/ChromaDBEmbedder.py b/embedders/ChromaDBEmbedder.py
similarity index 100%
rename from script_mteb_french/src/ChromaDBEmbedder.py
rename to embedders/ChromaDBEmbedder.py
diff --git a/script_mteb_french/src/CohereEmbeddingFunction.py b/embedders/CohereEmbeddingFunction.py
similarity index 100%
rename from script_mteb_french/src/CohereEmbeddingFunction.py
rename to embedders/CohereEmbeddingFunction.py
diff --git a/script_mteb_french/src/LaserEmbeddingFunction.py b/embedders/LaserEmbeddingFunction.py
similarity index 96%
rename from script_mteb_french/src/LaserEmbeddingFunction.py
rename to embedders/LaserEmbeddingFunction.py
index 79119dcf..e563dadc 100644
--- a/script_mteb_french/src/LaserEmbeddingFunction.py
+++ b/embedders/LaserEmbeddingFunction.py
@@ -33,7 +33,7 @@ def encode_documents(self, input: Documents) -> Embeddings:
 
     @staticmethod
     def _download_laser_models():
-        MODELS_DOWNLOAD_FOLDER = "models"
+        MODELS_DOWNLOAD_FOLDER = "downloads"
         if not os.path.exists(MODELS_DOWNLOAD_FOLDER):
             os.mkdir(MODELS_DOWNLOAD_FOLDER)
 
diff --git a/script_mteb_french/src/MistralAIEmbeddingFunction.py b/embedders/MistralAIEmbeddingFunction.py
similarity index 100%
rename from script_mteb_french/src/MistralAIEmbeddingFunction.py
rename to embedders/MistralAIEmbeddingFunction.py
diff --git a/script_mteb_french/src/ModelConfig.py b/embedders/ModelConfig.py
similarity index 100%
rename from script_mteb_french/src/ModelConfig.py
rename to embedders/ModelConfig.py
diff --git a/script_mteb_french/src/OpenAIEmbeddingFunction.py b/embedders/OpenAIEmbeddingFunction.py
similarity index 100%
rename from script_mteb_french/src/OpenAIEmbeddingFunction.py
rename to embedders/OpenAIEmbeddingFunction.py
diff --git a/script_mteb_french/src/SentenceTransformerEmbeddingFunction.py b/embedders/SentenceTransformerEmbeddingFunction.py
similarity index 100%
rename from script_mteb_french/src/SentenceTransformerEmbeddingFunction.py
rename to embedders/SentenceTransformerEmbeddingFunction.py
diff --git a/script_mteb_french/src/UniversalSentenceEncoderEmbeddingFunction.py b/embedders/UniversalSentenceEncoderEmbeddingFunction.py
similarity index 100%
rename from script_mteb_french/src/UniversalSentenceEncoderEmbeddingFunction.py
rename to embedders/UniversalSentenceEncoderEmbeddingFunction.py
diff --git a/script_mteb_french/src/VoyageAIEmbeddingFunction.py b/embedders/VoyageAIEmbeddingFunction.py
similarity index 100%
rename from script_mteb_french/src/VoyageAIEmbeddingFunction.py
rename to embedders/VoyageAIEmbeddingFunction.py
diff --git a/script_mteb_french/src/__init__.py b/embedders/__init__.py
similarity index 100%
rename from script_mteb_french/src/__init__.py
rename to embedders/__init__.py
diff --git a/fix_results.py b/fix_results.py
deleted file mode 100644
index bb095429..00000000
--- a/fix_results.py
+++ /dev/null
@@ -1,64 +0,0 @@
-"""
-Iterates over json results for custom fixes
-Usage: python fix_results.py results_folder_path
-"""
-import glob
-import json
-import sys
-import os
-
-from mteb import MTEB
-
-results_folder = sys.argv[1]
-files = glob.glob(f'{results_folder.strip("/")}/*/*.json')
-
-print("Found files: ", files)
-
-for file_name in files:
-    with open(file_name, 'r', encoding='utf-8') as f:
-        results = json.load(f)
-        if "dataset_version" in results:
-            results.pop("dataset_version")
-        if "mteb_version" not in results:
-            results["mteb_version"] = "0.0.2"
-        if "mteb_dataset_name" not in results:
-            results["mteb_dataset_name"] = file_name.split("/")[-1].replace(".json", "")
-        if "dataset_revision" not in results:
-            print(file_name)
-            mteb_desc = (
-                MTEB(tasks=[file_name.split("/")[-1].replace(".json", "").replace("CQADupstackRetrieval", "CQADupstackAndroidRetrieval")])
-                .tasks[0]
-                .description
-            )
-            import huggingface_hub
-            if "hf_hub_name" in mteb_desc:
-                hf_hub_name = mteb_desc.get("hf_hub_name")
-            else:
-                hf_hub_name = "BeIR/" + mteb_desc.get("beir_name")
-            if "cqadupstack" in hf_hub_name:
-                hf_hub_name = "BeIR/cqadupstack-qrels"
-            results["dataset_revision"] = huggingface_hub.hf_api.dataset_info(hf_hub_name).sha
-
-        if "STS22" in file_name:
-            for split, split_results in results.items():
-                if isinstance(split_results, dict):
-                    for metric, score in split_results.items():
-                        if isinstance(score, dict):
-                            for sub_metric, sub_score in score.items():
-                                if isinstance(sub_score, dict):
-                                    for sub_sub_metric, sub_sub_score in sub_score.items():
-                                        results[split][metric][sub_metric][sub_sub_metric] = abs(sub_sub_score)
-                                else:
-                                    results[split][metric][sub_metric] = abs(sub_score)
-                        else:
-                            results[split][metric] = abs(score)
-                results.setdefault(split, {})
-        # Merge MSMARCO dev & test split runs
-        elif "MSMARCO." in file_name and os.path.exists(file_name.replace("MSMARCO.", "MSMARCO-test.")):
-            with open(file_name.replace("MSMARCO.", "MSMARCO-test."), 'r', encoding='utf-8') as f:
-                results_test = json.load(f)
-            results["test"] = results_test["test"]
-
-        with open(file_name, 'w', encoding='utf-8') as f:
-            json.dump(results, f, indent=4)
-
diff --git a/results/Cohere-embed-multilingual-light-v3.0/AlloProfClusteringP2P.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/AlloProfClusteringP2P.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/AlloProfClusteringS2S.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/AlloProfClusteringS2S.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/AmazonReviewsClassification.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/AmazonReviewsClassification.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/DiaBLaBitextMining.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/DiaBLaBitextMining.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/FloresBitextMining.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/FloresBitextMining.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/HALClusteringS2S.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/HALClusteringS2S.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/MLSUMClusteringP2P.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/MLSUMClusteringP2P.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/MLSUMClusteringS2S.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/MLSUMClusteringS2S.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/MTOPDomainClassification.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/MTOPDomainClassification.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/MasakhaNEWSClassification.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/MasakhaNEWSClassification.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/MasakhaNEWSClusteringP2P.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/MasakhaNEWSClusteringP2P.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/MasakhaNEWSClusteringS2S.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/MasakhaNEWSClusteringS2S.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/MassiveIntentClassification.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/MassiveIntentClassification.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/MintakaRetrieval.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/MintakaRetrieval.json
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diff --git a/results/Cohere-embed-multilingual-light-v3.0/OpusparcusPC.json b/outputs/benchmark_results/Cohere-embed-multilingual-light-v3.0/OpusparcusPC.json
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-Dataset & Language & Glove & Komninos & BERT & SimCSE-BERT-unsup & SimCSE-BERT-sup & coCondenser-msmarco & Contriever & SPECTER & LaBSE & LASER2 & MiniLM-L6 & MiniLM-L12 & MiniLM-L12-multilingual & MPNet & MPNet-multilingual & Ada Similarity & SGPT-125M-nli & SGPT-5.8B-nli & SGPT-125M-msmarco & SGPT-1.3B-msmarco & SGPT-2.7B-msmarco & SGPT-5.8B-msmarco & SGPT-BLOOM-7.1B-msmarco & GTR-Base & GTR-Large & GTR-XL & GTR-XXL & ST5-Base & ST5-Large & ST5-XL & ST5-XXL \\
-AmazonCounterfactualClassification & en & 56.91 & 60.54 & 74.25 & 67.09 & 75.75 & 64.06 & 72.19 & 58.7 & 75.93 & 76.84 & 64.15 & 65.28 & 71.57 & 65.27 & 75.81 & 76.4 & 65.88 & 74.07 & 61.24 & 65.21 & 67.57 & 69.22 & 68.06 & 69.33 & 70.03 & 68.6 & 67.3 & 75.82 & 75.51 & 76.01 & 77.07 \\
-AmazonPolarityClassification & en & 60.32 & 59.59 & 71.33 & 74.48 & 82.47 & 66.88 & 68.63 & 57.77 & 68.95 & 61.01 & 62.58 & 62.98 & 69.21 & 67.13 & 76.41 & 92.83 & 74.94 & 82.31 & 65.4 & 73.21 & 71.44 & 71.26 & 68.97 & 67.82 & 73.92 & 74.58 & 75.05 & 85.12 & 92.87 & 93.17 & 92.79 \\
-AmazonReviewsClassification & en & 29.67 & 31.01 & 33.56 & 33.85 & 39.6 & 34.85 & 37.42 & 26.26 & 35.8 & 28.71 & 31.79 & 30.79 & 35.11 & 31.92 & 38.51 & 47.45 & 35.1 & 41.58 & 31.17 & 34.96 & 35.75 & 39.19 & 33.86 & 38.48 & 37.21 & 38.2 & 37.3 & 44.94 & 47.12 & 48.18 & 48.93 \\
-Banking77Classification & en & 67.69 & 67.05 & 63.41 & 73.55 & 75.76 & 82.35 & 80.02 & 66.66 & 69.85 & 57.76 & 79.75 & 80.4 & 79.77 & 81.86 & 81.07 & 68.04 & 74.68 & 81.74 & 77.7 & 82.06 & 83.22 & 84.49 & 84.33 & 79.26 & 81.21 & 82.22 & 82.32 & 76.48 & 78.46 & 80.88 & 82.31 \\
-EmotionClassification & en & 36.93 & 33.18 & 35.28 & 42.22 & 44.81 & 41.91 & 44.77 & 24.82 & 37.22 & 24.83 & 38.43 & 41.17 & 42.37 & 39.73 & 45.84 & 50.32 & 42.23 & 49.92 & 39.08 & 46.39 & 49.21 & 49.66 & 44.87 & 42.2 & 46.32 & 45.55 & 43.19 & 51.36 & 51.73 & 51.95 & 48.57 \\
-ImdbClassification & en & 62.57 & 63.98 & 65.35 & 69.63 & 73.53 & 60.17 & 67.04 & 56.35 & 62.04 & 57.58 & 60.66 & 59.76 & 60.46 & 70.72 & 64.57 & 89.38 & 62.9 & 74.33 & 58.67 & 64.05 & 63.53 & 66.64 & 61.77 & 65.99 & 70.86 & 68.15 & 70.8 & 77.34 & 87.01 & 87.54 & 90.23 \\
-MassiveIntentClassification & en & 56.19 & 57.21 & 59.88 & 59.84 & 65.95 & 70.4 & 67.78 & 51.73 & 61.46 & 47.91 & 67.4 & 67.15 & 66.84 & 69.57 & 69.32 & 65.17 & 58.08 & 70.0 & 61.41 & 68.65 & 69.01 & 70.39 & 69.67 & 67.05 & 70.06 & 70.23 & 70.61 & 69.74 & 71.78 & 72.09 & 73.44 \\
-MassiveScenarioClassification & en & 66.03 & 66.11 & 64.28 & 66.25 & 70.78 & 73.73 & 76.0 & 58.58 & 66.41 & 55.92 & 75.76 & 74.58 & 71.51 & 76.01 & 75.35 & 67.67 & 66.34 & 75.03 & 69.74 & 76.04 & 75.9 & 76.28 & 75.34 & 75.4 & 75.49 & 75.94 & 77.77 & 72.32 & 73.16 & 73.26 & 74.82 \\
-MTOPDomainClassification & en & 79.11 & 78.57 & 82.63 & 81.71 & 84.29 & 91.34 & 93.18 & 74.53 & 86.06 & 75.36 & 91.56 & 91.9 & 87.06 & 92.08 & 89.24 & 89.89 & 81.52 & 89.64 & 86.96 & 92.08 & 92.56 & 93.47 & 93.68 & 92.42 & 94.01 & 93.6 & 93.84 & 90.34 & 90.99 & 90.73 & 92.49 \\
-MTOPIntentClassification & en & 55.85 & 57.07 & 68.14 & 59.23 & 63.14 & 71.07 & 69.31 & 50.05 & 63.03 & 49.47 & 62.18 & 62.84 & 65.52 & 70.21 & 68.69 & 64.8 & 58.24 & 70.68 & 62.25 & 71.19 & 71.85 & 72.42 & 71.34 & 62.44 & 63.86 & 65.93 & 67.71 & 63.32 & 64.98 & 68.15 & 68.33 \\
-ToxicConversationsClassification & en & 65.4 & 67.76 & 70.0 & 68.82 & 72.04 & 64.01 & 67.77 & 57.44 & 66.9 & 54.05 & 66.99 & 67.47 & 66.07 & 60.86 & 71.02 & 70.0 & 62.79 & 69.93 & 62.66 & 68.73 & 68.84 & 67.71 & 66.55 & 66.6 & 68.65 & 67.56 & 68.48 & 68.2 & 71.73 & 70.95 & 70.04 \\
-TweetSentimentExtractionClassification & en & 50.8 & 49.68 & 51.81 & 53.36 & 59.73 & 55.74 & 56.1 & 45.52 & 58.82 & 48.73 & 55.41 & 54.25 & 56.12 & 55.46 & 59.03 & 63.35 & 54.82 & 62.44 & 52.41 & 55.67 & 56.69 & 56.85 & 55.85 & 56.02 & 54.09 & 54.77 & 54.54 & 62.71 & 62.33 & 61.21 & 62.01 \\
-ArxivClusteringP2P & en & 32.56 & 34.73 & 35.19 & 32.61 & 35.18 & 36.94 & 42.61 & 44.75 & 32.13 & 17.77 & 46.55 & 46.07 & 38.33 & 48.38 & 37.78 & 41.49 & 34.74 & 40.55 & 39.71 & 43.38 & 44.72 & 45.59 & 44.59 & 35.49 & 37.5 & 37.9 & 37.9 & 39.28 & 41.62 & 41.62 & 42.89 \\
-ArxivClusteringS2S & en & 23.14 & 26.01 & 27.51 & 24.68 & 27.54 & 29.03 & 32.32 & 35.27 & 22.05 & 12.39 & 37.86 & 37.5 & 31.55 & 39.72 & 31.68 & 28.47 & 24.68 & 32.49 & 28.24 & 33.71 & 35.08 & 38.86 & 38.03 & 27.18 & 30.55 & 30.45 & 32.39 & 27.26 & 29.44 & 31.17 & 33.47 \\
-BiorxivClusteringP2P & en & 29.27 & 29.76 & 30.12 & 24.9 & 30.15 & 32.35 & 34.97 & 39.52 & 29.84 & 12.4 & 38.48 & 36.99 & 33.49 & 39.62 & 33.09 & 36.86 & 28.93 & 33.59 & 33.63 & 35.06 & 34.41 & 36.55 & 36.03 & 27.66 & 29.59 & 30.52 & 30.48 & 33.99 & 35.99 & 36.43 & 36.53 \\
-BiorxivClusteringS2S & en & 19.18 & 20.71 & 24.77 & 19.55 & 24.67 & 28.16 & 29.08 & 34.53 & 20.57 & 8.83 & 33.17 & 33.21 & 29.44 & 35.02 & 29.6 & 27.55 & 23.08 & 29.13 & 27.04 & 30.71 & 30.53 & 33.7 & 32.48 & 23.25 & 25.72 & 26.06 & 27.5 & 22.92 & 24.02 & 26.47 & 28.66 \\
-MedrxivClusteringP2P & en & 26.12 & 26.65 & 26.09 & 23.6 & 26.25 & 30.23 & 31.19 & 35.04 & 30.13 & 17.91 & 34.41 & 34.25 & 31.52 & 35.58 & 31.96 & 31.09 & 28.3 & 30.33 & 31.37 & 32.08 & 31.35 & 31.51 & 31.05 & 27.57 & 28.72 & 28.69 & 29.12 & 33.2 & 32.4 & 32.3 & 32.09 \\
-MedrxivClusteringS2S & en & 20.38 & 21.5 & 23.6 & 21.97 & 24.12 & 27.01 & 27.27 & 31.66 & 24.82 & 16.63 & 32.29 & 32.24 & 30.87 & 32.87 & 31.7 & 26.5 & 24.93 & 28.02 & 26.87 & 29.45 & 28.77 & 28.76 & 29.26 & 25.13 & 27.39 & 26.69 & 27.56 & 26.13 & 26.33 & 26.93 & 26.82 \\
-RedditClustering & en & 28.46 & 28.84 & 27.24 & 32.18 & 40.23 & 48.04 & 54.89 & 24.13 & 28.79 & 9.96 & 50.67 & 51.18 & 42.02 & 54.82 & 45.24 & 42.47 & 33.76 & 42.17 & 40.23 & 48.23 & 46.47 & 40.45 & 35.53 & 56.13 & 61.69 & 61.34 & 64.13 & 52.93 & 54.53 & 57.03 & 58.99 \\
-RedditClusteringP2P & en & 35.82 & 7.37 & 43.32 & 45.14 & 47.74 & 53.53 & 57.58 & 35.06 & 49.14 & 26.42 & 54.15 & 54.8 & 50.73 & 56.77 & 51.31 & 58.1 & 41.01 & 48.02 & 49.09 & 53.18 & 54.17 & 55.75 & 54.52 & 58.53 & 61.67 & 61.11 & 62.84 & 59.67 & 62.5 & 62.34 & 64.46 \\
-StackExchangeClustering & en & 35.8 & 39.04 & 43.58 & 43.07 & 47.55 & 59.54 & 63.15 & 39.01 & 35.43 & 15.79 & 53.36 & 53.05 & 49.6 & 53.8 & 52.98 & 53.52 & 44.59 & 54.13 & 52.74 & 60.86 & 59.19 & 59.21 & 55.13 & 64.21 & 69.93 & 69.95 & 71.43 & 63.13 & 65.11 & 67.13 & 70.78 \\
-StackExchangeClusteringP2P & en & 28.51 & 30.23 & 26.55 & 28.5 & 29.45 & 30.48 & 32.25 & 31.46 & 28.83 & 18.63 & 38.0 & 33.13 & 31.69 & 34.28 & 32.94 & 30.43 & 28.23 & 31.12 & 32.66 & 32.36 & 32.57 & 33.95 & 34.31 & 33.01 & 33.21 & 32.73 & 32.85 & 35.68 & 36.86 & 34.79 & 35.25 \\
-TwentyNewsgroupsClustering & en & 25.83 & 27.42 & 23.35 & 23.21 & 34.86 & 38.68 & 46.82 & 24.22 & 23.28 & 11.38 & 46.86 & 47.47 & 39.28 & 49.74 & 44.1 & 36.26 & 28.24 & 37.2 & 32.13 & 40.06 & 40.89 & 39.46 & 37.28 & 46.72 & 51.64 & 51.15 & 50.44 & 48.1 & 49.33 & 49.53 & 50.93 \\
-SprintDuplicateQuestions & en & 86.96 & 85.55 & 36.81 & 69.41 & 69.39 & 96.09 & 95.55 & 71.63 & 89.26 & 65.54 & 94.55 & 92.45 & 89.46 & 90.15 & 90.55 & 77.85 & 77.73 & 80.54 & 89.89 & 92.58 & 93.47 & 93.84 & 94.93 & 94.55 & 95.05 & 95.45 & 95.68 & 91.23 & 89.01 & 91.44 & 88.89 \\
-TwitterSemEval2015 & en & 48.45 & 53.85 & 55.9 & 60.21 & 67.75 & 65.95 & 66.85 & 43.25 & 62.78 & 59.57 & 67.86 & 70.02 & 62.06 & 73.85 & 66.75 & 69.04 & 57.09 & 66.0 & 54.75 & 62.37 & 63.68 & 66.87 & 65.31 & 72.23 & 76.03 & 77.81 & 77.54 & 78.25 & 79.75 & 80.89 & 80.28 \\
-TwitterURLCorpus & en & 77.35 & 79.41 & 76.29 & 81.37 & 83.89 & 83.17 & 85.21 & 69.22 & 84.58 & 81.47 & 84.7 & 84.77 & 83.83 & 85.11 & 85.14 & 83.69 & 80.51 & 84.54 & 81.06 & 83.79 & 84.8 & 85.29 & 85.46 & 84.77 & 84.89 & 85.14 & 85.13 & 86.05 & 86.14 & 85.86 & 86.01 \\
-AskUbuntuDupQuestions & en & 49.57 & 50.88 & 45.84 & 51.57 & 51.8 & 58.99 & 56.69 & 50.07 & 52.75 & 48.99 & 63.48 & 64.06 & 60.49 & 65.85 & 60.16 & 53.49 & 52.63 & 55.9 & 55.84 & 58.13 & 59.63 & 61.63 & 59.97 & 60.86 & 61.64 & 63.08 & 63.23 & 59.73 & 61.51 & 62.86 & 66.16 \\
-MindSmallReranking & en & 27.01 & 28.92 & 28.37 & 28.62 & 29.3 & 27.13 & 31.58 & 24.8 & 29.81 & 24.79 & 30.8 & 31.02 & 30.37 & 30.97 & 30.15 & 30.71 & 29.27 & 31.11 & 30.4 & 31.34 & 31.72 & 32.29 & 31.79 & 31.33 & 31.84 & 31.5 & 31.93 & 30.2 & 30.27 & 29.77 & 30.6 \\
-SciDocsRR & en & 62.56 & 63.55 & 64.94 & 66.33 & 70.14 & 72.78 & 76.51 & 81.31 & 68.72 & 54.99 & 87.12 & 87.2 & 77.78 & 88.65 & 78.09 & 71.04 & 68.36 & 77.54 & 71.34 & 77.21 & 77.72 & 80.79 & 79.77 & 73.71 & 76.39 & 76.49 & 77.96 & 73.96 & 74.88 & 75.16 & 76.09 \\
-StackOverflowDupQuestions & en & 34.03 & 35.65 & 34.62 & 39.35 & 38.9 & 48.48 & 47.78 & 36.22 & 42.42 & 36.98 & 50.76 & 51.47 & 45.85 & 51.98 & 46.79 & 40.85 & 39.97 & 44.77 & 44.74 & 49.32 & 49.61 & 51.53 & 51.07 & 51.01 & 51.58 & 52.79 & 53.5 & 48.46 & 49.34 & 51.05 & 52.85 \\
-ArguAna & en & 36.3 & 30.96 & 28.29 & 38.34 & 38.33 & 45.15 & 48.32 & 32.67 & 34.18 & 12.86 & 50.17 & 47.13 & 44.88 & 46.52 & 48.91 &  & 31.04 & 35.07 & 45.42 & 49.68 & 50.49 & 51.38 & 47.28 & 50.83 & 52.09 & 52.81 & 53.77 & 44.85 & 39.27 & 39.4 & 39.85 \\
-ClimateFEVER & en & 14.44 & 14.87 & 5.41 & 11.8 & 11.98 & 16.96 & 24.79 & 6.86 & 3.83 & 0.36 & 20.27 & 21.57 & 18.49 & 21.97 & 15.27 &  & 11.01 & 17.57 & 21.86 & 26.6 & 27.11 & 30.46 & 29.39 & 24.88 & 26.9 & 27.01 & 27.21 & 10.37 & 11.36 & 10.61 & 14.63 \\
-CQADupstackRetrieval & en & 15.47 & 16.79 & 5.51 & 13.22 & 14.5 & 27.72 & 33.67 & 14.6 & 18.75 & 4.12 & 41.32 & 42.53 & 30.71 & 44.96 & 31.32 &  & 20.29 & 29.98 & 27.25 & 33.33 & 36.53 & 39.4 & 39.62 & 34.55 & 36.62 & 37.35 & 38.56 & 35.23 & 38.96 & 40.78 & 44.65 \\
-DBPedia & en & 18.29 & 15.88 & 4.13 & 15.04 & 19.73 & 27.86 & 38.1 & 4.14 & 15.57 & 1.53 & 32.33 & 33.36 & 22.63 & 32.09 & 26.22 &  & 10.87 & 26.1 & 22.72 & 31.51 & 34.7 & 39.87 & 39.03 & 35.24 & 39.55 & 39.74 & 41.28 & 27.77 & 31.55 & 33.65 & 39.19 \\
-FEVER & en & 14.99 & 15.56 & 3.3 & 21.05 & 20.41 & 45.68 & 59.29 & 5.45 & 12.17 & 0.77 & 51.93 & 55.91 & 52.66 & 50.86 & 56.76 &  & 18.4 & 38.64 & 60.45 & 68.12 & 72.73 & 78.24 & 73.97 & 68.93 & 72.66 & 72.18 & 74.08 & 26.16 & 36.21 & 36.12 & 51.2 \\
-FiQA2018 & en & 10.09 & 10.49 & 2.19 & 9.84 & 10.41 & 15.62 & 27.42 & 5.64 & 7.0 & 1.73 & 36.87 & 37.27 & 20.33 & 49.96 & 22.96 &  & 8.94 & 18.59 & 21.12 & 29.99 & 33.29 & 37.2 & 35.84 & 35.15 & 42.79 & 44.19 & 46.78 & 34.83 & 43.55 & 44.71 & 46.68 \\
-HotpotQA & en & 19.18 & 20.77 & 8.26 & 19.75 & 22.89 & 35.61 & 56.81 & 5.46 & 18.75 & 5.5 & 46.51 & 44.59 & 30.01 & 39.29 & 37.03 &  & 17.73 & 33.99 & 40.88 & 49.93 & 52.84 & 59.26 & 57.26 & 54.93 & 57.85 & 58.91 & 59.67 & 33.2 & 33.95 & 37.17 & 42.14 \\
-MSMARCO & en & 9.6 & 9.75 & 1.91 & 9.35 & 11.0 & 29.57 & 36.77 & 5.58 & 7.6 & 1.09 & 36.54 & 39.03 & 23.72 & 39.75 & 26.6 &  & 6.27 & 15.83 & 27.98 & 36.05 & 38.83 & 39.91 & 41.12 & 41.16 & 42.73 & 43.52 & 44.05 & 20.71 & 23.96 & 25.17 & 27.68 \\
-NFCorpus & en & 13.87 & 11.79 & 4.3 & 9.88 & 12.42 & 22.29 & 31.31 & 0.84 & 16.54 & 2.44 & 31.59 & 32.25 & 23.45 & 33.29 & 25.49 &  & 11.8 & 28.26 & 22.79 & 32.08 & 33.89 & 36.21 & 35.78 & 30.22 & 32.63 & 33.34 & 34.18 & 28.64 & 31.1 & 33.18 & 35.08 \\
-NQ & en & 12.87 & 12.75 & 2.61 & 11.69 & 16.08 & 29.85 & 41.83 & 5.99 & 8.42 & 0.64 & 43.87 & 46.47 & 29.8 & 50.45 & 33.6 &  & 7.63 & 24.63 & 29.73 & 42.94 & 46.7 & 52.41 & 53.15 & 50.47 & 55.09 & 56.16 & 57.24 & 36.32 & 42.02 & 46.29 & 52.87 \\
-QuoraRetrieval & en & 71.32 & 71.58 & 61.03 & 78.03 & 79.62 & 86.51 & 86.72 & 64.65 & 77.03 & 71.14 & 87.56 & 87.75 & 86.55 & 87.46 & 86.41 &  & 78.96 & 84.68 & 72.98 & 85.28 & 85.6 & 84.58 & 74.71 & 87.98 & 88.47 & 88.91 & 89.09 & 85.49 & 85.73 & 85.85 & 85.96 \\
-SCIDOCS & en & 8.04 & 8.47 & 2.81 & 5.5 & 7.53 & 10.13 & 17.12 & 0.0 & 5.63 & 0.78 & 21.64 & 21.82 & 0.03 & 23.77 & 13.96 &  & 7.13 & 13.55 & 12.21 & 16.18 & 16.57 & 19.87 & 18.62 & 14.0 & 15.51 & 15.71 & 15.88 & 14.16 & 15.38 & 15.97 & 17.17 \\
-SciFact & en & 29.58 & 29.53 & 13.34 & 25.72 & 29.59 & 52.31 & 65.51 & 47.88 & 38.2 & 4.04 & 64.51 & 62.64 & 48.37 & 65.57 & 50.3 &  & 31.79 & 46.66 & 56.9 & 68.29 & 70.17 & 74.7 & 72.11 & 59.74 & 63.42 & 64.2 & 66.77 & 45.76 & 49.91 & 50.91 & 55.38 \\
-Touche2020 & en & 13.99 & 13.17 & 0.97 & 8.9 & 9.89 & 8.57 & 15.79 & 8.46 & 4.88 & 1.06 & 16.9 & 17.22 & 16.06 & 19.93 & 17.4 &  & 12.27 & 16.18 & 22.97 & 24.45 & 23.44 & 25.43 & 23.98 & 25.89 & 28.29 & 25.26 & 26.76 & 20.3 & 21.63 & 22.51 & 21.65 \\
-TRECCOVID & en & 36.22 & 35.92 & 14.74 & 26.2 & 22.93 & 40.54 & 44.77 & 29.91 & 16.34 & 10.97 & 47.25 & 50.82 & 39.12 & 51.33 & 37.87 &  & 39.31 & 55.35 & 70.3 & 72.98 & 75.17 & 84.88 & 81.37 & 56.05 & 56.68 & 60.09 & 51.9 & 40.7 & 46.11 & 54.77 & 59.48 \\
-BIOSSES & en & 44.93 & 50.25 & 54.7 & 72.31 & 68.38 & 77.32 & 83.32 & 64.95 & 78.7 & 62.01 & 81.64 & 83.57 & 74.18 & 80.43 & 76.27 & 78.04 & 70.93 & 79.5 & 75.21 & 83.02 & 84.84 & 86.25 & 85.31 & 79.0 & 84.86 & 78.94 & 81.91 & 75.89 & 78.93 & 73.12 & 80.43 \\
-SICK-R & en & 55.43 & 55.49 & 58.65 & 72.24 & 80.77 & 72.0 & 70.2 & 56.39 & 69.99 & 62.86 & 77.58 & 79.32 & 79.61 & 80.59 & 79.62 & 77.48 & 74.57 & 79.59 & 65.93 & 67.23 & 68.2 & 69.63 & 69.82 & 71.45 & 73.39 & 73.63 & 74.29 & 80.18 & 80.34 & 79.98 & 80.47 \\
-STS12 & en & 54.64 & 53.51 & 30.87 & 66.05 & 75.3 & 68.19 & 64.34 & 62.49 & 65.08 & 62.6 & 72.37 & 73.08 & 76.02 & 72.63 & 77.9 & 72.3 & 69.17 & 74.29 & 66.53 & 66.59 & 66.99 & 67.5 & 69.66 & 68.59 & 70.33 & 69.11 & 70.12 & 78.05 & 79.11 & 79.02 & 78.85 \\
-STS13 & en & 69.16 & 70.8 & 59.89 & 81.49 & 84.67 & 80.4 & 80.03 & 58.7 & 67.98 & 59.62 & 80.6 & 82.13 & 80.7 & 83.48 & 85.11 & 81.49 & 77.23 & 85.35 & 76.17 & 77.33 & 77.58 & 79.16 & 79.67 & 79.09 & 82.19 & 81.82 & 82.72 & 85.85 & 87.33 & 88.8 & 88.94 \\
-STS14 & en & 60.81 & 63.56 & 47.73 & 73.61 & 80.19 & 74.02 & 74.51 & 54.87 & 64.03 & 57.03 & 75.59 & 76.73 & 78.85 & 78.0 & 80.81 & 74.74 & 70.99 & 79.21 & 69.05 & 71.83 & 72.78 & 74.46 & 74.61 & 74.64 & 77.16 & 77.07 & 78.24 & 82.19 & 83.17 & 84.33 & 84.86 \\
-STS15 & en & 72.31 & 74.08 & 60.29 & 79.72 & 85.4 & 82.57 & 83.3 & 62.54 & 76.59 & 71.57 & 85.39 & 85.58 & 85.84 & 85.66 & 87.48 & 84.28 & 79.74 & 85.52 & 79.24 & 80.66 & 82.62 & 84.47 & 83.81 & 84.85 & 86.31 & 86.01 & 86.26 & 87.46 & 88.28 & 88.89 & 89.32 \\
-STS16 & en & 65.34 & 64.6 & 63.73 & 78.12 & 80.82 & 79.78 & 79.67 & 64.27 & 72.98 & 70.75 & 78.99 & 80.23 & 81.05 & 80.03 & 83.2 & 82.06 & 77.93 & 82.54 & 76.07 & 78.91 & 80.1 & 80.96 & 80.4 & 81.57 & 81.85 & 82.23 & 81.61 & 84.03 & 84.36 & 85.31 & 84.67 \\
-STS17 & en-en & 77.95 & 76.91 & 64.1 & 83.58 & 89.44 & 85.94 & 86.32 & 69.63 & 79.45 & 76.73 & 87.59 & 88.63 & 86.87 & 90.6 & 86.99 & 87.08 & 87.33 & 90.44 & 84.95 & 86.99 & 87.25 & 87.78 & 87.07 & 85.8 & 83.93 & 84.9 & 85.18 & 89.57 & 88.99 & 88.91 & 89.46 \\
-STS22 & en & 56.35 & 53.89 & 56.37 & 59.65 & 61.96 & 67.54 & 64.64 & 55.06 & 60.97 & 39.75 & 67.21 & 65.67 & 61.72 & 67.95 & 63.06 & 64.71 & 59.64 & 63.2 & 65.66 & 67.3 & 68.75 & 69.35 & 66.13 & 66.17 & 64.3 & 66.61 & 65.76 & 62.66 & 62.39 & 64.32 & 65.33 \\
-STSBenchmark & en & 61.54 & 61.55 & 47.29 & 76.52 & 84.25 & 76.97 & 78.81 & 61.26 & 72.25 & 69.77 & 82.03 & 83.09 & 84.42 & 83.42 & 86.82 & 83.78 & 79.54 & 85.67 & 75.34 & 77.59 & 79.21 & 81.39 & 80.9 & 79.58 & 77.6 & 77.65 & 77.73 & 85.52 & 85.36 & 83.93 & 84.01 \\
-SummEval & en & 28.87 & 30.49 & 29.82 & 31.15 & 23.31 & 29.5 & 30.36 & 27.66 & 31.05 & 26.8 & 30.81 & 27.9 & 30.67 & 27.49 & 31.57 & 26.94 & 30.26 & 30.38 & 28.9 & 25.44 & 27.87 & 24.75 & 24.99 & 29.67 & 29.5 & 30.21 & 30.64 & 31.39 & 29.64 & 29.91 & 30.08 \\
-Average & en & 41.97 & 42.06 & 38.33 & 45.45 & 48.72 & 52.35 & 56.0 & 40.28 & 45.21 & 34.95 & 56.26 & 56.53 & 52.44 & 57.78 & 54.71 &  & 45.97 & 53.74 & 51.23 & 56.11 & 57.12 & 58.81 & 57.44 & 56.19 & 58.28 & 58.42 & 58.97 & 55.27 & 57.06 & 57.87 & 59.51 \\
diff --git a/plotstables/avg_table.txt b/plotstables/avg_table.txt
deleted file mode 100644
index 1885217e..00000000
--- a/plotstables/avg_table.txt
+++ /dev/null
@@ -1,34 +0,0 @@
-Task ($\ rightarrow$) & Class. & Clust. & PairClass. & Rerank. & Retr. & STS & Summ. & Avg. \\
-Num. Datasets ($\ rightarrow$) & 12 & 11 & 3 & 4 & 15 & 10 & 1 & 56 \\
-Model ($\downarrow$) & Class. & Clust. & PairClass. & Rerank. & Retr. & STS & Summ. & Avg. \\
-Glove & 57.29 & 27.73 & 70.92 & 43.29 & 21.62 & 61.85 & 28.87 & 41.97 \\
-Komninos & 57.65 & 26.57 & 72.94 & 44.75 & 21.22 & 62.47 & 30.49 & 42.06 \\
-LASER2 & 53.18 & 15.28 & 68.86 & 41.44 & 7.93 & 63.27 & 26.8 & 34.95 \\
-LaBSE & 62.71 & 29.55 & 78.87 & 48.42 & 18.99 & 70.8 & 31.05 & 45.21 \\
-BERT & 61.66 & 30.12 & 56.33 & 43.44 & 10.59 & 54.36 & 29.82 & 38.33 \\
-coCondenser-msmarco & 64.71 & 37.64 & 81.74 & 51.84 & 32.96 & 76.47 & 29.5 & 52.35 \\
-SPECTER & 52.37 & 34.06 & 61.37 & 48.1 & 15.88 & 61.02 & 27.66 & 40.28 \\
-SimCSE-BERT-unsup & 62.5 & 29.04 & 70.33 & 46.47 & 20.29 & 74.33 & 31.15 & 45.45 \\
-SimCSE-BERT-sup & 67.32 & 33.43 & 73.68 & 47.54 & 21.82 & 79.12 & 23.31 & 48.72 \\
-MiniLM-L6 & 63.06 & 42.35 & 82.37 & 58.04 & 41.95 & 78.9 & 30.81 & 56.26 \\
-MiniLM-L12 & 63.21 & 41.81 & 82.41 & 58.44 & 42.69 & 79.8 & 27.9 & 56.53 \\
-MiniLM-L12-multilingual & 64.3 & 37.14 & 78.45 & 53.62 & 32.45 & 78.92 & 30.67 & 52.44 \\
-MPNet & 65.07 & 43.69 & 83.04 & 59.36 & 43.81 & 80.28 & 27.49 & 57.78 \\
-MPNet-multilingual & 67.91 & 38.4 & 80.81 & 53.8 & 35.34 & 80.73 & 31.57 & 54.71 \\
-Contriever & 66.68 & 41.1 & 82.53 & 53.14 & 41.88 & 76.51 & 30.36 & 56.0 \\
-Ada Similarity & 70.44 & 37.52 & 76.86 & 49.02 &  & 78.6 & 26.94 &  \\
-SGPT-125M-nli & 61.46 & 30.95 & 71.78 & 47.56 & 20.9 & 74.71 & 30.26 & 45.97 \\
-SGPT-5.8B-nli & 70.14 & 36.98 & 77.03 & 52.33 & 32.34 & 80.53 & 30.38 & 53.74 \\
-SGPT-125M-msmarco & 60.72 & 35.79 & 75.23 & 50.58 & 37.04 & 73.41 & 28.9 & 51.23 \\
-SGPT-1.3B-msmarco & 66.52 & 39.92 & 79.58 & 54.0 & 44.49 & 75.74 & 25.44 & 56.11 \\
-SGPT-2.7B-msmarco & 67.13 & 39.83 & 80.65 & 54.67 & 46.54 & 76.83 & 27.87 & 57.12 \\
-SGPT-5.8B-msmarco & 68.13 & 40.35 & 82.0 & 56.56 & 50.25 & 78.1 & 24.75 & 58.81 \\
-SGPT-BLOOM-7.1B-msmarco & 66.19 & 38.93 & 81.9 & 55.65 & 48.21 & 77.74 & 24.99 & 57.44 \\
-GTR-Base & 65.25 & 38.63 & 83.85 & 54.23 & 44.67 & 77.07 & 29.67 & 56.19 \\
-GTR-Large & 67.14 & 41.6 & 85.33 & 55.36 & 47.42 & 78.19 & 29.5 & 58.28 \\
-GTR-XL & 67.11 & 41.51 & 86.13 & 55.96 & 47.96 & 77.8 & 30.21 & 58.42 \\
-GTR-XXL & 67.41 & 42.42 & 86.12 & 56.65 & 48.48 & 78.38 & 30.64 & 58.97 \\
-ST5-Base & 69.81 & 40.21 & 85.17 & 53.09 & 33.63 & 81.14 & 31.39 & 55.27 \\
-ST5-Large & 72.31 & 41.65 & 84.97 & 54.0 & 36.71 & 81.83 & 29.64 & 57.06 \\
-ST5-XL & 72.84 & 42.34 & 86.06 & 54.71 & 38.47 & 81.66 & 29.91 & 57.87 \\
-ST5-XXL & 73.42 & 43.71 & 85.06 & 56.43 & 42.24 & 82.63 & 30.08 & 59.51 \\
diff --git a/plotstables/benchmark.pdf b/plotstables/benchmark.pdf
deleted file mode 100644
index 4879780c..00000000
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diff --git a/plotstables/benchmark.png b/plotstables/benchmark.png
deleted file mode 100644
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diff --git a/plotstables/benchmark_gpu.json b/plotstables/benchmark_gpu.json
deleted file mode 100644
index e8f840fa..00000000
--- a/plotstables/benchmark_gpu.json
+++ /dev/null
@@ -1,134 +0,0 @@
-{
-    "LASER2": {
-        "STS15": {
-            "speed_ms": 1.095552682876587,
-            "embedding_size_kb": 4.096
-        }
-    },
-    "komninos": {
-        "STS15": {
-            "speed_ms": 0.014420787493387857,
-            "embedding_size_kb": 1.2
-        }
-    },
-    "glove.6B.300d": {
-        "STS15": {
-            "speed_ms": 0.015402833620707195,
-            "embedding_size_kb": 1.2
-        }
-    },
-    "SGPT-125M-weightedmean-nli-bitfit": {
-        "STS15": {
-            "speed_ms": 0.3835549751917521,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "SGPT-125M-weightedmean-msmarco-specb-bitfit": {
-        "STS15": {
-            "speed_ms": 0.40986963113149005,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "SGPT-5.8B-weightedmean-nli-bitfit": {
-        "STS15": {
-            "speed_ms": 13.105161627133688,
-            "embedding_size_kb": 16.384
-        }
-    },
-    "SGPT-5.8B-weightedmean-msmarco-specb-bitfit": {
-        "STS15": {
-            "speed_ms": 14.537005225817362,
-            "embedding_size_kb": 16.384
-        }
-    },
-    "all-MiniLM-L6-v2": {
-        "STS15": {
-            "speed_ms": 0.1488305727640788,
-            "embedding_size_kb": 1.536
-        }
-    },
-    "all-mpnet-base-v2": {
-        "STS15": {
-            "speed_ms": 0.35439515113830566,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "paraphrase-multilingual-mpnet-base-v2": {
-        "STS15": {
-            "speed_ms": 0.39108145236968994,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "sentence-t5-base": {
-        "STS15": {
-            "speed_ms": 0.41539565722147626,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "sentence-t5-xxl": {
-        "STS15": {
-            "speed_ms": 15.400389790534973,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "gtr-t5-base": {
-        "STS15": {
-            "speed_ms": 0.41492275396982825,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "gtr-t5-xxl": {
-        "STS15": {
-            "speed_ms": 15.39513130982717,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "contriever-base-msmarco": {
-        "STS15": {
-            "speed_ms": 0.34681657950083417,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "msmarco-bert-co-condensor": {
-        "STS15": {
-            "speed_ms": 0.3462672630945841,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "bert-base-uncased": {
-        "STS15": {
-            "speed_ms": 0.34756950537363684,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "sup-simcse-bert-base-uncased": {
-        "STS15": {
-            "speed_ms": 0.3426841100056966,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "unsup-simcse-bert-base-uncased": {
-        "STS15": {
-            "speed_ms": 0.3423287868499756,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "LaBSE": {
-        "STS15": {
-            "speed_ms": 0.3441281318664551,
-            "embedding_size_kb": 3.072
-        }
-    },
-    "all-MiniLM-L12-v2": {
-        "STS15": {
-            "speed_ms": 0.2361156940460205,
-            "embedding_size_kb": 1.536
-        }
-    },
-    "allenai-specter": {
-        "STS15": {
-            "speed_ms": 0.3867043654123942,
-            "embedding_size_kb": 3.072
-        }
-    }
-}
diff --git a/plotstables/benchmark_to_plot.py b/plotstables/benchmark_to_plot.py
deleted file mode 100644
index ea7b30d8..00000000
--- a/plotstables/benchmark_to_plot.py
+++ /dev/null
@@ -1,348 +0,0 @@
-"""
-Usage: 
-Inspired by Fig 3 from https://arxiv.org/pdf/2011.04006.pdf
-"""
-import json
-import os
-import sys
-
-import matplotlib.pyplot as plt
-from mteb import MTEB
-
-
-### GLOBAL VARIABLES ###
-
-TASK_LIST_CLASSIFICATION = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-]
-
-TASK_LIST_CLUSTERING = [
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-]
-
-TASK_LIST_PAIR_CLASSIFICATION = [
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-]
-
-TASK_LIST_RERANKING = [
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-]
-
-TASK_LIST_RETRIEVAL = [
-    "ArguAna",
-    "ClimateFEVER",
-    "CQADupstackRetrieval",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-]
-
-TASK_LIST_STS = [
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-]
-
-
-TASK_LIST_SUMMARIZATION = [
-    "SummEval",
-]
-
-TASK_LIST_EN = (
-    TASK_LIST_CLASSIFICATION
-    + TASK_LIST_CLUSTERING
-    + TASK_LIST_PAIR_CLASSIFICATION
-    + TASK_LIST_RERANKING
-    + TASK_LIST_RETRIEVAL
-    + TASK_LIST_STS
-    + TASK_LIST_SUMMARIZATION
-)
-
-MODEL_TO_NAME = {
-    "bert-base-uncased": "BERT",
-    "gtr-t5-base": "GTR-Base",
-    "gtr-t5-large": "GTR-Large",
-    "gtr-t5-xl": "GTR-XL",
-    "gtr-t5-xxl": "GTR-XXL",
-    "sentence-t5-base": "ST5-Base",
-    "sentence-t5-large": "ST5-Large",
-    "sentence-t5-xl": "ST5-XL",
-    "sentence-t5-xxl": "ST5-XXL",
-    "SGPT-125M-weightedmean-msmarco-specb-bitfit": "SGPT-125M-msmarco",
-    "SGPT-1.3B-weightedmean-msmarco-specb-bitfit": "SGPT-1.3B-msmarco",
-    "SGPT-2.7B-weightedmean-msmarco-specb-bitfit": "SGPT-2.7B-msmarco",
-    "SGPT-5.8B-weightedmean-msmarco-specb-bitfit": "SGPT-5.8B-msmarco",
-    "sgpt-bloom-7b1-msmarco": "SGPT-BLOOM-7.1B-msmarco",
-    "SGPT-125M-weightedmean-nli-bitfit": "SGPT-125M-nli",
-    "SGPT-5.8B-weightedmean-nli-bitfit": "SGPT-5.8B-nli",
-    "sup-simcse-bert-base-uncased": "SimCSE-BERT-sup",
-    "contriever-base-msmarco": "Contriever",
-    "msmarco-bert-co-condensor": "coCondenser-msmarco", # They write it as coCondenser in the paper
-    "unsup-simcse-bert-base-uncased": "SimCSE-BERT-unsup",
-    "glove.6B.300d": "Glove",
-    "komninos": "Komninos",
-    "all-MiniLM-L6-v2": "MiniLM-L6",
-    "all-MiniLM-L12-v2": "MiniLM-L12",
-    "paraphrase-multilingual-MiniLM-L12-v2": "MiniLM-L12-multilingual",
-    "all-mpnet-base-v2": "MPNet",
-    "paraphrase-multilingual-mpnet-base-v2": "MPNet-multilingual",
-    "allenai-specter": "SPECTER",
-    "text-similarity-ada-001": "Ada Similarity",
-}
-
-NAME_TO_ARCH = {
-    "gtr": "T5",
-    "st5": "T5",
-    "sgpt": "GPT",
-    "simcse": "BERT",
-    "contriever": "BERT",
-    "bert": "BERT",
-    "cocondenser": "BERT",
-    "specter": "SciBERT",
-    "mpnet": "MPNet",
-    "minilm": "MiniLM",
-    "laser2": "LASER",
-    "labse": "BERT",
-    "glove": "WordEmbeddings",
-    "komninos": "WordEmbeddings",
-}
-
-# Base from:
-# https://coolors.co/palette/ff5400-ff6d00-ff8500-ff9100-ff9e00-00b4d8-0096c7-0077b6-023e8a-03045e
-# Yellow tones from:
-# https://coolors.co/palette/6ab6dc-49a6d4-2f94c6-277ba5-1f6284-e0b700-ffd20a-ffda33-ffe15c-ffe570
-# Green from:
-# https://coolors.co/palette/f94144-f3722c-f8961e-f9844a-f9c74f-90be6d-43aa8b-4d908e-577590-277da1
-MODEL_TO_COLOR = {
-    "MiniLM": "#BAF19C",#"#017600", # Green
-    "MPNet": "#F94144",#"#007A7A", # Light Green
-    "GTR": "#FF5400",#"#221D91", # Blue 1
-    "ST5": "#FF9E00",#"#86D4F1", # Blue 2
-    "SGPT": "#00B4D8",#"#7B3FB9", # Purple
-    "SimCSE": "#F9C74F",#"#2070B4", # Blue 3
-    "LaBSE": "#F9C74F",#"#2070B4", # Blue 3
-    "SPECTER": "#E0B700", # Shade of #2070B4
-    "Glove": "#023E8A",#"#9BC7DD", # Light Blue
-    "LASER2": "#03045E", # Grey
-}
-
-ARCH_TO_COLOR = {
-    "T5": MODEL_TO_COLOR["GTR"],
-    "GPT": MODEL_TO_COLOR["SGPT"],
-    "BERT": MODEL_TO_COLOR["SimCSE"],
-    "SciBERT": MODEL_TO_COLOR["SPECTER"],
-    "MiniLM": MODEL_TO_COLOR["MiniLM"],
-    "MPNet": MODEL_TO_COLOR["MPNet"],
-    "WordEmbeddings": MODEL_TO_COLOR["Glove"],
-    "LASER": MODEL_TO_COLOR["LASER2"],
-}
-
-
-### LOGIC ###
-
-# Get average MTEB performance
-
-results_folder = sys.argv[1].strip("/")
-benchmark_json = sys.argv[2]
-
-all_results = {}
-
-for model_name in os.listdir(results_folder):
-    model_res_folder = os.path.join(results_folder, model_name)
-    if os.path.isdir(model_res_folder):
-        all_results.setdefault(model_name, {})
-        for file_name in os.listdir(model_res_folder):
-            if not file_name.endswith(".json"):
-                print(f"Skipping non-json {file_name}")
-                continue
-            with open(os.path.join(model_res_folder, file_name), "r", encoding="utf-8") as f:
-                results = json.load(f)
-                all_results[model_name] = {**all_results[model_name], **{file_name.replace(".json", ""): results}}
-
-def get_row(dataset, model_name, limit_langs=[], skip_langs=[]):
-    # CQADupstackRetrieval uses the same metric as its subsets
-    tasks = MTEB(tasks=[dataset.replace("CQADupstackRetrieval", "CQADupstackTexRetrieval")]).tasks
-    assert len(tasks) == 1, f"Found {len(tasks)} for {dataset}. Expected 1."
-    main_metric = tasks[0].description["main_score"]
-    test_result = all_results.get(model_name, {}). get(dataset, {})
-
-    # Dev / Val set is used for MSMARCO (See BEIR paper)
-    if "MSMARCO" in dataset:
-        test_result = (
-            test_result.get("dev") if "dev" in test_result else test_result.get("validation")
-        )
-    else:
-        test_result = test_result.get("test")
-
-    for lang in tasks[0].description["eval_langs"]:
-        if (limit_langs and lang not in limit_langs) or (skip_langs and lang in skip_langs):
-            continue
-        elif test_result is None:
-            raise NotImplementedError(f"Got no test result {test_result} for ds: {dataset} model: {model_name}")
-
-        test_result_lang = test_result.get(lang, test_result)
-        if main_metric == "cosine_spearman":
-            test_result_lang = test_result_lang.get("cos_sim", {}).get("spearman")
-        elif main_metric == "ap":
-            test_result_lang = test_result_lang.get("cos_sim", {}).get("ap")
-        else:
-            test_result_lang = test_result_lang.get(main_metric)
-
-        if test_result_lang is None:
-            raise NotImplementedError
-
-        return test_result_lang
-    raise NotImplementedError
-
-results_avg = {}
-
-for model in all_results:
-    try:
-        model_task_results = [get_row(task, model, limit_langs=["en", "en-en"]) for task in TASK_LIST_EN]
-    except:
-        continue
-    results_avg[model] = 100 * (sum(model_task_results) / len(model_task_results))
-
-
-with open(benchmark_json, "r") as f:
-    gpu_bench = json.load(f)
-
-import numpy as np
-
-fig, ax = plt.subplots(figsize=(14,8))
-
-for k, v in gpu_bench.items():
-    if k in ("specs", "sgpt-bloom-7b1-msmarco", "paraphrase-multilingual-MiniLM-L12-v2", "paraphrase-multilingual-mpnet-base-v2"):
-        continue
-
-    model_name = MODEL_TO_NAME.get(k, k)
-    model_arch = NAME_TO_ARCH.get(model_name.split(" ")[0].split("-")[0].lower(), (model_name))
-    color = ARCH_TO_COLOR[model_arch]
-
-    if k not in results_avg:
-        print(f"Missing average score for {k}")
-        continue
-
-    speed = 1000 / v["STS15"]["speed_ms"]
-    score = results_avg[k]
-    
-    ax.scatter(
-        speed, 
-        score,
-        label=model_arch,
-        color=color,
-        s=v["STS15"]["embedding_size_kb"] * 150, 
-        alpha=.5
-    )
-    # Empirical offsets
-    x_offset = y_offset = 0
-    if model_name in ("ST5-Base"):
-        x_offset = 0.5 * speed
-    elif model_name in ("GTR-Base"):
-        x_offset = 0.5 * speed
-        y_offset = -0.01 * score
-    elif model_name in ("Contriever"):
-        x_offset = -0.14 * speed
-        y_offset = 0.018 * score
-    elif model_name in ("LaBSE"):
-        x_offset = 0.45 * speed
-        y_offset = 0.01 * score
-    elif model_name in ("GTR-XXL", "ST5-XXL"):
-        x_offset = -0.65 * speed
-        if model_name == "GTR-XXL":
-            y_offset = 0.01 * score
-    elif model_name == "Komninos":
-        x_offset = 0.4 * speed
-        y_offset = 0.05 * score
-    elif model_name in ("Glove", "SPECTER"):
-        x_offset = 0.2 * speed
-        y_offset = -0.025 * score
-    elif model_name.startswith("SGPT-5.8B"):
-        x_offset = 0.3 * speed
-        y_offset = 0.05 * score
-    elif model_name.startswith("SGPT-125M-nli"):
-        x_offset = -0.45 * speed
-        y_offset = -0.008 * score
-    elif model_name.startswith("SGPT-125M-msmarco"):
-        x_offset = -0.2 * speed
-        y_offset = 0.01 * score
-    elif model_name.startswith("MiniLM-L12"):
-        y_offset = -0.01 * score
-        x_offset = -0.15 * speed
-    elif model_arch in ("BERT", "MiniLM", "MPNet", "LASER") or model_name.startswith("SGPT-125M"):
-        x_offset = -0.2 * speed
-    
-    ax.text(
-        speed - x_offset,
-        score - y_offset,
-        model_name,
-    )
-
-    # Annotate does not work with logscale, https://stackoverflow.com/questions/21140385/matplotlib-annotate-doesnt-work-on-log-scale
-    #ax.annotate(
-    #    MODEL_TO_NAME.get(k, k), 
-    #   xy=(np.log10(1000 / v["STS15"]["speed_ms"]), results_avg[k] - offset)
-    #)
-
-ax.set_xlabel("Speed (examples per sec)")
-ax.set_ylabel("MTEB Score")
-ax.set_xscale('log')
-ax.grid(alpha=0.5)
-
-# Create deduplicated Global Legend
-handles, labels = plt.gca().get_legend_handles_labels()
-by_label = dict(zip(labels, handles))
-lgnd = plt.legend(
-    by_label.values(), 
-    by_label.keys(), 
-    title="Base Architecture",
-    loc=(0.08,0.08), # "lower left", 
-)
-# Rescale bubbles to have the same size
-for handle in lgnd.legendHandles:
-    handle.set_sizes([70.0])
-
-
-plt.savefig('benchmark.pdf', dpi=300, bbox_inches='tight')
diff --git a/plotstables/bitext.txt b/plotstables/bitext.txt
deleted file mode 100644
index fbe13655..00000000
--- a/plotstables/bitext.txt
+++ /dev/null
@@ -1,118 +0,0 @@
-Dataset & Language & LASER2 & LaBSE & MiniLM-L12-multilingual & MPNet-multilingual & SGPT-BLOOM-7.1B-msmarco \\
-BUCC & de-en & 99.21 & 99.35 & 97.11 & 98.59 & 54.0 \\
-BUCC & fr-en & 98.39 & 98.72 & 94.99 & 96.89 & 97.06 \\
-BUCC & ru-en & 97.62 & 97.78 & 95.06 & 96.44 & 45.3 \\
-BUCC & zh-en & 97.7 & 99.16 & 95.63 & 97.56 & 97.96 \\
-Tatoeba & sqi-eng & 97.22 & 96.76 & 98.17 & 98.57 & 10.38 \\
-Tatoeba & fry-eng & 42.07 & 89.31 & 31.13 & 43.54 & 24.62 \\
-Tatoeba & kur-eng & 19.09 & 83.59 & 46.94 & 61.44 & 8.26 \\
-Tatoeba & tur-eng & 98.03 & 98.0 & 95.08 & 96.17 & 6.15 \\
-Tatoeba & deu-eng & 99.07 & 99.2 & 97.02 & 97.73 & 70.1 \\
-Tatoeba & nld-eng & 95.35 & 96.07 & 94.58 & 95.5 & 29.74 \\
-Tatoeba & ron-eng & 96.52 & 96.92 & 95.3 & 96.43 & 27.23 \\
-Tatoeba & ang-eng & 25.22 & 59.28 & 10.24 & 16.72 & 28.76 \\
-Tatoeba & ido-eng & 80.86 & 89.42 & 40.25 & 43.91 & 43.91 \\
-Tatoeba & jav-eng & 9.95 & 79.77 & 17.04 & 23.39 & 15.02 \\
-Tatoeba & isl-eng & 94.32 & 94.75 & 24.07 & 59.25 & 6.29 \\
-Tatoeba & slv-eng & 95.4 & 96.03 & 96.92 & 97.08 & 10.14 \\
-Tatoeba & cym-eng & 5.85 & 92.0 & 13.25 & 22.31 & 6.97 \\
-Tatoeba & kaz-eng & 53.3 & 87.49 & 34.89 & 61.49 & 3.32 \\
-Tatoeba & est-eng & 96.43 & 96.55 & 97.33 & 98.4 & 4.76 \\
-Tatoeba & heb-eng & 0.0 & 91.53 & 86.88 & 88.26 & 1.69 \\
-Tatoeba & gla-eng & 1.52 & 85.66 & 3.61 & 4.72 & 2.09 \\
-Tatoeba & mar-eng & 92.93 & 92.65 & 92.38 & 93.83 & 45.53 \\
-Tatoeba & lat-eng & 64.81 & 80.07 & 19.47 & 24.25 & 28.76 \\
-Tatoeba & bel-eng & 79.54 & 95.0 & 67.73 & 79.94 & 8.03 \\
-Tatoeba & pms-eng & 36.23 & 64.57 & 30.7 & 34.19 & 31.94 \\
-Tatoeba & gle-eng & 4.2 & 93.8 & 11.62 & 16.85 & 3.26 \\
-Tatoeba & pes-eng & 93.13 & 94.7 & 92.59 & 93.47 & 12.13 \\
-Tatoeba & nob-eng & 95.77 & 98.4 & 97.73 & 98.53 & 21.07 \\
-Tatoeba & bul-eng & 93.57 & 94.58 & 92.65 & 93.52 & 20.09 \\
-Tatoeba & cbk-eng & 77.17 & 79.44 & 55.37 & 58.68 & 64.63 \\
-Tatoeba & hun-eng & 95.2 & 96.55 & 91.58 & 94.18 & 5.07 \\
-Tatoeba & uig-eng & 56.49 & 92.4 & 24.39 & 48.35 & 1.27 \\
-Tatoeba & rus-eng & 92.58 & 93.75 & 91.87 & 92.92 & 59.84 \\
-Tatoeba & spa-eng & 97.33 & 98.4 & 95.42 & 97.0 & 94.48 \\
-Tatoeba & hye-eng & 88.72 & 94.09 & 93.28 & 94.38 & 0.5 \\
-Tatoeba & tel-eng & 96.72 & 97.86 & 36.4 & 79.73 & 64.62 \\
-Tatoeba & afr-eng & 92.59 & 96.18 & 58.22 & 72.96 & 16.62 \\
-Tatoeba & mon-eng & 3.42 & 95.91 & 95.04 & 96.14 & 2.85 \\
-Tatoeba & arz-eng & 66.16 & 76.0 & 51.26 & 55.69 & 70.66 \\
-Tatoeba & hrv-eng & 96.72 & 96.95 & 95.98 & 97.0 & 12.79 \\
-Tatoeba & nov-eng & 60.02 & 74.38 & 47.99 & 50.23 & 52.23 \\
-Tatoeba & gsw-eng & 27.52 & 46.5 & 25.74 & 25.12 & 21.03 \\
-Tatoeba & nds-eng & 77.13 & 79.42 & 32.16 & 38.88 & 23.92 \\
-Tatoeba & ukr-eng & 93.52 & 93.97 & 92.82 & 92.67 & 22.06 \\
-Tatoeba & uzb-eng & 23.2 & 84.23 & 17.14 & 23.19 & 4.71 \\
-Tatoeba & lit-eng & 96.2 & 96.47 & 93.16 & 95.37 & 4.49 \\
-Tatoeba & ina-eng & 93.93 & 95.37 & 79.13 & 84.32 & 73.67 \\
-Tatoeba & lfn-eng & 63.39 & 67.54 & 47.02 & 49.56 & 44.85 \\
-Tatoeba & zsm-eng & 95.41 & 95.62 & 95.31 & 95.8 & 79.95 \\
-Tatoeba & ita-eng & 94.32 & 92.72 & 93.05 & 93.76 & 65.04 \\
-Tatoeba & cmn-eng & 85.62 & 95.1 & 94.93 & 95.83 & 91.45 \\
-Tatoeba & lvs-eng & 95.33 & 95.88 & 97.87 & 97.53 & 6.55 \\
-Tatoeba & glg-eng & 96.14 & 96.82 & 94.0 & 95.32 & 79.86 \\
-Tatoeba & ceb-eng & 9.93 & 64.42 & 8.05 & 7.39 & 6.64 \\
-Tatoeba & bre-eng & 31.2 & 15.07 & 5.56 & 6.42 & 4.67 \\
-Tatoeba & ben-eng & 89.43 & 88.55 & 36.48 & 64.9 & 75.98 \\
-Tatoeba & swg-eng & 33.1 & 59.36 & 26.31 & 22.8 & 16.89 \\
-Tatoeba & arq-eng & 26.63 & 42.69 & 18.6 & 19.84 & 27.75 \\
-Tatoeba & kab-eng & 65.88 & 4.31 & 1.16 & 1.41 & 1.69 \\
-Tatoeba & fra-eng & 94.28 & 94.86 & 91.72 & 93.12 & 91.44 \\
-Tatoeba & por-eng & 94.54 & 94.14 & 92.13 & 93.02 & 92.62 \\
-Tatoeba & tat-eng & 34.74 & 85.92 & 10.25 & 10.89 & 3.59 \\
-Tatoeba & oci-eng & 58.13 & 65.81 & 38.57 & 43.49 & 40.17 \\
-Tatoeba & pol-eng & 97.32 & 97.22 & 94.28 & 96.95 & 14.09 \\
-Tatoeba & war-eng & 8.25 & 60.29 & 7.25 & 7.42 & 10.38 \\
-Tatoeba & aze-eng & 82.41 & 94.93 & 62.1 & 76.36 & 6.32 \\
-Tatoeba & vie-eng & 96.73 & 97.2 & 95.12 & 97.23 & 94.2 \\
-Tatoeba & nno-eng & 72.75 & 94.48 & 76.34 & 81.41 & 16.28 \\
-Tatoeba & cha-eng & 14.86 & 31.77 & 15.98 & 12.59 & 23.26 \\
-Tatoeba & mhr-eng & 6.86 & 15.74 & 6.89 & 7.57 & 1.56 \\
-Tatoeba & dan-eng & 95.22 & 95.71 & 94.8 & 96.17 & 23.52 \\
-Tatoeba & ell-eng & 96.2 & 95.35 & 95.43 & 94.93 & 5.34 \\
-Tatoeba & amh-eng & 80.82 & 91.47 & 36.21 & 53.49 & 0.03 \\
-Tatoeba & pam-eng & 3.24 & 10.73 & 5.41 & 5.39 & 5.85 \\
-Tatoeba & hsb-eng & 45.75 & 67.11 & 36.1 & 44.32 & 9.68 \\
-Tatoeba & srp-eng & 93.64 & 94.43 & 92.24 & 94.12 & 11.69 \\
-Tatoeba & epo-eng & 96.61 & 98.2 & 41.73 & 55.12 & 26.2 \\
-Tatoeba & kzj-eng & 4.46 & 11.33 & 6.24 & 5.88 & 5.17 \\
-Tatoeba & awa-eng & 33.74 & 71.7 & 33.43 & 42.83 & 35.01 \\
-Tatoeba & fao-eng & 57.04 & 87.4 & 27.51 & 38.24 & 12.61 \\
-Tatoeba & mal-eng & 98.16 & 98.45 & 32.2 & 88.46 & 83.3 \\
-Tatoeba & ile-eng & 87.88 & 85.58 & 57.71 & 60.36 & 59.59 \\
-Tatoeba & bos-eng & 95.86 & 94.92 & 93.27 & 94.02 & 13.65 \\
-Tatoeba & cor-eng & 4.45 & 10.11 & 3.42 & 3.53 & 2.83 \\
-Tatoeba & cat-eng & 95.8 & 95.38 & 94.42 & 96.05 & 88.31 \\
-Tatoeba & eus-eng & 93.32 & 95.01 & 23.18 & 31.33 & 53.38 \\
-Tatoeba & yue-eng & 87.75 & 89.58 & 71.45 & 77.58 & 77.03 \\
-Tatoeba & swe-eng & 95.31 & 95.63 & 94.42 & 95.45 & 19.53 \\
-Tatoeba & dtp-eng & 7.39 & 10.85 & 5.69 & 5.03 & 3.41 \\
-Tatoeba & kat-eng & 81.16 & 95.02 & 95.44 & 95.46 & 0.42 \\
-Tatoeba & jpn-eng & 93.78 & 95.38 & 90.41 & 92.51 & 71.36 \\
-Tatoeba & csb-eng & 27.03 & 52.57 & 21.56 & 23.73 & 10.03 \\
-Tatoeba & xho-eng & 4.68 & 91.55 & 4.52 & 6.53 & 5.51 \\
-Tatoeba & orv-eng & 23.24 & 38.93 & 15.1 & 23.77 & 5.79 \\
-Tatoeba & ind-eng & 92.98 & 93.66 & 92.74 & 93.5 & 88.04 \\
-Tatoeba & tuk-eng & 16.35 & 75.27 & 15.16 & 14.91 & 5.48 \\
-Tatoeba & max-eng & 36.96 & 63.26 & 45.25 & 48.77 & 36.14 \\
-Tatoeba & swh-eng & 55.66 & 84.5 & 14.48 & 16.02 & 16.74 \\
-Tatoeba & hin-eng & 95.32 & 96.87 & 97.62 & 97.75 & 85.23 \\
-Tatoeba & dsb-eng & 42.34 & 64.81 & 33.43 & 36.85 & 8.78 \\
-Tatoeba & ber-eng & 77.63 & 8.4 & 4.43 & 4.88 & 4.92 \\
-Tatoeba & tam-eng & 87.32 & 89.0 & 24.64 & 73.6 & 72.76 \\
-Tatoeba & slk-eng & 95.82 & 96.5 & 95.15 & 96.62 & 9.98 \\
-Tatoeba & tgl-eng & 63.19 & 96.02 & 13.09 & 17.67 & 10.7 \\
-Tatoeba & ast-eng & 76.35 & 90.68 & 62.17 & 70.08 & 71.13 \\
-Tatoeba & mkd-eng & 93.63 & 93.6 & 91.0 & 93.02 & 10.47 \\
-Tatoeba & khm-eng & 74.19 & 78.37 & 32.11 & 58.8 & 0.37 \\
-Tatoeba & ces-eng & 95.52 & 96.68 & 95.12 & 95.73 & 9.55 \\
-Tatoeba & tzl-eng & 36.56 & 58.88 & 25.46 & 34.21 & 27.82 \\
-Tatoeba & urd-eng & 84.23 & 93.22 & 94.57 & 95.12 & 70.1 \\
-Tatoeba & ara-eng & 90.14 & 88.8 & 87.93 & 90.19 & 85.37 \\
-Tatoeba & kor-eng & 87.97 & 90.95 & 92.52 & 93.07 & 22.39 \\
-Tatoeba & yid-eng & 2.49 & 88.79 & 14.38 & 30.73 & 0.16 \\
-Tatoeba & fin-eng & 96.98 & 96.37 & 93.1 & 95.92 & 3.41 \\
-Tatoeba & tha-eng & 96.38 & 96.14 & 96.72 & 95.99 & 2.22 \\
-Tatoeba & wuu-eng & 75.09 & 90.18 & 76.0 & 78.25 & 79.58 \\
-Average & mix & 67.42 & 81.75 & 57.98 & 63.38 & 31.08 \\
diff --git a/plotstables/dataset_sim.py b/plotstables/dataset_sim.py
deleted file mode 100644
index f61eaf96..00000000
--- a/plotstables/dataset_sim.py
+++ /dev/null
@@ -1,304 +0,0 @@
-# pip install GitPython mteb beir seaborn
-import os
-import random
-
-import seaborn as sns
-import matplotlib.pyplot as plt
-from mteb import MTEB
-from mteb.evaluation.evaluators.utils import cos_sim
-import numpy as np
-import pandas as pd
-from sentence_transformers import SentenceTransformer
-import torch
-
-
-if os.path.exists("sim_data.csv"):
-    data_emb_df = (pd.read_csv("sim_data.csv", index_col=0) * 100).round(0).astype(int)
-    plt.figure(figsize=(40, 24))
-    # define the mask to set the values in the upper triangle to True
-    mask = np.triu(np.ones_like(data_emb_df, dtype=np.bool))
-    heatmap = sns.heatmap(
-        data_emb_df, 
-        mask=mask, 
-        vmin=data_emb_df.values.min(), 
-        vmax=data_emb_df.values.max(), 
-        annot=True, 
-        cmap='Blues',
-        fmt='g',
-    )
-    heatmap.set_xticklabels(heatmap.get_xmajorticklabels(), fontsize=16)#, fontweight="bold")
-    heatmap.set_yticklabels(heatmap.get_ymajorticklabels(), fontsize=16)#, fontweight="bold")
-    # Save
-    plt.savefig('heatmap_data.pdf', dpi=450, bbox_inches='tight')
-    exit()
-
-
-### GLOBAL VARIABLES ###
-
-DATAPATH = "./"
-
-SEED = 42
-
-K_SAMPLES = 100
-LEN_KEYS = {
-    "text", 
-    "sentences", 
-    "sentence1", 
-    "sentence2",
-    "sent1",
-    "sent2"
-    "query",
-    "positive",
-    "negative"
-    "queries", 
-    "corpus",
-    "machine_summaries",
-    "human_summaries",
-}
-
-TASK_LIST_CLASSIFICATION = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-]
-
-TASK_LIST_CLUSTERING = [
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-]
-
-TASK_LIST_PAIR_CLASSIFICATION = [
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-]
-
-TASK_LIST_RERANKING = [
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-]
-
-TASK_LIST_RETRIEVAL = [
-    "ArguAna",
-    "ClimateFEVER",
-    "CQADupstackAndroidRetrieval",
-    "CQADupstackEnglishRetrieval",
-    "CQADupstackGamingRetrieval",
-    "CQADupstackGisRetrieval",
-    "CQADupstackMathematicaRetrieval",
-    "CQADupstackPhysicsRetrieval",
-    "CQADupstackProgrammersRetrieval",
-    "CQADupstackStatsRetrieval",
-    "CQADupstackTexRetrieval",
-    "CQADupstackUnixRetrieval",
-    "CQADupstackWebmastersRetrieval",
-    "CQADupstackWordpressRetrieval",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-]
-
-TASK_LIST_STS = [
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-]
-
-
-TASK_LIST_SUMMARIZATION = [
-    "SummEval",
-]
-
-TASK_LIST_EN = (
-    TASK_LIST_CLASSIFICATION
-    + TASK_LIST_CLUSTERING
-    + TASK_LIST_PAIR_CLASSIFICATION
-    + TASK_LIST_RERANKING
-    + TASK_LIST_RETRIEVAL
-    + TASK_LIST_STS
-    + TASK_LIST_SUMMARIZATION
-)
-
-### LOGIC ###
-
-def get_samples_beir(hf_hub_name):
-    # Somehow needs to be set in the function scope
-    random.seed(SEED)
-    from beir.datasets.data_loader import GenericDataLoader as BeirDataLoader
-    path = os.path.join(DATAPATH, hf_hub_name)
-    print("GOT PATH", path)
-    split = "validation" if "MSMARCO" in hf_hub_name else "test"
-    if not os.path.exists(path):
-        from beir import util
-        if "cqadupstack" in hf_hub_name:
-            hf_hub_name = "cqadupstack"
-        url = f"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{hf_hub_name}.zip"
-        util.download_and_unzip(url, DATAPATH)
-    corpus, queries, relevant_docs = BeirDataLoader(path).load(split=split)
-    # Pick shortest k samples
-    samples = [v["text"] + " " + v["title"] for v in sorted(list(corpus.values()), key=lambda x: len(x["text"]))[:K_SAMPLES]]
-    # Optionally randomly pick
-    #samples = [v["text"] + " " + v["title"] for v in random.choices(sorted(list(corpus.values()), key=lambda x: len(x["text"])), k=K_SAMPLES)]
-    return samples
-
-def load_data(hf_hub_name, subset=None):
-    """
-    Load dataset from Hub via cloning for easy offline usage with HF_DATASETS_OFFLINE=1
-    Can be replaced with just `load_dataset(hf_hub_name, subset)` if preferred
-    """
-    from datasets import load_dataset
-    path = os.path.join(DATAPATH, hf_hub_name)
-    if os.path.exists(path):
-        dataset = load_dataset(path, subset)
-    else:
-        from git import Repo
-        Repo.clone_from("https://huggingface.co/datasets/mteb/" + hf_hub_name, path)
-        dataset = load_dataset(path, subset)
-    return dataset
-
-def get_samples_ds(hf_hub_name):
-    ds = load_data(hf_hub_name)
-    # Optionally shuffle
-    # .shuffle(seed=SEED)
-    assert "test" in ds, f"No test set for {hf_hub_name}"
-    len_keys = list(set(ds["test"].features.keys()) & LEN_KEYS)
-    split = "test"
-    k = len_keys[0]
-    if isinstance(ds[split][k][0], str):
-        # Select K shortest examples
-        samples = sorted([x for x in ds[split][k]], key=len)[:K_SAMPLES]
-    elif isinstance(ds[split][k][0], list):
-        assert isinstance(ds[split][k][0][0], str), f"Too nested: {k}"
-        # Select K shortest examples
-        samples = [y for x in ds[split][k] for y in x]
-        samples = sorted(samples, key=len)[:K_SAMPLES]
-        # Optionally randomly select
-        # random.choices(samples, k=K_SAMPLES)
-    else:
-        raise ValueError(f"Unknown type {type(ds[split][k])}")
-    return samples
-
-
-embeddings = {}
-model = SentenceTransformer("sentence-transformers/sentence-t5-xxl")
-
-# Optionally custom selection
-# TASKS = ["ArguAna", "ClimateFEVER", "DBPedia", "FEVER", "FiQA2018", "HotpotQA", "NFCorpus", "NQ", "QuoraRetrieval", "SCIDOCS", "SciFact", "Touche2020", "TRECCOVID"]
-
-TASKS = TASK_LIST_EN
-
-
-for task in MTEB(tasks=TASKS).tasks:
-    print("Task: ", task)
-    if "hf_hub_name" in task.description:
-        hub_name = hub_url = task.description.get("hf_hub_name")
-        samples = get_samples_ds(hub_name.split("/")[-1])
-    if "beir_name" in task.description:
-        hub_name = hub_url = "BeIR/" + task.description.get("beir_name")
-        samples = get_samples_beir("/".join(hub_name.split("/")[1:]))
-    embeddings[task.description["name"]] = model.encode(samples)
-
-# Plot 1: Compute all cos sims & then average
-"""
-data_dict = []
-for i, task_1 in enumerate(TASKS):
-    data_dict.append({task_2: torch.mean(cos_sim(embeddings[task_1], embeddings[task_2])).item() for j, task_2 in enumerate(TASKS)})
-
-data_df = pd.DataFrame(data_dict)
-data_df.set_index(data_df.columns, inplace=True)
-
-
-# Save
-data_df.to_csv("data.csv")
-
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-plt.figure(figsize=(32, 16))
-# define the mask to set the values in the upper triangle to True
-mask = np.triu(np.ones_like(data_df, dtype=np.bool))
-#heatmap = sns.heatmap(data_df, mask=mask, vmin=-1, vmax=1, annot=True, cmap='Blues')
-heatmap = sns.heatmap(data_df, mask=mask, vmin=data_df.values.min(), vmax=data_df.values.max(), annot=True, cmap='Blues')
-heatmap.set_title('Similarity of MTEB datasets', fontdict={'fontsize':18}, pad=16)
-
-plt.savefig('heatmap_data.pdf', dpi=300, bbox_inches='tight')
-"""
-
-
-# Plot 2: Average embeddings & then compute cos_sim
-
-data_dict_emb = []
-for i, task_1 in enumerate(TASKS):
-    data_dict_emb.append({task_2: cos_sim(np.mean(embeddings[task_1], axis=0), np.mean(embeddings[task_2], axis=0)).item() for j, task_2 in enumerate(TASKS)})
-
-data_emb_df = pd.DataFrame(data_dict_emb)
-data_emb_df.set_index(data_emb_df.columns, inplace=True)
-
-
-plt.figure(figsize=(36, 24))
-# define the mask to set the values in the upper triangle to True
-mask = np.triu(np.ones_like(data_emb_df, dtype=np.bool))
-heatmap = sns.heatmap(data_emb_df, mask=mask, vmin=data_emb_df.values.min(), vmax=data_emb_df.values.max(), annot=True, cmap='Blues')
-#heatmap.set_title('Similarity of MTEB datasets', fontdict={'fontsize':18}, pad=16)
-
-# Save
-data_emb_df.to_csv("sim_data.csv")
-plt.savefig('heatmap_data.pdf', dpi=450, bbox_inches='tight')
-
-
-# Plot 3: Min (/Max) embeddings & then compute cos_sim
-"""
-data_dict_emb = []
-for i, task_1 in enumerate(TASKS):
-    data_dict_emb.append({task_2: cos_sim(np.min(embeddings[i], axis=0), np.min(embeddings[j], axis=0)).item() for j, task_2 in enumerate(TASKS)})
-
-data_emb_df = pd.DataFrame(data_dict_emb)
-data_emb_df.set_index(data_emb_df.columns, inplace=True)
-
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-plt.figure(figsize=(32, 16))
-# define the mask to set the values in the upper triangle to True
-mask = np.triu(np.ones_like(data_emb_df, dtype=np.bool))
-heatmap = sns.heatmap(data_emb_df, mask=mask, vmin=data_emb_df.values.min(), vmax=data_emb_df.values.max(), annot=True, cmap='Blues')
-heatmap.set_title('Similarity of MTEB datasets', fontdict={'fontsize':18}, pad=16)
-
-plt.savefig('heatmap_data.pdf', dpi=300, bbox_inches='tight')
-"""
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deleted file mode 100644
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+++ /dev/null
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</diagram></mxfile>
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diff --git a/plotstables/mteb_diagram.pdf b/plotstables/mteb_diagram.pdf
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diff --git a/plotstables/mteb_diagram.png b/plotstables/mteb_diagram.png
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diff --git a/plotstables/multilingclf.txt b/plotstables/multilingclf.txt
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--- a/plotstables/multilingclf.txt
+++ /dev/null
@@ -1,119 +0,0 @@
-Dataset & Language & LASER2 & LaBSE & MiniLM-L12-multilingual & MPNet-multilingual & SGPT-BLOOM-7.1B-msmarco \\
-AmazonCounterfactualClassification & de & 67.82 & 73.17 & 68.35 & 69.95 & 61.35 \\
-AmazonCounterfactualClassification & ja & 68.76 & 76.42 & 63.45 & 69.79 & 58.23 \\
-AmazonReviewsClassification & de & 31.07 & 39.92 & 35.91 & 39.52 & 29.7 \\
-AmazonReviewsClassification & es & 32.72 & 39.39 & 37.49 & 39.99 & 35.97 \\
-AmazonReviewsClassification & fr & 31.12 & 38.52 & 35.3 & 39.0 & 35.92 \\
-AmazonReviewsClassification & ja & 28.94 & 36.44 & 33.24 & 36.64 & 27.64 \\
-AmazonReviewsClassification & zh & 30.89 & 36.45 & 35.26 & 37.74 & 32.63 \\
-MassiveIntentClassification & af & 38.01 & 56.12 & 45.88 & 52.32 & 47.85 \\
-MassiveIntentClassification & am & 12.7 & 55.71 & 36.75 & 41.55 & 33.3 \\
-MassiveIntentClassification & ar & 37.16 & 50.86 & 45.14 & 51.43 & 59.25 \\
-MassiveIntentClassification & az & 19.98 & 58.97 & 47.42 & 56.98 & 45.24 \\
-MassiveIntentClassification & bn & 42.51 & 58.22 & 35.34 & 48.79 & 61.59 \\
-MassiveIntentClassification & cy & 17.33 & 50.16 & 26.12 & 27.87 & 44.92 \\
-MassiveIntentClassification & da & 45.61 & 58.25 & 57.73 & 62.77 & 51.23 \\
-MassiveIntentClassification & de & 44.79 & 56.21 & 50.71 & 59.57 & 56.1 \\
-MassiveIntentClassification & el & 46.71 & 57.03 & 58.7 & 62.62 & 46.13 \\
-MassiveIntentClassification & es & 45.44 & 58.32 & 59.66 & 64.43 & 66.35 \\
-MassiveIntentClassification & fa & 45.01 & 62.33 & 61.02 & 65.34 & 51.2 \\
-MassiveIntentClassification & fi & 45.94 & 60.12 & 57.54 & 62.28 & 45.33 \\
-MassiveIntentClassification & fr & 46.13 & 60.47 & 60.25 & 64.82 & 66.95 \\
-MassiveIntentClassification & he & 42.55 & 56.55 & 52.51 & 58.21 & 43.18 \\
-MassiveIntentClassification & hi & 40.2 & 59.4 & 58.37 & 62.77 & 63.54 \\
-MassiveIntentClassification & hu & 42.77 & 59.52 & 60.41 & 63.87 & 44.73 \\
-MassiveIntentClassification & hy & 28.07 & 56.2 & 51.6 & 57.74 & 38.13 \\
-MassiveIntentClassification & id & 45.81 & 61.12 & 59.85 & 65.43 & 64.06 \\
-MassiveIntentClassification & is & 39.86 & 54.9 & 30.83 & 37.05 & 44.35 \\
-MassiveIntentClassification & it & 48.25 & 59.83 & 59.61 & 64.68 & 60.77 \\
-MassiveIntentClassification & ja & 45.3 & 63.11 & 60.89 & 63.74 & 61.22 \\
-MassiveIntentClassification & jv & 24.3 & 50.98 & 32.37 & 36.49 & 50.94 \\
-MassiveIntentClassification & ka & 22.7 & 48.35 & 43.03 & 49.85 & 33.84 \\
-MassiveIntentClassification & km & 22.48 & 48.55 & 40.04 & 45.47 & 37.34 \\
-MassiveIntentClassification & kn & 4.32 & 56.24 & 40.98 & 50.63 & 53.54 \\
-MassiveIntentClassification & ko & 44.26 & 60.99 & 50.3 & 61.82 & 53.36 \\
-MassiveIntentClassification & lv & 39.75 & 57.1 & 54.68 & 61.29 & 46.5 \\
-MassiveIntentClassification & ml & 41.33 & 57.91 & 42.41 & 54.34 & 58.27 \\
-MassiveIntentClassification & mn & 16.2 & 58.5 & 51.77 & 56.59 & 40.28 \\
-MassiveIntentClassification & ms & 43.23 & 58.6 & 54.76 & 60.7 & 59.65 \\
-MassiveIntentClassification & my & 25.37 & 57.35 & 52.01 & 57.09 & 37.42 \\
-MassiveIntentClassification & nb & 37.74 & 57.91 & 55.5 & 62.6 & 49.41 \\
-MassiveIntentClassification & nl & 45.0 & 59.37 & 59.51 & 63.57 & 52.09 \\
-MassiveIntentClassification & pl & 44.99 & 59.71 & 59.43 & 64.3 & 50.48 \\
-MassiveIntentClassification & pt & 48.55 & 60.16 & 61.27 & 64.89 & 66.69 \\
-MassiveIntentClassification & ro & 44.3 & 57.92 & 58.39 & 62.8 & 50.53 \\
-MassiveIntentClassification & ru & 44.29 & 60.67 & 59.04 & 63.26 & 58.32 \\
-MassiveIntentClassification & sl & 44.72 & 59.37 & 57.36 & 63.51 & 47.74 \\
-MassiveIntentClassification & sq & 46.12 & 58.03 & 56.59 & 62.49 & 48.94 \\
-MassiveIntentClassification & sv & 45.95 & 59.66 & 59.43 & 64.73 & 50.79 \\
-MassiveIntentClassification & sw & 31.89 & 51.62 & 29.57 & 31.95 & 49.81 \\
-MassiveIntentClassification & ta & 29.63 & 55.04 & 36.77 & 50.17 & 56.4 \\
-MassiveIntentClassification & te & 36.03 & 58.32 & 40.72 & 52.82 & 54.71 \\
-MassiveIntentClassification & th & 43.39 & 56.58 & 58.97 & 61.11 & 44.43 \\
-MassiveIntentClassification & tl & 29.73 & 55.28 & 33.67 & 38.83 & 50.21 \\
-MassiveIntentClassification & tr & 43.93 & 60.91 & 59.9 & 64.54 & 46.56 \\
-MassiveIntentClassification & ur & 26.11 & 56.7 & 52.8 & 56.37 & 56.75 \\
-MassiveIntentClassification & vi & 44.33 & 56.67 & 56.61 & 59.68 & 64.53 \\
-MassiveIntentClassification & zh-CN & 40.62 & 63.86 & 61.99 & 65.33 & 67.07 \\
-MassiveIntentClassification & zh-TW & 32.93 & 59.51 & 58.77 & 62.35 & 62.89 \\
-MassiveScenarioClassification & af & 47.1 & 63.39 & 53.64 & 59.67 & 51.47 \\
-MassiveScenarioClassification & am & 17.7 & 62.02 & 41.89 & 48.97 & 34.87 \\
-MassiveScenarioClassification & ar & 45.21 & 57.72 & 51.74 & 57.78 & 65.21 \\
-MassiveScenarioClassification & az & 28.21 & 63.48 & 52.06 & 61.53 & 45.58 \\
-MassiveScenarioClassification & bn & 50.52 & 61.84 & 41.17 & 54.53 & 67.3 \\
-MassiveScenarioClassification & cy & 22.58 & 56.13 & 31.72 & 35.26 & 46.29 \\
-MassiveScenarioClassification & da & 54.87 & 65.24 & 66.87 & 71.0 & 53.52 \\
-MassiveScenarioClassification & de & 54.34 & 62.39 & 57.4 & 67.34 & 61.74 \\
-MassiveScenarioClassification & el & 55.47 & 64.58 & 66.14 & 68.81 & 48.96 \\
-MassiveScenarioClassification & es & 52.77 & 63.61 & 65.04 & 70.42 & 73.34 \\
-MassiveScenarioClassification & fa & 52.5 & 67.46 & 65.86 & 69.88 & 53.17 \\
-MassiveScenarioClassification & fi & 52.63 & 64.58 & 63.75 & 67.6 & 44.69 \\
-MassiveScenarioClassification & fr & 54.32 & 65.1 & 66.06 & 70.69 & 72.91 \\
-MassiveScenarioClassification & he & 52.41 & 63.53 & 59.2 & 65.16 & 43.1 \\
-MassiveScenarioClassification & hi & 47.37 & 64.4 & 65.21 & 67.92 & 69.27 \\
-MassiveScenarioClassification & hu & 53.43 & 65.82 & 66.56 & 70.3 & 45.16 \\
-MassiveScenarioClassification & hy & 33.57 & 61.25 & 56.11 & 63.02 & 38.73 \\
-MassiveScenarioClassification & id & 54.38 & 65.84 & 66.16 & 70.73 & 70.13 \\
-MassiveScenarioClassification & is & 49.78 & 61.94 & 37.52 & 44.16 & 44.21 \\
-MassiveScenarioClassification & it & 54.84 & 64.09 & 65.0 & 69.73 & 65.57 \\
-MassiveScenarioClassification & ja & 54.12 & 67.72 & 66.5 & 69.69 & 65.76 \\
-MassiveScenarioClassification & jv & 32.71 & 58.29 & 38.6 & 44.2 & 54.79 \\
-MassiveScenarioClassification & ka & 26.92 & 53.38 & 50.66 & 57.3 & 32.99 \\
-MassiveScenarioClassification & km & 27.23 & 56.18 & 46.96 & 53.14 & 39.34 \\
-MassiveScenarioClassification & kn & 10.06 & 61.74 & 45.73 & 56.08 & 60.5 \\
-MassiveScenarioClassification & ko & 52.01 & 67.26 & 55.66 & 68.52 & 55.69 \\
-MassiveScenarioClassification & lv & 44.82 & 61.87 & 59.8 & 66.28 & 44.35 \\
-MassiveScenarioClassification & ml & 49.1 & 62.26 & 47.69 & 60.13 & 65.53 \\
-MassiveScenarioClassification & mn & 21.51 & 62.6 & 57.07 & 60.85 & 38.72 \\
-MassiveScenarioClassification & ms & 53.6 & 65.63 & 61.71 & 65.81 & 64.99 \\
-MassiveScenarioClassification & my & 29.72 & 62.94 & 59.1 & 63.03 & 36.84 \\
-MassiveScenarioClassification & nb & 43.9 & 64.29 & 64.25 & 70.24 & 51.8 \\
-MassiveScenarioClassification & nl & 53.33 & 65.16 & 65.52 & 70.37 & 56.32 \\
-MassiveScenarioClassification & pl & 52.92 & 64.56 & 65.04 & 68.99 & 49.98 \\
-MassiveScenarioClassification & pt & 53.41 & 63.28 & 65.79 & 70.09 & 71.46 \\
-MassiveScenarioClassification & ro & 50.48 & 62.41 & 64.17 & 67.95 & 53.69 \\
-MassiveScenarioClassification & ru & 51.84 & 65.25 & 65.24 & 69.92 & 61.6 \\
-MassiveScenarioClassification & sl & 51.29 & 64.25 & 64.01 & 70.81 & 48.04 \\
-MassiveScenarioClassification & sq & 55.65 & 64.54 & 64.31 & 69.63 & 50.06 \\
-MassiveScenarioClassification & sv & 54.64 & 66.01 & 67.14 & 71.6 & 51.73 \\
-MassiveScenarioClassification & sw & 42.04 & 58.36 & 34.86 & 37.29 & 54.22 \\
-MassiveScenarioClassification & ta & 36.72 & 59.08 & 42.62 & 55.96 & 62.77 \\
-MassiveScenarioClassification & te & 42.08 & 64.13 & 46.46 & 58.81 & 62.59 \\
-MassiveScenarioClassification & th & 52.15 & 64.34 & 67.01 & 69.44 & 45.18 \\
-MassiveScenarioClassification & tl & 37.34 & 60.23 & 37.37 & 43.99 & 52.06 \\
-MassiveScenarioClassification & tr & 52.56 & 65.43 & 66.55 & 70.4 & 47.21 \\
-MassiveScenarioClassification & ur & 32.6 & 61.52 & 60.43 & 62.9 & 64.26 \\
-MassiveScenarioClassification & vi & 50.97 & 61.05 & 60.72 & 65.71 & 70.61 \\
-MassiveScenarioClassification & zh-CN & 50.22 & 70.85 & 67.44 & 71.23 & 73.95 \\
-MassiveScenarioClassification & zh-TW & 42.32 & 67.08 & 65.7 & 68.73 & 70.3 \\
-MTOPDomainClassification & de & 74.08 & 86.95 & 79.2 & 85.73 & 82.05 \\
-MTOPDomainClassification & es & 73.47 & 84.07 & 83.04 & 86.96 & 93.55 \\
-MTOPDomainClassification & fr & 72.26 & 84.14 & 78.63 & 81.21 & 90.98 \\
-MTOPDomainClassification & hi & 72.95 & 85.11 & 81.36 & 84.76 & 89.33 \\
-MTOPDomainClassification & th & 72.68 & 81.24 & 79.99 & 82.51 & 60.49 \\
-MTOPIntentClassification & de & 51.62 & 63.42 & 54.23 & 61.27 & 61.92 \\
-MTOPIntentClassification & es & 52.75 & 64.44 & 60.28 & 66.59 & 74.49 \\
-MTOPIntentClassification & fr & 50.12 & 62.01 & 54.05 & 59.76 & 69.12 \\
-MTOPIntentClassification & hi & 45.55 & 62.58 & 59.9 & 62.37 & 64.85 \\
-MTOPIntentClassification & th & 50.07 & 64.61 & 61.96 & 64.8 & 49.36 \\
-Average & mix & 42.85 & 60.77 & 54.87 & 60.39 & 54.4 \\
diff --git a/plotstables/multilingsts.txt b/plotstables/multilingsts.txt
deleted file mode 100644
index 2cd0b762..00000000
--- a/plotstables/multilingsts.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-Dataset & Language & LASER2 & LaBSE & MiniLM-L12-multilingual & MPNet-multilingual & SGPT-BLOOM-7.1B-msmarco \\
-STS17 & ko-ko & 70.52 & 71.32 & 77.03 & 83.41 & 66.89 \\
-STS17 & ar-ar & 67.47 & 69.07 & 79.16 & 79.1 & 76.42 \\
-STS17 & en-ar & 65.05 & 74.51 & 81.22 & 80.85 & 78.07 \\
-STS17 & en-de & 66.66 & 73.85 & 84.22 & 83.28 & 59.1 \\
-STS17 & en-tr & 70.05 & 72.07 & 76.74 & 74.9 & 11.8 \\
-STS17 & es-en & 55.3 & 65.71 & 84.44 & 86.11 & 78.22 \\
-STS17 & es-es & 79.67 & 80.83 & 85.56 & 85.14 & 86.0 \\
-STS17 & fr-en & 70.82 & 76.98 & 76.59 & 81.17 & 80.46 \\
-STS17 & it-en & 70.98 & 76.99 & 82.35 & 84.24 & 51.58 \\
-STS17 & nl-en & 68.12 & 75.22 & 81.71 & 82.51 & 45.85 \\
-STS22 & de & 25.69 & 48.58 & 44.64 & 46.7 & 30.05 \\
-STS22 & es & 54.92 & 63.18 & 56.56 & 59.91 & 65.41 \\
-STS22 & pl & 18.34 & 39.3 & 33.74 & 33.65 & 31.13 \\
-STS22 & tr & 36.97 & 58.15 & 53.39 & 56.3 & 47.14 \\
-STS22 & ar & 42.57 & 57.67 & 46.2 & 52.19 & 58.67 \\
-STS22 & ru & 39.24 & 57.49 & 57.08 & 58.74 & 43.36 \\
-STS22 & zh & 49.41 & 63.02 & 58.75 & 61.75 & 66.78 \\
-STS22 & fr & 58.61 & 77.95 & 70.55 & 74.3 & 80.38 \\
-STS22 & de-en & 32.35 & 50.14 & 52.65 & 50.81 & 51.16 \\
-STS22 & es-en & 54.34 & 71.86 & 67.33 & 70.26 & 75.06 \\
-STS22 & it & 60.31 & 72.22 & 55.22 & 60.65 & 65.65 \\
-STS22 & pl-en & 53.63 & 69.41 & 69.02 & 73.07 & 53.31 \\
-STS22 & zh-en & 46.19 & 64.02 & 65.71 & 67.96 & 68.45 \\
-STS22 & es-it & 42.21 & 69.69 & 47.67 & 53.7 & 65.5 \\
-STS22 & de-fr & 37.41 & 53.28 & 51.73 & 62.34 & 53.28 \\
-STS22 & de-pl & 15.67 & 58.69 & 44.22 & 40.53 & 43.05 \\
-STS22 & fr-pl & 39.44 & 61.98 & 50.71 & 84.52 & 28.17 \\
-Average & mix & 51.55 & 65.67 & 64.23 & 67.71 & 57.81 \\
diff --git a/plotstables/multilingual.png b/plotstables/multilingual.png
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diff --git a/plotstables/results_to_avg_table.py b/plotstables/results_to_avg_table.py
deleted file mode 100644
index 8ede5a2b..00000000
--- a/plotstables/results_to_avg_table.py
+++ /dev/null
@@ -1,259 +0,0 @@
-import json
-import os
-import sys
-
-from mteb import MTEB
-
-### GLOBAL VARIABLES ###
-
-TASK_LIST_CLASSIFICATION = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-]
-
-TASK_LIST_CLUSTERING = [
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-]
-
-TASK_LIST_PAIR_CLASSIFICATION = [
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-]
-
-TASK_LIST_RERANKING = [
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-]
-
-TASK_LIST_RETRIEVAL = [
-    "ArguAna",
-    "ClimateFEVER",
-    "CQADupstackRetrieval",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-]
-
-TASK_LIST_STS = [
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-]
-
-
-TASK_LIST_SUMMARIZATION = [
-    "SummEval",
-]
-
-TASK_LIST_EN = (
-    TASK_LIST_CLASSIFICATION
-    + TASK_LIST_CLUSTERING
-    + TASK_LIST_PAIR_CLASSIFICATION
-    + TASK_LIST_RERANKING
-    + TASK_LIST_RETRIEVAL
-    + TASK_LIST_STS
-    + TASK_LIST_SUMMARIZATION
-)
-
-
-TASK_LIST_NAMES = [
-    ("Class.", TASK_LIST_CLASSIFICATION, ["en", "en-en"]),
-    ("Clust.", TASK_LIST_CLUSTERING, ["en", "en-en"]),
-    ("PairClass.", TASK_LIST_PAIR_CLASSIFICATION, ["en", "en-en"]),
-    ("Rerank.", TASK_LIST_RERANKING, ["en", "en-en"]),
-    ("Retr.", TASK_LIST_RETRIEVAL, ["en", "en-en"]),
-    ("STS", TASK_LIST_STS, ["en", "en-en"]),
-    ("Summ.", TASK_LIST_SUMMARIZATION, ["en", "en-en"]),
-    # ("BitextMining", TASK_LIST_BITEXT, []),
-    ("Avg.", TASK_LIST_EN, ["en", "en-en"]),
-]
-
-SELFSUPERVISED_MODELS = [
-    "glove.6B.300d",
-    "komninos",
-    "bert-base-uncased",
-    "unsup-simcse-bert-base-uncased",
-]
-
-SUPERVISED_MODELS = [
-    "sup-simcse-bert-base-uncased",
-    "msmarco-bert-co-condensor",
-    "contriever-base-msmarco",
-    "allenai-specter",
-    "LaBSE",
-    "LASER2",
-    "all-MiniLM-L6-v2",
-    "all-MiniLM-L12-v2",
-    "paraphrase-multilingual-MiniLM-L12-v2",
-    "all-mpnet-base-v2",
-    "paraphrase-multilingual-mpnet-base-v2",
-    "text-similarity-ada-001",
-    "SGPT-125M-weightedmean-nli-bitfit",
-    "SGPT-5.8B-weightedmean-nli-bitfit",
-    "SGPT-125M-weightedmean-msmarco-specb-bitfit",
-    "SGPT-1.3B-weightedmean-msmarco-specb-bitfit",
-    "SGPT-2.7B-weightedmean-msmarco-specb-bitfit",
-    "SGPT-5.8B-weightedmean-msmarco-specb-bitfit",
-    "sgpt-bloom-7b1-msmarco",
-    "gtr-t5-base", # 110M
-    "gtr-t5-large",
-    "gtr-t5-xl",
-    "gtr-t5-xxl", # 4.8B
-    "sentence-t5-base", # 110M
-    "sentence-t5-large",
-    "sentence-t5-xl",
-    "sentence-t5-xxl", # 4.8B
-]
-
-MODEL_TO_NAME = {
-    "bert-base-uncased": "BERT",
-    "gtr-t5-base": "GTR-Base",
-    "gtr-t5-large": "GTR-Large",
-    "gtr-t5-xl": "GTR-XL",
-    "gtr-t5-xxl": "GTR-XXL",
-    "sentence-t5-base": "ST5-Base",
-    "sentence-t5-large": "ST5-Large",
-    "sentence-t5-xl": "ST5-XL",
-    "sentence-t5-xxl": "ST5-XXL",
-    "SGPT-125M-weightedmean-msmarco-specb-bitfit": "SGPT-125M-msmarco",
-    "SGPT-1.3B-weightedmean-msmarco-specb-bitfit": "SGPT-1.3B-msmarco",
-    "SGPT-2.7B-weightedmean-msmarco-specb-bitfit": "SGPT-2.7B-msmarco",
-    "SGPT-5.8B-weightedmean-msmarco-specb-bitfit": "SGPT-5.8B-msmarco",
-    "sgpt-bloom-7b1-msmarco": "SGPT-BLOOM-7.1B-msmarco",
-    "SGPT-125M-weightedmean-nli-bitfit": "SGPT-125M-nli",
-    "SGPT-5.8B-weightedmean-nli-bitfit": "SGPT-5.8B-nli",
-    "sup-simcse-bert-base-uncased": "SimCSE-BERT-sup",
-    "contriever-base-msmarco": "Contriever",
-    "msmarco-bert-co-condensor": "coCondenser-msmarco", # They write it as coCondenser in the paper
-    "unsup-simcse-bert-base-uncased": "SimCSE-BERT-unsup",
-    "glove.6B.300d": "Glove",
-    "komninos": "Komninos",
-    "all-MiniLM-L6-v2": "MiniLM-L6",
-    "all-MiniLM-L12-v2": "MiniLM-L12",
-    "paraphrase-multilingual-MiniLM-L12-v2": "MiniLM-L12-multilingual",
-    "all-mpnet-base-v2": "MPNet",
-    "paraphrase-multilingual-mpnet-base-v2": "MPNet-multilingual",
-    "allenai-specter": "SPECTER",
-    "text-similarity-ada-001": "Ada Similarity",
-}
-
-
-### LOGIC ###
-
-results_folder = sys.argv[1].strip("/")
-all_results = {}
-
-for model_name in os.listdir(results_folder):
-    model_res_folder = os.path.join(results_folder, model_name)
-    if os.path.isdir(model_res_folder):
-        all_results.setdefault(model_name, {})
-        for file_name in os.listdir(model_res_folder):
-            if not file_name.endswith(".json"):
-                print(f"Skipping non-json {file_name}")
-                continue
-            with open(os.path.join(model_res_folder, file_name), "r", encoding="utf-8") as f:
-                results = json.load(f)
-                all_results[model_name] = {**all_results[model_name], **{file_name.replace(".json", ""): results}}
-
-def get_row(dataset, model_name, limit_langs=[], skip_langs=[]):
-    # CQADupstackRetrieval uses the same metric as its subsets
-    tasks = MTEB(tasks=[dataset.replace("CQADupstackRetrieval", "CQADupstackTexRetrieval")]).tasks
-    assert len(tasks) == 1, f"Found {len(tasks)} for {dataset}. Expected 1."
-    main_metric = tasks[0].description["main_score"]
-    test_result = all_results.get(model_name, {}). get(dataset, {})
-
-    # Dev / Val set is used for MSMARCO (See BEIR paper)
-    if "MSMARCO" in dataset:
-        test_result = (
-            test_result.get("dev") if "dev" in test_result else test_result.get("validation")
-        )
-    else:
-        test_result = test_result.get("test")
-
-    for lang in tasks[0].description["eval_langs"]:
-        if (limit_langs and lang not in limit_langs) or (skip_langs and lang in skip_langs):
-            continue
-        elif test_result is None:
-            raise NotImplementedError(f"Got no test result {test_result} for ds: {dataset} model: {model_name}")
-
-        test_result_lang = test_result.get(lang, test_result)
-        if main_metric == "cosine_spearman":
-            test_result_lang = test_result_lang.get("cos_sim", {}).get("spearman")
-        elif main_metric == "ap":
-            test_result_lang = test_result_lang.get("cos_sim", {}).get("ap")
-        else:
-            test_result_lang = test_result_lang.get(main_metric)
-
-        if test_result_lang is None:
-            raise NotImplementedError
-
-        return test_result_lang
-    raise NotImplementedError
-
-
-table = "Task ($\ rightarrow$) & " + " & ".join([x[0] for x in TASK_LIST_NAMES]) + " \\\\" + "\n"
-table += "Num. Datasets ($\ rightarrow$) & " + " & ".join([str(len(x[1])) for x in TASK_LIST_NAMES]) + " \\\\" + "\n"
-table += "Model ($\downarrow$) & " + " & ".join([x[0] for x in TASK_LIST_NAMES]) + " \\\\" + "\n"
-
-
-def add_to_table(model_list, table):
-    for model in model_list:
-        results = []
-        for (task_name, task_list, limit_langs) in TASK_LIST_NAMES:
-            try:
-                model_task_results = [get_row(task, model, limit_langs=limit_langs) for task in task_list]
-            except:
-                results.append("")
-                continue
-            results.append(str(round(100 * (sum(model_task_results) / len(model_task_results)), 2)))
-
-        model_name = MODEL_TO_NAME.get(model, model)
-        table += model_name + " & " + " & ".join(results) + " \\\\" + "\n"
-    return table
-
-
-table = add_to_table(SELFSUPERVISED_MODELS, table)
-table = add_to_table(SUPERVISED_MODELS, table)
-
-with open("avg_table.txt", "w") as f:
-    f.write(table)
-
diff --git a/plotstables/results_to_heatmap.py b/plotstables/results_to_heatmap.py
deleted file mode 100644
index 3d9461e7..00000000
--- a/plotstables/results_to_heatmap.py
+++ /dev/null
@@ -1,313 +0,0 @@
-"""
-Usage: python results_to_heatmap.py results_folder_path
-results_folder_path contains results of multiple models whose folders should be named after them
-Source: https://medium.com/@szabo.bibor/how-to-create-a-seaborn-correlation-heatmap-in-python-834c0686b88e
-"""
-import json
-import os
-import sys
-
-from mteb import MTEB
-import numpy as np
-import pandas as pd
-
-TASK_LIST_BITEXT = [
-    "BUCC",
-    "Tatoeba",
-]
-
-TASK_LIST_CLASSIFICATION = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-]
-
-TASK_LIST_CLUSTERING = [
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-]
-
-TASK_LIST_PAIR_CLASSIFICATION = [
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-]
-
-TASK_LIST_RERANKING = [
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-]
-
-TASK_LIST_RETRIEVAL = [
-    "ArguAna",
-    "ClimateFEVER",
-    "CQADupstackRetrieval",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-]
-
-TASK_LIST_STS = [
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-]
-
-TASK_LIST_SUMMARIZATION = [
-    "SummEval",
-]
-
-TASK_LIST = (
-    TASK_LIST_BITEXT
-    + TASK_LIST_CLASSIFICATION
-    + TASK_LIST_CLUSTERING
-    + TASK_LIST_PAIR_CLASSIFICATION
-    + TASK_LIST_RERANKING
-    + TASK_LIST_RETRIEVAL
-    + TASK_LIST_STS
-    + TASK_LIST_SUMMARIZATION
-)
-
-TASK_LIST_EN = (
-    TASK_LIST_CLASSIFICATION
-    + TASK_LIST_CLUSTERING
-    + TASK_LIST_PAIR_CLASSIFICATION
-    + TASK_LIST_RERANKING
-    + TASK_LIST_RETRIEVAL
-    + TASK_LIST_STS
-    + TASK_LIST_SUMMARIZATION
-)
-
-TASK_LIST_NAMES = [
-    ("Class.", TASK_LIST_CLASSIFICATION, ["en", "en-en"]),
-    ("Clust.", TASK_LIST_CLUSTERING, ["en", "en-en"]),
-    ("PairClass.", TASK_LIST_PAIR_CLASSIFICATION, ["en", "en-en"]),
-    ("Rerank.", TASK_LIST_RERANKING, ["en", "en-en"]),
-    ("Retr.", TASK_LIST_RETRIEVAL, ["en", "en-en"]),
-    ("STS", TASK_LIST_STS, ["en", "en-en"]),
-    ("Summ.", TASK_LIST_SUMMARIZATION, ["en", "en-en"]),
-    # ("BitextMining", TASK_LIST_BITEXT, []),
-    # ("Avg.", TASK_LIST_EN, ["en", "en-en"]),
-]
-
-MODEL_TO_NAME = {
-    "bert-base-uncased": "BERT",
-    "gtr-t5-base": "GTR-Base",
-    "gtr-t5-large": "GTR-Large",
-    "gtr-t5-xl": "GTR-XL",
-    "gtr-t5-xxl": "GTR-XXL",
-    "sentence-t5-base": "ST5-Base",
-    "sentence-t5-large": "ST5-Large",
-    "sentence-t5-xl": "ST5-XL",
-    "sentence-t5-xxl": "ST5-XXL",
-    "SGPT-125M-weightedmean-msmarco-specb-bitfit": "SGPT-125M-msmarco",
-    "SGPT-1.3B-weightedmean-msmarco-specb-bitfit": "SGPT-1.3B-msmarco",
-    "SGPT-2.7B-weightedmean-msmarco-specb-bitfit": "SGPT-2.7B-msmarco",
-    "SGPT-5.8B-weightedmean-msmarco-specb-bitfit": "SGPT-5.8B-msmarco",
-    "sgpt-bloom-7b1-msmarco": "SGPT-BLOOM-7.1B-msmarco",
-    "SGPT-125M-weightedmean-nli-bitfit": "SGPT-125M-nli",
-    "SGPT-5.8B-weightedmean-nli-bitfit": "SGPT-5.8B-nli",
-    "sup-simcse-bert-base-uncased": "SimCSE-BERT-sup",
-    "contriever-base-msmarco": "Contriever",
-    "msmarco-bert-co-condensor": "coCondenser-msmarco", # They write it as coCondenser in the paper
-    "unsup-simcse-bert-base-uncased": "SimCSE-BERT-unsup",
-    "glove.6B.300d": "Glove",
-    "komninos": "Komninos",
-    "all-MiniLM-L6-v2": "MiniLM-L6",
-    "all-MiniLM-L12-v2": "MiniLM-L12",
-    "paraphrase-multilingual-MiniLM-L12-v2": "MiniLM-L12-multilingual",
-    "all-mpnet-base-v2": "MPNet",
-    "paraphrase-multilingual-mpnet-base-v2": "MPNet-multilingual",
-    "allenai-specter": "SPECTER",
-    # "text-similarity-ada-001": "Ada Similarity",
-}
-
-SELFSUPERVISED_MODELS = [
-    "glove.6B.300d",
-    "komninos",
-    "bert-base-uncased",
-    "unsup-simcse-bert-base-uncased",
-]
-
-SUPERVISED_MODELS = [
-    "sup-simcse-bert-base-uncased",
-    "msmarco-bert-co-condensor",
-    "contriever-base-msmarco",
-    "allenai-specter",
-    "LaBSE",
-    "LASER2",
-    "all-MiniLM-L6-v2",
-    "all-MiniLM-L12-v2",
-    "paraphrase-multilingual-MiniLM-L12-v2",
-    "all-mpnet-base-v2",
-    "paraphrase-multilingual-mpnet-base-v2",
-    # "text-similarity-ada-001",
-    "SGPT-125M-weightedmean-nli-bitfit",
-    "SGPT-5.8B-weightedmean-nli-bitfit",
-    "SGPT-125M-weightedmean-msmarco-specb-bitfit",
-    "SGPT-1.3B-weightedmean-msmarco-specb-bitfit",
-    "SGPT-2.7B-weightedmean-msmarco-specb-bitfit",
-    "SGPT-5.8B-weightedmean-msmarco-specb-bitfit",
-    "sgpt-bloom-7b1-msmarco",
-    "gtr-t5-base", # 110M
-    "gtr-t5-large",
-    "gtr-t5-xl",
-    "gtr-t5-xxl", # 4.8B
-    "sentence-t5-base", # 110M
-    "sentence-t5-large",
-    "sentence-t5-xl",
-    "sentence-t5-xxl", # 4.8B
-]
-
-results_folder = sys.argv[1].strip("/")
-
-all_results = {}
-
-for model_name in os.listdir(results_folder):
-    model_res_folder = os.path.join(results_folder, model_name)
-    if os.path.isdir(model_res_folder):
-        all_results.setdefault(model_name, {})
-        for file_name in os.listdir(model_res_folder):
-            if not file_name.endswith(".json"):
-                print(f"Skipping non-json {file_name}")
-                continue
-            with open(os.path.join(model_res_folder, file_name), "r", encoding="utf-8") as f:
-                results = json.load(f)
-                all_results[model_name] = {**all_results[model_name], **{file_name.replace(".json", ""): results}}
-
-def get_row(dataset, model_name, limit_langs=[], skip_langs=[]):
-    # CQADupstackRetrieval uses the same metric as its subsets
-    tasks = MTEB(tasks=[dataset.replace("CQADupstackRetrieval", "CQADupstackTexRetrieval")]).tasks
-    assert len(tasks) == 1, f"Found {len(tasks)} for {dataset}. Expected 1."
-    main_metric = tasks[0].description["main_score"]
-    test_result = all_results.get(model_name, {}). get(dataset, {})
-
-    # Dev / Val set is used for MSMARCO (See BEIR paper)
-    if "MSMARCO" in dataset:
-        test_result = (
-            test_result.get("dev") if "dev" in test_result else test_result.get("validation")
-        )
-    else:
-        test_result = test_result.get("test")
-
-    for lang in tasks[0].description["eval_langs"]:
-        if (limit_langs and lang not in limit_langs) or (skip_langs and lang in skip_langs):
-            continue
-        elif test_result is None:
-            raise NotImplementedError(f"Got no test result {test_result} for ds: {dataset} model: {model_name}")
-
-        test_result_lang = test_result.get(lang, test_result)
-        if main_metric == "cosine_spearman":
-            test_result_lang = test_result_lang.get("cos_sim", {}).get("spearman")
-        elif main_metric == "ap":
-            test_result_lang = test_result_lang.get("cos_sim", {}).get("ap")
-        else:
-            test_result_lang = test_result_lang.get(main_metric)
-
-        if test_result_lang is None:
-            raise NotImplementedError
-
-        return test_result_lang
-    raise NotImplementedError
-
-
-### MODEL HEATMAP
-
-model_dict = []
-
-for ds in TASK_LIST_EN:
-    model_dict.append({MODEL_TO_NAME.get(model,model): get_row(ds, model, limit_langs=["en", "en-en"]) for model in SELFSUPERVISED_MODELS + SUPERVISED_MODELS})
-
-model_df = pd.DataFrame(model_dict)
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-plt.figure(figsize=(20, 10))
-
-model_df = (model_df.corr() * 100).round(0).astype(int)
-
-# define the mask to set the values in the upper triangle to True
-mask = np.triu(np.ones_like(model_df, dtype=np.bool))
-heatmap = sns.heatmap(model_df, mask=mask, vmin=model_df.values.min(), vmax=model_df.values.max(), annot=True, fmt='g', cmap='Blues')
-# heatmap.set_title('Pearson Correlations of scores on MTEB', fontdict={'fontsize':18}, pad=16);
-
-plt.savefig('heatmap_model.pdf', dpi=300, bbox_inches='tight')
-
-data_dict = []
-
-
-### TASK HEATMAP
-for model in MODEL_TO_NAME:
-    results = {}
-    for (task_name, task_list, limit_langs) in TASK_LIST_NAMES:
-        model_task_results = [get_row(task, model, limit_langs=limit_langs) for task in task_list]
-        results[task_name] = np.mean(model_task_results)
-    data_dict.append(results)
-
-data_df = pd.DataFrame(data_dict)
-data_df = (data_df.corr() * 100).round(0).astype(int)
-
-plt.figure(figsize=(20, 10))
-# define the mask to set the values in the upper triangle to True
-mask = np.triu(np.ones_like(data_df, dtype=np.bool))
-heatmap = sns.heatmap(data_df, mask=mask, vmin=data_df.values.min(), vmax=data_df.values.max(), annot=True, fmt='g', cmap='Blues')
-# heatmap.set_title('Pearson Correlations of tasks on MTEB', fontdict={'fontsize':18}, pad=16)
-
-plt.savefig('heatmap_tasks.pdf', dpi=300, bbox_inches='tight')
-
-exit()
-# The last heatmap is not used
-
-### DATA HEATMAP
-# This is to be differentiated from a heatmap of actual data content (e.g. via unigram Jaccard similarity)
-# E.g. for BEIR SciFact & HotpotQA have very low unigram Jaccard similarity, but in this method,
-# they get a high similarity score, because model scores seem to correlate on the datasrt
-
-for model, name in MODEL_TO_NAME.items():
-    data_dict.append({ds: get_row(ds, model, limit_langs=["en", "en-en"]) for ds in TASK_LIST_EN})
-
-data_df = pd.DataFrame(data_dict)
-
-plt.figure(figsize=(128, 48))
-# define the mask to set the values in the upper triangle to True
-mask = np.triu(np.ones_like(data_df.corr(), dtype=np.bool))
-heatmap = sns.heatmap(data_df.corr(), mask=mask, vmin=-1, vmax=1, annot=True, cmap='Blues')
-heatmap.set_title('Pearson Correlations of scores on MTEB', fontdict={'fontsize':18}, pad=16)
-
-plt.savefig('heatmap_data.pdf', dpi=300, bbox_inches='tight')
diff --git a/plotstables/results_to_multilingual.py b/plotstables/results_to_multilingual.py
deleted file mode 100644
index f89263f9..00000000
--- a/plotstables/results_to_multilingual.py
+++ /dev/null
@@ -1,329 +0,0 @@
-"""
-Usage: python results_to_multilingual.py results_folder_path
-Make sure the final directory results_folder_path is the name of your model
-"""
-import json
-import os
-import sys
-
-### GLOBAL VARIABLES ###
-
-TASK_LIST_BITEXT = [
-    "BUCC",
-    "Tatoeba",
-]
-
-BITEXT_MODELS = MULTILING_MODELS = [
-    "LaBSE",
-    "LASER2",
-    "paraphrase-multilingual-MiniLM-L12-v2",
-    "paraphrase-multilingual-mpnet-base-v2",
-    "sgpt-bloom-7b1-msmarco",
-    # "sgpt-bloom-1b3-nli", # Not too interesting
-]
-
-MODEL_TO_NAME = {
-    "bert-base-uncased": "BERT",
-    "gtr-t5-base": "GTR-Base",
-    "gtr-t5-large": "GTR-Large",
-    "gtr-t5-xl": "GTR-XL",
-    "gtr-t5-xxl": "GTR-XXL",
-    "sentence-t5-base": "ST5-Base",
-    "sentence-t5-large": "ST5-Large",
-    "sentence-t5-xl": "ST5-XL",
-    "sentence-t5-xxl": "ST5-XXL",
-    "SGPT-125M-weightedmean-msmarco-specb-bitfit": "SGPT-125M-msmarco",
-    "SGPT-1.3B-weightedmean-msmarco-specb-bitfit": "SGPT-1.3B-msmarco",
-    "SGPT-2.7B-weightedmean-msmarco-specb-bitfit": "SGPT-2.7B-msmarco",
-    "SGPT-5.8B-weightedmean-msmarco-specb-bitfit": "SGPT-5.8B-msmarco",
-    "sgpt-bloom-7b1-msmarco": "SGPT-BLOOM-7.1B-msmarco",
-    "SGPT-125M-weightedmean-nli-bitfit": "SGPT-125M-nli",
-    "SGPT-5.8B-weightedmean-nli-bitfit": "SGPT-5.8B-nli",
-    "sup-simcse-bert-base-uncased": "SimCSE-BERT-sup",
-    "contriever-base-msmarco": "Contriever",
-    "msmarco-bert-co-condensor": "coCondenser-msmarco", # They write it as coCondenser in the paper
-    "unsup-simcse-bert-base-uncased": "SimCSE-BERT-unsup",
-    "glove.6B.300d": "Glove",
-    "komninos": "Komninos",
-    "all-MiniLM-L6-v2": "MiniLM-L6",
-    "all-MiniLM-L12-v2": "MiniLM-L12",
-    "paraphrase-multilingual-MiniLM-L12-v2": "MiniLM-L12-multilingual",
-    "all-mpnet-base-v2": "MPNet",
-    "paraphrase-multilingual-mpnet-base-v2": "MPNet-multilingual",
-    "allenai-specter": "SPECTER",
-    "text-similarity-ada-001": "Ada Similarity",
-}
-
-# Base from:
-# https://coolors.co/palette/ff5400-ff6d00-ff8500-ff9100-ff9e00-00b4d8-0096c7-0077b6-023e8a-03045e
-# Yellow tones from:
-# https://coolors.co/palette/6ab6dc-49a6d4-2f94c6-277ba5-1f6284-e0b700-ffd20a-ffda33-ffe15c-ffe570
-# Green from:
-# https://coolors.co/palette/f94144-f3722c-f8961e-f9844a-f9c74f-90be6d-43aa8b-4d908e-577590-277da1
-MODEL_TO_COLOR = {
-    "MiniLM": "#BAF19C",#"#017600", # Green
-    "MPNet": "#F94144",#"#007A7A", # Light Green
-    "GTR": "#FF5400",#"#221D91", # Blue 1
-    "ST5": "#FF9E00",#"#86D4F1", # Blue 2
-    "SGPT": "#00B4D8",#"#7B3FB9", # Purple
-    "SimCSE": "#F9C74F",#"#2070B4", # Blue 3
-    "LaBSE": "#F9C74F",#"#2070B4", # Blue 3
-    "SPECTER": "#E0B700", # Shade of #2070B4
-    "Glove": "#023E8A",#"#9BC7DD", # Light Blue
-    "LASER2": "#03045E", # Grey
-}
-
-
-MULTILINGUAL_CLF = [
-    "AmazonCounterfactualClassification",
-    "AmazonReviewsClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-]
-
-MULTILINGUAL_STS = [
-    "STS17",
-    "STS22",
-]
-
-### LOGIC ###
-
-results_folder = sys.argv[1].strip("/")
-all_results = {}
-
-for model_name in os.listdir(results_folder):
-    model_res_folder = os.path.join(results_folder, model_name)
-    if os.path.isdir(model_res_folder):
-        all_results.setdefault(model_name, {})
-        for file_name in os.listdir(model_res_folder):
-            if not file_name.endswith(".json"):
-                print(f"Skipping non-json {file_name}")
-                continue
-            with open(os.path.join(model_res_folder, file_name), "r", encoding="utf-8") as f:
-                results = json.load(f)
-                all_results[model_name] = {**all_results[model_name], **{file_name.replace(".json", ""): results}}
-
-
-
-# Create a plot for each task with scaling of the model performances on this task
-import matplotlib.pyplot as plt
-import numpy as np
-
-fig, ax = plt.subplots(figsize=(64,12))
-
-markers = ["x", "o", "v", "*", "p"]
-
-# Compute averages
-scores = {}
-for i, model in enumerate(BITEXT_MODELS):
-    if not(all_results.get(model, []).get("Tatoeba")):
-        continue
-    for lang, res in all_results[model]["Tatoeba"]["test"].items():
-        if lang == "evaluation_time":
-            continue
-        scores.setdefault(lang, [])
-        scores[lang].append(res["f1"])
-# Average
-scores = {k: np.mean(v) for k,v in scores.items()}
-scores_sorted = sorted(scores.items(), key=lambda x: x[-1], reverse=True)
-langs_sorted = [x[0] for x in scores_sorted]
-global_idx = {lang: langs_sorted.index(lang) for lang in scores}
-
-for i, model in enumerate(BITEXT_MODELS):
-    scores = {}
-
-    if not(all_results.get(model, []).get("Tatoeba")):
-        continue
-
-    for lang, res in all_results[model]["Tatoeba"]["test"].items():
-        if lang == "evaluation_time":
-            continue
-        scores[lang] = res["f1"]
-    
-    # Optionally sort by LaBSE scores
-    if i == 0: 
-        assert model == "LaBSE"
-        scores_sorted = sorted(scores.items(), key=lambda x: x[-1], reverse=True)
-        langs_sorted = [x[0] for x in scores_sorted]
-        global_idx = {lang: langs_sorted.index(lang) for lang in scores}
-
-    # Reverse is already accounted for in global_idx
-    scores_sorted = sorted(scores.items(), key=lambda x: global_idx[x[0]], reverse=False)
-    x_langs = [x[0] for x in scores_sorted]
-    y_scores = [x[1] for x in scores_sorted]
-
-    model_name = MODEL_TO_NAME.get(model, model)
-    ax.plot(x_langs, y_scores, linewidth=6.0, label=model_name, marker=markers[i], color=MODEL_TO_COLOR.get(model_name.split("-")[0]))
-
-ax.set_ylabel("F1 score", fontsize=22)
-ax.margins(x=0.01) # Reduce whitespace left & right
-
-plt.xticks(rotation=45, fontsize=20) #plt.xticks(rotation=90, ha='right')
-plt.legend(fontsize=25)
-plt.savefig('multilingual_tatoeba.pdf', dpi=300, bbox_inches='tight')
-
-
-### CLASSIFICATION ###
-
-# Compute averages
-scores = {}
-for i, model in enumerate(BITEXT_MODELS):
-    for ds in MULTILINGUAL_CLF:
-        if not(all_results.get(model, []).get(ds)):
-            continue
-        for lang, res in all_results[model][ds]["test"].items():
-            if lang == "evaluation_time":
-                continue
-            elif lang == "en-ext":
-                lang = "en"
-            
-            scores.setdefault(lang, [])
-            scores[lang].append(res["accuracy"])
-# Average
-scores = {k: np.mean(v) for k,v in scores.items()}
-scores_sorted = sorted(scores.items(), key=lambda x: x[-1], reverse=True)
-langs_sorted = [x[0] for x in scores_sorted]
-global_idx = {lang: langs_sorted.index(lang) for lang in scores}
-
-
-fig, ax = plt.subplots(figsize=(32,8))
-
-
-for i, model in enumerate(BITEXT_MODELS):
-    scores = {}
-    for ds in MULTILINGUAL_CLF:
-        if not(all_results.get(model, []).get(ds)):
-            continue
-        for lang, res in all_results[model][ds]["test"].items():
-            if lang == "evaluation_time":
-                continue
-            elif lang == "en-ext":
-                lang = "en"
-            
-            scores.setdefault(lang, [])
-            scores[lang].append(res["accuracy"])
-
-    # Average scores for langs
-    scores = {k: np.mean(v) for k,v in scores.items()}
-
-    # Reverse is already accounted for in global_idx
-    scores_sorted = sorted(scores.items(), key=lambda x: global_idx[x[0]], reverse=False)
-    x_langs = [x[0] for x in scores_sorted]
-    y_scores = [x[1] for x in scores_sorted]
-    model_name = MODEL_TO_NAME.get(model, model)
-    ax.plot(x_langs, y_scores, linewidth=6.0, label=model_name, marker=markers[i], color=MODEL_TO_COLOR.get(model_name.split("-")[0]))
-
-ax.set_ylabel("Accuracy", fontsize=22)
-
-plt.xticks(rotation=45, fontsize=20) #plt.xticks(rotation=90, ha='right')
-plt.legend(fontsize=25)
-
-plt.savefig('multilingual_clf.pdf', dpi=300, bbox_inches='tight')
-
-
-
-### STS ###
-
-# Compute averages
-scores_multi = {}
-scores_cross = {}
-
-for i, model in enumerate(BITEXT_MODELS):
-    for ds in MULTILINGUAL_STS:
-        if not(all_results.get(model, []).get(ds)):
-            continue
-        for lang, res in all_results[model][ds]["test"].items():
-            if lang == "evaluation_time":
-                continue
-            multi = True
-            if "-" in lang:
-                l1, l2 = lang.split("-")
-                if l1 != l2:
-                    multi = False
-                else:
-                    lang = l1
-            if multi:
-                scores_multi.setdefault(lang, [])
-                scores_multi[lang].append(res["cos_sim"]["spearman"])
-            else:
-                scores_cross.setdefault(lang, [])
-                scores_cross[lang].append(res["cos_sim"]["spearman"])
-
-# Average
-scores = {k: np.mean(v) for k,v in scores_multi.items()}
-scores_sorted = sorted(scores.items(), key=lambda x: x[-1], reverse=True)
-langs_sorted = [x[0] for x in scores_sorted]
-global_idx_multi = {lang: langs_sorted.index(lang) for lang in scores}
-
-scores = {k: np.mean(v) for k,v in scores_cross.items()}
-scores_sorted = sorted(scores.items(), key=lambda x: x[-1], reverse=True)
-langs_sorted = [x[0] for x in scores_sorted]
-global_idx_cross = {lang: langs_sorted.index(lang) for lang in scores}
-
-
-
-fig, axes = plt.subplots(figsize=(32,8), ncols=2, nrows=1, sharey=True)
-
-ax_multi, ax_cross = axes
-
-for i, model in enumerate(BITEXT_MODELS):
-    scores_multi = {}
-    scores_cross = {}
-    for ds in MULTILINGUAL_STS:
-        if not(all_results.get(model, []).get(ds)):
-            continue
-        for lang, res in all_results[model][ds]["test"].items():
-            if lang == "evaluation_time":
-                continue
-            multi = True
-            if "-" in lang:
-                l1, l2 = lang.split("-")
-                if l1 != l2:
-                    multi = False
-                else:
-                    lang = l1
-            
-            if multi:
-                scores_multi.setdefault(lang, [])
-                scores_multi[lang].append(res["cos_sim"]["spearman"])
-            else:
-                scores_cross.setdefault(lang, [])
-                scores_cross[lang].append(res["cos_sim"]["spearman"])              
-
-    scores_multi = {k: np.mean(v) for k,v in scores_multi.items()}
-    scores_cross = {k: np.mean(v) for k,v in scores_cross.items()}
-
-    scores_sorted_multi = sorted(scores_multi.items(), key=lambda x: global_idx_multi[x[0]], reverse=False)
-    scores_sorted_cross = sorted(scores_cross.items(), key=lambda x: global_idx_cross[x[0]], reverse=False)
-
-    model_name = MODEL_TO_NAME.get(model, model)
-    model_color = MODEL_TO_COLOR.get(model_name.split("-")[0])
-
-    ax_multi.plot(
-        [x[0] for x in scores_sorted_multi],
-        [x[1] for x in scores_sorted_multi],
-        label=model_name, 
-        marker=markers[i], 
-        color=model_color,
-        linewidth=6.0,
-    )
-
-    ax_cross.plot(        
-        [x[0] for x in scores_sorted_cross],
-        [x[1] for x in scores_sorted_cross],
-        label=model_name,
-        marker=markers[i], 
-        color=model_color,
-        linewidth=6.0,
-    )
-
-ax_multi.set_ylabel("Cos. Sim. Spearman Corr.", fontsize=22)
-
-ax_multi.tick_params(axis='both', which='minor', labelsize=20)
-ax_multi.tick_params(axis='both', which='major', labelsize=20)
-ax_cross.tick_params(axis='both', which='minor', labelsize=20, rotation=45)
-ax_cross.tick_params(axis='both', which='major', labelsize=20, rotation=45)
-
-plt.savefig('multilingual_sts.pdf', dpi=300, bbox_inches='tight')
diff --git a/plotstables/results_to_scale.py b/plotstables/results_to_scale.py
deleted file mode 100644
index 36bf8013..00000000
--- a/plotstables/results_to_scale.py
+++ /dev/null
@@ -1,274 +0,0 @@
-"""
-Creates scaling graphs
-Usage: python results_to_scale.py results_folder_path
-results_folder_path contains results of multiple models whose folders should be named after them
-"""
-import json
-import os
-import sys
-
-from mteb import MTEB
-import numpy as np
-
-### GLOBAL VARIABLES ###
-
-TASK_LIST_CLASSIFICATION = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-]
-
-TASK_LIST_CLUSTERING = [
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-]
-
-TASK_LIST_PAIR_CLASSIFICATION = [
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-]
-
-TASK_LIST_RERANKING = [
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-]
-
-TASK_LIST_RETRIEVAL = [
-    "ArguAna",
-    "ClimateFEVER",
-    "CQADupstackRetrieval",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-]
-
-TASK_LIST_STS = [
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-]
-
-# Parameter counts in millions
-MODELS = [
-# Doesnt add a lot of value to the figure
-#    [
-#        ("MiniLM-L6", "all-MiniLM-L6-v2", 22.713216), # 22.7 M
-#        ("MiniLM-L12", "all-MiniLM-L12-v2", 33.360000), # 33.4 M
-#    ],
-    [
-        ("GTR-Base", "gtr-t5-base", 110),
-        ("GTR-Large", "gtr-t5-large", 335),
-        ("GTR-XL", "gtr-t5-xl", 1240),
-        ("GTR-XXL", "gtr-t5-xxl", 4800),
-    ],
-    [
-        ("ST5-Base", "sentence-t5-base", 110),
-        ("ST5-Large", "sentence-t5-large", 335),
-        ("ST5-XL", "sentence-t5-xl", 1240),
-        ("ST5-XXL", "sentence-t5-xxl", 4800),
-    ],
-    [
-        ("SGPT-125M-msmarco", "SGPT-125M-weightedmean-msmarco-specb-bitfit", 125),
-        ("SGPT-1.3B-msmarco", "SGPT-1.3B-weightedmean-msmarco-specb-bitfit", 1300),
-        ("SGPT-2.7B-msmarco", "SGPT-2.7B-weightedmean-msmarco-specb-bitfit", 2700),
-        ("SGPT-5.8B-msmarco", "SGPT-5.8B-weightedmean-msmarco-specb-bitfit", 5800),
-    ],
-]
-
-# todo: remove
-lines = ["blue2", "blue", "purple"]
-shades = ["lightblue2", "lightblue", "lightpurple"]
-colors = {
-    "purple": "#7B3FB9",
-    "lightpurple": "#CBB3E3",
-    "blue": "#221D91",
-    "lightblue": "#B6B4DB",
-    "blue2": "#86D4F1",
-    "lightblue2": "#AAF2F2",
-}
-
-
-MODEL_TO_MARKER = {
-    "MiniLM": "o",
-    "GTR": "x",
-    "ST5": "*",
-    "SGPT": "v",
-}
-
-# Base from:
-# https://coolors.co/palette/ff5400-ff6d00-ff8500-ff9100-ff9e00-00b4d8-0096c7-0077b6-023e8a-03045e
-# Yellow tones from:
-# https://coolors.co/palette/6ab6dc-49a6d4-2f94c6-277ba5-1f6284-e0b700-ffd20a-ffda33-ffe15c-ffe570
-# Green from:
-# https://coolors.co/palette/f94144-f3722c-f8961e-f9844a-f9c74f-90be6d-43aa8b-4d908e-577590-277da1
-MODEL_TO_COLOR = {
-    "MiniLM": "#BAF19C",#"#017600", # Green
-    "MPNet": "#F94144",#"#007A7A", # Light Green
-    "GTR": "#FF5400",#"#221D91", # Blue 1
-    "ST5": "#FF9E00",#"#86D4F1", # Blue 2
-    "SGPT": "#00B4D8",#"#7B3FB9", # Purple
-    "SimCSE": "#F9C74F",#"#2070B4", # Blue 3
-    "LaBSE": "#F9C74F",#"#2070B4", # Blue 3
-    "SPECTER": "#E0B700", # Shade of #2070B4
-    "Glove": "#023E8A",#"#9BC7DD", # Light Blue
-    "LASER2": "#03045E", # Grey
-}
-
-
-TASK_LIST_NAMES = [
-    ("Classification", TASK_LIST_CLASSIFICATION, ["en", "en-en"], "accuracy"),
-    ("Clustering", TASK_LIST_CLUSTERING, ["en", "en-en"], "v_measure"),
-    ("PairClassification", TASK_LIST_PAIR_CLASSIFICATION, ["en", "en-en"], "ap"),
-    ("Reranking", TASK_LIST_RERANKING, ["en", "en-en"], "map"),
-    ("Retrieval", TASK_LIST_RETRIEVAL, ["en", "en-en"], "nDCG@10"),
-    ("STS", TASK_LIST_STS, ["en", "en-en"], "cos. sim. spearman corr."),
-]
-
-
-### LOGIC ###
-
-results_folder = sys.argv[1].strip("/")
-
-all_results = {}
-
-for model_name in os.listdir(results_folder):
-    model_res_folder = os.path.join(results_folder, model_name)
-    if os.path.isdir(model_res_folder):
-        all_results.setdefault(model_name, {})
-        for file_name in os.listdir(model_res_folder):
-            if not file_name.endswith(".json"):
-                print(f"Skipping non-json {file_name}")
-                continue
-            with open(os.path.join(model_res_folder, file_name), "r", encoding="utf-8") as f:
-                results = json.load(f)
-                all_results[model_name] = {**all_results[model_name], **{file_name.replace(".json", ""): results}}
-
-
-def get_row(dataset, model_name, limit_langs=[], skip_langs=[]):
-    # CQADupstackRetrieval uses the same metric as its subsets
-    tasks = MTEB(tasks=[dataset.replace("CQADupstackRetrieval", "CQADupstackTexRetrieval")]).tasks
-    assert len(tasks) == 1, f"Found {len(tasks)} for {dataset}. Expected 1."
-    main_metric = tasks[0].description["main_score"]
-    test_result = all_results.get(model_name, {}). get(dataset, {})
-
-    # Dev / Val set is used for MSMARCO (See BEIR paper)
-    if "MSMARCO" in dataset:
-        test_result = (
-            test_result.get("dev") if "dev" in test_result else test_result.get("validation")
-        )
-    else:
-        test_result = test_result.get("test")
-
-    for lang in tasks[0].description["eval_langs"]:
-        if (limit_langs and lang not in limit_langs) or (skip_langs and lang in skip_langs):
-            continue
-        elif test_result is None:
-            raise NotImplementedError(f"Got no test result {test_result} for ds: {dataset} model: {model_name}")
-
-        test_result_lang = test_result.get(lang, test_result)
-        if main_metric == "cosine_spearman":
-            test_result_lang = test_result_lang.get("cos_sim", {}).get("spearman")
-        elif main_metric == "ap":
-            test_result_lang = test_result_lang.get("cos_sim", {}).get("ap")
-        else:
-            test_result_lang = test_result_lang.get(main_metric)
-
-        if test_result_lang is None:
-            raise NotImplementedError
-
-        return test_result_lang
-    raise NotImplementedError
-
-
-# Create a plot for each task with scaling of the model performances on this task
-import matplotlib.pyplot as plt
-
-fig, axes = plt.subplots(figsize=(16, 20), facecolor='w', edgecolor='k', ncols=2, nrows=3, sharey=False)
-
-
-# Create each boxplot
-model_xticks_global = ['0.1B', '1B','2B','4B']
-model_xticks_num_global = [np.log10(100_000_000), np.log10(1_000_000_000), np.log10(2_000_000_000), np.log10(4_000_000_000)]
-
-for ax, (task_name, task_list, limit_langs, metric) in zip(axes.flatten(), TASK_LIST_NAMES):
-    for i, model_group in enumerate(MODELS):
-        model_xticks_num = [np.log10(x[-1] * 1_000_000) for x in model_group]
-        avg_scores = []
-        std_scores = []
-        for model in model_group:
-            model_name = model[0]
-            try:
-                model_task_results = [get_row(task, model[1], limit_langs=limit_langs) for task in task_list]
-            except:
-                model_task_results = [0.5]
-            
-            avg_scores.append(np.mean(np.array(model_task_results)).item())
-            std_scores.append(np.std(np.array(model_task_results)).item())
-
-        ax.plot(
-            model_xticks_num, 
-            avg_scores, 
-            label=model_name.split("-")[0], 
-            color=MODEL_TO_COLOR.get(model_name.split("-")[0]),
-            marker=MODEL_TO_MARKER.get(model_name.split("-")[0])
-        )
-
-        # Shade doesn't look good, as std is too big
-        # ax.fill_between(model_xticks_num, [avg-std for avg, std in zip(avg_scores, std_scores)], [avg+std for avg, std in zip(avg_scores, std_scores)], color=colors.get(shades[i]), alpha=0.5)
-
-    ax.set_ylabel(f"Average Performance ({metric})", fontsize=16)
-    ax.set_xlabel("Model Parameters (Billions)", fontsize=16)
-    ax.set_xticks(model_xticks_num_global, model_xticks_global)
-    ax.set_title(task_name, fontweight="bold", fontsize=20)
-    ax.grid(alpha=0.5)
-
-# Create deduplicated Global Legend
-handles, labels = plt.gca().get_legend_handles_labels()
-by_label = dict(zip(labels, handles))
-fig.legend(
-    by_label.values(), 
-    by_label.keys(), 
-    loc=(0.35, 0.94), # "upper center",
-    ncol=len(by_label),
-    frameon=False,
-    fontsize=15,
-)
-
-plt.savefig('scale.pdf', dpi=300, bbox_inches='tight')
diff --git a/plotstables/results_to_tex.py b/plotstables/results_to_tex.py
deleted file mode 100644
index aea339b1..00000000
--- a/plotstables/results_to_tex.py
+++ /dev/null
@@ -1,308 +0,0 @@
-"""
-Usage: python results_to_tex.py results_folder_path
-results_folder_path contains results of multiple models whose folders should be named after them
-"""
-import json
-import os
-import sys
-
-from mteb import MTEB
-import numpy as np
-
-
-### GLOBAL VARIABLES ###
-
-
-TASK_LIST_BITEXT = [
-    "BUCC",
-    "Tatoeba",
-]
-
-TASK_LIST_CLASSIFICATION = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-]
-
-TASK_LIST_CLUSTERING = [
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-]
-
-TASK_LIST_PAIR_CLASSIFICATION = [
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-]
-
-TASK_LIST_RERANKING = [
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-]
-
-TASK_LIST_RETRIEVAL = [
-    "ArguAna",
-    "ClimateFEVER",
-    "CQADupstackRetrieval",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-]
-
-TASK_LIST_STS = [
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-]
-
-TASK_LIST_SUMMARIZATION = [
-    "SummEval",
-]
-
-TASK_LIST = (
-    TASK_LIST_BITEXT
-    + TASK_LIST_CLASSIFICATION
-    + TASK_LIST_CLUSTERING
-    + TASK_LIST_PAIR_CLASSIFICATION
-    + TASK_LIST_RERANKING
-    + TASK_LIST_RETRIEVAL
-    + TASK_LIST_STS
-    + TASK_LIST_SUMMARIZATION
-)
-
-TASK_LIST_EN = (
-    TASK_LIST_CLASSIFICATION
-    + TASK_LIST_CLUSTERING
-    + TASK_LIST_PAIR_CLASSIFICATION
-    + TASK_LIST_RERANKING
-    + TASK_LIST_RETRIEVAL
-    + TASK_LIST_STS
-    + TASK_LIST_SUMMARIZATION
-)
-
-TASK_LIST_NAMES = [
-    ("Classification", TASK_LIST_CLASSIFICATION, ["en", "en-en"]),
-    ("Clustering", TASK_LIST_CLUSTERING, ["en", "en-en"]),
-    ("PairClassification", TASK_LIST_PAIR_CLASSIFICATION, ["en", "en-en"]),
-    ("Reranking", TASK_LIST_RERANKING, ["en", "en-en"]),
-    ("Retrieval", TASK_LIST_RETRIEVAL, ["en", "en-en"]),
-    ("STS", TASK_LIST_STS, ["en", "en-en"]),
-    ("all", TASK_LIST, ["en", "en-en"]),
-    ("BitextMining", TASK_LIST_BITEXT, []),
-]
-
-BITEXT_MODELS = MULTILING_MODELS = [
-    # "glove.6B.300d",
-    # "komninos",
-    "LASER2",
-    "LaBSE",
-    "paraphrase-multilingual-MiniLM-L12-v2",
-    "paraphrase-multilingual-mpnet-base-v2",
-    "sgpt-bloom-7b1-msmarco",
-    # "sgpt-bloom-1b3-nli",
-]
-
-
-SELFSUPERVISED_MODELS = [
-    "glove.6B.300d",
-    "komninos",
-    "bert-base-uncased",
-    "unsup-simcse-bert-base-uncased",
-]
-
-SUPERVISED_MODELS = [
-    "sup-simcse-bert-base-uncased",
-    "msmarco-bert-co-condensor",
-    "contriever-base-msmarco",
-    "allenai-specter",
-    "LaBSE",
-    "LASER2",
-    "all-MiniLM-L6-v2",
-    "all-MiniLM-L12-v2",
-    "paraphrase-multilingual-MiniLM-L12-v2",
-    "all-mpnet-base-v2",
-    "paraphrase-multilingual-mpnet-base-v2",
-    "text-similarity-ada-001",
-    "SGPT-125M-weightedmean-nli-bitfit",
-    "SGPT-5.8B-weightedmean-nli-bitfit",
-    "SGPT-125M-weightedmean-msmarco-specb-bitfit",
-    "SGPT-1.3B-weightedmean-msmarco-specb-bitfit",
-    "SGPT-2.7B-weightedmean-msmarco-specb-bitfit",
-    "SGPT-5.8B-weightedmean-msmarco-specb-bitfit",
-    "sgpt-bloom-7b1-msmarco",
-    "gtr-t5-base", # 110M
-    "gtr-t5-large",
-    "gtr-t5-xl",
-    "gtr-t5-xxl", # 4.8B
-    "sentence-t5-base", # 110M
-    "sentence-t5-large",
-    "sentence-t5-xl",
-    "sentence-t5-xxl", # 4.8B
-]
-
-
-MODEL_TO_NAME = {
-    "bert-base-uncased": "BERT",
-    "gtr-t5-base": "GTR-Base",
-    "gtr-t5-large": "GTR-Large",
-    "gtr-t5-xl": "GTR-XL",
-    "gtr-t5-xxl": "GTR-XXL",
-    "sentence-t5-base": "ST5-Base",
-    "sentence-t5-large": "ST5-Large",
-    "sentence-t5-xl": "ST5-XL",
-    "sentence-t5-xxl": "ST5-XXL",
-    "SGPT-125M-weightedmean-msmarco-specb-bitfit": "SGPT-125M-msmarco",
-    "SGPT-1.3B-weightedmean-msmarco-specb-bitfit": "SGPT-1.3B-msmarco",
-    "SGPT-2.7B-weightedmean-msmarco-specb-bitfit": "SGPT-2.7B-msmarco",
-    "SGPT-5.8B-weightedmean-msmarco-specb-bitfit": "SGPT-5.8B-msmarco",
-    "sgpt-bloom-7b1-msmarco": "SGPT-BLOOM-7.1B-msmarco",
-    "SGPT-125M-weightedmean-nli-bitfit": "SGPT-125M-nli",
-    "SGPT-5.8B-weightedmean-nli-bitfit": "SGPT-5.8B-nli",
-    "sup-simcse-bert-base-uncased": "SimCSE-BERT-sup",
-    "contriever-base-msmarco": "Contriever",
-    "msmarco-bert-co-condensor": "coCondenser-msmarco", # They write it as coCondenser in the paper
-    "unsup-simcse-bert-base-uncased": "SimCSE-BERT-unsup",
-    "glove.6B.300d": "Glove",
-    "komninos": "Komninos",
-    "all-MiniLM-L6-v2": "MiniLM-L6",
-    "all-MiniLM-L12-v2": "MiniLM-L12",
-    "paraphrase-multilingual-MiniLM-L12-v2": "MiniLM-L12-multilingual",
-    "all-mpnet-base-v2": "MPNet",
-    "paraphrase-multilingual-mpnet-base-v2": "MPNet-multilingual",
-    "allenai-specter": "SPECTER",
-    "text-similarity-ada-001": "Ada Similarity",
-    "text-search-ada-query-001": "Ada Search Query"
-}
-
-
-
-### LOGIC ###
-
-results_folder = sys.argv[1].strip("/")
-
-all_results = {}
-
-for model_name in os.listdir(results_folder):
-    model_res_folder = os.path.join(results_folder, model_name)
-    if os.path.isdir(model_res_folder):
-        all_results.setdefault(model_name, {})
-        for file_name in os.listdir(model_res_folder):
-            if not file_name.endswith(".json"):
-                print(f"Skipping non-json {file_name}")
-                continue
-            with open(os.path.join(model_res_folder, file_name), "r", encoding="utf-8") as f:
-                results = json.load(f)
-                all_results[model_name] = {**all_results[model_name], **{file_name.replace(".json", ""): results}}
-
-
-def get_rows(dataset, model_name, limit_langs=[], skip_langs=[]):
-    rows = []
-    # CQADupstackRetrieval uses the same metric as its subsets
-    tasks = MTEB(tasks=[dataset.replace("CQADupstackRetrieval", "CQADupstackTexRetrieval")]).tasks
-    assert len(tasks) == 1, f"Found {len(tasks)} for {dataset}. Expected 1."
-    main_metric = tasks[0].description["main_score"]
-    test_result = all_results.get(model_name, {}). get(dataset, {})
-
-    # Dev / Val set is used for MSMARCO (See BEIR paper)
-    if "MSMARCO" in dataset:
-        test_result = (
-            test_result.get("dev") if "dev" in test_result else test_result.get("validation")
-        )
-    else:
-        test_result = test_result.get("test")
-
-    for lang in tasks[0].description["eval_langs"]:
-        if (limit_langs and lang not in limit_langs) or (skip_langs and lang in skip_langs):
-            continue
-        elif test_result is None:
-            rows.append([lang, main_metric, None])
-            continue
-
-        test_result_lang = test_result.get(lang, test_result)
-        if main_metric == "cosine_spearman":
-            test_result_lang = test_result_lang.get("cos_sim", {}).get("spearman")
-        elif main_metric == "ap":
-            test_result_lang = test_result_lang.get("cos_sim", {}).get("ap")
-        else:
-            test_result_lang = test_result_lang.get(main_metric)
-
-        if test_result_lang is None:
-            rows.append([lang, main_metric, None])
-            continue
-
-        rows.append([lang, main_metric, test_result_lang])
-    return rows
-
-
-def get_table(models, task_list, limit_langs=[], skip_langs=[], name="table"):
-    TABLE = "Dataset & Language & " + " & ".join([MODEL_TO_NAME.get(model, model) for model in models]) + " \\\\" + "\n"
-    scores_all = []
-    for ds in task_list:
-        results =  [get_rows(dataset=ds, model_name=model, limit_langs=limit_langs, skip_langs=skip_langs) for model in models]
-        assert all(len(sub) == len(results[0]) for sub in results)
-        for lang_idx in range(len(results[0])):
-            scores = [x[lang_idx][-1] for x in results]
-            scores_all.append(scores)
-            lang = results[0][lang_idx][0]
-            one_line = " & ".join([ds, lang] + [str(round(x*100, 2)) if x is not None else "" for x in scores])
-            TABLE += one_line + " \\\\" + "\n"
-
-    arr = np.array(scores_all, dtype=np.float32)
-    # Get an index of columns which has any NaN value
-    index = np.isnan(arr).any(axis=0)
-    # Delete columns (models) with any NaN value from 2D NumPy Array
-    arr = np.delete(arr, index, axis=1)
-    # Average
-    scores_avg = list(np.mean(arr, axis=0))
-    # Insert empty string for NaN columns
-    for i, val in enumerate(index):
-        if val == True:
-            scores_avg.insert(i, "")
-    lang = "mix" if not(limit_langs) else limit_langs[0]
-    TABLE += " & ".join(["Average", lang] + [str(round(x*100, 2)) if x else "" for x in scores_avg]) + " \\\\" + "\n"
-
-    with open(f"{name}.txt", "w") as f:
-        f.write(TABLE)
-
-
-get_table(SELFSUPERVISED_MODELS + SUPERVISED_MODELS, TASK_LIST_EN, limit_langs=["en", "en-en",], name="all_en")
-get_table(BITEXT_MODELS, TASK_LIST_BITEXT, limit_langs=[], name="bitext")
-get_table(MULTILING_MODELS, TASK_LIST_CLASSIFICATION, limit_langs=[], skip_langs=["en", "en-en", "en-ext"], name="multilingclf")
-get_table(MULTILING_MODELS, TASK_LIST_STS, limit_langs=[], skip_langs=["en", "en-en", "en-ext"], name="multilingsts")
-    
diff --git a/plotstables/scale.pdf b/plotstables/scale.pdf
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diff --git a/plotstables/scale.png b/plotstables/scale.png
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diff --git a/plotstables/sim_data.csv b/plotstables/sim_data.csv
deleted file mode 100644
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+++ /dev/null
@@ -1,68 +0,0 @@
-,AmazonCounterfactualClassification,AmazonPolarityClassification,AmazonReviewsClassification,Banking77Classification,EmotionClassification,ImdbClassification,MassiveIntentClassification,MassiveScenarioClassification,MTOPDomainClassification,MTOPIntentClassification,ToxicConversationsClassification,TweetSentimentExtractionClassification,ArxivClusteringP2P,ArxivClusteringS2S,BiorxivClusteringP2P,BiorxivClusteringS2S,MedrxivClusteringP2P,MedrxivClusteringS2S,RedditClustering,RedditClusteringP2P,StackExchangeClustering,StackExchangeClusteringP2P,TwentyNewsgroupsClustering,SprintDuplicateQuestions,TwitterSemEval2015,TwitterURLCorpus,AskUbuntuDupQuestions,MindSmallReranking,SciDocsRR,StackOverflowDupQuestions,ArguAna,ClimateFEVER,CQADupstackAndroidRetrieval,CQADupstackEnglishRetrieval,CQADupstackGamingRetrieval,CQADupstackGisRetrieval,CQADupstackMathematicaRetrieval,CQADupstackPhysicsRetrieval,CQADupstackProgrammersRetrieval,CQADupstackStatsRetrieval,CQADupstackTexRetrieval,CQADupstackUnixRetrieval,CQADupstackWebmastersRetrieval,CQADupstackWordpressRetrieval,DBPedia,FEVER,FiQA2018,HotpotQA,MSMARCO,NFCorpus,NQ,QuoraRetrieval,SCIDOCS,SciFact,Touche2020,TRECCOVID,BIOSSES,SICK-R,STS12,STS13,STS14,STS15,STS16,STS17,STS22,STSBenchmark,SummEval
-AmazonCounterfactualClassification,0.9999998807907104,0.9725366830825806,0.8517286777496338,0.901214599609375,0.8986697196960449,0.9143805503845215,0.915182888507843,0.915182888507843,0.9136463403701782,0.9136463403701782,0.9268499612808228,0.9379464387893677,0.9061737060546875,0.9226172566413879,0.8816869854927063,0.9111778140068054,0.8716050982475281,0.8851615786552429,0.9361067414283752,0.9444894790649414,0.9205214381217957,0.8694825768470764,0.927936851978302,0.7448422908782959,0.8848876357078552,0.9181852340698242,0.8771646618843079,0.8432382345199585,0.9121214151382446,0.8809256553649902,0.9206087589263916,0.8687456250190735,0.8753382563591003,0.9054211974143982,0.9050220847129822,0.8575048446655273,0.8769376277923584,0.8788719773292542,0.8834834098815918,0.8732110261917114,0.8720847964286804,0.8778753876686096,0.8800479173660278,0.8742043972015381,0.8960760831832886,0.8687456250190735,0.9194890260696411,0.8983748555183411,0.9003165364265442,0.8930765986442566,0.9119835495948792,0.9182446002960205,0.889691174030304,0.892997145652771,0.9215371608734131,0.8833832144737244,0.8625791668891907,0.8299649357795715,0.9155313968658447,0.9190161824226379,0.9440998435020447,0.9286789298057556,0.9331569671630859,0.908374547958374,0.8873857855796814,0.933322012424469,0.9260546565055847
-AmazonPolarityClassification,0.9725366830825806,1.0000001192092896,0.8430818319320679,0.8914044499397278,0.8869525194168091,0.9380159974098206,0.9221842288970947,0.9221842288970947,0.9159311652183533,0.9159311652183533,0.9314469695091248,0.9369900822639465,0.9092541337013245,0.9261904954910278,0.8834359645843506,0.9139003753662109,0.8740972876548767,0.8870366811752319,0.9363611340522766,0.9457828998565674,0.9190340638160706,0.8677751421928406,0.9307129383087158,0.7418009638786316,0.8866813778877258,0.9222720861434937,0.8745728135108948,0.8573817014694214,0.9169661998748779,0.8784961700439453,0.911296546459198,0.8777851462364197,0.8737571239471436,0.9069114327430725,0.9039636254310608,0.8530020713806152,0.874477207660675,0.8754789233207703,0.8846838474273682,0.870514988899231,0.8698432445526123,0.8746495246887207,0.8826687932014465,0.8705847859382629,0.9033768773078918,0.8777851462364197,0.91923987865448,0.9041053652763367,0.9071121215820312,0.8932070136070251,0.9180147051811218,0.922761857509613,0.8992704749107361,0.8951342701911926,0.9281871914863586,0.8897658586502075,0.8574753999710083,0.8390056490898132,0.915518581867218,0.9148537516593933,0.93932044506073,0.9213941097259521,0.9270878434181213,0.904332160949707,0.8904899954795837,0.9300788640975952,0.924091637134552
-AmazonReviewsClassification,0.8517286777496338,0.8430818319320679,1.0,0.8274011611938477,0.8377322554588318,0.8060175180435181,0.8927789330482483,0.8927789330482483,0.8721326589584351,0.8721326589584351,0.8651610612869263,0.8857579231262207,0.8268718719482422,0.8719925284385681,0.8097570538520813,0.8545474410057068,0.8083184361457825,0.8327248692512512,0.8771207332611084,0.8643344044685364,0.8922589421272278,0.7934136986732483,0.8788571953773499,0.6903976202011108,0.829818844795227,0.8436835408210754,0.8549239039421082,0.7972355484962463,0.8631322979927063,0.8426644802093506,0.8367676734924316,0.8256171345710754,0.7958477735519409,0.8322862386703491,0.8206374049186707,0.7885207533836365,0.7998864650726318,0.8036501407623291,0.8091654181480408,0.7987720966339111,0.8001028299331665,0.8078638315200806,0.8033185601234436,0.7957671284675598,0.857245147228241,0.8256171345710754,0.8700592517852783,0.861984133720398,0.8672728538513184,0.8301711678504944,0.8646405339241028,0.8830270767211914,0.8478741645812988,0.831039309501648,0.8493306040763855,0.8365864753723145,0.797872006893158,0.8078639507293701,0.8909081220626831,0.8852756023406982,0.8971998691558838,0.8883463144302368,0.8896051645278931,0.8645649552345276,0.9054290056228638,0.8723177313804626,0.8509836196899414
-Banking77Classification,0.901214599609375,0.8914044499397278,0.8274011611938477,1.0000001192092896,0.8723608255386353,0.8470839262008667,0.9069743156433105,0.9069743156433105,0.9206961393356323,0.9206961393356323,0.8975388407707214,0.9144454002380371,0.8722931742668152,0.896972119808197,0.8474944233894348,0.8749076724052429,0.8394017219543457,0.8544522523880005,0.9231041669845581,0.925590991973877,0.9119146466255188,0.8638715744018555,0.9062290787696838,0.7831484079360962,0.8524944186210632,0.8785852193832397,0.8910298943519592,0.8121806979179382,0.8894634246826172,0.8714233040809631,0.868624746799469,0.8610327243804932,0.891751766204834,0.8907471895217896,0.9007715582847595,0.8569079041481018,0.8734966516494751,0.866807758808136,0.8690007328987122,0.8641847968101501,0.860200822353363,0.8753891587257385,0.8771096467971802,0.8702175617218018,0.8732821941375732,0.8610327243804932,0.9026527404785156,0.8878933191299438,0.9106489419937134,0.8546255826950073,0.8853597640991211,0.9191234707832336,0.85566246509552,0.8536851406097412,0.8839954733848572,0.8441707491874695,0.8158388733863831,0.8224376440048218,0.9016413688659668,0.8994392156600952,0.9188550710678101,0.903643786907196,0.9121543169021606,0.8826841711997986,0.8817184567451477,0.9049835801124573,0.8948301672935486
-EmotionClassification,0.8986697196960449,0.8869525194168091,0.8377322554588318,0.8723608255386353,0.9999998807907104,0.8484991192817688,0.8875608444213867,0.8875608444213867,0.8829092979431152,0.8829092979431152,0.8891539573669434,0.9219067692756653,0.8317292332649231,0.8686325550079346,0.8193415999412537,0.8483346104621887,0.8102065920829773,0.8282783031463623,0.9020230770111084,0.9169633984565735,0.8777654767036438,0.8238407969474792,0.8720272779464722,0.7159684896469116,0.8473405838012695,0.8711422085762024,0.8389461040496826,0.8027688264846802,0.8528178930282593,0.8284692168235779,0.8480105400085449,0.8250551819801331,0.8178394436836243,0.8577408790588379,0.847434937953949,0.7987120747566223,0.8172357678413391,0.8205375075340271,0.8239988684654236,0.8105478286743164,0.8049406409263611,0.8208318948745728,0.8235769867897034,0.8167813420295715,0.8425841331481934,0.8250551819801331,0.8733782172203064,0.8516587615013123,0.8590372204780579,0.840248167514801,0.8614929914474487,0.8839017748832703,0.8242060542106628,0.8306145071983337,0.8763502836227417,0.8282589912414551,0.7966073751449585,0.8122089505195618,0.8802585005760193,0.8810560703277588,0.9074888229370117,0.8856732845306396,0.9061300754547119,0.8690868020057678,0.8543643951416016,0.8943052887916565,0.8744396567344666
-ImdbClassification,0.9143805503845215,0.9380159974098206,0.8060175180435181,0.8470839262008667,0.8484991192817688,0.9999999403953552,0.8814461827278137,0.8814461827278137,0.8795197010040283,0.8795197010040283,0.8966728448867798,0.8972986340522766,0.8641327023506165,0.8861985206604004,0.8488188982009888,0.8755455613136292,0.8384004235267639,0.8527776598930359,0.8933579325675964,0.9051699042320251,0.8781872391700745,0.8293363451957703,0.8849860429763794,0.6948404908180237,0.8515918254852295,0.8860101699829102,0.8357095718383789,0.8353019952774048,0.877151370048523,0.8337087631225586,0.8854134678840637,0.839382529258728,0.8364291787147522,0.8700346946716309,0.8654309511184692,0.8066450357437134,0.8283482193946838,0.8346224427223206,0.8459446430206299,0.8239307999610901,0.8178578019142151,0.8369230031967163,0.8369060158729553,0.825982391834259,0.8585302829742432,0.839382529258728,0.8759719133377075,0.8769184350967407,0.8686230182647705,0.8685956597328186,0.8820010423660278,0.8861343860626221,0.8516706228256226,0.8627360463142395,0.8969246745109558,0.8567239046096802,0.8268139362335205,0.8363828063011169,0.8844971656799316,0.8787712454795837,0.9043587446212769,0.8874390721321106,0.8882074952125549,0.8814995884895325,0.8508477807044983,0.9050480723381042,0.9031330347061157
-MassiveIntentClassification,0.915182888507843,0.9221842288970947,0.8927789330482483,0.9069743156433105,0.8875608444213867,0.8814461827278137,0.9999998807907104,0.9999998807907104,0.9750701189041138,0.9750701189041138,0.9595458507537842,0.9734818339347839,0.8967283368110657,0.9652600884437561,0.8725947141647339,0.9290119409561157,0.8716907501220703,0.9026644825935364,0.9533762335777283,0.9527404308319092,0.9526662826538086,0.9006844758987427,0.9625997543334961,0.7721297740936279,0.9069065451622009,0.9225729703903198,0.921728789806366,0.8853965401649475,0.9450154900550842,0.9193835258483887,0.9042443037033081,0.9111378788948059,0.8964847922325134,0.9208599328994751,0.9270387291908264,0.8749427199363708,0.8865760564804077,0.8897450566291809,0.8953050374984741,0.8811706304550171,0.8818027973175049,0.8984538316726685,0.897138774394989,0.8913282155990601,0.9272512793540955,0.9111378788948059,0.9463034868240356,0.9470813274383545,0.9404415488243103,0.8946558833122253,0.9522876143455505,0.9682824611663818,0.9068668484687805,0.8946090340614319,0.9345963597297668,0.8986713886260986,0.8533274531364441,0.8902477025985718,0.9739812016487122,0.9600082635879517,0.9729264974594116,0.9575598835945129,0.9471473097801208,0.9507492780685425,0.9367305040359497,0.9577158689498901,0.9361376762390137
-MassiveScenarioClassification,0.915182888507843,0.9221842288970947,0.8927789330482483,0.9069743156433105,0.8875608444213867,0.8814461827278137,0.9999998807907104,0.9999998807907104,0.9750701189041138,0.9750701189041138,0.9595458507537842,0.9734818339347839,0.8967283368110657,0.9652600884437561,0.8725947141647339,0.9290119409561157,0.8716907501220703,0.9026644825935364,0.9533762335777283,0.9527404308319092,0.9526662826538086,0.9006844758987427,0.9625997543334961,0.7721297740936279,0.9069065451622009,0.9225729703903198,0.921728789806366,0.8853965401649475,0.9450154900550842,0.9193835258483887,0.9042443037033081,0.9111378788948059,0.8964847922325134,0.9208599328994751,0.9270387291908264,0.8749427199363708,0.8865760564804077,0.8897450566291809,0.8953050374984741,0.8811706304550171,0.8818027973175049,0.8984538316726685,0.897138774394989,0.8913282155990601,0.9272512793540955,0.9111378788948059,0.9463034868240356,0.9470813274383545,0.9404415488243103,0.8946558833122253,0.9522876143455505,0.9682824611663818,0.9068668484687805,0.8946090340614319,0.9345963597297668,0.8986713886260986,0.8533274531364441,0.8902477025985718,0.9739812016487122,0.9600082635879517,0.9729264974594116,0.9575598835945129,0.9471473097801208,0.9507492780685425,0.9367305040359497,0.9577158689498901,0.9361376762390137
-MTOPDomainClassification,0.9136463403701782,0.9159311652183533,0.8721326589584351,0.9206961393356323,0.8829092979431152,0.8795197010040283,0.9750701189041138,0.9750701189041138,1.000000238418579,1.000000238418579,0.9477394223213196,0.9620302319526672,0.8916090726852417,0.9507513046264648,0.8692935109138489,0.9198922514915466,0.8673161864280701,0.8943023681640625,0.9499332308769226,0.9520930647850037,0.9415560960769653,0.8926861882209778,0.9499171376228333,0.7859740853309631,0.9024993777275085,0.9156029224395752,0.9076282382011414,0.8778815865516663,0.9327757358551025,0.9088919162750244,0.8972400426864624,0.8968261480331421,0.900844395160675,0.917822539806366,0.922167956829071,0.8686424493789673,0.8871181607246399,0.8848662972450256,0.8901574015617371,0.8792017698287964,0.8758128881454468,0.892038881778717,0.8904502391815186,0.8847219944000244,0.9200598001480103,0.8968261480331421,0.9321253299713135,0.9370198249816895,0.9469432234764099,0.8838071823120117,0.9368411898612976,0.956068754196167,0.8929500579833984,0.8861535787582397,0.9225219488143921,0.8865776658058167,0.8508256077766418,0.8823556900024414,0.9523103833198547,0.9431838989257812,0.960503101348877,0.9454217553138733,0.9361295700073242,0.9454723000526428,0.918997585773468,0.9517570734024048,0.932615339756012
-MTOPIntentClassification,0.9136463403701782,0.9159311652183533,0.8721326589584351,0.9206961393356323,0.8829092979431152,0.8795197010040283,0.9750701189041138,0.9750701189041138,1.000000238418579,1.000000238418579,0.9477394223213196,0.9620302319526672,0.8916090726852417,0.9507513046264648,0.8692935109138489,0.9198922514915466,0.8673161864280701,0.8943023681640625,0.9499332308769226,0.9520930647850037,0.9415560960769653,0.8926861882209778,0.9499171376228333,0.7859740853309631,0.9024993777275085,0.9156029224395752,0.9076282382011414,0.8778815865516663,0.9327757358551025,0.9088919162750244,0.8972400426864624,0.8968261480331421,0.900844395160675,0.917822539806366,0.922167956829071,0.8686424493789673,0.8871181607246399,0.8848662972450256,0.8901574015617371,0.8792017698287964,0.8758128881454468,0.892038881778717,0.8904502391815186,0.8847219944000244,0.9200598001480103,0.8968261480331421,0.9321253299713135,0.9370198249816895,0.9469432234764099,0.8838071823120117,0.9368411898612976,0.956068754196167,0.8929500579833984,0.8861535787582397,0.9225219488143921,0.8865776658058167,0.8508256077766418,0.8823556900024414,0.9523103833198547,0.9431838989257812,0.960503101348877,0.9454217553138733,0.9361295700073242,0.9454723000526428,0.918997585773468,0.9517570734024048,0.932615339756012
-ToxicConversationsClassification,0.9268499612808228,0.9314469695091248,0.8651610612869263,0.8975388407707214,0.8891539573669434,0.8966728448867798,0.9595458507537842,0.9595458507537842,0.9477394223213196,0.9477394223213196,1.0,0.9791338443756104,0.8923237323760986,0.9557365775108337,0.8709883689880371,0.9188441038131714,0.871146023273468,0.8937941193580627,0.9509134292602539,0.9578840732574463,0.939154326915741,0.8850978016853333,0.9539644122123718,0.7365511655807495,0.9154583811759949,0.9275434613227844,0.8940021991729736,0.88185054063797,0.9295150637626648,0.8942537307739258,0.9117645025253296,0.8989847898483276,0.8807247281074524,0.9173315763473511,0.9148895144462585,0.8601332306861877,0.8703606724739075,0.8878904581069946,0.8843247294425964,0.8718209862709045,0.8644421100616455,0.8797895312309265,0.8873119354248047,0.8789352178573608,0.9204174280166626,0.8989847898483276,0.9462225437164307,0.9388979077339172,0.9315915107727051,0.8950788378715515,0.9423636198043823,0.9756679534912109,0.8898159265518188,0.8889317512512207,0.9373823404312134,0.8933861255645752,0.8529271483421326,0.8919810056686401,0.9546875357627869,0.9443492889404297,0.968707799911499,0.9534297585487366,0.9462233185768127,0.9519502520561218,0.9268797636032104,0.9642019867897034,0.9413120746612549
-TweetSentimentExtractionClassification,0.9379464387893677,0.9369900822639465,0.8857579231262207,0.9144454002380371,0.9219067692756653,0.8972986340522766,0.9734818339347839,0.9734818339347839,0.9620302319526672,0.9620302319526672,0.9791338443756104,0.9999998807907104,0.8941646814346313,0.9579667448997498,0.8712595701217651,0.9201945066452026,0.8699737787246704,0.89603590965271,0.9627991318702698,0.9685115814208984,0.9449712634086609,0.8842088580131531,0.957473874092102,0.7514926791191101,0.9206107258796692,0.9304682612419128,0.9066123366355896,0.8845974802970886,0.9322013854980469,0.902407705783844,0.9012815952301025,0.9066250324249268,0.8804208636283875,0.9191873669624329,0.9144876003265381,0.8603336215019226,0.8727058172225952,0.8835071921348572,0.8789454698562622,0.8683345913887024,0.8654515743255615,0.8802311420440674,0.8835926055908203,0.8761081099510193,0.9267283082008362,0.9066250324249268,0.9446496963500977,0.9437984228134155,0.9354615807533264,0.8943512439727783,0.9478681683540344,0.9679496884346008,0.8895792961120605,0.8876237869262695,0.9310782551765442,0.8951513767242432,0.8504247069358826,0.8916918635368347,0.9640517830848694,0.952302873134613,0.9752659201622009,0.960588812828064,0.9546923041343689,0.9575697183609009,0.9292759299278259,0.9668652415275574,0.9422594308853149
-ArxivClusteringP2P,0.9061737060546875,0.9092541337013245,0.8268718719482422,0.8722931742668152,0.8317292332649231,0.8641327023506165,0.8967283368110657,0.8967283368110657,0.8916090726852417,0.8916090726852417,0.8923237323760986,0.8941646814346313,1.0000001192092896,0.9296634197235107,0.9502661228179932,0.9453719854354858,0.9198501706123352,0.9335336685180664,0.9047669172286987,0.9180348515510559,0.9226759076118469,0.8873042464256287,0.9214056134223938,0.7285578846931458,0.849682629108429,0.8937832117080688,0.8850733637809753,0.8498433828353882,0.942658007144928,0.8952110409736633,0.9187605381011963,0.8781493902206421,0.8702689409255981,0.9049964547157288,0.8932080864906311,0.8815957903862,0.9128819108009338,0.9258798360824585,0.9027954936027527,0.916979193687439,0.9049314856529236,0.8934173583984375,0.8883233666419983,0.8882037997245789,0.9017259478569031,0.8781493902206421,0.9082144498825073,0.9008392095565796,0.8916330933570862,0.9167720675468445,0.910786509513855,0.9078540205955505,0.9503505229949951,0.9326635599136353,0.9119592308998108,0.9338494539260864,0.9067458510398865,0.8065285086631775,0.8979881405830383,0.912732720375061,0.9206886291503906,0.9100639820098877,0.9006866812705994,0.8714514374732971,0.8883957266807556,0.8937532901763916,0.9103951454162598
-ArxivClusteringS2S,0.9226172566413879,0.9261904954910278,0.8719925284385681,0.896972119808197,0.8686325550079346,0.8861985206604004,0.9652600884437561,0.9652600884437561,0.9507513046264648,0.9507513046264648,0.9557365775108337,0.9579667448997498,0.9296634197235107,1.0000001192092896,0.8961361050605774,0.9565531611442566,0.8940218091011047,0.9300719499588013,0.9504541754722595,0.9541244506835938,0.9593493342399597,0.9132787585258484,0.9759801030158997,0.7547072768211365,0.9124619960784912,0.9184166789054871,0.9116992354393005,0.8834333419799805,0.9691587686538696,0.9220894575119019,0.9125237464904785,0.9121813178062439,0.8931977152824402,0.9257218837738037,0.9272482991218567,0.888041615486145,0.9053133130073547,0.9213353395462036,0.9087139964103699,0.9069965481758118,0.8965097069740295,0.9025301933288574,0.9069535732269287,0.8937448859214783,0.9494740962982178,0.9121813178062439,0.947498619556427,0.956941545009613,0.9396511316299438,0.9095065593719482,0.9639177918434143,0.9652320742607117,0.9295390248298645,0.9176734685897827,0.9311457872390747,0.9181896448135376,0.8817151784896851,0.8841482996940613,0.9600266218185425,0.9491159915924072,0.9618067741394043,0.9499640464782715,0.9319314360618591,0.942611038684845,0.933143138885498,0.949581503868103,0.9360114932060242
-BiorxivClusteringP2P,0.8816869854927063,0.8834359645843506,0.8097570538520813,0.8474944233894348,0.8193415999412537,0.8488188982009888,0.8725947141647339,0.8725947141647339,0.8692935109138489,0.8692935109138489,0.8709883689880371,0.8712595701217651,0.9502661228179932,0.8961361050605774,0.9999998211860657,0.9384621381759644,0.956779956817627,0.9311316013336182,0.8793036341667175,0.8974093794822693,0.8921758532524109,0.8562670350074768,0.8946423530578613,0.7087208032608032,0.8287373781204224,0.8686756491661072,0.8585236072540283,0.8195667266845703,0.9144576191902161,0.8649075031280518,0.9078131318092346,0.8496026396751404,0.8520771265029907,0.8786687850952148,0.8723767995834351,0.8544682264328003,0.8758416771888733,0.8830129504203796,0.8758763670921326,0.8930484056472778,0.8660279512405396,0.8672094345092773,0.8678568005561829,0.8641262650489807,0.8874456882476807,0.8496026396751404,0.8788062334060669,0.8783058524131775,0.8714765310287476,0.9372479319572449,0.8799590468406677,0.8842073082923889,0.9247989654541016,0.9538105130195618,0.8883203864097595,0.942850649356842,0.9347233772277832,0.7822513580322266,0.8725536465644836,0.8873900175094604,0.8985730409622192,0.8854580521583557,0.8810415267944336,0.8527474999427795,0.8675625324249268,0.8723320960998535,0.8993033170700073
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-CQADupstackPhysicsRetrieval,0.8788719773292542,0.8754789233207703,0.8036501407623291,0.866807758808136,0.8205375075340271,0.8346224427223206,0.8897450566291809,0.8897450566291809,0.8848662972450256,0.8848662972450256,0.8878904581069946,0.8835071921348572,0.9258798360824585,0.9213353395462036,0.8830129504203796,0.9188753366470337,0.870018720626831,0.8929807543754578,0.8964251279830933,0.9109740257263184,0.9299076795578003,0.9218308925628662,0.9096159934997559,0.7272855639457703,0.8345741033554077,0.8722454309463501,0.8661698698997498,0.8156949281692505,0.9176410436630249,0.8843101859092712,0.8957704305648804,0.8617082834243774,0.9050965905189514,0.9414180517196655,0.9342014789581299,0.903142511844635,0.9277931451797485,0.9999999403953552,0.9381371736526489,0.9415716528892517,0.91279536485672,0.9261923432350159,0.9212412238121033,0.9054916501045227,0.8781377077102661,0.8617082834243774,0.8984333276748657,0.8824734091758728,0.8800548315048218,0.8915558457374573,0.8959563970565796,0.922303318977356,0.9024848937988281,0.9060347676277161,0.8913036584854126,0.8834498524665833,0.8713915348052979,0.8070156574249268,0.892284631729126,0.9096480011940002,0.9132447838783264,0.903823971748352,0.899546205997467,0.8698975443840027,0.869685173034668,0.8923594355583191,0.8838998079299927
-CQADupstackProgrammersRetrieval,0.8834834098815918,0.8846838474273682,0.8091654181480408,0.8690007328987122,0.8239988684654236,0.8459446430206299,0.8953050374984741,0.8953050374984741,0.8901574015617371,0.8901574015617371,0.8843247294425964,0.8789454698562622,0.9027954936027527,0.9087139964103699,0.8758763670921326,0.911128044128418,0.8732233643531799,0.8892922401428223,0.8974623084068298,0.9104718565940857,0.9368252158164978,0.9459747672080994,0.9153035879135132,0.7467317581176758,0.8340392112731934,0.8732454776763916,0.8830022215843201,0.8110246062278748,0.9275285005569458,0.9159318804740906,0.9052194356918335,0.8505048155784607,0.9206304550170898,0.9488763213157654,0.9393653869628906,0.9301380515098572,0.9406094551086426,0.9381371736526489,1.0000001192092896,0.9467190504074097,0.9259558320045471,0.9530035257339478,0.9550228714942932,0.9337886571884155,0.8744832277297974,0.8505048155784607,0.9110053181648254,0.8822770714759827,0.8804248571395874,0.8899571895599365,0.8902618885040283,0.9181737303733826,0.9190900325775146,0.9017314910888672,0.8959858417510986,0.8842517137527466,0.8579108119010925,0.8032951951026917,0.8920109868049622,0.9053783416748047,0.9127124547958374,0.89433753490448,0.9096778035163879,0.8691129088401794,0.8748772144317627,0.8964371681213379,0.8863435387611389
-CQADupstackStatsRetrieval,0.8732110261917114,0.870514988899231,0.7987720966339111,0.8641847968101501,0.8105478286743164,0.8239307999610901,0.8811706304550171,0.8811706304550171,0.8792017698287964,0.8792017698287964,0.8718209862709045,0.8683345913887024,0.916979193687439,0.9069965481758118,0.8930484056472778,0.923565149307251,0.9020135402679443,0.9127675890922546,0.8893818259239197,0.901867687702179,0.9241059422492981,0.9272336959838867,0.9074497222900391,0.7388317584991455,0.8295093774795532,0.8640527129173279,0.8678272366523743,0.8064138293266296,0.9317771792411804,0.9021614193916321,0.8878010511398315,0.8545312881469727,0.9030284285545349,0.9316865801811218,0.9243125319480896,0.9305801391601562,0.9559803009033203,0.9415716528892517,0.9467190504074097,1.0000001192092896,0.9336675405502319,0.9365205764770508,0.930739164352417,0.9157169461250305,0.8788878917694092,0.8545312881469727,0.897214412689209,0.8763482570648193,0.8824243545532227,0.895236074924469,0.8826169371604919,0.9051744341850281,0.9343346357345581,0.9186573028564453,0.8812727332115173,0.8962944149971008,0.8794823884963989,0.7893253564834595,0.8797302842140198,0.8905830383300781,0.8992865085601807,0.878086507320404,0.8895502090454102,0.8537505865097046,0.8672860264778137,0.879453182220459,0.8792035579681396
-CQADupstackTexRetrieval,0.8720847964286804,0.8698432445526123,0.8001028299331665,0.860200822353363,0.8049406409263611,0.8178578019142151,0.8818027973175049,0.8818027973175049,0.8758128881454468,0.8758128881454468,0.8644421100616455,0.8654515743255615,0.9049314856529236,0.8965097069740295,0.8660279512405396,0.8992398977279663,0.8579853177070618,0.8801870942115784,0.8851460814476013,0.8874367475509644,0.9255838990211487,0.9189807772636414,0.8974940180778503,0.7514438033103943,0.8197506666183472,0.8527172803878784,0.880608856678009,0.8085086941719055,0.9046880602836609,0.9091837406158447,0.8700098991394043,0.8441368937492371,0.8972444534301758,0.9194899201393127,0.9149504899978638,0.9221766591072083,0.9574756026268005,0.91279536485672,0.9259558320045471,0.9336675405502319,1.0,0.9387102723121643,0.9309241771697998,0.9312326312065125,0.8696514964103699,0.8441368937492371,0.8951022624969482,0.8696152567863464,0.8712650537490845,0.8649015426635742,0.8828864097595215,0.8967395424842834,0.8998434543609619,0.8823222517967224,0.876192569732666,0.874040961265564,0.8481307625770569,0.7818848490715027,0.8804553747177124,0.8856875896453857,0.8912297487258911,0.8708868026733398,0.885320246219635,0.843300998210907,0.8693545460700989,0.8727013468742371,0.8655053973197937
-CQADupstackUnixRetrieval,0.8778753876686096,0.8746495246887207,0.8078638315200806,0.8753891587257385,0.8208318948745728,0.8369230031967163,0.8984538316726685,0.8984538316726685,0.892038881778717,0.892038881778717,0.8797895312309265,0.8802311420440674,0.8934173583984375,0.9025301933288574,0.8672094345092773,0.9039843082427979,0.8595551252365112,0.8826228976249695,0.8965474367141724,0.9019376039505005,0.9274458289146423,0.9332571029663086,0.9126835465431213,0.7605757713317871,0.8345680236816406,0.8612546324729919,0.93036949634552,0.8037967681884766,0.9125511646270752,0.9131677150726318,0.8799136877059937,0.8518548607826233,0.9373556971549988,0.9315387010574341,0.940504789352417,0.9310668110847473,0.9464672207832336,0.9261923432350159,0.9530035257339478,0.9365205764770508,0.9387102723121643,0.9999998211860657,0.9425334334373474,0.9361576437950134,0.8726716041564941,0.8518548607826233,0.9011945724487305,0.8829306364059448,0.8788676857948303,0.8737623691558838,0.8897589445114136,0.9121820330619812,0.8979055881500244,0.8855134844779968,0.8814226984977722,0.8759041428565979,0.8541761040687561,0.8000640273094177,0.8939105272293091,0.8999499082565308,0.9092024564743042,0.8901516199111938,0.9038639068603516,0.8625014424324036,0.8721633553504944,0.8905507326126099,0.873734712600708
-CQADupstackWebmastersRetrieval,0.8800479173660278,0.8826687932014465,0.8033185601234436,0.8771096467971802,0.8235769867897034,0.8369060158729553,0.897138774394989,0.897138774394989,0.8904502391815186,0.8904502391815186,0.8873119354248047,0.8835926055908203,0.8883233666419983,0.9069535732269287,0.8678568005561829,0.9041688442230225,0.8668990731239319,0.8879578709602356,0.8993308544158936,0.9086974263191223,0.9290803074836731,0.9323667287826538,0.9110020995140076,0.7364763021469116,0.8336871266365051,0.87152498960495,0.8850497603416443,0.8270236253738403,0.9187279343605042,0.9141953587532043,0.8878820538520813,0.8488019108772278,0.9290414452552795,0.9308363199234009,0.9380438923835754,0.9317100048065186,0.9315505027770996,0.9212412238121033,0.9550228714942932,0.930739164352417,0.9309241771697998,0.9425334334373474,0.9999998807907104,0.9648351073265076,0.8810744285583496,0.8488019108772278,0.9122025966644287,0.8880552053451538,0.8899712562561035,0.8789516091346741,0.8950729966163635,0.9161275029182434,0.9036228060722351,0.8879486918449402,0.888551652431488,0.8810858726501465,0.8505773544311523,0.7996203303337097,0.8922863602638245,0.89543616771698,0.9074068665504456,0.8887396454811096,0.9075014591217041,0.8670238256454468,0.8819707036018372,0.8923331499099731,0.8873686790466309
-CQADupstackWordpressRetrieval,0.8742043972015381,0.8705847859382629,0.7957671284675598,0.8702175617218018,0.8167813420295715,0.825982391834259,0.8913282155990601,0.8913282155990601,0.8847219944000244,0.8847219944000244,0.8789352178573608,0.8761081099510193,0.8882037997245789,0.8937448859214783,0.8641262650489807,0.8961097002029419,0.8557989597320557,0.8784561157226562,0.8881715536117554,0.8998266458511353,0.9175654649734497,0.9188860058784485,0.8972079753875732,0.7487673163414001,0.8268483281135559,0.8667789697647095,0.8874524235725403,0.810706615447998,0.907027006149292,0.9168320894241333,0.8797154426574707,0.8423066735267639,0.9209067821502686,0.917028546333313,0.9254974722862244,0.918504536151886,0.9326183199882507,0.9054916501045227,0.9337886571884155,0.9157169461250305,0.9312326312065125,0.9361576437950134,0.9648351073265076,1.0000001192092896,0.8684195876121521,0.8423066735267639,0.9004048705101013,0.8746203780174255,0.8731535077095032,0.8685926198959351,0.8860312104225159,0.9065831899642944,0.8904937505722046,0.8772619366645813,0.8826008439064026,0.8709307909011841,0.8430542945861816,0.793935239315033,0.8826149106025696,0.8886222243309021,0.8997173309326172,0.8829312920570374,0.8971396088600159,0.8602017760276794,0.8727341890335083,0.886046290397644,0.8786991834640503
-DBPedia,0.8960760831832886,0.9033768773078918,0.857245147228241,0.8732821941375732,0.8425841331481934,0.8585302829742432,0.9272512793540955,0.9272512793540955,0.9200598001480103,0.9200598001480103,0.9204174280166626,0.9267283082008362,0.9017259478569031,0.9494740962982178,0.8874456882476807,0.9335931539535522,0.8835738897323608,0.9113901853561401,0.9286068081855774,0.92087322473526,0.9289190769195557,0.865909218788147,0.9430718421936035,0.7269722819328308,0.8812722563743591,0.8917800784111023,0.8765844106674194,0.8491460084915161,0.932131826877594,0.8864024877548218,0.8886457085609436,0.9115601778030396,0.8635401725769043,0.9058995842933655,0.8909529447555542,0.8589087724685669,0.8738807439804077,0.8781377077102661,0.8744832277297974,0.8788878917694092,0.8696514964103699,0.8726716041564941,0.8810744285583496,0.8684195876121521,1.0000001192092896,0.9115601778030396,0.9128028154373169,0.9553118348121643,0.9282314777374268,0.9043858051300049,0.9467520117759705,0.9322096705436707,0.908551037311554,0.9075112342834473,0.9047530293464661,0.908862829208374,0.8717136979103088,0.84193354845047,0.9324358105659485,0.9196749925613403,0.9341995120048523,0.9155308604240417,0.9073795676231384,0.9020201563835144,0.9053149819374084,0.9130927324295044,0.9138416051864624
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diff --git a/plotstables/thumbnail.png b/plotstables/thumbnail.png
deleted file mode 100644
index 27bc78c8..00000000
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diff --git a/plotstables/thumbnail_v1.drawio b/plotstables/thumbnail_v1.drawio
deleted file mode 100644
index b025fbca..00000000
--- a/plotstables/thumbnail_v1.drawio
+++ /dev/null
@@ -1 +0,0 @@
-<mxGraphModel><root><mxCell id="0"/><mxCell id="1" parent="0"/></root></mxGraphModel>
\ No newline at end of file
diff --git a/results_to_csv.bash b/results_to_csv.bash
deleted file mode 100644
index 17ac0355..00000000
--- a/results_to_csv.bash
+++ /dev/null
@@ -1,31 +0,0 @@
-
-results=(
-LASER2
-SGPT-125M-weightedmean-msmarco-specb-bitfit
-SGPT-125M-weightedmean-msmarco-specb-bitfit-doc
-SGPT-125M-weightedmean-msmarco-specb-bitfit-que
-SGPT-125M-weightedmean-nli-bitfit
-SGPT-5.8B-weightedmean-msmarco-specb-bitfit
-SGPT-5.8B-weightedmean-nli-bitfit
-all-MiniLM-L6-v2
-all-mpnet-base-v2
-bert-base-uncased
-contriever-base-msmarco
-glove.6B.300d
-gtr-t5-base
-gtr-t5-xxl
-komninos
-msmarco-bert-co-condensor
-sentence-t5-base
-sentence-t5-xxl
-sgpt-bloom-1b3-nli
-sgpt-bloom-7b1-msmarco
-sup-simcse-bert-base-uncased
-unsup-simcse-bert-base-uncased
-)
-
-for i in "${results[@]}"
-do
-   echo "$i"
-   python results_to_csv.py results/$i
-done
diff --git a/results_to_csv.py b/results_to_csv.py
deleted file mode 100644
index 993dbbc6..00000000
--- a/results_to_csv.py
+++ /dev/null
@@ -1,212 +0,0 @@
-"""
-Usage: python results_to_csv.py results_folder_path
-Make sure the final directory results_folder_path is the name of your model
-"""
-import csv
-import json
-import os
-import sys
-
-from mteb import MTEB
-
-TASK_LIST_CLASSIFICATION = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-]
-
-TASK_LIST_CLUSTERING = [
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-]
-
-TASK_LIST_PAIR_CLASSIFICATION = [
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-]
-
-TASK_LIST_RERANKING = [
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-]
-
-TASK_LIST_RETRIEVAL = [
-    "ArguAna",
-    "ClimateFEVER",
-    "CQADupstackRetrieval",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-]
-
-TASK_LIST_STS = [
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-]
-
-TASK_LIST_SUMMARIZATION = [
-    "SummEval",
-]
-
-TASK_LIST_BITEXTMINING = [
-    "BUCC",
-    "Tatoeba",
-]
-
-TASK_LIST = (
-    TASK_LIST_BITEXTMINING
-    + TASK_LIST_CLASSIFICATION
-    + TASK_LIST_CLUSTERING
-    + TASK_LIST_PAIR_CLASSIFICATION
-    + TASK_LIST_RERANKING
-    + TASK_LIST_RETRIEVAL
-    + TASK_LIST_STS
-    + TASK_LIST_SUMMARIZATION
-)
-
-TASK_LIST_NAMES = [
-    ("Classification", TASK_LIST_CLASSIFICATION, ["en", "en-en"]),
-    ("Clustering", TASK_LIST_CLUSTERING, ["en", "en-en"]),
-    ("PairClassification", TASK_LIST_PAIR_CLASSIFICATION, ["en", "en-en"]),
-    ("Reranking", TASK_LIST_RERANKING, ["en", "en-en"]),
-    ("Retrieval", TASK_LIST_RETRIEVAL, ["en", "en-en"]),
-    ("STS", TASK_LIST_STS, ["en", "en-en"]),
-    ("all", TASK_LIST, ["en", "en-en"]),
-    ("BitextMining", TASK_LIST_BITEXTMINING, []),
-]
-
-results_folder = sys.argv[1]
-results_folder = results_folder.strip("/")
-model_name = results_folder.split("/")[-1]
-print(f"Using model name {model_name}")
-
-all_results = {}
-
-for file_name in os.listdir(results_folder):
-    if not file_name.endswith(".json"):
-        print(f"Skipping non-json {file_name}")
-        continue
-    with open(os.path.join(results_folder, file_name), "r", encoding="utf-8") as f:
-        results = json.load(f)
-        all_results = {**all_results, **{file_name.replace(".json", ""): results}}
-
-csv_file = f"{results_folder}_results.csv"
-print(f"Converting {results_folder} to {csv_file}")
-
-NOT_FOUND = []
-
-
-def get_rows(task, dataset, limit_langs=[]):
-    rows = []
-    # CQADupstackRetrieval uses the same metric as its subsets
-    tasks = MTEB(tasks=[dataset.replace("CQADupstackRetrieval", "CQADupstackTexRetrieval")]).tasks
-    assert len(tasks) == 1, f"Found {len(tasks)} for {dataset}. Expected 1."
-    main_metric = tasks[0].description["main_score"]
-    test_result = all_results.get(dataset, {})
-
-    # Dev / Val set is used for MSMARCO (See BEIR paper)
-    if "MSMARCO" in dataset:
-        test_result = (
-            test_result.get("dev") if "dev" in test_result else test_result.get("validation")
-        )
-    else:
-        test_result = test_result.get("test")
-    if test_result is None:
-        print(f"{dataset} - test set not found")
-        NOT_FOUND.append(dataset)
-        return [[model_name, task, dataset, "", main_metric, ""]]
-
-    for lang in tasks[0].description["eval_langs"]:
-        if limit_langs and lang not in limit_langs:
-            continue
-        test_result_lang = test_result.get(lang, test_result)
-        if main_metric == "cosine_spearman":
-            test_result_lang = test_result_lang.get("cos_sim", {}).get("spearman")
-        elif main_metric == "ap":
-            test_result_lang = test_result_lang.get("cos_sim", {}).get("ap")
-        else:
-            test_result_lang = test_result_lang.get(main_metric)
-
-        if test_result_lang is None:
-            print(f"{lang} & {main_metric} not found for task {dataset}.")
-            rows.append([model_name, task, dataset, lang, main_metric, ""])
-        rows.append([model_name, task, dataset, lang, main_metric, test_result_lang])
-    return rows
-
-
-with open(csv_file, "w", encoding="utf-8") as f:
-    writer = csv.writer(f)
-    writer.writerow(["model", "task", "dataset", "language", "metric", "value"])
-    for task, dataset_list in [
-        ("BitextMining", TASK_LIST_BITEXTMINING),
-        ("Classification", TASK_LIST_CLASSIFICATION),
-        ("Clustering", TASK_LIST_CLUSTERING),
-        ("PairClassification", TASK_LIST_PAIR_CLASSIFICATION),
-        ("Reranking", TASK_LIST_RERANKING),
-        ("Retrieval", TASK_LIST_RETRIEVAL),
-        ("STS", TASK_LIST_STS),
-        ("Summarization", TASK_LIST_SUMMARIZATION),
-    ]:
-        for dataset in dataset_list:
-            writer.writerows(get_rows(task, dataset))
-
-    # Add average scores
-    for task, dataset_list, limit_langs in [
-        ("BitextMining", TASK_LIST_BITEXTMINING, []),
-        ("Classification", TASK_LIST_CLASSIFICATION, ["en", "en-en"]),
-        ("Clustering", TASK_LIST_CLUSTERING, ["en", "en-en"]),
-        ("PairClassification", TASK_LIST_PAIR_CLASSIFICATION, ["en", "en-en"]),
-        ("Reranking", TASK_LIST_RERANKING, ["en", "en-en"]),
-        ("Retrieval", TASK_LIST_RETRIEVAL, ["en", "en-en"]),
-        ("STS", TASK_LIST_STS, ["en", "en-en"]),
-        ("all", TASK_LIST, ["en", "en-en"]),
-    ]:
-        if all([x in all_results for x in dataset_list]):
-            rows = [y for x in dataset_list for y in get_rows(task, x, limit_langs=limit_langs)]
-            try:
-                avg = sum([float(x[-1]) for x in rows]) / len(rows)
-            except:
-                continue
-            metric = "multiple" if task == "all" else rows[-1][-2]
-            writer.writerow([model_name, task, "average", "en", metric, avg])
-
-if NOT_FOUND:
-    print("Not found: " + "'" + "','".join(NOT_FOUND) + "'", len(NOT_FOUND))
diff --git a/run_array.py b/run_array.py
deleted file mode 100644
index ac172e82..00000000
--- a/run_array.py
+++ /dev/null
@@ -1,247 +0,0 @@
-import argparse
-import logging
-import os
-from typing import Dict, List, Union
-
-logging.basicConfig(level=logging.INFO)
-
-logger = logging.getLogger("main")
-
-os.environ["HF_DATASETS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_CACHE"]="/gpfswork/rech/six/commun/models"
-os.environ["HF_DATASETS_CACHE"]="/gpfswork/rech/six/commun/datasets"
-os.environ["HF_MODULES_CACHE"]="/gpfswork/rech/six/commun/modules"
-os.environ["HF_METRICS_CACHE"]="/gpfswork/rech/six/commun/metrics"
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-from mteb import MTEB
-import numpy as np
-from sentence_transformers import SentenceTransformer
-from torch import Tensor
-import torch.multiprocessing as mp
-
-
-TASK_LIST_CLASSIFICATION = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-]
-
-TASK_LIST_CLUSTERING = [
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-]
-
-TASK_LIST_PAIR_CLASSIFICATION = [
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-]
-
-TASK_LIST_RERANKING = [
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-]
-
-TASK_LIST_RETRIEVAL = [
-    "ArguAna",
-    "ClimateFEVER",
-    "CQADupstackAndroidRetrieval",
-    "CQADupstackEnglishRetrieval",
-    "CQADupstackGamingRetrieval",
-    "CQADupstackGisRetrieval",
-    "CQADupstackMathematicaRetrieval",
-    "CQADupstackPhysicsRetrieval",
-    "CQADupstackProgrammersRetrieval",
-    "CQADupstackStatsRetrieval",
-    "CQADupstackTexRetrieval",
-    "CQADupstackUnixRetrieval",
-    "CQADupstackWebmastersRetrieval",
-    "CQADupstackWordpressRetrieval",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-]
-
-TASK_LIST_STS = [
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-    "SummEval",
-]
-
-TASK_LIST = TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS
-
-
-class SentenceTransformerSpecb:
-    # Requires: 
-    # https://github.com/Muennighoff/sentence-transformers/tree/sgpt_poolings_specb
-    # pip install git+https://github.com/Muennighoff/sentence-transformers.git@sgpt_poolings_specb
-    def __init__(self, model):
-        self.model = SentenceTransformer(model)
-        self.sep = " "
-        tokens = ["[SOS]", "{SOS}"]
-        self.model._first_module().tokenizer.add_tokens(tokens, special_tokens=True)
-        self.model._first_module().auto_model.resize_token_embeddings(len(self.model._first_module().tokenizer))
-        # Will be replaced with the rep tokens in the model ones
-        # The problem is we don't know if a text is query or document when tokenizing in the Transformer.py module, 
-        # so we use the SOS tokens as an identifier if we have a query or document at hand & then replace them
-        # If we would directly use the brackets here, they may become part of another token
-        self.model._first_module().bos_spec_token_q = self.model._first_module().tokenizer.encode("[SOS]", add_special_tokens=False)[0]
-        self.model._first_module().bos_spec_token_d = self.model._first_module().tokenizer.encode("{SOS}", add_special_tokens=False)[0]
-        self.model._first_module().bos_spec_token_q_rep = self.model._first_module().tokenizer.encode("[", add_special_tokens=False)[0]
-        self.model._first_module().eos_spec_token_q = self.model._first_module().tokenizer.encode("]", add_special_tokens=False)[0]
-        self.model._first_module().bos_spec_token_d_rep = self.model._first_module().tokenizer.encode("{", add_special_tokens=False)[0]
-        self.model._first_module().eos_spec_token_d = self.model._first_module().tokenizer.encode("}", add_special_tokens=False)[0]
-        self.model._first_module().replace_bos = True
-
-    def encode(self, sentences, **kwargs):
-        """Returns a list of embeddings for the given sentences.
-        Args:
-            sentences (`List[str]`): List of sentences to encode
-            batch_size (`int`): Batch size for the encoding
-
-        Returns:
-            `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences
-        """
-        # Add specb query token
-        sentences = ["[SOS]" + sent for sent in sentences]
-        return self.model.encode(sentences, **kwargs)
-
-    def encode_queries(self, queries: List[str], batch_size: int = 16, **kwargs) -> Union[List[Tensor], np.ndarray, Tensor]:
-        # Will be replaced with [ in the models tokenization
-        # If we would put [ here, there is a risk of it getting chained with a different token when encoding
-        queries = ["[SOS]" + q for q in queries]
-        return self.model.encode(queries, batch_size=batch_size, **kwargs)
-    
-    def encode_corpus(self, corpus: List[Dict[str, str]], batch_size: int = 8, **kwargs) -> Union[List[Tensor], np.ndarray, Tensor]:
-        # Will be replaced with { in the models tokenization
-        # If we would put { here, there is a risk of it getting chained with a different token when encoding
-        sentences = [("{SOS}" + doc["title"] + self.sep + doc["text"]).strip() if "title" in doc else "{SOS}" + doc["text"].strip() for doc in corpus]
-        return self.model.encode(sentences, batch_size=batch_size, **kwargs)
-
-    def encode_corpus_parallel(
-        self, corpus: List[Dict[str, str]], pool: Dict[str, object], batch_size: int, chunk_id: int, **kwargs
-    ):
-        if type(corpus) is dict:
-            sentences = [
-                ("{SOS}" + corpus["title"][i] + self.sep + corpus["text"][i]).strip()
-                if "title" in corpus
-                else "{SOS}" + corpus["text"][i].strip()
-                for i in range(len(corpus["text"]))
-            ]
-        else:
-            sentences = [
-                ("{SOS}" + doc["title"] + self.sep + doc["text"]).strip() if "title" in doc else "{SOS}" + doc["text"].strip()
-                for doc in corpus
-            ]
-
-        if chunk_id is not None and chunk_id >= len(pool["processes"]):
-            output_queue = pool["output"]
-            output_queue.get()
-
-        input_queue = pool["input"]
-        input_queue.put([chunk_id, batch_size, sentences])
-
-
-    def start_multi_process_pool(self, target_devices: List[str] = None) -> Dict[str, object]:
-        logger.info("Start multi-process pool on devices: {}".format(", ".join(map(str, target_devices))))
-
-        ctx = mp.get_context("spawn")
-        input_queue = ctx.Queue()
-        output_queue = ctx.Queue()
-        processes = []
-
-        for process_id, device_name in enumerate(target_devices):
-            p = ctx.Process(
-                target=SentenceTransformer._encode_multi_process_worker,
-                args=(process_id, device_name, self.model, input_queue, output_queue),
-                daemon=True,
-            )
-            p.start()
-            processes.append(p)
-
-        return {"input": input_queue, "output": output_queue, "processes": processes}
-
-    def stop_multi_process_pool(self, pool: Dict[str, object]):
-        output_queue = pool["output"]
-        [output_queue.get() for _ in range(len(pool["processes"]))]
-        return self.model.stop_multi_process_pool(pool)
-
-
-def parse_args():
-    # Parse command line arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--startid", type=int)
-    parser.add_argument("--endid", type=int)
-    parser.add_argument("--addspecbdoc", action='store_true')
-    parser.add_argument("--addspecbquery", action='store_true')
-    parser.add_argument("--modelpath", type=str, default="/gpfswork/rech/six/commun/models/sentence-transformers_sentence-t5-base")
-    parser.add_argument("--lang", type=str, default="en")
-    parser.add_argument("--taskname", type=str, default=None)
-    parser.add_argument("--batchsize", type=int, default=128)
-    args = parser.parse_args()
-    return args
-
-def main(args):
-
-    if args.addspecbdoc or args.addspecbquery:
-        model = SentenceTransformerSpecb(args.modelpath) # Only used for SGPT-msmarco models
-    else:
-        model = SentenceTransformer(args.modelpath)
-
-    if args.taskname is not None:
-        task = args.taskname
-        model_name = args.modelpath.split("/")[-1].split("_")[-1]
-        eval_splits = ["validation"] if task == "MSMARCO" else ["test"]
-        evaluation = MTEB(tasks=[task], task_langs=[args.lang])
-        evaluation.run(model, output_folder=f"results/{model_name}", batch_size=args.batchsize, eval_splits=eval_splits)
-        exit()
-
-    for task in TASK_LIST[args.startid:args.endid]:
-        print("Running task: ", task)
-        eval_splits = ["validation"] if task == "MSMARCO" else ["test"]
-        model_name = args.modelpath.split("/")[-1].split("_")[-1]
-        evaluation = MTEB(tasks=[task], task_langs=[args.lang])
-        evaluation.run(model, output_folder=f"results/{model_name}", batch_size=args.batchsize, eval_splits=eval_splits)
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)
diff --git a/run_array_laser.py b/run_array_laser.py
deleted file mode 100644
index a3f61208..00000000
--- a/run_array_laser.py
+++ /dev/null
@@ -1,178 +0,0 @@
-"""
-See https://github.com/facebookresearch/LASER/issues/211
-"""
-
-import argparse
-import logging
-import os
-
-import numpy as np
-import subprocess
-
-logging.basicConfig(level=logging.INFO)
-
-os.environ["HF_DATASETS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_CACHE"]="/gpfswork/rech/six/commun/models"
-os.environ["HF_DATASETS_CACHE"]="/gpfswork/rech/six/commun/datasets"
-os.environ["HF_MODULES_CACHE"]="/gpfswork/rech/six/commun/modules"
-os.environ["HF_METRICS_CACHE"]="/gpfswork/rech/six/commun/metrics"
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-from mteb import MTEB
-
-TASK_LIST_CLASSIFICATION = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-]
-
-TASK_LIST_CLUSTERING = [
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-]
-
-TASK_LIST_PAIR_CLASSIFICATION = [
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-]
-
-TASK_LIST_RERANKING = [
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-]
-
-TASK_LIST_RETRIEVAL = [
-    "ArguAna",
-    "ClimateFEVER",
-    "CQADupstackAndroidRetrieval",
-    "CQADupstackEnglishRetrieval",
-    "CQADupstackGamingRetrieval",
-    "CQADupstackGisRetrieval",
-    "CQADupstackMathematicaRetrieval",
-    "CQADupstackPhysicsRetrieval",
-    "CQADupstackProgrammersRetrieval",
-    "CQADupstackStatsRetrieval",
-    "CQADupstackTexRetrieval",
-    "CQADupstackUnixRetrieval",
-    "CQADupstackWebmastersRetrieval",
-    "CQADupstackWordpressRetrieval",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-]
-
-TASK_LIST_STS = [
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-    "SummEval",
-]
-
-TASK_LIST = TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS
-
-### Setup prior to running ###
-#with open("LASER_script.sh", "w") as f:
-#    f.write("LASER=/content/LASER ./LASER/tasks/embed/embed.sh tmp.txt tmp.bin")
-# Run `chmod u+rx LASER_script.sh` to give permissions
-# !chmod u+rx LASER_script.sh
-
-class LASER():
-    def encode(self, sentences, batch_size=32, **kwargs):
-        """
-        Returns a list of embeddings for the given sentences.
-        Args:
-            sentences (`List[str]`): List of sentences to encode
-            batch_size (`int`): Batch size for the encoding
-
-        Returns:
-            `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences
-        """
-        if os.path.exists("tmp.txt"):
-            os.remove("tmp.txt")
-        if os.path.exists("tmp.bin"):
-            os.remove("tmp.bin")
-
-        # LASER expects one text per line, so we need to replace newlines
-        sentences = [s.replace("\n", " ") for s in sentences]
-        with open("tmp.txt", "w") as f:
-            f.write("\n".join(sentences))
-        
-        print(len(sentences))
-        rc = subprocess.call("./LASER_script.sh", shell=True)
-              
-        dim = 1024
-        X = np.fromfile("tmp.bin", dtype=np.float32, count=-1)                                                                          
-        X.resize(X.shape[0] // dim, dim)
-        print(X.shape)
-        return X
-
-def parse_args():
-    # Parse command line arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--startid", type=int)
-    parser.add_argument("--endid", type=int)
-    parser.add_argument("--lang", type=str, default="en")
-    parser.add_argument("--taskname", type=str, default=None)
-    parser.add_argument("--batchsize", type=int, default=128)
-    args = parser.parse_args()
-    return args
-
-def main(args):
-
-    model = LASER()
-    model_name = "LASER2"
-
-    if args.taskname is not None:
-        task = args.taskname
-        eval_splits = ["validation"] if task == "MSMARCO" else ["test"]
-        evaluation = MTEB(tasks=[task], task_langs=[args.lang])
-        evaluation.run(model, output_folder=f"results/{model_name}", batch_size=args.batchsize, eval_splits=eval_splits)
-        exit()
-
-    for task in TASK_LIST[args.startid:args.endid]:
-        print("Running task: ", task)
-        eval_splits = ["validation"] if task == "MSMARCO" else ["test"]
-        evaluation = MTEB(tasks=[task], task_langs=[args.lang])
-        evaluation.run(model, output_folder=f"results/{model_name}", batch_size=args.batchsize, eval_splits=eval_splits)
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)
diff --git a/run_array_openai.py b/run_array_openai.py
deleted file mode 100644
index c3a95d55..00000000
--- a/run_array_openai.py
+++ /dev/null
@@ -1,220 +0,0 @@
-"""
-openai==0.11.4
-"""
-import argparse
-import logging
-import os
-import pathlib
-import pickle
-
-import openai
-from transformers import GPT2TokenizerFast
-
-logging.basicConfig(level=logging.INFO)
-
-os.environ["HF_DATASETS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_CACHE"]="/gpfswork/rech/six/commun/models"
-os.environ["HF_DATASETS_CACHE"]="/gpfswork/rech/six/commun/datasets"
-os.environ["HF_MODULES_CACHE"]="/gpfswork/rech/six/commun/modules"
-os.environ["HF_METRICS_CACHE"]="/gpfswork/rech/six/commun/metrics"
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-API_KEY = "YOUR_KEY"
-
-from mteb import MTEB
-
-
-TASK_LIST_CLASSIFICATION = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-]
-
-TASK_LIST_CLUSTERING = [
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-]
-
-TASK_LIST_PAIR_CLASSIFICATION = [
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-]
-
-TASK_LIST_RERANKING = [
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-]
-
-TASK_LIST_RETRIEVAL = [
-    "ArguAna",
-    "ClimateFEVER",
-    "CQADupstackAndroidRetrieval",
-    "CQADupstackEnglishRetrieval",
-    "CQADupstackGamingRetrieval",
-    "CQADupstackGisRetrieval",
-    "CQADupstackMathematicaRetrieval",
-    "CQADupstackPhysicsRetrieval",
-    "CQADupstackProgrammersRetrieval",
-    "CQADupstackStatsRetrieval",
-    "CQADupstackTexRetrieval",
-    "CQADupstackUnixRetrieval",
-    "CQADupstackWebmastersRetrieval",
-    "CQADupstackWordpressRetrieval",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-]
-
-TASK_LIST_STS = [
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-    "SummEval",
-]
-
-TASK_LIST = TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS
-class OpenAIEmbedder:
-    """
-    Benchmark OpenAIs embeddings endpoint on USEB.
-    """
-    def __init__(self, engine, task_name=None, batch_size=32, save_emb=False, **kwargs):
-        self.engine = engine
-        self.max_token_len = 2046 # 2048 - 2 special tokens
-        self.batch_size = batch_size
-        self.save_emb = False # Problematic as the filenames end up being the same
-        self.base_path = f"embeddings/{engine.split('/')[-1]}/"
-        self.tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
-        self.task_name = task_name
-
-        if save_emb:
-            assert self.task_name is not None
-
-        pathlib.Path(self.base_path).mkdir(parents=True, exist_ok=True)
-        
-    def encode(self, 
-            sentences,
-            decode=True,
-            idx=None,
-            **kwargs
-        ):
-
-        openai.api_key = API_KEY
-
-        fin_embeddings = []
-
-        embedding_path = f"{self.base_path}/{self.task_name}_{sentences[0][:5]}_{sentences[-1][-5:]}.pickle"
-        if sentences and os.path.exists(embedding_path):
-            loaded = pickle.load(open(embedding_path, "rb"))
-            fin_embeddings = loaded["fin_embeddings"]
-        else:
-            for i in range(0, len(sentences), self.batch_size):
-                batch = sentences[i : i + self.batch_size]
-
-                all_tokens = []
-                used_indices = []
-                for j, txt in enumerate(batch):
-                    tokens = self.tokenizer.encode(txt, add_special_tokens=False)
-                    token_len = len(tokens)
-                    if token_len == 0:
-                        raise ValueError("Empty items should be cleaned prior to running")
-                    if token_len > self.max_token_len:
-                        tokens = tokens[:self.max_token_len]
-                    # For some characters the API raises weird errors, e.g. input=[[126]]
-                    if decode:
-                        tokens = self.tokenizer.decode(tokens)
-                    all_tokens.append(tokens)
-                    used_indices.append(j)
-
-                out = [[]] * len(batch)
-                if all_tokens:
-                    response = openai.Engine(id=self.engine).embeddings(input=all_tokens)
-                    assert len(response["data"]) == len(
-                        all_tokens
-                    ), f"Sent {len(all_tokens)}, got {len(response['data'])}"
-
-                    for data in response["data"]:
-                        idx = data["index"]
-                        # OpenAI seems to return them ordered, but to be save use the index and insert
-                        idx = used_indices[idx]
-                        embedding = data["embedding"]
-                        out[idx] = embedding
-
-                fin_embeddings.extend(out)
-        # Save embeddings
-        if fin_embeddings and self.save_emb:
-            dump = {
-                "fin_embeddings": fin_embeddings,
-            }
-            pickle.dump(dump, open(embedding_path, "wb"))
-
-        assert len(sentences) == len(fin_embeddings)
-        return fin_embeddings
-
-
-def parse_args():
-    # Parse command line arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--startid", type=int)
-    parser.add_argument("--endid", type=int)
-    parser.add_argument("--engine", type=str, default="text-similarity-ada-001")
-    parser.add_argument("--lang", type=str, default="en")
-    parser.add_argument("--taskname", type=str, default=None)
-    parser.add_argument("--batchsize", type=int, default=2048)
-    args = parser.parse_args()
-    return args
-
-def main(args):
-
-    # Different batch size than the arg
-    # The below is used to send X embeddings to the API
-    # The CLI arg is how much will be saved / pickle file
-
-    for task in TASK_LIST[args.startid:args.endid]:
-        print("Running task: ", task)
-        model = OpenAIEmbedder(args.engine, task_name=task, batchsize=256, save_emb=True)
-        eval_splits = ["validation"] if task == "MSMARCO" else ["test"]
-        model_name = args.engine.split("/")[-1].split("_")[-1]
-        evaluation = MTEB(tasks=[task], task_langs=[args.lang])
-        evaluation.run(model, output_folder=f"results/{model_name}", batch_size=args.batchsize, eval_splits=eval_splits)
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)
diff --git a/run_array_openaiv2.py b/run_array_openaiv2.py
deleted file mode 100644
index d29c6cc4..00000000
--- a/run_array_openaiv2.py
+++ /dev/null
@@ -1,226 +0,0 @@
-"""
-openai==0.26.4
-tiktoken==0.2.0
-"""
-import argparse
-import logging
-import os
-import pathlib
-import pickle
-
-from mteb import MTEB
-import openai
-import tiktoken
-from transformers import GPT2TokenizerFast
-
-logging.basicConfig(level=logging.INFO)
-
-os.environ["HF_DATASETS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_CACHE"]="/gpfswork/rech/six/commun/models"
-os.environ["HF_DATASETS_CACHE"]="/gpfswork/rech/six/commun/datasets"
-os.environ["HF_MODULES_CACHE"]="/gpfswork/rech/six/commun/modules"
-os.environ["HF_METRICS_CACHE"]="/gpfswork/rech/six/commun/metrics"
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-API_KEY = "YOUR_KEY"
-
-TASK_LIST_CLASSIFICATION = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-]
-
-TASK_LIST_CLUSTERING = [
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-]
-
-TASK_LIST_PAIR_CLASSIFICATION = [
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-]
-
-TASK_LIST_RERANKING = [
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-]
-
-TASK_LIST_RETRIEVAL = [
-    "ArguAna",
-    "ClimateFEVER",
-    "CQADupstackAndroidRetrieval",
-    "CQADupstackEnglishRetrieval",
-    "CQADupstackGamingRetrieval",
-    "CQADupstackGisRetrieval",
-    "CQADupstackMathematicaRetrieval",
-    "CQADupstackPhysicsRetrieval",
-    "CQADupstackProgrammersRetrieval",
-    "CQADupstackStatsRetrieval",
-    "CQADupstackTexRetrieval",
-    "CQADupstackUnixRetrieval",
-    "CQADupstackWebmastersRetrieval",
-    "CQADupstackWordpressRetrieval",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-]
-
-TASK_LIST_STS = [
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-    "SummEval",
-]
-
-TASK_LIST = TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS
-
-class OpenAIEmbedder:
-    """
-    Benchmark OpenAIs embeddings endpoint.
-    """
-    def __init__(self, engine, task_name=None, batch_size=32, save_emb=False, **kwargs):
-        self.engine = engine
-        self.max_token_len = 8191
-        self.batch_size = batch_size
-        self.save_emb = save_emb # Problematic as the filenames may end up being the same
-        self.base_path = f"embeddings/{engine.split('/')[-1]}/"
-        # self.tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
-        self.tokenizer = tiktoken.encoding_for_model(engine)
-        self.task_name = task_name
-
-        if save_emb:
-            assert self.task_name is not None
-
-        pathlib.Path(self.base_path).mkdir(parents=True, exist_ok=True)
-        
-    def encode(self, 
-            sentences,
-            decode=True,
-            idx=None,
-            **kwargs
-        ):
-
-        openai.api_key = API_KEY
-
-        fin_embeddings = []
-
-        embedding_path = f"{self.base_path}/{self.task_name}_{sentences[0][:10]}_{sentences[-1][-10:]}.pickle"
-        if sentences and os.path.exists(embedding_path):
-            loaded = pickle.load(open(embedding_path, "rb"))
-            fin_embeddings = loaded["fin_embeddings"]
-        else:
-            for i in range(0, len(sentences), self.batch_size):
-                batch = sentences[i : i + self.batch_size]
-
-                all_tokens = []
-                used_indices = []
-                for j, txt in enumerate(batch):
-                    # tokens = self.tokenizer.encode(txt, add_special_tokens=False)
-                    if not(txt):
-                        print("Detected empty item, which is not allowed by the OpenAI API - Replacing with empty space")
-                        txt = " "
-                    tokens = self.tokenizer.encode(txt)
-                    token_len = len(tokens)
-                    if token_len > self.max_token_len:
-                        tokens = tokens[:self.max_token_len]
-                    # For some characters the API raises weird errors, e.g. input=[[126]]
-                    if decode:
-                        tokens = self.tokenizer.decode(tokens)
-                    all_tokens.append(tokens)
-                    used_indices.append(j)
-
-                out = [[]] * len(batch)
-                if all_tokens:
-                    response = openai.Embedding.create(input=all_tokens, model=self.engine)
-                    # May want to sleep here to avoid getting too many requests error
-                    # time.sleep(1)
-                    assert len(response["data"]) == len(
-                        all_tokens
-                    ), f"Sent {len(all_tokens)}, got {len(response['data'])}"
-
-                    for data in response["data"]:
-                        idx = data["index"]
-                        # OpenAI seems to return them ordered, but to be save use the index and insert
-                        idx = used_indices[idx]
-                        embedding = data["embedding"]
-                        out[idx] = embedding
-
-                fin_embeddings.extend(out)
-        # Save embeddings
-        if fin_embeddings and self.save_emb:
-            dump = {
-                "fin_embeddings": fin_embeddings,
-            }
-            pickle.dump(dump, open(embedding_path, "wb"))
-
-        assert len(sentences) == len(fin_embeddings)
-        return fin_embeddings
-
-
-def parse_args():
-    # Parse command line arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--startid", type=int)
-    parser.add_argument("--endid", type=int)
-    parser.add_argument("--engine", type=str, default="text-embedding-ada-002")
-    parser.add_argument("--lang", type=str, default="en")
-    parser.add_argument("--taskname", type=str, default=None)
-    parser.add_argument("--batchsize", type=int, default=2048)
-    args = parser.parse_args()
-    return args
-
-def main(args):
-
-    # There are two different batch sizes
-    # OpenAIEmbedder(...) batch size arg is used to send X embeddings to the API
-    # evaluation.run(...) batch size arg is how much will be saved / pickle file (as it's the total sent to the embed function)
-
-    for task in TASK_LIST[args.startid:args.endid]:
-        print("Running task: ", task)
-        model = OpenAIEmbedder(args.engine, task_name=task, batch_size=args.batchsize, save_emb=True)
-        eval_splits = ["validation"] if task == "MSMARCO" else ["test"]
-        model_name = args.engine.split("/")[-1].split("_")[-1]
-        evaluation = MTEB(tasks=[task], task_langs=[args.lang])
-        evaluation.run(model, output_folder=f"results/{model_name}", batch_size=args.batchsize, eval_splits=eval_splits, corpus_chunk_size=10000)
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)
diff --git a/run_array_sgpt.py b/run_array_sgpt.py
deleted file mode 100644
index f95da0a6..00000000
--- a/run_array_sgpt.py
+++ /dev/null
@@ -1,247 +0,0 @@
-import argparse
-import logging
-import os
-from typing import Dict, List, Union
-
-from mteb import MTEB
-import numpy as np
-from sentence_transformers import SentenceTransformer
-import torch.multiprocessing as mp
-from torch import Tensor
-
-logging.basicConfig(level=logging.INFO)
-
-logger = logging.getLogger("main")
-
-os.environ["HF_DATASETS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_CACHE"]="/gpfswork/rech/six/commun/models"
-os.environ["HF_DATASETS_CACHE"]="/gpfswork/rech/six/commun/datasets"
-os.environ["HF_MODULES_CACHE"]="/gpfswork/rech/six/commun/modules"
-os.environ["HF_METRICS_CACHE"]="/gpfswork/rech/six/commun/metrics"
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-
-TASK_LIST_CLASSIFICATION = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-]
-
-TASK_LIST_CLUSTERING = [
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-]
-
-TASK_LIST_PAIR_CLASSIFICATION = [
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-]
-
-TASK_LIST_RERANKING = [
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-]
-
-TASK_LIST_RETRIEVAL = [
-    "ArguAna",
-    "ClimateFEVER",
-    "CQADupstackAndroidRetrieval",
-    "CQADupstackEnglishRetrieval",
-    "CQADupstackGamingRetrieval",
-    "CQADupstackGisRetrieval",
-    "CQADupstackMathematicaRetrieval",
-    "CQADupstackPhysicsRetrieval",
-    "CQADupstackProgrammersRetrieval",
-    "CQADupstackStatsRetrieval",
-    "CQADupstackTexRetrieval",
-    "CQADupstackUnixRetrieval",
-    "CQADupstackWebmastersRetrieval",
-    "CQADupstackWordpressRetrieval",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-]
-
-TASK_LIST_STS = [
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-    "SummEval",
-]
-
-TASK_LIST = TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS
-
-
-class SentenceTransformerSpecb(SentenceTransformer):
-    # Requires: 
-    # https://github.com/Muennighoff/sentence-transformers/tree/sgpt_poolings_specb
-    # pip install git+https://github.com/Muennighoff/sentence-transformers.git@sgpt_poolings_specb
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        tokens = ["[SOS]", "{SOS}"]
-        self.sep = " "
-        self._first_module().tokenizer.add_tokens(tokens, special_tokens=True)
-        self._first_module().auto_model.resize_token_embeddings(len(self._first_module().tokenizer))
-        # Will be replaced with the rep tokens in the model ones
-        # The problem is we don't know if a text is query or document when tokenizing in the Transformer.py module, 
-        # so we use the SOS tokens as an identifier if we have a query or document at hand & then replace them
-        # If we would directly use the brackets here, they may become part of another token
-        self._first_module().bos_spec_token_q = self._first_module().tokenizer.encode("[SOS]", add_special_tokens=False)[0]
-        self._first_module().bos_spec_token_d = self._first_module().tokenizer.encode("{SOS}", add_special_tokens=False)[0]
-        self._first_module().bos_spec_token_q_rep = self._first_module().tokenizer.encode("[", add_special_tokens=False)[0]
-        self._first_module().eos_spec_token_q = self._first_module().tokenizer.encode("]", add_special_tokens=False)[0]
-        self._first_module().bos_spec_token_d_rep = self._first_module().tokenizer.encode("{", add_special_tokens=False)[0]
-        self._first_module().eos_spec_token_d = self._first_module().tokenizer.encode("}", add_special_tokens=False)[0]
-        self._first_module().replace_bos = True
-
-    def encode(self, sentences, **kwargs):
-        """Returns a list of embeddings for the given sentences.
-        Args:
-            sentences (`List[str]`): List of sentences to encode
-            batch_size (`int`): Batch size for the encoding
-
-        Returns:
-            `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences
-        """
-        # Add specb query token
-        sentences = ["[SOS]" + sent for sent in sentences]
-        return super().encode(sentences, **kwargs)
-
-    def encode_queries(self, queries: List[str], batch_size: int = 16, **kwargs) -> Union[List[Tensor], np.ndarray, Tensor]:
-        # Will be replaced with [ in the models tokenization
-        # If we would put [ here, there is a risk of it getting chained with a different token when encoding
-        queries = ["[SOS]" + q for q in queries]
-        return super().encode(queries, batch_size=batch_size, **kwargs)
-    
-    def encode_corpus(self, corpus: List[Dict[str, str]], batch_size: int = 8, **kwargs) -> Union[List[Tensor], np.ndarray, Tensor]:
-        # Will be replaced with { in the models tokenization
-        # If we would put { here, there is a risk of it getting chained with a different token when encoding
-        sentences = [("{SOS}" + doc["title"] + self.sep + doc["text"]).strip() if "title" in doc else "{SOS}" + doc["text"].strip() for doc in corpus]
-        return super().encode(sentences, batch_size=batch_size, **kwargs)
-
-    def encode_corpus_parallel(
-        self, corpus: List[Dict[str, str]], pool: Dict[str, object], batch_size: int, chunk_id: int, **kwargs
-    ):
-        if type(corpus) is dict:
-            sentences = [
-                ("{SOS}" + corpus["title"][i] + self.sep + corpus["text"][i]).strip()
-                if "title" in corpus
-                else "{SOS}" + corpus["text"][i].strip()
-                for i in range(len(corpus["text"]))
-            ]
-        else:
-            sentences = [
-                ("{SOS}" + doc["title"] + self.sep + doc["text"]).strip() if "title" in doc else "{SOS}" + doc["text"].strip()
-                for doc in corpus
-            ]
-
-        if chunk_id is not None and chunk_id >= len(pool["processes"]):
-            output_queue = pool["output"]
-            output_queue.get()
-
-        input_queue = pool["input"]
-        input_queue.put([chunk_id, batch_size, sentences])
-
-
-    def start_multi_process_pool(self, target_devices: List[str] = None) -> Dict[str, object]:
-        logger.info("Start multi-process pool on devices: {}".format(", ".join(map(str, target_devices))))
-
-        ctx = mp.get_context("spawn")
-        input_queue = ctx.Queue()
-        output_queue = ctx.Queue()
-        processes = []
-
-        for process_id, device_name in enumerate(target_devices):
-            p = ctx.Process(
-                target=SentenceTransformer._encode_multi_process_worker,
-                args=(process_id, device_name, self.model, input_queue, output_queue),
-                daemon=True,
-            )
-            p.start()
-            processes.append(p)
-
-        return {"input": input_queue, "output": output_queue, "processes": processes}
-
-    def stop_multi_process_pool(self, pool: Dict[str, object]):
-        output_queue = pool["output"]
-        [output_queue.get() for _ in range(len(pool["processes"]))]
-        return self.model.stop_multi_process_pool(pool)
-
-
-def parse_args():
-    # Parse command line arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--startid", type=int)
-    parser.add_argument("--endid", type=int)
-    parser.add_argument("--addspecbdoc", action='store_true')
-    parser.add_argument("--addspecbquery", action='store_true')
-    parser.add_argument("--modelpath", type=str, default="/gpfswork/rech/six/commun/models/Muennighoff_SGPT-125M-weightedmean-msmarco-specb-bitfit")
-    parser.add_argument("--lang", type=str, default="en")
-    parser.add_argument("--taskname", type=str, default=None)
-    parser.add_argument("--batchsize", type=int, default=128)
-    args = parser.parse_args()
-    return args
-
-def main(args):
-
-    if args.addspecbdoc or args.addspecbquery:
-        model = SentenceTransformerSpecb(args.modelpath) # Only used for SGPT-msmarco models
-    else:
-        model = SentenceTransformer(args.modelpath)
-
-    if args.taskname is not None:
-        task = args.taskname
-        model_name = args.modelpath.split("/")[-1].split("_")[-1]
-        eval_splits = ["validation"] if task == "MSMARCO" else ["test"]
-        evaluation = MTEB(tasks=[task], task_langs=[args.lang])
-        evaluation.run(model, output_folder=f"results/{model_name}", batch_size=args.batchsize, eval_splits=eval_splits)
-        exit()
-
-    for task in TASK_LIST[args.startid:args.endid]:
-        print("Running task: ", task)
-        eval_splits = ["validation"] if task == "MSMARCO" else ["test"]
-        model_name = args.modelpath.split("/")[-1].split("_")[-1]
-        evaluation = MTEB(tasks=[task], task_langs=[args.lang])
-        evaluation.run(model, output_folder=f"results/{model_name}", batch_size=args.batchsize, eval_splits=eval_splits)
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)
diff --git a/run_array_simcse.py b/run_array_simcse.py
deleted file mode 100644
index cc3eefd8..00000000
--- a/run_array_simcse.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import argparse
-import logging
-import os
-
-logging.basicConfig(level=logging.INFO)
-
-os.environ["HF_DATASETS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_CACHE"]="/gpfswork/rech/six/commun/models"
-os.environ["HF_DATASETS_CACHE"]="/gpfswork/rech/six/commun/datasets"
-os.environ["HF_MODULES_CACHE"]="/gpfswork/rech/six/commun/modules"
-os.environ["HF_METRICS_CACHE"]="/gpfswork/rech/six/commun/metrics"
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-import numpy as np
-from mteb import MTEB
-from transformers import AutoModel, AutoTokenizer
-import torch
-
-
-TASK_LIST_CLASSIFICATION = [
-    "AmazonCounterfactualClassification",
-    "AmazonPolarityClassification",
-    "AmazonReviewsClassification",
-    "Banking77Classification",
-    "EmotionClassification",
-    "ImdbClassification",
-    "MassiveIntentClassification",
-    "MassiveScenarioClassification",
-    "MTOPDomainClassification",
-    "MTOPIntentClassification",
-    "ToxicConversationsClassification",
-    "TweetSentimentExtractionClassification",
-]
-
-TASK_LIST_CLUSTERING = [
-    "ArxivClusteringP2P",
-    "ArxivClusteringS2S",
-    "BiorxivClusteringP2P",
-    "BiorxivClusteringS2S",
-    "MedrxivClusteringP2P",
-    "MedrxivClusteringS2S",
-    "RedditClustering",
-    "RedditClusteringP2P",
-    "StackExchangeClustering",
-    "StackExchangeClusteringP2P",
-    "TwentyNewsgroupsClustering",
-]
-
-TASK_LIST_PAIR_CLASSIFICATION = [
-    "SprintDuplicateQuestions",
-    "TwitterSemEval2015",
-    "TwitterURLCorpus",
-]
-
-TASK_LIST_RERANKING = [
-    "AskUbuntuDupQuestions",
-    "MindSmallReranking",
-    "SciDocsRR",
-    "StackOverflowDupQuestions",
-]
-
-TASK_LIST_RETRIEVAL = [
-    "ArguAna",
-    "ClimateFEVER",
-    "CQADupstackAndroidRetrieval",
-    "CQADupstackEnglishRetrieval",
-    "CQADupstackGamingRetrieval",
-    "CQADupstackGisRetrieval",
-    "CQADupstackMathematicaRetrieval",
-    "CQADupstackPhysicsRetrieval",
-    "CQADupstackProgrammersRetrieval",
-    "CQADupstackStatsRetrieval",
-    "CQADupstackTexRetrieval",
-    "CQADupstackUnixRetrieval",
-    "CQADupstackWebmastersRetrieval",
-    "CQADupstackWordpressRetrieval",
-    "DBPedia",
-    "FEVER",
-    "FiQA2018",
-    "HotpotQA",
-    "MSMARCO",
-    "NFCorpus",
-    "NQ",
-    "QuoraRetrieval",
-    "SCIDOCS",
-    "SciFact",
-    "Touche2020",
-    "TRECCOVID",
-]
-
-TASK_LIST_STS = [
-    "BIOSSES",
-    "SICK-R",
-    "STS12",
-    "STS13",
-    "STS14",
-    "STS15",
-    "STS16",
-    "STS17",
-    "STS22",
-    "STSBenchmark",
-    "SummEval",
-]
-
-TASK_LIST = TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS
-
-
-class SimCSEWrapper:
-    def __init__(self, modelpath="princeton-nlp/sup-simcse-bert-base-uncased"):
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.tokenizer = AutoTokenizer.from_pretrained(modelpath)
-        self.model = AutoModel.from_pretrained(modelpath).to(self.device)
-        self.model.eval()
-
-    def encode(self, sentences, batch_size=32, **kwargs):
-        """ Returns a list of embeddings for the given sentences.
-        Args:
-            sentences (`List[str]`): List of sentences to encode
-            batch_size (`int`): Batch size for the encoding
-
-        Returns:
-            `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences
-        """
-        all_embeddings = []
-        length_sorted_idx = np.argsort([len(sen) for sen in sentences])
-        sentences_sorted = [sentences[idx] for idx in length_sorted_idx]
-
-        for start_index in range(0, len(sentences), batch_size):
-            sentences_batch = sentences_sorted[start_index:start_index+batch_size]
-            inputs = self.tokenizer(sentences_batch, padding=True, truncation=True, return_tensors="pt")
-            inputs = {k: v.to(self.device) for k,v in inputs.items()}
-            # Get the embeddings
-            with torch.no_grad():
-                embeddings = self.model(**inputs, output_hidden_states=True, return_dict=True).pooler_output
-            all_embeddings.extend(embeddings.cpu().numpy())
-        all_embeddings = [all_embeddings[idx] for idx in np.argsort(length_sorted_idx)]
-        return all_embeddings
-
-def parse_args():
-    # Parse command line arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--startid", type=int)
-    parser.add_argument("--endid", type=int)
-    parser.add_argument("--modelpath", type=str, default="/gpfswork/rech/six/commun/models/princeton-nlp/sup-simcse-bert-base-uncased")
-    parser.add_argument("--lang", type=str, default="en")
-    parser.add_argument("--taskname", type=str, default=None)
-    parser.add_argument("--batchsize", type=int, default=128)
-    args = parser.parse_args()
-    return args
-
-def main(args):
-
-    model = SimCSEWrapper(args.modelpath)
-
-    if args.taskname is not None:
-        task = args.taskname
-        eval_splits = ["validation"] if task == "MSMARCO" else ["test"]
-        model_name = args.modelpath.split("/")[-1].split("_")[-1]
-        evaluation = MTEB(tasks=[task], task_langs=[args.lang], eval_splits=eval_splits)
-        evaluation.run(model, output_folder=f"results/{model_name}", batch_size=args.batchsize)
-        exit()
-
-    for task in TASK_LIST[args.startid:args.endid]:
-        print("Running task: ", task)
-        eval_splits = ["validation"] if task == "MSMARCO" else ["test"]
-        model_name = args.modelpath.split("/")[-1].split("_")[-1]
-        evaluation = MTEB(tasks=[task], task_langs=[args.lang])
-        evaluation.run(model, output_folder=f"results/{model_name}", batch_size=args.batchsize, eval_splits=eval_splits)
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)
diff --git a/run_benchmark.py b/run_benchmark.py
deleted file mode 100644
index c333ee4c..00000000
--- a/run_benchmark.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import argparse
-import logging
-import json
-import os
-import subprocess
-import time
-
-logging.basicConfig(level=logging.INFO)
-
-os.environ["HF_DATASETS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_OFFLINE"]="1" # 1 for offline
-os.environ["TRANSFORMERS_CACHE"]="/gpfswork/rech/six/commun/models"
-os.environ["HF_DATASETS_CACHE"]="/gpfswork/rech/six/commun/datasets"
-os.environ["HF_MODULES_CACHE"]="/gpfswork/rech/six/commun/modules"
-os.environ["HF_METRICS_CACHE"]="/gpfswork/rech/six/commun/metrics"
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-import numpy as np
-from mteb import MTEB
-from sentence_transformers import SentenceTransformer
-import torch
-from transformers import AutoModel, AutoTokenizer 
-
-
-MODELS = [
-    "LASER2",
-    "/gpfswork/rech/six/commun/models/sentence-transformers_average_word_embeddings_komninos",
-    "/gpfswork/rech/six/commun/models/sentence-transformers_average_word_embeddings_glove.6B.300d",
-    "/gpfswork/rech/six/commun/models/Muennighoff_SGPT-125M-weightedmean-nli-bitfit",
-    "/gpfswork/rech/six/commun/models/Muennighoff_SGPT-125M-weightedmean-msmarco-specb-bitfit",
-    "/gpfswork/rech/six/commun/models/Muennighoff_SGPT-5.8B-weightedmean-nli-bitfit",
-    "/gpfswork/rech/six/commun/models/Muennighoff_SGPT-5.8B-weightedmean-msmarco-specb-bitfit",
-    "/gpfswork/rech/six/commun/models/bigscience_sgpt-bloom-7b1-msmarco",
-    "/gpfswork/rech/six/commun/models/bigscience-catalogue-lm-data_sgpt-bloom-1b3-nli",
-    "/gpfswork/rech/six/commun/models/sentence-transformers_all-MiniLM-L6-v2",
-    "/gpfswork/rech/six/commun/models/sentence-transformers_all-mpnet-base-v2",
-    "/gpfswork/rech/six/commun/models/sentence-transformers_paraphrase-multilingual-mpnet-base-v2",
-    "/gpfswork/rech/six/commun/models/sentence-transformers_sentence-t5-base",
-    "/gpfswork/rech/six/commun/models/sentence-transformers_sentence-t5-xxl",
-    "/gpfswork/rech/six/commun/models/sentence-transformers_gtr-t5-base",
-    "/gpfswork/rech/six/commun/models/sentence-transformers_gtr-t5-xxl",
-    "/gpfswork/rech/six/commun/models/nthakur_contriever-base-msmarco",
-    "/gpfswork/rech/six/commun/models/sentence-transformers_msmarco-bert-co-condensor",
-    "/gpfswork/rech/six/commun/models/bert-base-uncased",
-    "/gpfswork/rech/six/commun/models/princeton-nlp_sup-simcse-bert-base-uncased",
-    "/gpfswork/rech/six/commun/models/princeton-nlp_unsup-simcse-bert-base-uncased",
-    "/gpfswork/rech/six/commun/models/sentence-transformers_LaBSE",
-]
-
-MODELS = [
-    "/gpfswork/rech/six/commun/models/sentence-transformers_all-MiniLM-L12-v2",
-    "/gpfswork/rech/six/commun/models/sentence-transformers_allenai-specter",
-]
-
-TASKS = [
-    "STS15",
-]
-
-class SentenceTransformerSpecb(SentenceTransformer):
-    # Requires: 
-    # https://github.com/Muennighoff/sentence-transformers/tree/sgpt_poolings_specb
-    # pip install git+https://github.com/Muennighoff/sentence-transformers.git@sgpt_poolings_specb
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        tokens = ["[SOS]", "{SOS}"]
-        self._first_module().tokenizer.add_tokens(tokens, special_tokens=True)
-        self._first_module().auto_model.resize_token_embeddings(len(self._first_module().tokenizer))
-        # Will be replaced with the rep tokens in the model ones
-        # The problem is we don't know if a text is query or document when tokenizing in the Transformer.py module, 
-        # so we use the SOS tokens as an identifier if we have a query or document at hand & then replace them
-        # If we would directly use the brackets here, they may become part of another token
-        self._first_module().bos_spec_token_q = self._first_module().tokenizer.encode("[SOS]", add_special_tokens=False)[0]
-        self._first_module().bos_spec_token_d = self._first_module().tokenizer.encode("{SOS}", add_special_tokens=False)[0]
-        self._first_module().bos_spec_token_q_rep = self._first_module().tokenizer.encode("[", add_special_tokens=False)[0]
-        self._first_module().eos_spec_token_q = self._first_module().tokenizer.encode("]", add_special_tokens=False)[0]
-        self._first_module().bos_spec_token_d_rep = self._first_module().tokenizer.encode("{", add_special_tokens=False)[0]
-        self._first_module().eos_spec_token_d = self._first_module().tokenizer.encode("}", add_special_tokens=False)[0]
-        self._first_module().replace_bos = True
-
-    def encode(self, sentences, **kwargs):
-        """Returns a list of embeddings for the given sentences.
-        Args:
-            sentences (`List[str]`): List of sentences to encode
-            batch_size (`int`): Batch size for the encoding
-
-        Returns:
-            `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences
-        """
-        # Add specb query token
-        sentences = ["[SOS]" + sent for sent in sentences]
-        return super().encode(sentences, **kwargs)
-
-class SimCSEWrapper:
-    def __init__(self, modelpath="princeton-nlp/sup-simcse-bert-base-uncased"):
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.tokenizer = AutoTokenizer.from_pretrained(modelpath)
-        self.model = AutoModel.from_pretrained(modelpath).to(self.device)
-        self.model.eval()
-
-    def encode(self, sentences, batch_size=32, **kwargs):
-        """ Returns a list of embeddings for the given sentences.
-        Args:
-            sentences (`List[str]`): List of sentences to encode
-            batch_size (`int`): Batch size for the encoding
-
-        Returns:
-            `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences
-        """
-        all_embeddings = []
-        length_sorted_idx = np.argsort([len(sen) for sen in sentences])
-        sentences_sorted = [sentences[idx] for idx in length_sorted_idx]
-
-        for start_index in range(0, len(sentences), batch_size):
-            sentences_batch = sentences_sorted[start_index:start_index+batch_size]
-            inputs = self.tokenizer(sentences_batch, padding=True, truncation=True, return_tensors="pt")
-            inputs = {k: v.to(self.device) for k,v in inputs.items()}
-            # Get the embeddings
-            with torch.no_grad():
-                embeddings = self.model(**inputs, output_hidden_states=True, return_dict=True).pooler_output
-            all_embeddings.extend(embeddings.cpu().numpy())
-        all_embeddings = [all_embeddings[idx] for idx in np.argsort(length_sorted_idx)]
-        return all_embeddings
-
-class LASER():
-    def encode(self, sentences, batch_size=32, **kwargs):
-        """
-        Returns a list of embeddings for the given sentences.
-        Args:
-            sentences (`List[str]`): List of sentences to encode
-            batch_size (`int`): Batch size for the encoding
-
-        Returns:
-            `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences
-        """
-        if os.path.exists("tmp.txt"):
-            os.remove("tmp.txt")
-        if os.path.exists("tmp.bin"):
-            os.remove("tmp.bin")
-
-        # LASER expects one text per line, so we need to replace newlines
-        sentences = [s.replace("\n", " ") for s in sentences]
-        with open("tmp.txt", "w") as f:
-            f.write("\n".join(sentences))
-
-        rc = subprocess.call("/gpfsscratch/rech/six/commun/commun/experiments/muennighoff/mteb/LASER/LASER_script.sh", shell=True)
-              
-        dim = 1024
-        X = np.fromfile("tmp.bin", dtype=np.float32, count=-1)                                                                          
-        X.resize(X.shape[0] // dim, dim)
-        print(X.shape)
-        return X
-
-
-def parse_args():
-    # Parse command line arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--lang", type=str, default="en")
-    parser.add_argument("--batchsize", type=int, default=32)
-    args = parser.parse_args()
-    return args
-
-def main(args):
-
-    out = {}
-    for model_name in MODELS:
-        if ("sgpt" in model_name.lower()) and ("msmarco" in model_name.lower()):
-            model = SentenceTransformerSpecb(model_name) # Only used for SGPT-msmarco models
-        elif "simcse" in model_name.lower():
-            model = SimCSEWrapper(model_name)
-        elif "LASER2" == model_name:
-            model = LASER()
-        else:
-            model = SentenceTransformer(model_name)
-
-        evaluation = MTEB(tasks=TASKS, task_langs=[args.lang])
-        model_name = model_name.split("/")[-1].split("_")[-1]
-        for task, task_name in zip(evaluation.tasks, TASKS):
-            task.load_data()
-
-            # Encode all with the same batch size for a fair comparison of speed / sentence
-            data = task.dataset["test"]["sentence1"] + task.dataset["test"]["sentence2"]
-            data_len = len(data)
-            # Warmup run to build py caches etc
-            embeddings = np.asarray(model.encode(data, batch_size=args.batchsize))
-            tick = time.time()
-            embeddings = np.asarray(model.encode(data, batch_size=args.batchsize))
-            tock = time.time()
-
-            out.setdefault(model_name, {})
-            out[model_name].setdefault(task_name, {})
-            out[model_name][task_name]["speed_ms"] = ((tock - tick) / data_len) * 1000
-            out[model_name][task_name]["embedding_size_kb"] = embeddings.nbytes / data_len / 1000
-
-        # Overwrite every iteration for intermed results
-        with open("benchmark.json", "w") as f:
-            json.dump(out, f)
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)
diff --git a/script_mteb_french/README.md b/script_mteb_french/README.md
deleted file mode 100644
index 03e6d2cf..00000000
--- a/script_mteb_french/README.md
+++ /dev/null
@@ -1,70 +0,0 @@
-# Scripts to run the French MTEB benchmark
-
-This folder contains the scripts used to generate the French tab results on the [MTEB](https://github.com/embeddings-benchmark/mteb) benchmark.
-
-Below are instructions to run the main scripts.
-
-## Benchmark
-
-### Running on host using venv
-
-* Navigate to the repository root folder
-* Create your virtual env:
-
-```bash
-python3 -m venv .venv
-```
-* Activate it and install the requirements:
-```bash
-source .venv/bin/activate
-pip install -r requirements.txt
-```
-* Run the benchmark:
-```bash
-cd script_mteb_french
-python run_benchmark.py
-```
-
-By default the benchmark runs on sentence_transformer models but you can specify the type with the argument `--model_type`:
-```bash
-# default ['sentence_transformer']
-python run_benchmark.py
-# choosing other type ['voyage_ai']
-python run_benchmark.py --model_type voyage_ai
-# running on two types ['voyage_ai', 'sentence_transformer']
-python run_benchmark.py --model_type voyage_ai sentence_transformer
-```
-
-You can also run the benchmark on one model only by specifying `--model_name`:
-```bash
-# default ['sentence_transformer'] -> all models of this type
-python run_benchmark.py
-# running on one model 'camembert-base'
-python run_benchmark.py --model_type sentence_transformer --model_name "xlm-roberta-base"
-```
-Note that the `model_name` should be included in models of specified `model_type`.
-
-You can run the benchmark on one task type in ["all", "classification", "clustering", "reranking", "retrieval", "pair_classification", "sts", "summarization", "bitextmining"], default is set to "all" and will run all tasks :
-```bash
-# running 'sentence_transformer' models on 'classification' task
-python run_benchmark.py --model_type sentence_transformer --task_type classification
-```
-
-## Running using Docker
-
-* Navigate to the repository root folder
-* Build the docker image:
-```bash
-docker build -t mtebscripts_image .
-```
-* Run the benchmark in the container as follows:
-```
-docker run -v $(pwd):/mtebscripts mtebscripts_image sh -c "cd script_mteb_french && python run_benchmark.py"
-```
-If you want to use the gpu, make sure to add the `--gpus` option to your run command, or `--runtime=nvidia` if you are using an older version of docker.
-
-Note: Because the volume is shared between the host and the container, the results will be available in the host at the end.
-
-## Models' characteristics
-
-Additionnaly, you can find a script `get_model_specs.py` to compute models' characteristics (size, number of params, embeddings dimension). You can run it similarly to the benchmark by substituting `run_benchmark.py` with `get_model_specs.py`.
diff --git a/script_mteb_french/results_analysis/__init__.py b/script_mteb_french/results_analysis/__init__.py
deleted file mode 100644
index 11e8bfb7..00000000
--- a/script_mteb_french/results_analysis/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .results_parser import ResultsParser
\ No newline at end of file
diff --git a/slurmscripts/run_array_8a100_st5xxl.slurm b/slurmscripts/run_array_8a100_st5xxl.slurm
deleted file mode 100644
index a6b37b60..00000000
--- a/slurmscripts/run_array_8a100_st5xxl.slurm
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-a100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=64         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=20:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@a100
-#SBATCH --reservation=hug
-#SBATCH --constraint=a100
-#SBATCH --partition=gpu_p5
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-	--modelpath /gpfswork/rech/six/commun/models/sentence-transformers_sentence-t5-xxl
diff --git a/slurmscripts/run_array_a100_100_gtrxxl.slurm b/slurmscripts/run_array_a100_100_gtrxxl.slurm
deleted file mode 100644
index 02a73c0c..00000000
--- a/slurmscripts/run_array_a100_100_gtrxxl.slurm
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-a100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=8         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time 100:00:00              # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@a100
-#SBATCH --reservation=hug
-#SBATCH --constraint=a100
-#SBATCH --partition=gpu_p5
-#SBATCH --qos=qos_gpu-gc             # up to 100h
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-	--modelpath /gpfswork/rech/six/commun/models/sentence-transformers_gtr-t5-xxl
diff --git a/slurmscripts/run_array_a100_100_sgpt5b8_asym.slurm b/slurmscripts/run_array_a100_100_sgpt5b8_asym.slurm
deleted file mode 100644
index faa0fe98..00000000
--- a/slurmscripts/run_array_a100_100_sgpt5b8_asym.slurm
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-a100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=8         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time 100:00:00              # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@a100
-#SBATCH --reservation=hug
-#SBATCH --constraint=a100
-#SBATCH --partition=gpu_p5
-#SBATCH --qos=qos_gpu-gc             # up to 100h
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-	--modelpath /gpfswork/rech/six/commun/models/Muennighoff_SGPT-5.8B-weightedmean-msmarco-specb-bitfit \
-    --addspecbquery
diff --git a/slurmscripts/run_array_a100_100_sgpt5b8_asym_specb.slurm b/slurmscripts/run_array_a100_100_sgpt5b8_asym_specb.slurm
deleted file mode 100644
index 1c3dd343..00000000
--- a/slurmscripts/run_array_a100_100_sgpt5b8_asym_specb.slurm
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-a100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=8         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time 80:00:00              # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@a100
-#SBATCH --reservation=hug
-#SBATCH --constraint=a100
-#SBATCH --partition=gpu_p5
-#SBATCH --qos=qos_gpu-gc             # up to 100h
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array_sgpt.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-	--modelpath /gpfswork/rech/six/commun/models/Muennighoff_SGPT-5.8B-weightedmean-msmarco-specb-bitfit \
-    --addspecbquery
diff --git a/slurmscripts/run_array_a100_100_sgpt5b8_sym.slurm b/slurmscripts/run_array_a100_100_sgpt5b8_sym.slurm
deleted file mode 100644
index b667f34d..00000000
--- a/slurmscripts/run_array_a100_100_sgpt5b8_sym.slurm
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-a100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=8         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time 100:00:00              # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@a100
-#SBATCH --reservation=hug
-#SBATCH --constraint=a100
-#SBATCH --partition=gpu_p5
-#SBATCH --qos=qos_gpu-gc             # up to 100h
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-	--modelpath /gpfswork/rech/six/commun/models/Muennighoff_SGPT-5.8B-weightedmean-nli-bitfit
diff --git a/slurmscripts/run_array_a100_100_st5xxl.slurm b/slurmscripts/run_array_a100_100_st5xxl.slurm
deleted file mode 100644
index 5982a42b..00000000
--- a/slurmscripts/run_array_a100_100_st5xxl.slurm
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-a100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=8         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time 100:00:00              # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@a100
-#SBATCH --reservation=hug
-#SBATCH --constraint=a100
-#SBATCH --partition=gpu_p5
-#SBATCH --qos=qos_gpu-gc             # up to 100h
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-	--modelpath /gpfswork/rech/six/commun/models/sentence-transformers_sentence-t5-xxl
diff --git a/slurmscripts/run_array_a100_sgpt5b8_sym.slurm b/slurmscripts/run_array_a100_sgpt5b8_sym.slurm
deleted file mode 100644
index 98bfb93f..00000000
--- a/slurmscripts/run_array_a100_sgpt5b8_sym.slurm
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-a100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=8         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=20:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@a100
-#SBATCH --reservation=hug
-#SBATCH --constraint=a100
-#SBATCH --partition=gpu_p5
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-	--modelpath /gpfswork/rech/six/commun/models/Muennighoff_SGPT-5.8B-weightedmean-nli-bitfit
diff --git a/slurmscripts/run_array_a100_st5xxl.slurm b/slurmscripts/run_array_a100_st5xxl.slurm
deleted file mode 100644
index d8edab6e..00000000
--- a/slurmscripts/run_array_a100_st5xxl.slurm
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-a100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=8         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=20:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@a100
-#SBATCH --reservation=hug
-#SBATCH --constraint=a100
-#SBATCH --partition=gpu_p5
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-	--modelpath /gpfswork/rech/six/commun/models/sentence-transformers_sentence-t5-xxl
diff --git a/slurmscripts/run_array_ada.slurm b/slurmscripts/run_array_ada.slurm
deleted file mode 100644
index 2581afe9..00000000
--- a/slurmscripts/run_array_ada.slurm
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=ada
-#SBATCH --partition=prepost
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
-#SBATCH --cpus-per-task=10           # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time 20:00:00              # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@cpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/mteb
-
-NUM_TASKS_PER_JOB=10
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python run_array_openai.py \
-	--startid $TASK_START \
-	--endid $TASK_END
diff --git a/slurmscripts/run_array_v10032_bert.slurm b/slurmscripts/run_array_v10032_bert.slurm
deleted file mode 100644
index 46a2c11f..00000000
--- a/slurmscripts/run_array_v10032_bert.slurm
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=10:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-#SBATCH -C v100-32g
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/bert-base-uncased
diff --git a/slurmscripts/run_array_v10032_cocondensor.slurm b/slurmscripts/run_array_v10032_cocondensor.slurm
deleted file mode 100644
index a08fbc51..00000000
--- a/slurmscripts/run_array_v10032_cocondensor.slurm
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --qos=qos_gpu-t3
-#SBATCH -C v100-32g
-#SBATCH --time 20:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/sentence-transformers_msmarco-bert-co-condensor
diff --git a/slurmscripts/run_array_v10032_contriever.slurm b/slurmscripts/run_array_v10032_contriever.slurm
deleted file mode 100644
index 26d5045b..00000000
--- a/slurmscripts/run_array_v10032_contriever.slurm
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --qos=qos_gpu-t3
-#SBATCH -C v100-32g
-#SBATCH --time 20:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/nthakur_contriever-base-msmarco
diff --git a/slurmscripts/run_array_v10032_gtr.slurm b/slurmscripts/run_array_v10032_gtr.slurm
deleted file mode 100644
index 2cd303ff..00000000
--- a/slurmscripts/run_array_v10032_gtr.slurm
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH -C v100-32g
-#SBATCH --time 10:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/sentence-transformers_gtr-t5-base
diff --git a/slurmscripts/run_array_v10032_st5.slurm b/slurmscripts/run_array_v10032_st5.slurm
deleted file mode 100644
index 6c476c45..00000000
--- a/slurmscripts/run_array_v10032_st5.slurm
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:4
-#SBATCH --cpus-per-task=40         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --qos=qos_gpu-t3
-#SBATCH -C v100-32g
-#SBATCH --time 20:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS=19
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/sentence-transformers_sentence-t5-base
diff --git a/slurmscripts/run_array_v100_glove.slurm b/slurmscripts/run_array_v100_glove.slurm
deleted file mode 100644
index 4ed2a08a..00000000
--- a/slurmscripts/run_array_v100_glove.slurm
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=10:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/sentence-transformers_average_word_embeddings_glove.6B.300d
diff --git a/slurmscripts/run_array_v100_internet_sgpt125m_sym.slurm b/slurmscripts/run_array_v100_internet_sgpt125m_sym.slurm
deleted file mode 100644
index 8050ee48..00000000
--- a/slurmscripts/run_array_v100_internet_sgpt125m_sym.slurm
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-mteb-125M    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=10:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@cpu
-#SBATCH --partition=prepost
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS=54
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-	--modelpath /gpfswork/rech/six/commun/models/Muennighoff_SGPT-125M-weightedmean-nli-bitfit
diff --git a/slurmscripts/run_array_v100_komninos.slurm b/slurmscripts/run_array_v100_komninos.slurm
deleted file mode 100644
index f3f0753f..00000000
--- a/slurmscripts/run_array_v100_komninos.slurm
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=10:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/sentence-transformers_average_word_embeddings_komninos
diff --git a/slurmscripts/run_array_v100_labse.slurm b/slurmscripts/run_array_v100_labse.slurm
deleted file mode 100644
index 2a9a9a34..00000000
--- a/slurmscripts/run_array_v100_labse.slurm
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100-labse    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=20:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-#SBATCH -C v100-32g
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-    --taskname BUCC \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/sentence-transformers_LaBSE
diff --git a/slurmscripts/run_array_v100_laser.slurm b/slurmscripts/run_array_v100_laser.slurm
deleted file mode 100644
index 608ca482..00000000
--- a/slurmscripts/run_array_v100_laser.slurm
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=10:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array_laser.py \
-	--startid $TASK_START \
-	--endid $TASK_END
diff --git a/slurmscripts/run_array_v100_minilm.slurm b/slurmscripts/run_array_v100_minilm.slurm
deleted file mode 100644
index 6b600bf1..00000000
--- a/slurmscripts/run_array_v100_minilm.slurm
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=10:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/sentence-transformers_all-MiniLM-L6-v2
diff --git a/slurmscripts/run_array_v100_mpnet.slurm b/slurmscripts/run_array_v100_mpnet.slurm
deleted file mode 100644
index 1b779454..00000000
--- a/slurmscripts/run_array_v100_mpnet.slurm
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=10:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/sentence-transformers_all-mpnet-base-v2
diff --git a/slurmscripts/run_array_v100_multimini.slurm b/slurmscripts/run_array_v100_multimini.slurm
deleted file mode 100644
index 7044300a..00000000
--- a/slurmscripts/run_array_v100_multimini.slurm
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100-multimini    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=20:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-#SBATCH -C v100-32g
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-    --taskname BUCC \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2
diff --git a/slurmscripts/run_array_v100_multimpnet.slurm b/slurmscripts/run_array_v100_multimpnet.slurm
deleted file mode 100644
index 0e1098fb..00000000
--- a/slurmscripts/run_array_v100_multimpnet.slurm
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100-multimpnet    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=20:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-#SBATCH -C v100-32g
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-    --taskname BUCC \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/sentence-transformers_paraphrase-multilingual-mpnet-base-v2
diff --git a/slurmscripts/run_array_v100_sgpt125m_asym.slurm b/slurmscripts/run_array_v100_sgpt125m_asym.slurm
deleted file mode 100644
index b6352cb9..00000000
--- a/slurmscripts/run_array_v100_sgpt125m_asym.slurm
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=20:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS=20
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array_sgpt.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-	--modelpath /gpfswork/rech/six/commun/models/Muennighoff_SGPT-125M-weightedmean-msmarco-specb-bitfit \
-	--addspecbquery
diff --git a/slurmscripts/run_array_v100_sgpt1b3_sym.slurm b/slurmscripts/run_array_v100_sgpt1b3_sym.slurm
deleted file mode 100644
index a1726be7..00000000
--- a/slurmscripts/run_array_v100_sgpt1b3_sym.slurm
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=10:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-#SBATCH -C v100-32g
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --lang zh \
-    --modelpath /gpfswork/rech/six/commun/models/bigscience-catalogue-lm-data_sgpt-nli-bloom-1b3
diff --git a/slurmscripts/run_array_v100_simcsesup.slurm b/slurmscripts/run_array_v100_simcsesup.slurm
deleted file mode 100644
index c260c907..00000000
--- a/slurmscripts/run_array_v100_simcsesup.slurm
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=10:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array_simcse.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/princeton-nlp_sup-simcse-bert-base-uncased
-
-echo "END TIME: $(date)"
diff --git a/slurmscripts/run_array_v100_simcseunsup.slurm b/slurmscripts/run_array_v100_simcseunsup.slurm
deleted file mode 100644
index 023af0b2..00000000
--- a/slurmscripts/run_array_v100_simcseunsup.slurm
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=10:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array_simcse.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/princeton-nlp_unsup-simcse-bert-base-uncased
diff --git a/slurmscripts/run_array_v100_st5.slurm b/slurmscripts/run_array_v100_st5.slurm
deleted file mode 100644
index 6f2af715..00000000
--- a/slurmscripts/run_array_v100_st5.slurm
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=run-array-v100    # job name
-#SBATCH --ntasks=1                   # number of MP tasks
-#SBATCH --nodes=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=10         # number of cores per tasks
-#SBATCH --hint=nomultithread         # we get physical cores not logical
-#SBATCH --time=10:00:00             # maximum execution time (HH:MM:SS)
-#SBATCH --output=%x-%j.out           # output file name
-#SBATCH --account=six@gpu
-
-set -x -e
-
-source $six_ALL_CCFRWORK/start-prod
-conda activate muennighoffmtb
-
-echo "START TIME: $(date)"
-
-cd /gpfsscratch/rech/six/commun/commun/experiments/muennighoff/
-
-NUM_TASKS=19
-NUM_TASKS_PER_JOB=1
-
-TASK_START=$(expr $SLURM_ARRAY_TASK_ID \* $NUM_TASKS_PER_JOB )
-TASK_END=$(expr $TASK_START + $NUM_TASKS_PER_JOB )
-
-python mteb/run_array.py \
-	--startid $TASK_START \
-	--endid $TASK_END \
-    --modelpath /gpfswork/rech/six/commun/models/sentence-transformers_sentence-t5-base
diff --git a/script_mteb_french/results_analysis/README.md b/tools/README.md
similarity index 91%
rename from script_mteb_french/results_analysis/README.md
rename to tools/README.md
index 28e9990c..6a9da674 100644
--- a/script_mteb_french/results_analysis/README.md
+++ b/tools/README.md
@@ -6,11 +6,11 @@ Before starting, you can create your environment using the packages listed in *r
 
 ### result_parser.py
 
-This scripts intent is to ***format results from json files in the results folder to a table*** (csv, excel or latex).
+This script's intent is to ***format results from json files in the results folder to a table*** (csv, excel or latex).
 
 #### Usage
 
-You can use the class ResultParser like so:
+You can use the class ``ResultParser`` like so:
 
 ```py
 from results_analysis.results_parser import ResultParser
@@ -30,7 +30,7 @@ rp = ResultParser()
 results_df = rp(RESULT_FOLDER_PATH, output_format="latex", apply_style=True)
 ```
 
-Alternatively, you can use a command line :
+Alternatively, you can use a command line:
 ```
 python .\script_mteb_french\results_analysis\results_parser.py --results_folder ./results --output_format csv
 ```
diff --git a/tools/analysis_tools/__init__.py b/tools/analysis_tools/__init__.py
new file mode 100644
index 00000000..e6a677d2
--- /dev/null
+++ b/tools/analysis_tools/__init__.py
@@ -0,0 +1 @@
+from ...tools.results_parser import ResultsParser
\ No newline at end of file
diff --git a/script_mteb_french/results_analysis/dataset_correlation.py b/tools/analysis_tools/dataset_correlation.py
similarity index 96%
rename from script_mteb_french/results_analysis/dataset_correlation.py
rename to tools/analysis_tools/dataset_correlation.py
index 137e1be4..c2afdaa5 100644
--- a/script_mteb_french/results_analysis/dataset_correlation.py
+++ b/tools/analysis_tools/dataset_correlation.py
@@ -4,7 +4,7 @@
 import seaborn as sns
 import numpy as np
 
-from results_parser import ResultsParser
+from mtebscripts.tools.results_parser import ResultsParser
 
 
 def parse_args() -> Namespace:
@@ -15,7 +15,7 @@ def parse_args() -> Namespace:
     """
     parser = ArgumentParser()
     parser.add_argument("--results_folder", required=True, type=str)
-    parser.add_argument("--output_folder", type=str, default="./analyses_outputs/results_correlations")
+    parser.add_argument("--output_folder", type=str, default="./analysis_outputs/results_correlations")
     parser.add_argument(
         "--output_format",
         type=str,
diff --git a/script_mteb_french/results_analysis/datasets_similarity.py b/tools/analysis_tools/datasets_similarity.py
similarity index 99%
rename from script_mteb_french/results_analysis/datasets_similarity.py
rename to tools/analysis_tools/datasets_similarity.py
index c7c2e95f..9af5cb93 100644
--- a/script_mteb_french/results_analysis/datasets_similarity.py
+++ b/tools/analysis_tools/datasets_similarity.py
@@ -169,7 +169,7 @@ def parse_args() -> Namespace:
     parser = ArgumentParser()
     parser.add_argument("--task_type", type=str, default="all")
     parser.add_argument("--langs", type=list[str], default=["fr"])
-    parser.add_argument("--output_folder", type=str, default="./analyses_outputs/datasets_similarity")
+    parser.add_argument("--output_folder", type=str, default="./analysis_outputs/datasets_similarity")
     parser.add_argument("--model_name", type=str, default="intfloat/multilingual-e5-large")
     parser.add_argument("--seed", type=int, default=42)
     parser.add_argument("--n_samples", type=int, default=90)
diff --git a/script_mteb_french/results_analysis/performance_vs_characteristics.py b/tools/analysis_tools/performance_vs_characteristics.py
similarity index 97%
rename from script_mteb_french/results_analysis/performance_vs_characteristics.py
rename to tools/analysis_tools/performance_vs_characteristics.py
index 979598a2..458a1bd1 100644
--- a/script_mteb_french/results_analysis/performance_vs_characteristics.py
+++ b/tools/analysis_tools/performance_vs_characteristics.py
@@ -4,7 +4,7 @@
 import os
 from argparse import ArgumentParser, Namespace
 
-from results_parser import ResultsParser
+from mtebscripts.tools.results_parser import ResultsParser
 import numpy as np
 
 # model,pretrained_or_tuned,multilingual_or_french,number_params,size_gb,seq_len,embedding_dim,model_type,license
@@ -60,7 +60,7 @@ def parse_args() -> Namespace:
     parser.add_argument(
         "--output_folder",
         type=str,
-        default="./analyses_outputs/performance_vs_characteristics",
+        default="./analysis_outputs/performance_vs_characteristics",
     )
     parser.add_argument(
         "--output_format",
diff --git a/script_mteb_french/results_analysis/results_parser.py b/tools/analysis_tools/results_parser.py
similarity index 99%
rename from script_mteb_french/results_analysis/results_parser.py
rename to tools/analysis_tools/results_parser.py
index 95837835..8e56cf4e 100644
--- a/script_mteb_french/results_analysis/results_parser.py
+++ b/tools/analysis_tools/results_parser.py
@@ -262,7 +262,7 @@ def parse_args() -> Namespace:
     parser.add_argument("--results_folder", required=True, type=str)
     parser.add_argument("--output_format", type=str, choices=["excel", "csv", "latex"], default="excel")
     parser.add_argument("--apply_style", type=bool, default=True)
-    parser.add_argument("--output_folder", type=str, default="./analyses_outputs/")
+    parser.add_argument("--output_folder", type=str, default="./analysis_outputs/")
     args = parser.parse_args()
 
     return args
diff --git a/script_mteb_french/results_analysis/statistical_tests.py b/tools/analysis_tools/statistical_tests.py
similarity index 98%
rename from script_mteb_french/results_analysis/statistical_tests.py
rename to tools/analysis_tools/statistical_tests.py
index 4030075b..2a590481 100644
--- a/script_mteb_french/results_analysis/statistical_tests.py
+++ b/tools/analysis_tools/statistical_tests.py
@@ -19,7 +19,7 @@ def parse_args() -> Namespace:
     parser.add_argument(
         "--output_folder",
         type=str,
-        default="./analyses_outputs/statistical_tests",
+        default="./analysis_outputs/statistical_tests",
     )
     parser.add_argument(
         "--output_format",
diff --git a/script_mteb_french/estimate_evaluation_cost.py b/tools/model_tools/estimate_evaluation_cost.py
similarity index 100%
rename from script_mteb_french/estimate_evaluation_cost.py
rename to tools/model_tools/estimate_evaluation_cost.py
diff --git a/script_mteb_french/get_model_specs.py b/tools/model_tools/get_model_specs.py
similarity index 98%
rename from script_mteb_french/get_model_specs.py
rename to tools/model_tools/get_model_specs.py
index a8469597..a2edf905 100644
--- a/script_mteb_french/get_model_specs.py
+++ b/tools/model_tools/get_model_specs.py
@@ -4,7 +4,7 @@
 import os
 
 from huggingface_hub import HfFileSystem
-import model_spec_utils
+import mtebscripts.tools.model_tools.model_spec_utils as model_spec_utils
 from run_benchmark import TYPES_TO_MODELS
 import pandas as pd
 
diff --git a/script_mteb_french/model_spec_utils.py b/tools/model_tools/model_spec_utils.py
similarity index 100%
rename from script_mteb_french/model_spec_utils.py
rename to tools/model_tools/model_spec_utils.py
diff --git a/script_mteb_french/preload_models.py b/tools/preload_models.py
similarity index 100%
rename from script_mteb_french/preload_models.py
rename to tools/preload_models.py
diff --git a/script_mteb_french/preload_tasks.py b/tools/preload_tasks.py
similarity index 100%
rename from script_mteb_french/preload_tasks.py
rename to tools/preload_tasks.py
diff --git a/script_mteb_french/run_benchmark.py b/tools/run_benchmark.py
similarity index 70%
rename from script_mteb_french/run_benchmark.py
rename to tools/run_benchmark.py
index febe880a..9b96278c 100644
--- a/script_mteb_french/run_benchmark.py
+++ b/tools/run_benchmark.py
@@ -6,6 +6,7 @@
 
 from src.ModelConfig import ModelConfig
 from utils.tasks_list import get_tasks
+from utils.models_list import TYPES_TO_MODELS, SENTENCE_TRANSORMER_MODELS_WITH_ERRORS
 
 logging.basicConfig(
     stream=sys.stdout,
@@ -29,83 +30,9 @@
 Example: MODELS = [ModelConfig("intfloat/multilingual-e5-base", model_type="sentence_transformer")]
 """
 
-#############################
-# Step 1 : Setup model list #
-#############################
-SENTENCE_TRANSORMER_MODELS = [
-    "bert-base-multilingual-cased",
-    "bert-base-multilingual-uncased",
-    "flaubert/flaubert_base_uncased",
-    "flaubert/flaubert_base_cased",
-    "flaubert/flaubert_large_cased",
-    "dangvantuan/sentence-camembert-base",
-    "sentence-transformers/distiluse-base-multilingual-cased-v2",
-    "sentence-transformers/all-MiniLM-L6-v2",
-    "sentence-transformers/all-MiniLM-L12-v2",
-    "sentence-transformers/LaBSE",
-    "sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
-    "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
-    "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
-    "intfloat/multilingual-e5-base",
-    "intfloat/multilingual-e5-large",
-    "intfloat/multilingual-e5-small",
-    "distilbert-base-uncased",
-    "Geotrend/distilbert-base-25lang-cased",
-    "Geotrend/distilbert-base-en-fr-es-pt-it-cased",
-    "Geotrend/distilbert-base-en-fr-cased",
-    "Geotrend/distilbert-base-fr-cased",
-    "Geotrend/bert-base-25lang-cased",
-    "Geotrend/bert-base-15lang-cased",
-    "Geotrend/bert-base-10lang-cased",
-    "shibing624/text2vec-base-multilingual",
-    "izhx/udever-bloom-560m",
-    "izhx/udever-bloom-1b1",
-    "sentence-transformers/sentence-t5-base",
-    "sentence-transformers/sentence-t5-large",
-    "sentence-transformers/sentence-t5-xl",
-    "sentence-transformers/sentence-t5-xxl",
-    "intfloat/e5-mistral-7b-instruct",
-    "Wissam42/sentence-croissant-llm-base"
-]
-
-# these models max_length is indicated to be 514 whereas the embedding layer actually supports 512
-SENTENCE_TRANSORMER_MODELS_WITH_ERRORS = [
-    "camembert/camembert-base",
-    "camembert/camembert-large",
-    "dangvantuan/sentence-camembert-large",
-    "xlm-roberta-base",
-    "xlm-roberta-large",
-]
-
-UNIVERSAL_SENTENCE_ENCODER_MODELS = [
-    "vprelovac/universal-sentence-encoder-multilingual-3",
-    "vprelovac/universal-sentence-encoder-multilingual-large-3",
-]
-
-LASER_MODELS = ["laser2"]
-
-VOYAGE_MODELS = ["voyage-2", "voyage-code-2"]
-
-OPEN_AI_MODELS = ["text-embedding-ada-002", "text-embedding-3-small", "text-embedding-3-large"]
-
-COHERE_MODELS = ["embed-multilingual-light-v3.0", "embed-multilingual-v3.0"]
-
-MISTRAL_MODELS = ["mistral-embed"]
-
-TYPES_TO_MODELS = {
-    "sentence_transformer": SENTENCE_TRANSORMER_MODELS
-    + SENTENCE_TRANSORMER_MODELS_WITH_ERRORS,
-    "universal_sentence_encoder": UNIVERSAL_SENTENCE_ENCODER_MODELS,
-    "laser": LASER_MODELS,
-    "voyage_ai": VOYAGE_MODELS,
-    "open_ai": OPEN_AI_MODELS,
-    "cohere": COHERE_MODELS,
-    "mistral_ai": MISTRAL_MODELS,
-
-}
 
 ##########################
-# Step 3 : Run benchmark #
+# Step : Run benchmark #
 ##########################
 
 
diff --git a/script_mteb_french/utils/__init__.py b/tools/utils/__init__.py
similarity index 100%
rename from script_mteb_french/utils/__init__.py
rename to tools/utils/__init__.py
diff --git a/tools/utils/models_list.py b/tools/utils/models_list.py
new file mode 100644
index 00000000..26fdd926
--- /dev/null
+++ b/tools/utils/models_list.py
@@ -0,0 +1,71 @@
+SENTENCE_TRANSORMER_MODELS = [
+    "bert-base-multilingual-cased",
+    "bert-base-multilingual-uncased",
+    "flaubert/flaubert_base_uncased",
+    "flaubert/flaubert_base_cased",
+    "flaubert/flaubert_large_cased",
+    "dangvantuan/sentence-camembert-base",
+    "sentence-transformers/distiluse-base-multilingual-cased-v2",
+    "sentence-transformers/all-MiniLM-L6-v2",
+    "sentence-transformers/all-MiniLM-L12-v2",
+    "sentence-transformers/LaBSE",
+    "sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
+    "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+    "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
+    "intfloat/multilingual-e5-base",
+    "intfloat/multilingual-e5-large",
+    "intfloat/multilingual-e5-small",
+    "distilbert-base-uncased",
+    "Geotrend/distilbert-base-25lang-cased",
+    "Geotrend/distilbert-base-en-fr-es-pt-it-cased",
+    "Geotrend/distilbert-base-en-fr-cased",
+    "Geotrend/distilbert-base-fr-cased",
+    "Geotrend/bert-base-25lang-cased",
+    "Geotrend/bert-base-15lang-cased",
+    "Geotrend/bert-base-10lang-cased",
+    "shibing624/text2vec-base-multilingual",
+    "izhx/udever-bloom-560m",
+    "izhx/udever-bloom-1b1",
+    "sentence-transformers/sentence-t5-base",
+    "sentence-transformers/sentence-t5-large",
+    "sentence-transformers/sentence-t5-xl",
+    "sentence-transformers/sentence-t5-xxl",
+    "intfloat/e5-mistral-7b-instruct",
+    "Wissam42/sentence-croissant-llm-base"
+]
+
+# these models max_length is indicated to be 514 whereas the embedding layer actually supports 512
+SENTENCE_TRANSORMER_MODELS_WITH_ERRORS = [
+    "camembert/camembert-base",
+    "camembert/camembert-large",
+    "dangvantuan/sentence-camembert-large",
+    "xlm-roberta-base",
+    "xlm-roberta-large",
+]
+
+UNIVERSAL_SENTENCE_ENCODER_MODELS = [
+    "vprelovac/universal-sentence-encoder-multilingual-3",
+    "vprelovac/universal-sentence-encoder-multilingual-large-3",
+]
+# TODO: use json file keys
+
+LASER_MODELS = ["laser2"]
+
+VOYAGE_MODELS = ["voyage-2", "voyage-code-2"]
+
+OPEN_AI_MODELS = ["text-embedding-ada-002", "text-embedding-3-small", "text-embedding-3-large"]
+
+COHERE_MODELS = ["embed-multilingual-light-v3.0", "embed-multilingual-v3.0"]
+
+MISTRAL_MODELS = ["mistral-embed"]
+
+TYPES_TO_MODELS = {
+    "sentence_transformer": SENTENCE_TRANSORMER_MODELS
+    + SENTENCE_TRANSORMER_MODELS_WITH_ERRORS,
+    "universal_sentence_encoder": UNIVERSAL_SENTENCE_ENCODER_MODELS,
+    "laser": LASER_MODELS,
+    "voyage_ai": VOYAGE_MODELS,
+    "open_ai": OPEN_AI_MODELS,
+    "cohere": COHERE_MODELS,
+    "mistral_ai": MISTRAL_MODELS,
+}
diff --git a/script_mteb_french/utils/tasks_list.py b/tools/utils/tasks_list.py
similarity index 100%
rename from script_mteb_french/utils/tasks_list.py
rename to tools/utils/tasks_list.py
diff --git a/script_mteb_french/universal_sentence_encoder_models_paths.json b/tools/utils/universal_sentence_encoder_models_paths.json
similarity index 100%
rename from script_mteb_french/universal_sentence_encoder_models_paths.json
rename to tools/utils/universal_sentence_encoder_models_paths.json