elser

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,6 +1,2 @@
 *.pyc
-model_infos.json
-space
-.venv
-results
-mteb
+model_infos.json

DESCRIPTION.md

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+Massive Text Embedding Benchmark (MTEB) Leaderboard.

Dockerfile

@@ -1,19 +0,0 @@
-FROM python:3.12-bookworm
-
-RUN apt update && apt install -y git make
-RUN useradd -m -u 1000 user
-ENV PATH="/home/user/.local/bin:$PATH"
-
-RUN git clone https://github.com/embeddings-benchmark/mteb.git
-RUN chown -R user:user /mteb
-
-USER user
-WORKDIR /mteb
-
-RUN pip install "pydantic<2.11"
-RUN pip install ".[leaderboard]"
-# ENV XDG_CACHE_HOME=/home/user/.cache
-ENV GRADIO_SERVER_NAME="0.0.0.0"
-EXPOSE 7860
-
-CMD ["make", "run-leaderboard"]

README.md

@@ -1,20 +1,14 @@
 ---
-title: MTEB Leaderboard
+title: MTEB Leaderboard 
 emoji: 🥇
 colorFrom: blue
 colorTo: indigo
-sdk: docker
-app_port: 7860
+sdk: gradio
+sdk_version: 4.20.0
 app_file: app.py
-pinned: true
+pinned: false
 tags:
-- leaderboard
+  - leaderboard
 startup_duration_timeout: 1h
 fullWidth: true
-license: mit
-short_description: Embedding Leaderboard
 ---
-
-# MTEB Leaderboard
-
-Embedding Leaderboard

app.py

@@ -0,0 +1,674 @@
+from functools import partial, reduce
+import json
+import os
+import re
+
+from datasets import load_dataset
+import gradio as gr
+from huggingface_hub import hf_hub_download
+from huggingface_hub.repocard import metadata_load
+import pandas as pd
+from tqdm.autonotebook import tqdm
+
+from utils.model_size import get_model_parameters_memory
+from envs import LEADERBOARD_CONFIG, MODEL_META, REPO_ID, RESULTS_REPO, API
+
+TASKS_CONFIG = LEADERBOARD_CONFIG["tasks"]
+BOARDS_CONFIG = LEADERBOARD_CONFIG["boards"]
+
+TASKS = list(TASKS_CONFIG.keys())
+PRETTY_NAMES = {
+    "InstructionRetrieval": "Retrieval w/Instructions",
+    "PairClassification": "Pair Classification",
+    "BitextMining": "Bitext Mining",
+}
+
+TASK_TO_METRIC = {k:v["metric"] for k,v in TASKS_CONFIG.items()}
+
+def make_clickable_model(model_name, link=None):
+    if link is None:
+        link = "https://huggingface.co/" + model_name
+    # Remove user from model name
+    return (
+        f'<a target="_blank" style="text-decoration: underline" href="{link}">{model_name.split("/")[-1]}</a>'
+    )
+
+EXTERNAL_MODELS = {k for k,v in MODEL_META["model_meta"].items() if v.get("is_external", False)}
+EXTERNAL_MODEL_TO_LINK = {k: v["link"] for k,v in MODEL_META["model_meta"].items() if v.get("link", False)}
+EXTERNAL_MODEL_TO_DIM = {k: v["dim"] for k,v in MODEL_META["model_meta"].items() if v.get("dim", False)}
+EXTERNAL_MODEL_TO_SEQLEN = {k: v["seq_len"] for k,v in MODEL_META["model_meta"].items() if v.get("seq_len", False)}
+EXTERNAL_MODEL_TO_SIZE = {k: v["size"] for k,v in MODEL_META["model_meta"].items() if v.get("size", False)}
+PROPRIETARY_MODELS = {k for k,v in MODEL_META["model_meta"].items() if v.get("is_proprietary", False)}
+TASK_DESCRIPTIONS = {k: v["task_description"] for k,v in TASKS_CONFIG.items()}
+TASK_DESCRIPTIONS["Overall"] = "Overall performance across MTEB tasks."
+SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS = {k for k,v in MODEL_META["model_meta"].items() if v.get("is_sentence_transformers_compatible", False)}
+MODELS_TO_SKIP = MODEL_META["models_to_skip"]
+CROSS_ENCODERS = MODEL_META["cross_encoders"]
+BI_ENCODERS = [k for k, _ in MODEL_META["model_meta"].items() if k not in CROSS_ENCODERS + ["bm25"]]
+
+PROPRIETARY_MODELS = {
+    make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
+    for model in PROPRIETARY_MODELS
+}
+SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS = {
+    make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
+    for model in SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS
+}
+CROSS_ENCODERS = {
+    make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
+    for model in CROSS_ENCODERS
+}
+BI_ENCODERS = {
+    make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
+    for model in BI_ENCODERS
+}
+
+
+TASK_TO_TASK_TYPE = {task_category: [] for task_category in TASKS}
+for board_config in BOARDS_CONFIG.values():
+    for task_category, task_list in board_config["tasks"].items():
+        TASK_TO_TASK_TYPE[task_category].extend(task_list)
+
+def add_lang(examples):
+    if not(examples["eval_language"]):
+        examples["mteb_dataset_name_with_lang"] = examples["mteb_dataset_name"]
+    else:
+        examples["mteb_dataset_name_with_lang"] = examples["mteb_dataset_name"] + f' ({examples["eval_language"]})'
+    return examples
+
+def norm(names): return set([name.split(" ")[0] for name in names])
+
+def add_task(examples):
+    # Could be added to the dataset loading script instead
+    task_name = examples["mteb_dataset_name"]
+    task_type = None
+    for task_category, task_list in TASK_TO_TASK_TYPE.items():
+        if task_name in norm(task_list):
+            task_type = task_category
+            break
+    if task_type is not None:
+        examples["mteb_task"] = task_type
+    else:
+        print("WARNING: Task not found for dataset", examples["mteb_dataset_name"])
+        examples["mteb_task"] = "Unknown"
+    return examples
+
+if os.path.exists("EXTERNAL_MODEL_RESULTS.json"):
+    with open("EXTERNAL_MODEL_RESULTS.json") as f:
+        EXTERNAL_MODEL_RESULTS = json.load(f)
+    # Update with models not contained
+    models_to_run = []
+    for model in EXTERNAL_MODELS:
+        if model not in EXTERNAL_MODEL_RESULTS:
+            models_to_run.append(model)
+            EXTERNAL_MODEL_RESULTS[model] = {k: {v: []} for k, v in TASK_TO_METRIC.items()}
+else:
+    EXTERNAL_MODEL_RESULTS = {model: {k: {v: []} for k, v in TASK_TO_METRIC.items()} for model in EXTERNAL_MODELS}
+    models_to_run = EXTERNAL_MODELS
+
+pbar = tqdm(models_to_run, desc="Fetching external model results")
+for model in pbar:
+    pbar.set_description(f"Fetching external model results for {model!r}")
+    ds = load_dataset(RESULTS_REPO, model, trust_remote_code=True)
+    # For local debugging:
+    #, download_mode='force_redownload', verification_mode="no_checks")
+    ds = ds.map(add_lang)
+    ds = ds.map(add_task)
+    base_dict = {"Model": make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))}
+    # For now only one metric per task - Could add more metrics lateron
+    for task, metric in TASK_TO_METRIC.items():
+        ds_dict = ds.filter(lambda x: (x["mteb_task"] == task) and (x["metric"] == metric))["test"].to_dict()
+        ds_dict = {k: round(v, 2) for k, v in zip(ds_dict["mteb_dataset_name_with_lang"], ds_dict["score"])}
+        EXTERNAL_MODEL_RESULTS[model][task][metric].append({**base_dict, **ds_dict})
+
+# Save & cache EXTERNAL_MODEL_RESULTS
+with open("EXTERNAL_MODEL_RESULTS.json", "w") as f:
+    json.dump(EXTERNAL_MODEL_RESULTS, f)
+
+def get_dim_seq_size(model):
+    filenames = [sib.rfilename for sib in model.siblings]
+    dim, seq = "", ""
+    for filename in filenames:
+        if re.match("\d+_Pooling/config.json", filename):
+            st_config_path = hf_hub_download(model.modelId, filename=filename)
+            dim = json.load(open(st_config_path)).get("word_embedding_dimension", "")
+            break
+    for filename in filenames:
+        if re.match("\d+_Dense/config.json", filename):
+            st_config_path = hf_hub_download(model.modelId, filename=filename)
+            dim = json.load(open(st_config_path)).get("out_features", dim)
+    if "config.json" in filenames:
+        config_path = hf_hub_download(model.modelId, filename="config.json")
+        config = json.load(open(config_path))
+        if not dim:
+            dim = config.get("hidden_dim", config.get("hidden_size", config.get("d_model", "")))
+        seq = config.get("n_positions", config.get("max_position_embeddings", config.get("n_ctx", config.get("seq_length", ""))))
+    # Get model file size without downloading. Parameters in million parameters and memory in GB
+    parameters, memory = get_model_parameters_memory(model)
+    return dim, seq, parameters, memory
+
+def make_datasets_clickable(df):
+    """Does not work"""
+    if "BornholmBitextMining" in df.columns:
+        link = "https://huggingface.co/datasets/strombergnlp/bornholmsk_parallel"
+        df = df.rename(
+            columns={f'BornholmBitextMining': '<a target="_blank" style="text-decoration: underline" href="{link}">BornholmBitextMining</a>',})
+    return df
+
+def add_rank(df):
+    cols_to_rank = [col for col in df.columns if col not in ["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens"]]
+    if len(cols_to_rank) == 1:
+        df.sort_values(cols_to_rank[0], ascending=False, inplace=True)
+    else:
+        df.insert(len(df.columns) - len(cols_to_rank), "Average", df[cols_to_rank].mean(axis=1, skipna=False))
+        df.sort_values("Average", ascending=False, inplace=True)
+    df.insert(0, "Rank", list(range(1, len(df) + 1)))
+    df = df.round(2)
+    # Fill NaN after averaging
+    df.fillna("", inplace=True)
+    return df
+
+model_infos_path = "model_infos.json"
+MODEL_INFOS = {}
+if os.path.exists(model_infos_path):
+    with open(model_infos_path) as f:
+        MODEL_INFOS = json.load(f)
+
+def get_mteb_data(tasks=["Clustering"], langs=[], datasets=[], fillna=True, add_emb_dim=True, task_to_metric=TASK_TO_METRIC, rank=True, refresh=True):
+    global MODEL_INFOS
+    api = API
+    models = api.list_models(filter="mteb")
+    # Initialize list to models that we cannot fetch metadata from
+    df_list = []
+    for model in EXTERNAL_MODEL_RESULTS:
+        results_list = []
+        for task in tasks:
+            # Not all models have InstructionRetrieval, other new tasks
+            if task not in EXTERNAL_MODEL_RESULTS[model]:
+                continue
+            results_list += EXTERNAL_MODEL_RESULTS[model][task][task_to_metric[task]]
+        
+        if len(datasets) > 0:
+            res = {k: v for d in results_list for k, v in d.items() if (k == "Model") or any([x in k for x in datasets])}
+        elif langs:
+            # Would be cleaner to rely on an extra language column instead
+            langs_format = [f"({lang})" for lang in langs]
+            res = {k: v for d in results_list for k, v in d.items() if any([k.split(" ")[-1] in (k, x) for x in langs_format])}
+        else:
+            res = {k: v for d in results_list for k, v in d.items()}
+        # Model & at least one result
+        if len(res) > 1:
+            if add_emb_dim:
+                res["Model Size (Million Parameters)"] = EXTERNAL_MODEL_TO_SIZE.get(model, "")
+                res["Memory Usage (GB, fp32)"] = round(res["Model Size (Million Parameters)"] * 1e6 * 4 / 1024**3, 2) if res["Model Size (Million Parameters)"] != "" else ""
+                res["Embedding Dimensions"] = EXTERNAL_MODEL_TO_DIM.get(model, "")
+                res["Max Tokens"] = EXTERNAL_MODEL_TO_SEQLEN.get(model, "")
+            df_list.append(res)
+
+    for model in models:
+        if model.modelId in MODELS_TO_SKIP: continue
+        print("MODEL", model.modelId)
+        if model.modelId not in MODEL_INFOS or refresh:
+            readme_path = hf_hub_download(model.modelId, filename="README.md")
+            meta = metadata_load(readme_path)
+            MODEL_INFOS[model.modelId] = {
+                "metadata": meta
+            }
+        meta = MODEL_INFOS[model.modelId]["metadata"]
+        if "model-index" not in meta:
+            continue
+        # meta['model-index'][0]["results"] is list of elements like:
+        # {
+        #    "task": {"type": "Classification"},
+        #    "dataset": {
+        #        "type": "mteb/amazon_massive_intent",
+        #        "name": "MTEB MassiveIntentClassification (nb)",
+        #        "config": "nb",
+        #        "split": "test",
+        #    },
+        #    "metrics": [
+        #        {"type": "accuracy", "value": 39.81506388702084},
+        #        {"type": "f1", "value": 38.809586587791664},
+        #    ],
+        # },
+        # Use "get" instead of dict indexing to skip incompat metadata instead of erroring out
+        if len(datasets) > 0:
+            task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks) and any([x in sub_res.get("dataset", {}).get("name", "") for x in datasets])]
+        elif langs:
+            task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks) and (sub_res.get("dataset", {}).get("config", "default") in ("default", *langs))]
+        else:
+            task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks)]
+        out = [{res["dataset"]["name"].replace("MTEB ", ""): [round(score["value"], 2) for score in res["metrics"] if score["type"] == task_to_metric.get(res["task"]["type"])][0]} for res in task_results]
+        out = {k: v for d in out for k, v in d.items()}
+        out["Model"] = make_clickable_model(model.modelId)
+        # Model & at least one result
+        if len(out) > 1:
+            if add_emb_dim:
+                try:
+                    # Fails on gated repos, so we only include scores for them
+                    if "dim_seq_size" not in MODEL_INFOS[model.modelId] or refresh:
+                        MODEL_INFOS[model.modelId]["dim_seq_size"] = list(get_dim_seq_size(model))
+                    out["Embedding Dimensions"], out["Max Tokens"], out["Model Size (Million Parameters)"], out["Memory Usage (GB, fp32)"] = tuple(MODEL_INFOS[model.modelId]["dim_seq_size"])
+                except:
+                    MODEL_INFOS[model.modelId]["dim_seq_size"] = "", "", "", ""
+            df_list.append(out)
+        if model.library_name == "sentence-transformers" or "sentence-transformers" in model.tags or "modules.json" in {file.rfilename for file in model.siblings}:
+            SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS.add(out["Model"])
+            
+    # Save & cache MODEL_INFOS
+    with open("model_infos.json", "w") as f:
+        json.dump(MODEL_INFOS, f)
+
+    df = pd.DataFrame(df_list)
+    # If there are any models that are the same, merge them
+    # E.g. if out["Model"] has the same value in two places, merge & take whichever one is not NaN else just take the first one
+    df = df.groupby("Model", as_index=False).first()
+    # Put 'Model' column first
+    cols = sorted(list(df.columns))
+    base_columns = ["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens"]
+    if len(datasets) > 0:
+        #filter invalid columns
+        cols = [col for col in cols if col in base_columns + datasets]
+    i = 0
+    for column in base_columns:
+        if column in cols:
+            cols.insert(i, cols.pop(cols.index(column)))
+            i += 1
+    df = df[cols]
+    if rank:
+        df = add_rank(df)       
+    if fillna:
+        df.fillna("", inplace=True)
+    return df
+
+# Get dict with a task list for each task category
+# E.g. {"Classification": ["AmazonMassiveIntentClassification (en)", ...], "PairClassification": ["SprintDuplicateQuestions", ...]}
+def get_mteb_average(task_dict: dict, refresh=True):
+    all_tasks = reduce(lambda x, y: x + y, task_dict.values())
+    DATA_OVERALL = get_mteb_data(
+        tasks=list(task_dict.keys()),
+        datasets=all_tasks,
+        fillna=False,
+        add_emb_dim=True,
+        rank=False,
+        refresh=refresh
+    )
+    # Debugging:
+    # DATA_OVERALL.to_csv("overall.csv")
+    
+    DATA_OVERALL.insert(1, f"Average ({len(all_tasks)} datasets)", DATA_OVERALL[all_tasks].mean(axis=1, skipna=False))
+    for i, (task_category, task_category_list) in enumerate(task_dict.items()):
+        DATA_OVERALL.insert(i+2, f"{task_category} Average ({len(task_category_list)} datasets)", DATA_OVERALL[task_category_list].mean(axis=1, skipna=False))
+    DATA_OVERALL.sort_values(f"Average ({len(all_tasks)} datasets)", ascending=False, inplace=True)
+    # Start ranking from 1
+    DATA_OVERALL.insert(0, "Rank", list(range(1, len(DATA_OVERALL) + 1)))
+
+    DATA_OVERALL = DATA_OVERALL.round(2)
+
+    DATA_TASKS = {}
+    for task_category, task_category_list in task_dict.items():
+        DATA_TASKS[task_category] = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + task_category_list])
+        DATA_TASKS[task_category] = DATA_TASKS[task_category][DATA_TASKS[task_category].iloc[:, 4:].ne("").any(axis=1)]
+
+    # Fill NaN after averaging
+    DATA_OVERALL.fillna("", inplace=True)
+
+    data_overall_rows = ["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens", f"Average ({len(all_tasks)} datasets)"]
+    for task_category, task_category_list in task_dict.items():
+        data_overall_rows.append(f"{task_category} Average ({len(task_category_list)} datasets)")
+
+    DATA_OVERALL = DATA_OVERALL[data_overall_rows]
+    DATA_OVERALL = DATA_OVERALL[DATA_OVERALL.iloc[:, 5:].ne("").any(axis=1)]
+
+    return DATA_OVERALL, DATA_TASKS
+
+boards_data = {}
+all_data_tasks = []
+for board, board_config in BOARDS_CONFIG.items():
+    boards_data[board] = {
+        "data_overall": None,
+        "data_tasks": {}
+    }
+    if board_config["has_overall"]:
+        data_overall, data_tasks = get_mteb_average(board_config["tasks"], refresh=False)
+        boards_data[board]["data_overall"] = data_overall
+        boards_data[board]["data_tasks"] = data_tasks
+        all_data_tasks.extend(data_tasks.values())
+    else:
+        for task_category, task_category_list in board_config["tasks"].items():
+            data_task_category = get_mteb_data(tasks=[task_category], datasets=task_category_list, refresh=False)
+            data_task_category.drop(columns=["Embedding Dimensions", "Max Tokens"], inplace=True)
+            boards_data[board]["data_tasks"][task_category] = data_task_category
+            all_data_tasks.append(data_task_category)
+
+# Exact, add all non-nan integer values for every dataset
+NUM_SCORES = 0
+DATASETS = []
+MODELS = []
+# LANGUAGES = []
+for d in all_data_tasks:
+    # NUM_SCORES += d.iloc[:, 1:].apply(lambda x: sum([1 for y in x if isinstance(y, float) and not np.isnan(y)]), axis=1).sum()
+    cols_to_ignore = 4 if "Average" in d.columns else 3
+    # Count number of scores including only non-nan floats & excluding the rank column
+    NUM_SCORES += d.iloc[:, cols_to_ignore:].notna().sum().sum()
+    # Exclude rank & model name column (first two); Do not count different language versions as different datasets
+    DATASETS += [i.split(" ")[0] for i in d.columns[cols_to_ignore:]]
+    # LANGUAGES += [i.split(" ")[-1] for i in d.columns[cols_to_ignore:]]
+    MODELS += d["Model"].tolist()
+
+NUM_DATASETS = len(set(DATASETS))
+# NUM_LANGUAGES = len(set(LANGUAGES))
+NUM_MODELS = len(set(MODELS))
+
+# 1. Force headers to wrap
+# 2. Force model column (maximum) width
+# 3. Prevent model column from overflowing, scroll instead
+# 4. Prevent checkbox groups from taking up too much space
+css = """
+table > thead {
+    white-space: normal
+}
+
+table {
+    --cell-width-1: 250px
+}
+
+table > tbody > tr > td:nth-child(2) > div {
+    overflow-x: auto
+}
+
+.filter-checkbox-group {
+    max-width: max-content;
+}
+"""
+
+"""
+Each inner tab can have the following keys:
+- language: The language of the leaderboard
+- language_long: [optional] The long form of the language
+- description: The description of the leaderboard
+- credits: [optional] The credits for the leaderboard
+- data: The data for the leaderboard
+- refresh: The function to refresh the leaderboard
+"""
+
+def get_refresh_function(task_category, task_list):
+    def _refresh():
+        data_task_category = get_mteb_data(tasks=[task_category], datasets=task_list)
+        data_task_category.drop(columns=["Embedding Dimensions", "Max Tokens"], inplace=True)
+        return data_task_category
+    return _refresh
+
+data = {
+    "Overall": {"metric": "Various, refer to task tabs", "data": []}
+}
+for task in TASKS:
+    data[task] = {"metric": TASKS_CONFIG[task]["metric_description"], "data": []}
+
+for board, board_config in BOARDS_CONFIG.items():
+    init_name = board_config["title"]
+    if init_name in PRETTY_NAMES:
+        init_name = PRETTY_NAMES[init_name]
+    board_pretty_name = f"{init_name} leaderboard"
+    acronym = board_config.get("acronym", None)
+    board_icon = board_config.get("icon", None)
+    if board_icon is None:
+        board_icon = ""
+    credits = board_config.get("credits", None)
+
+    if board_config["has_overall"]:
+        overall_pretty_name = board_pretty_name
+        if acronym is not None:
+            overall_pretty_name += f" ({board_config['acronym']})"
+        data["Overall"]["data"].append({
+            "language": board_config["title"],
+            "language_long": board_config["language_long"],
+            "description": f"**Overall MTEB {overall_pretty_name}** 🔮{board_icon}",
+            "data": boards_data[board]["data_overall"],
+            "refresh": lambda: get_mteb_average(board_config["tasks"])[0],#partial(get_mteb_average, board_config["tasks"]),
+            "credits": credits,
+        })
+    for task_category, task_category_list in board_config["tasks"].items():
+        task_icon = TASKS_CONFIG[task_category]['icon']
+        if "special_icons" in board_config and isinstance(board_config["special_icons"], dict):
+            task_icon = board_config["special_icons"].get(task_category, task_icon)
+        data[task_category]["data"].append({
+            "language": board_config["title"],
+            "language_long": board_config["language_long"],
+            "description": f"**{task_category} {board_pretty_name}** {task_icon}{board_icon}",
+            "data": boards_data[board]["data_tasks"][task_category],
+            "refresh": get_refresh_function(task_category, task_category_list),
+            "credits": credits,
+        })
+
+dataframes = []
+full_dataframes = []
+tabs = []
+
+# The following JavaScript function updates the URL parameters based on the selected task and language
+# Additionally, `update_url_task` and `update_url_language` are used to update the current task and language
+# The current task and language are stored in the `current_task_language` and `language_per_task` JSON objects
+# This is all a bit hacky, but it might be the only way to pass options to a JavaScript function via Gradio
+set_window_url_params = """
+function(goalUrlObject) {
+    const params = new URLSearchParams(window.location.search);
+    for (const [key, value] of Object.entries(goalUrlObject)) {
+        params.set(key, value);
+    };
+    const queryString = '?' + params.toString();
+    console.log(queryString);
+    window.history.replaceState({}, '', queryString);
+    return [];
+}
+"""
+
+def update_url_task(event: gr.SelectData, current_task_language: dict, language_per_task: dict):
+    current_task_language["task"] = event.target.id
+    # Either use the cached language for this task or the 1st language
+    try:
+        current_task_language["language"] = language_per_task.get(event.target.id, event.target.children[1].children[0].id)
+    except Exception as e: # is Overall tab, no description
+        current_task_language["language"] = language_per_task.get(event.target.id, event.target.children[0].children[0].id)
+
+    return current_task_language, language_per_task
+
+def update_url_language(event: gr.SelectData, current_task_language: dict, language_per_task: dict):
+    current_task_language["language"] = event.target.id
+    if "task" not in current_task_language:
+        current_task_language["task"] = "overall"
+    language_per_task[current_task_language["task"]] = event.target.id
+    return current_task_language, language_per_task
+
+NUMERIC_INTERVALS = {
+    "<100M": pd.Interval(0, 100, closed="right"),
+    "100M to 250M": pd.Interval(100, 250, closed="right"),
+    "250M to 500M": pd.Interval(250, 500, closed="right"),
+    "500M to 1B": pd.Interval(500, 1000, closed="right"),
+    ">1B": pd.Interval(1000, 1_000_000, closed="right"),
+}
+
+MODEL_TYPES = [
+    "Open",
+    "Proprietary",
+    "Sentence Transformers",
+    "Cross-Encoders",
+    "Bi-Encoders"
+]
+
+def filter_data(search_query, model_types, model_sizes, *full_dataframes):
+    output_dataframes = []
+    for df in full_dataframes:
+        # Apply the search query
+        if search_query:
+            names = df["Model"].map(lambda x: re.match("<a .+?>(.+)</a>", x).group(1))
+            masks = []
+            for query in search_query.split(";"):
+                masks.append(names.str.contains(query))
+            df = df[reduce(lambda a, b: a | b, masks)]
+
+        # Apply the model type filtering
+        if set(model_types) != set(MODEL_TYPES):
+            masks = []
+            for model_type in model_types:
+                if model_type == "Open":
+                    masks.append(~df["Model"].isin(PROPRIETARY_MODELS))
+                elif model_type == "Proprietary":
+                    masks.append(df["Model"].isin(PROPRIETARY_MODELS))
+                elif model_type == "Sentence Transformers":
+                    masks.append(df["Model"].isin(SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS))
+                elif model_type == "Cross-Encoders":
+                    masks.append(df["Model"].isin(CROSS_ENCODERS))
+                elif model_type == "Bi-Encoders":
+                    masks.append(df["Model"].isin(BI_ENCODERS))
+            if masks:
+                df = df[reduce(lambda a, b: a | b, masks)]
+            else:
+                df = pd.DataFrame(columns=df.columns)
+
+        # Apply the model size filtering
+        if set(model_sizes) != set(NUMERIC_INTERVALS.keys()):
+            numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[model_size] for model_size in model_sizes]))
+            sizes = df["Model Size (Million Parameters)"].replace('', 0)
+            mask = sizes.apply(lambda size: any(numeric_interval.contains(size)))
+            df = df[mask]
+
+        output_dataframes.append(df)
+    return output_dataframes
+
+
+with gr.Blocks(css=css) as block:
+
+    # Store the current task and language for updating the URL. This is a bit hacky, but it works
+    # for passing the current task and language to the JavaScript function via Gradio
+    current_task_language = gr.JSON(value=dict(), visible=False)
+    language_per_task = gr.JSON(value=dict(), visible=False)
+
+    gr.Markdown(f"""
+    Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the <a href="https://github.com/embeddings-benchmark/mteb/blob/main/docs/adding_a_model.md" target="_blank" style="text-decoration: underline">MTEB GitHub repository</a> 🤗 Refer to the [MTEB paper](https://arxiv.org/abs/2210.07316) for details on metrics, tasks and models.
+    """)
+
+    with gr.Row():
+        search_bar = gr.Textbox(
+            label="Search Bar (separate multiple queries with `;`)",
+            placeholder=" 🔍 Search for a model and press enter...",
+        )
+        filter_model_type = gr.CheckboxGroup(
+            label="Model types",
+            choices=MODEL_TYPES,
+            value=MODEL_TYPES,
+            interactive=True,
+            elem_classes=["filter-checkbox-group"]
+        )
+        filter_model_sizes = gr.CheckboxGroup(
+            label="Model sizes (in number of parameters)",
+            choices=list(NUMERIC_INTERVALS.keys()),
+            value=list(NUMERIC_INTERVALS.keys()),
+            interactive=True,
+            elem_classes=["filter-checkbox-group"],
+            scale=2,
+        )
+
+    with gr.Tabs() as outer_tabs:
+        # Store the tabs for updating them on load based on URL parameters
+        tabs.append(outer_tabs)
+        for task, task_values in data.items():
+            metric = task_values["metric"]
+            task_tab_id = task.lower().replace(" ", "-")
+
+            # Overall, Bitext Mining, Classification, etc.
+            pretty_task_name = task if task not in PRETTY_NAMES.keys() else PRETTY_NAMES[task]
+            with gr.Tab(pretty_task_name, id=task_tab_id) as task_tab:
+                # For updating the 'task' in the URL
+                task_tab.select(update_url_task, [current_task_language, language_per_task], [current_task_language, language_per_task]).then(None, [current_task_language], [], js=set_window_url_params)
+                if "Overall" != task:
+                    gr.Markdown(TASK_DESCRIPTIONS[task])
+                with gr.Tabs() as task_tabs:
+                    # Store the task tabs for updating them on load based on URL parameters
+                    tabs.append(task_tabs)
+
+                    for item in task_values["data"]:
+                        item_tab_id = item["language"].lower().replace(" ", "-")
+
+                        # English, Chinese, French, etc.
+                        with gr.Tab(item["language"], id=item_tab_id) as item_tab:
+                            # For updating the 'language' in the URL
+                            item_tab.select(update_url_language, [current_task_language, language_per_task], [current_task_language, language_per_task], trigger_mode="always_last").then(None, [current_task_language], [], js=set_window_url_params)
+
+                            with gr.Row():
+                                gr.Markdown(f"""
+                                {item['description']}
+
+                                - **Metric:** {metric}
+                                - **Languages:** {item['language_long'] if 'language_long' in item else item['language']}
+                                {"- **Credits:** " + item['credits'] if ("credits" in item and item["credits"] is not None) else ''}
+                                """)
+
+                            with gr.Row():
+                                datatype = ["number", "markdown"] + ["number"] * len(item["data"])
+                                dataframe = gr.Dataframe(item["data"], datatype=datatype, type="pandas", height=500)
+                                dataframes.append(dataframe)
+
+                                full_dataframe = gr.Dataframe(item["data"], datatype=datatype, type="pandas", visible=False)
+                                full_dataframes.append(full_dataframe)
+
+                            with gr.Row():
+                                refresh_button = gr.Button("Refresh")
+                                refresh_button.click(item["refresh"], inputs=None, outputs=dataframe, concurrency_limit=20)
+
+    gr.Markdown(f"""
+    - **Total Datasets**: {NUM_DATASETS}
+    - **Total Languages**: 113
+    - **Total Scores**: {NUM_SCORES}
+    - **Total Models**: {NUM_MODELS}
+    """ + r"""
+    Made with ❤️ for NLP. If this work is useful to you, please consider citing:
+
+    ```bibtex
+    @article{muennighoff2022mteb,
+        doi = {10.48550/ARXIV.2210.07316},
+        url = {https://arxiv.org/abs/2210.07316},
+        author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
+        title = {MTEB: Massive Text Embedding Benchmark},
+        publisher = {arXiv},
+        journal={arXiv preprint arXiv:2210.07316},  
+        year = {2022}
+    }
+    ```
+    """)
+
+    def set_tabs_on_load(request: gr.Request):
+        """Set the selected tab based on the URL parameters on load."""
+        global tabs
+        valid_task_keys = [child.id for child in tabs[0].children]
+        return_tabs = [gr.Tabs()] * len(tabs)
+
+        query_params = request.request.query_params
+        task_key = query_params.get("task", "overall")
+        if task_key not in valid_task_keys:
+            task_key = "overall"
+        return_tabs[0] = gr.Tabs(selected=task_key)
+
+        tabs_idx = valid_task_keys.index(task_key) + 1
+        language_key = query_params.get("language", "english")
+        return_tabs[tabs_idx] = gr.Tabs(selected=language_key)
+        current_task_language = {"task": task_key, "language": language_key}
+        language_per_task = {task_key: language_key}
+        return return_tabs + [current_task_language, language_per_task]
+
+    block.load(set_tabs_on_load, inputs=[], outputs=tabs + [current_task_language, language_per_task])
+
+    search_bar.submit(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
+    filter_model_type.change(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
+    filter_model_sizes.change(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
+
+block.queue(max_size=10)
+block.launch()
+
+# Possible changes:
+# Could add graphs / other visual content
+# Could add verification marks
+
+# Sources:
+# https://huggingface.co/spaces/gradio/leaderboard
+# https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
+# https://getemoji.com/

config.yaml

@@ -0,0 +1,389 @@
+config:
+  REPO_ID: "mteb/leaderboard"
+  RESULTS_REPO: mteb/results
+  LEADERBOARD_NAME: "MTEB Leaderboard"
+tasks:
+  BitextMining:
+    icon: "🎌"
+    metric: f1
+    metric_description: "[F1](https://huggingface.co/spaces/evaluate-metric/f1)"
+    task_description: "Bitext mining is the task of finding parallel sentences in two languages."
+  Classification:
+    icon: "❤️"
+    metric: accuracy
+    metric_description: "[Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)"
+    task_description: "Classification is the task of assigning a label to a text."
+  Clustering:
+    icon: "✨"
+    metric: v_measure
+    metric_description: "Validity Measure (v_measure)"
+    task_description: "Clustering is the task of grouping similar documents together."
+  PairClassification:
+    icon: "🎭"
+    metric: cos_sim_ap
+    metric_description: "Average Precision based on Cosine Similarities (cos_sim_ap)"
+    task_description: "Pair classification is the task of determining whether two texts are similar."
+  Reranking: 
+    icon: "🥈"
+    metric: map
+    metric_description: "Mean Average Precision (MAP)"
+    task_description: "Reranking is the task of reordering a list of documents to improve relevance."
+  Retrieval:
+    icon: "🔎"
+    metric: ndcg_at_10
+    metric_description: "Normalized Discounted Cumulative Gain @ k (ndcg_at_10)"
+    task_description: "Retrieval is the task of finding relevant documents for a query."
+  STS:
+    icon: "🤖"
+    metric: cos_sim_spearman
+    metric_description: "Spearman correlation based on cosine similarity"
+    task_description: "Semantic Textual Similarity is the task of determining how similar two texts are."
+  Summarization:
+    icon: "📜"
+    metric: cos_sim_spearman
+    metric_description: "Spearman correlation	based on cosine similarity"
+    task_description: "Summarization is the task of generating a summary of a text."
+  InstructionRetrieval:
+    icon: "🔎📋"
+    metric: "p-MRR"
+    metric_description: "paired mean reciprocal rank"
+    task_description: "Retrieval w/Instructions is the task of finding relevant documents for a query that has detailed instructions."
+boards:
+  en:
+    title: English
+    language_long: "English"
+    has_overall: true
+    acronym: null
+    icon: null
+    special_icons: null
+    credits: null
+    tasks:
+      Classification:
+        - AmazonCounterfactualClassification (en)
+        - AmazonPolarityClassification
+        - AmazonReviewsClassification (en)
+        - Banking77Classification
+        - EmotionClassification
+        - ImdbClassification
+        - MassiveIntentClassification (en)
+        - MassiveScenarioClassification (en)
+        - MTOPDomainClassification (en)
+        - MTOPIntentClassification (en)
+        - ToxicConversationsClassification
+        - TweetSentimentExtractionClassification
+      Clustering:
+        - ArxivClusteringP2P
+        - ArxivClusteringS2S
+        - BiorxivClusteringP2P
+        - BiorxivClusteringS2S
+        - MedrxivClusteringP2P
+        - MedrxivClusteringS2S
+        - RedditClustering
+        - RedditClusteringP2P
+        - StackExchangeClustering
+        - StackExchangeClusteringP2P
+        - TwentyNewsgroupsClustering
+      PairClassification:
+        - SprintDuplicateQuestions
+        - TwitterSemEval2015
+        - TwitterURLCorpus
+      Reranking:
+        - AskUbuntuDupQuestions
+        - MindSmallReranking
+        - SciDocsRR
+        - StackOverflowDupQuestions
+      Retrieval:
+        - ArguAna
+        - ClimateFEVER
+        - CQADupstackRetrieval
+        - DBPedia
+        - FEVER
+        - FiQA2018
+        - HotpotQA
+        - MSMARCO
+        - NFCorpus
+        - NQ
+        - QuoraRetrieval
+        - SCIDOCS
+        - SciFact
+        - Touche2020
+        - TRECCOVID
+      STS:
+        - BIOSSES
+        - SICK-R
+        - STS12
+        - STS13
+        - STS14
+        - STS15
+        - STS16
+        - STS17 (en-en)
+        - STS22 (en)
+        - STSBenchmark
+      Summarization:
+        - SummEval
+  en-x:
+    title: "English-X"
+    language_long: "117 (Pairs of: English & other language)"
+    has_overall: false
+    acronym: null
+    icon: null
+    special_icons: null
+    credits: null
+    tasks:
+      BitextMining: ['BUCC (de-en)', 'BUCC (fr-en)', 'BUCC (ru-en)', 'BUCC (zh-en)', 'Tatoeba (afr-eng)', 'Tatoeba (amh-eng)', 'Tatoeba (ang-eng)', 'Tatoeba (ara-eng)', 'Tatoeba (arq-eng)', 'Tatoeba (arz-eng)', 'Tatoeba (ast-eng)', 'Tatoeba (awa-eng)', 'Tatoeba (aze-eng)', 'Tatoeba (bel-eng)', 'Tatoeba (ben-eng)', 'Tatoeba (ber-eng)', 'Tatoeba (bos-eng)', 'Tatoeba (bre-eng)', 'Tatoeba (bul-eng)', 'Tatoeba (cat-eng)', 'Tatoeba (cbk-eng)', 'Tatoeba (ceb-eng)', 'Tatoeba (ces-eng)', 'Tatoeba (cha-eng)', 'Tatoeba (cmn-eng)', 'Tatoeba (cor-eng)', 'Tatoeba (csb-eng)', 'Tatoeba (cym-eng)', 'Tatoeba (dan-eng)', 'Tatoeba (deu-eng)', 'Tatoeba (dsb-eng)', 'Tatoeba (dtp-eng)', 'Tatoeba (ell-eng)', 'Tatoeba (epo-eng)', 'Tatoeba (est-eng)', 'Tatoeba (eus-eng)', 'Tatoeba (fao-eng)', 'Tatoeba (fin-eng)', 'Tatoeba (fra-eng)', 'Tatoeba (fry-eng)', 'Tatoeba (gla-eng)', 'Tatoeba (gle-eng)', 'Tatoeba (glg-eng)', 'Tatoeba (gsw-eng)', 'Tatoeba (heb-eng)', 'Tatoeba (hin-eng)', 'Tatoeba (hrv-eng)', 'Tatoeba (hsb-eng)', 'Tatoeba (hun-eng)', 'Tatoeba (hye-eng)', 'Tatoeba (ido-eng)', 'Tatoeba (ile-eng)', 'Tatoeba (ina-eng)', 'Tatoeba (ind-eng)', 'Tatoeba (isl-eng)', 'Tatoeba (ita-eng)', 'Tatoeba (jav-eng)', 'Tatoeba (jpn-eng)', 'Tatoeba (kab-eng)', 'Tatoeba (kat-eng)', 'Tatoeba (kaz-eng)', 'Tatoeba (khm-eng)', 'Tatoeba (kor-eng)', 'Tatoeba (kur-eng)', 'Tatoeba (kzj-eng)', 'Tatoeba (lat-eng)', 'Tatoeba (lfn-eng)', 'Tatoeba (lit-eng)', 'Tatoeba (lvs-eng)', 'Tatoeba (mal-eng)', 'Tatoeba (mar-eng)', 'Tatoeba (max-eng)', 'Tatoeba (mhr-eng)', 'Tatoeba (mkd-eng)', 'Tatoeba (mon-eng)', 'Tatoeba (nds-eng)', 'Tatoeba (nld-eng)', 'Tatoeba (nno-eng)', 'Tatoeba (nob-eng)', 'Tatoeba (nov-eng)', 'Tatoeba (oci-eng)', 'Tatoeba (orv-eng)', 'Tatoeba (pam-eng)', 'Tatoeba (pes-eng)', 'Tatoeba (pms-eng)', 'Tatoeba (pol-eng)', 'Tatoeba (por-eng)', 'Tatoeba (ron-eng)', 'Tatoeba (rus-eng)', 'Tatoeba (slk-eng)', 'Tatoeba (slv-eng)', 'Tatoeba (spa-eng)', 'Tatoeba (sqi-eng)', 'Tatoeba (srp-eng)', 'Tatoeba (swe-eng)', 'Tatoeba (swg-eng)', 'Tatoeba (swh-eng)', 'Tatoeba (tam-eng)', 'Tatoeba (tat-eng)', 'Tatoeba (tel-eng)', 'Tatoeba (tgl-eng)', 'Tatoeba (tha-eng)', 'Tatoeba (tuk-eng)', 'Tatoeba (tur-eng)', 'Tatoeba (tzl-eng)', 'Tatoeba (uig-eng)', 'Tatoeba (ukr-eng)', 'Tatoeba (urd-eng)', 'Tatoeba (uzb-eng)', 'Tatoeba (vie-eng)', 'Tatoeba (war-eng)', 'Tatoeba (wuu-eng)', 'Tatoeba (xho-eng)', 'Tatoeba (yid-eng)', 'Tatoeba (yue-eng)', 'Tatoeba (zsm-eng)']
+  zh:
+    title: Chinese
+    language_long: Chinese
+    has_overall: true
+    acronym: C-MTEB
+    icon: "🇨🇳"
+    special_icons:
+      Classification: "🧡"
+    credits: "[FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)"
+    tasks:
+      Classification:
+        - AmazonReviewsClassification (zh)
+        - IFlyTek
+        - JDReview
+        - MassiveIntentClassification (zh-CN)
+        - MassiveScenarioClassification (zh-CN)
+        - MultilingualSentiment
+        - OnlineShopping
+        - TNews
+        - Waimai
+      Clustering:
+        - CLSClusteringP2P
+        - CLSClusteringS2S
+        - ThuNewsClusteringP2P
+        - ThuNewsClusteringS2S
+      PairClassification:
+        - Cmnli
+        - Ocnli
+      Reranking:
+        - CMedQAv1
+        - CMedQAv2
+        - MMarcoReranking
+        - T2Reranking
+      Retrieval:
+        - CmedqaRetrieval
+        - CovidRetrieval
+        - DuRetrieval
+        - EcomRetrieval
+        - MedicalRetrieval
+        - MMarcoRetrieval
+        - T2Retrieval
+        - VideoRetrieval
+      STS:
+        - AFQMC
+        - ATEC
+        - BQ
+        - LCQMC
+        - PAWSX
+        - QBQTC
+        - STS22 (zh)
+        - STSB
+  da:
+    title: Danish
+    language_long: Danish
+    has_overall: false
+    acronym: null
+    icon: "🇩🇰"
+    special_icons:
+      Classification: "🤍"
+    credits: "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)"
+    tasks:
+      BitextMining:
+        - BornholmBitextMining
+      Classification:
+        - AngryTweetsClassification
+        - DanishPoliticalCommentsClassification
+        - DKHateClassification
+        - LccSentimentClassification
+        - MassiveIntentClassification (da)
+        - MassiveScenarioClassification (da)
+        - NordicLangClassification
+        - ScalaDaClassification
+  fr:
+    title: French
+    language_long: "French"
+    has_overall: true
+    acronym: "F-MTEB"
+    icon: "🇫🇷"
+    special_icons:
+      Classification: "💙"
+    credits: "[Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [Wissam Siblini](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)"
+    tasks:
+      Classification:
+        - AmazonReviewsClassification (fr)
+        - MasakhaNEWSClassification (fra)
+        - MassiveIntentClassification (fr)
+        - MassiveScenarioClassification (fr)
+        - MTOPDomainClassification (fr)
+        - MTOPIntentClassification (fr)
+      Clustering:
+        - AlloProfClusteringP2P
+        - AlloProfClusteringS2S
+        - HALClusteringS2S
+        - MLSUMClusteringP2P
+        - MLSUMClusteringS2S
+        - MasakhaNEWSClusteringP2P (fra)
+        - MasakhaNEWSClusteringS2S (fra)
+      PairClassification:
+        - OpusparcusPC (fr)
+        - PawsX (fr)
+      Reranking:
+        - AlloprofReranking
+        - SyntecReranking
+      Retrieval:
+        - AlloprofRetrieval
+        - BSARDRetrieval
+        - MintakaRetrieval (fr)
+        - SyntecRetrieval
+        - XPQARetrieval (fr)
+      STS:
+        - STS22 (fr)
+        - STSBenchmarkMultilingualSTS (fr)
+        - SICKFr
+      Summarization:
+        - SummEvalFr
+  'no':
+    title: Norwegian
+    language_long: "Norwegian Bokmål"
+    has_overall: false
+    acronym: null
+    icon: "🇳🇴"
+    special_icons:
+      Classification: "💙"
+    credits: "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)"
+    tasks:
+      Classification: &id001
+        - NoRecClassification
+        - NordicLangClassification
+        - NorwegianParliament
+        - MassiveIntentClassification (nb)
+        - MassiveScenarioClassification (nb)
+        - ScalaNbClassification
+  instructions:
+    title: English
+    language_long: "English"
+    has_overall: false
+    acronym: null
+    icon: null
+    credits: "[Orion Weller, FollowIR](https://arxiv.org/abs/2403.15246)"
+    tasks:
+      InstructionRetrieval:
+      - Robust04InstructionRetrieval
+      - News21InstructionRetrieval
+      - Core17InstructionRetrieval
+  law:
+    title: Law
+    language_long: "English, German, Chinese"
+    has_overall: false
+    acronym: null
+    icon: "⚖️"
+    special_icons: null
+    credits: "[Voyage AI](https://www.voyageai.com/)"
+    tasks:
+      Retrieval:
+        - AILACasedocs
+        - AILAStatutes
+        - GerDaLIRSmall
+        - LeCaRDv2
+        - LegalBenchConsumerContractsQA
+        - LegalBenchCorporateLobbying
+        - LegalQuAD
+        - LegalSummarization
+  de:
+    title: German
+    language_long: "German"
+    has_overall: false
+    acronym: null
+    icon: "🇩🇪"
+    special_icons: null
+    credits: "[Silvan](https://github.com/slvnwhrl)"
+    tasks:
+      Clustering:
+      - BlurbsClusteringP2P
+      - BlurbsClusteringS2S
+      - TenKGnadClusteringP2P
+      - TenKGnadClusteringS2S
+  pl:
+    title: Polish
+    language_long: Polish
+    has_overall: true
+    acronym: null
+    icon: "🇵🇱"
+    special_icons:
+      Classification: "🤍"
+    credits: "[Rafał Poświata](https://github.com/rafalposwiata)"
+    tasks:
+      Classification:
+        - AllegroReviews
+        - CBD
+        - MassiveIntentClassification (pl)
+        - MassiveScenarioClassification (pl)
+        - PAC
+        - PolEmo2.0-IN
+        - PolEmo2.0-OUT
+      Clustering:
+        - 8TagsClustering
+      PairClassification:
+        - CDSC-E
+        - PPC
+        - PSC
+        - SICK-E-PL
+      Retrieval:
+        - ArguAna-PL
+        - DBPedia-PL
+        - FiQA-PL
+        - HotpotQA-PL
+        - MSMARCO-PL
+        - NFCorpus-PL
+        - NQ-PL
+        - Quora-PL
+        - SCIDOCS-PL
+        - SciFact-PL
+        - TRECCOVID-PL
+      STS:
+        - CDSC-R
+        - SICK-R-PL
+        - STS22 (pl)
+  se:
+    title: Swedish
+    language_long: Swedish
+    has_overall: false
+    acronym: null
+    icon: "🇸🇪"
+    special_icons:
+      Classification: "💛"
+    credits: "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)"
+    tasks:
+      Classification:
+        - NoRecClassification
+        - NordicLangClassification
+        - NorwegianParliament
+        - MassiveIntentClassification (nb)
+        - MassiveScenarioClassification (nb)
+        - ScalaNbClassification
+  other-cls:
+    title: "Other Languages"
+    language_long: "47 (Only languages not included in the other tabs)"
+    has_overall: false
+    acronym: null
+    icon: null
+    special_icons:
+      Classification: "💜💚💙"
+    credits: null
+    tasks:
+      Classification: ['AmazonCounterfactualClassification (de)', 'AmazonCounterfactualClassification (ja)', 'AmazonReviewsClassification (de)', 'AmazonReviewsClassification (es)', 'AmazonReviewsClassification (fr)', 'AmazonReviewsClassification (ja)', 'AmazonReviewsClassification (zh)', 'MTOPDomainClassification (de)', 'MTOPDomainClassification (es)', 'MTOPDomainClassification (fr)', 'MTOPDomainClassification (hi)', 'MTOPDomainClassification (th)', 'MTOPIntentClassification (de)', 'MTOPIntentClassification (es)', 'MTOPIntentClassification (fr)', 'MTOPIntentClassification (hi)', 'MTOPIntentClassification (th)', 'MassiveIntentClassification (af)', 'MassiveIntentClassification (am)', 'MassiveIntentClassification (ar)', 'MassiveIntentClassification (az)', 'MassiveIntentClassification (bn)', 'MassiveIntentClassification (cy)', 'MassiveIntentClassification (de)', 'MassiveIntentClassification (el)', 'MassiveIntentClassification (es)', 'MassiveIntentClassification (fa)', 'MassiveIntentClassification (fi)', 'MassiveIntentClassification (fr)', 'MassiveIntentClassification (he)', 'MassiveIntentClassification (hi)', 'MassiveIntentClassification (hu)', 'MassiveIntentClassification (hy)', 'MassiveIntentClassification (id)', 'MassiveIntentClassification (is)', 'MassiveIntentClassification (it)', 'MassiveIntentClassification (ja)', 'MassiveIntentClassification (jv)', 'MassiveIntentClassification (ka)', 'MassiveIntentClassification (km)', 'MassiveIntentClassification (kn)', 'MassiveIntentClassification (ko)', 'MassiveIntentClassification (lv)', 'MassiveIntentClassification (ml)', 'MassiveIntentClassification (mn)', 'MassiveIntentClassification (ms)', 'MassiveIntentClassification (my)', 'MassiveIntentClassification (nl)', 'MassiveIntentClassification (pt)', 'MassiveIntentClassification (ro)', 'MassiveIntentClassification (ru)', 'MassiveIntentClassification (sl)', 'MassiveIntentClassification (sq)', 'MassiveIntentClassification (sw)', 'MassiveIntentClassification (ta)', 'MassiveIntentClassification (te)', 'MassiveIntentClassification (th)', 'MassiveIntentClassification (tl)', 'MassiveIntentClassification (tr)', 'MassiveIntentClassification (ur)', 'MassiveIntentClassification (vi)', 'MassiveIntentClassification (zh-TW)', 'MassiveScenarioClassification (af)', 'MassiveScenarioClassification (am)', 'MassiveScenarioClassification (ar)', 'MassiveScenarioClassification (az)', 'MassiveScenarioClassification (bn)', 'MassiveScenarioClassification (cy)', 'MassiveScenarioClassification (de)', 'MassiveScenarioClassification (el)', 'MassiveScenarioClassification (es)', 'MassiveScenarioClassification (fa)', 'MassiveScenarioClassification (fi)', 'MassiveScenarioClassification (fr)', 'MassiveScenarioClassification (he)', 'MassiveScenarioClassification (hi)', 'MassiveScenarioClassification (hu)', 'MassiveScenarioClassification (hy)', 'MassiveScenarioClassification (id)', 'MassiveScenarioClassification (is)', 'MassiveScenarioClassification (it)', 'MassiveScenarioClassification (ja)', 'MassiveScenarioClassification (jv)', 'MassiveScenarioClassification (ka)', 'MassiveScenarioClassification (km)', 'MassiveScenarioClassification (kn)', 'MassiveScenarioClassification (ko)', 'MassiveScenarioClassification (lv)', 'MassiveScenarioClassification (ml)', 'MassiveScenarioClassification (mn)', 'MassiveScenarioClassification (ms)', 'MassiveScenarioClassification (my)', 'MassiveScenarioClassification (nl)', 'MassiveScenarioClassification (pt)', 'MassiveScenarioClassification (ro)', 'MassiveScenarioClassification (ru)', 'MassiveScenarioClassification (sl)', 'MassiveScenarioClassification (sq)', 'MassiveScenarioClassification (sw)', 'MassiveScenarioClassification (ta)', 'MassiveScenarioClassification (te)', 'MassiveScenarioClassification (th)', 'MassiveScenarioClassification (tl)', 'MassiveScenarioClassification (tr)', 'MassiveScenarioClassification (ur)', 'MassiveScenarioClassification (vi)', 'MassiveScenarioClassification (zh-TW)']
+  other-sts:
+    title: Other
+    language_long: "Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Russian, Spanish (Only language combos not included in the other tabs)"
+    has_overall: false
+    acronym: null
+    icon: null
+    special_icons:
+      STS: "👽"
+    credits: null
+    tasks:
+      STS: ["STS17 (ar-ar)", "STS17 (en-ar)", "STS17 (en-de)", "STS17 (en-tr)", "STS17 (es-en)", "STS17 (es-es)", "STS17 (fr-en)", "STS17 (it-en)", "STS17 (ko-ko)", "STS17 (nl-en)", "STS22 (ar)", "STS22 (de)", "STS22 (de-en)", "STS22 (de-fr)", "STS22 (de-pl)", "STS22 (es)", "STS22 (es-en)", "STS22 (es-it)", "STS22 (fr)", "STS22 (fr-pl)", "STS22 (it)", "STS22 (pl)", "STS22 (pl-en)", "STS22 (ru)", "STS22 (tr)", "STS22 (zh-en)", "STSBenchmark"]

envs.py

@@ -0,0 +1,48 @@
+import os
+from yaml import safe_load
+
+from huggingface_hub import HfApi
+
+LEADERBOARD_CONFIG_PATH = "config.yaml"
+with open(LEADERBOARD_CONFIG_PATH, 'r', encoding='utf-8') as f:
+    LEADERBOARD_CONFIG = safe_load(f)
+MODEL_META_PATH = "model_meta.yaml"
+with open(MODEL_META_PATH, 'r', encoding='utf-8') as f:
+    MODEL_META = safe_load(f)
+
+# Try first to get the config from the environment variables, then from the config.yaml file
+def get_config(name, default):
+    res = None
+
+    if name in os.environ:
+        res = os.environ[name]
+    elif 'config' in LEADERBOARD_CONFIG:
+        res = LEADERBOARD_CONFIG['config'].get(name, None)
+
+    if res is None:
+        return default
+    return res
+
+def str2bool(v):
+  return str(v).lower() in ("yes", "true", "t", "1")
+
+# clone / pull the lmeh eval data
+HF_TOKEN = get_config("HF_TOKEN", None)
+
+LEADERBOARD_NAME = get_config("LEADERBOARD_NAME", "MTEB Leaderboard")
+
+REPO_ID = get_config("REPO_ID", "mteb/leaderboard")
+RESULTS_REPO = get_config("RESULTS_REPO", "mteb/results")
+
+CACHE_PATH=get_config("HF_HOME", ".")
+os.environ["HF_HOME"] = CACHE_PATH
+
+# Check if it is using persistent storage
+if not os.access(CACHE_PATH, os.W_OK):
+    print(f"No write access to HF_HOME: {CACHE_PATH}. Resetting to current directory.")
+    CACHE_PATH = "."
+    os.environ["HF_HOME"] = CACHE_PATH
+else:
+    print(f"Write access confirmed for HF_HOME")
+
+API = HfApi(token=HF_TOKEN)

model_meta.yaml

@@ -0,0 +1,1327 @@
+model_meta:
+  gte-Qwen1.5-7B-instruct:
+    link: https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct
+    seq_len: 32768
+    size: 7099
+    dim: 4096
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  Baichuan-text-embedding:
+    link: https://platform.baichuan-ai.com/docs/text-Embedding
+    seq_len: 512
+    size: null
+    dim: 1024
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  Cohere-embed-english-v3.0:
+    link: https://huggingface.co/Cohere/Cohere-embed-english-v3.0
+    seq_len: 512
+    size: null
+    dim: 1024
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  Cohere-embed-multilingual-light-v3.0:
+    link: https://huggingface.co/Cohere/Cohere-embed-multilingual-light-v3.0
+    seq_len: 512
+    size: null
+    dim: 384
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  Cohere-embed-multilingual-v3.0:
+    link: https://huggingface.co/Cohere/Cohere-embed-multilingual-v3.0
+    seq_len: 512
+    size: null
+    dim: 1024
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  DanskBERT:
+    link: https://huggingface.co/vesteinn/DanskBERT
+    seq_len: 514
+    size: 125
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  FollowIR-7B:
+    link: https://huggingface.co/jhu-clsp/FollowIR-7B
+    seq_len: 4096
+    size: 7240
+    is_external: true
+    is_propietary: false
+    is_sentence_transformer_compatible: false
+  GritLM-7B:
+    link: https://huggingface.co/GritLM/GritLM-7B
+    seq_len: 4096
+    size: 7240
+    is_external: true
+    is_propietary: false
+    is_sentence_transformer_compatible: false
+  LASER2:
+    link: https://github.com/facebookresearch/LASER
+    seq_len: N/A
+    size: 43
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  LLM2Vec-Llama-2-supervised:
+    link: https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised
+    seq_len: 4096
+    size: 6607
+    dim: 4096
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  LLM2Vec-Llama-2-unsupervised:
+    link: https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse
+    seq_len: 4096
+    size: 6607
+    dim: 4096
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  LLM2Vec-Meta-Llama-3-supervised:
+    link: https://huggingface.co/McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised
+    seq_len: 8192
+    size: 7505
+    dim: 4096
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  LLM2Vec-Meta-Llama-3-unsupervised:
+    link: https://huggingface.co/McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse
+    seq_len: 8192
+    size: 7505
+    dim: 4096
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  LLM2Vec-Mistral-supervised:
+    link: https://huggingface.co/McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised
+    seq_len: 32768
+    size: 7111
+    dim: 4096
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  LLM2Vec-Mistral-unsupervised:
+    link: https://huggingface.co/McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-unsup-simcse
+    seq_len: 32768
+    size: 7111
+    dim: 4096
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  LLM2Vec-Sheared-Llama-supervised:
+    link: https://huggingface.co/McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised
+    seq_len: 4096
+    size: 1280
+    dim: 2048
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  LLM2Vec-Sheared-Llama-unsupervised:
+    link: https://huggingface.co/McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse
+    seq_len: 4096
+    size: 1280
+    dim: 2048
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  LaBSE:
+    link: https://huggingface.co/sentence-transformers/LaBSE
+    seq_len: 512
+    size: 471
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  OpenSearch-text-hybrid:
+    link: https://help.aliyun.com/zh/open-search/vector-search-edition/hybrid-retrieval
+    seq_len: 512
+    size: null
+    dim: 1792
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  all-MiniLM-L12-v2:
+    link: https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2
+    seq_len: 512
+    size: 33
+    dim: 384
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  all-MiniLM-L6-v2:
+    link: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
+    seq_len: 512
+    size: 23
+    dim: 384
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  all-mpnet-base-v2:
+    link: https://huggingface.co/sentence-transformers/all-mpnet-base-v2
+    seq_len: 514
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  allenai-specter:
+    link: https://huggingface.co/sentence-transformers/allenai-specter
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  bert-base-10lang-cased:
+    link: https://huggingface.co/Geotrend/bert-base-10lang-cased
+    seq_len: 512
+    size: 138
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  bert-base-15lang-cased:
+    link: https://huggingface.co/Geotrend/bert-base-15lang-cased
+    seq_len: 512
+    size: 138
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  bert-base-25lang-cased:
+    link: https://huggingface.co/Geotrend/bert-base-25lang-cased
+    seq_len: 512
+    size: 138
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  bert-base-multilingual-cased:
+    link: https://huggingface.co/google-bert/bert-base-multilingual-cased
+    seq_len: 512
+    size: 179
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  bert-base-multilingual-uncased:
+    link: https://huggingface.co/google-bert/bert-base-multilingual-uncased
+    seq_len: 512
+    size: 168
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  bert-base-swedish-cased:
+    link: https://huggingface.co/KB/bert-base-swedish-cased
+    seq_len: 512
+    size: 125
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  bert-base-uncased:
+    link: https://huggingface.co/bert-base-uncased
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  bge-base-zh-v1.5:
+    link: https://huggingface.co/BAAI/bge-base-zh-v1.5
+    seq_len: 512
+    size: 102
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  bge-large-en-v1.5:
+    link: https://huggingface.co/BAAI/bge-large-en-v1.5
+    seq_len: 512
+    size: null
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  bge-large-zh-noinstruct:
+    link: https://huggingface.co/BAAI/bge-large-zh-noinstruct
+    seq_len: 512
+    size: 326
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  bge-large-zh-v1.5:
+    link: https://huggingface.co/BAAI/bge-large-zh-v1.5
+    seq_len: 512
+    size: 326
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  bge-small-zh-v1.5:
+    link: https://huggingface.co/BAAI/bge-small-zh-v1.5
+    seq_len: 512
+    size: 24
+    dim: 512
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  bm25: 
+    link: https://en.wikipedia.org/wiki/Okapi_BM25
+    size: 0
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  camembert-base:
+    link: https://huggingface.co/almanach/camembert-base
+    seq_len: 512
+    size: 111
+    dim: 512
+    is_external: false
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  camembert-large:
+    link: https://huggingface.co/almanach/camembert-large
+    seq_len: 512
+    size: 338
+    dim: 768
+    is_external: false
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  contriever-base-msmarco:
+    link: https://huggingface.co/nthakur/contriever-base-msmarco
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  cross-en-de-roberta-sentence-transformer:
+    link: https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer
+    seq_len: 514
+    size: 278
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  dfm-encoder-large-v1:
+    link: https://huggingface.co/chcaa/dfm-encoder-large-v1
+    seq_len: 512
+    size: 355
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  dfm-sentence-encoder-large-1:
+    link: https://huggingface.co/chcaa/dfm-encoder-large-v1
+    seq_len: 512
+    size: 355
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  distilbert-base-25lang-cased:
+    link: https://huggingface.co/Geotrend/distilbert-base-25lang-cased
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: false
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  distilbert-base-en-fr-cased:
+    link: https://huggingface.co/Geotrend/distilbert-base-en-fr-cased
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: false
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  distilbert-base-en-fr-es-pt-it-cased:
+    link: https://huggingface.co/Geotrend/distilbert-base-en-fr-es-pt-it-cased
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: false
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  distilbert-base-fr-cased:
+    link: https://huggingface.co/Geotrend/distilbert-base-fr-cased
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: false
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  distilbert-base-uncased:
+    link: https://huggingface.co/distilbert-base-uncased
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: false
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  distiluse-base-multilingual-cased-v2:
+    link: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2
+    seq_len: 512
+    size: 135
+    dim: 512
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  e5-base-v2:
+    link: https://huggingface.co/intfloat/e5-base-v2
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  e5-base:
+    link: https://huggingface.co/intfloat/e5-base
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  e5-large-v2:
+    link: https://huggingface.co/intfloat/e5-large-v2
+    seq_len: 512
+    size: 335
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  e5-large:
+    link: https://huggingface.co/intfloat/e5-large
+    seq_len: 512
+    size: 335
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  e5-mistral-7b-instruct:
+    link: https://huggingface.co/intfloat/e5-mistral-7b-instruct
+    seq_len: 32768
+    size: 7111
+    dim: 4096
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  e5-small:
+    link: https://huggingface.co/intfloat/e5-small
+    seq_len: 512
+    size: 33
+    dim: 384
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  electra-small-nordic:
+    link: https://huggingface.co/jonfd/electra-small-nordic
+    seq_len: 512
+    size: 23
+    dim: 256
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  electra-small-swedish-cased-discriminator:
+    link: https://huggingface.co/KBLab/electra-small-swedish-cased-discriminator
+    seq_len: 512
+    size: 16
+    dim: 256
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  flan-t5-base:
+    link: https://huggingface.co/google/flan-t5-base
+    seq_len: 512
+    size: 220
+    dim: -1
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  flan-t5-large:
+    link: https://huggingface.co/google/flan-t5-large
+    seq_len: 512
+    size: 770
+    dim: -1
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  flaubert_base_cased:
+    link: https://huggingface.co/flaubert/flaubert_base_cased
+    seq_len: 512
+    size: 138
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  flaubert_base_uncased:
+    link: https://huggingface.co/flaubert/flaubert_base_uncased
+    seq_len: 512
+    size: 138
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  flaubert_large_cased:
+    link: https://huggingface.co/flaubert/flaubert_large_cased
+    seq_len: 512
+    size: 372
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  gbert-base:
+    link: https://huggingface.co/deepset/gbert-base
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  gbert-large:
+    link: https://huggingface.co/deepset/gbert-large
+    seq_len: 512
+    size: 337
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  gelectra-base:
+    link: https://huggingface.co/deepset/gelectra-base
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  gelectra-large:
+    link: https://huggingface.co/deepset/gelectra-large
+    seq_len: 512
+    size: 335
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  glove.6B.300d:
+    link: https://huggingface.co/sentence-transformers/average_word_embeddings_glove.6B.300d
+    seq_len: N/A
+    size: 120
+    dim: 300
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  google-gecko-256.text-embedding-preview-0409:
+    link: https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#latest_models
+    seq_len: 2048
+    size: 1200
+    dim: 256
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  google-gecko.text-embedding-preview-0409:
+    link: https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#latest_models
+    seq_len: 2048
+    size: 1200
+    dim: 768
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  gottbert-base:
+    link: https://huggingface.co/uklfr/gottbert-base
+    seq_len: 512
+    size: 127
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  gtr-t5-base:
+    link: https://huggingface.co/sentence-transformers/gtr-t5-base
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  gtr-t5-large:
+    link: https://huggingface.co/sentence-transformers/gtr-t5-large
+    seq_len: 512
+    size: 168
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  gtr-t5-xl:
+    link: https://huggingface.co/sentence-transformers/gtr-t5-xl
+    seq_len: 512
+    size: 1240
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  gtr-t5-xxl:
+    link: https://huggingface.co/sentence-transformers/gtr-t5-xxl
+    seq_len: 512
+    size: 4865
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  herbert-base-retrieval-v2:
+    link: https://huggingface.co/ipipan/herbert-base-retrieval-v2
+    seq_len: 514
+    size: 125
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  instructor-base:
+    link: https://huggingface.co/hkunlp/instructor-base
+    seq_len: N/A
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  instructor-xl:
+    link: https://huggingface.co/hkunlp/instructor-xl
+    seq_len: N/A
+    size: 1241
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  komninos:
+    link: https://huggingface.co/sentence-transformers/average_word_embeddings_komninos
+    seq_len: N/A
+    size: 134
+    dim: 300
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  llama-2-7b-chat:
+    link: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
+    seq_len: 4096
+    size: 7000
+    dim: -1
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  luotuo-bert-medium:
+    link: https://huggingface.co/silk-road/luotuo-bert-medium
+    seq_len: 512
+    size: 328
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  m3e-base:
+    link: https://huggingface.co/moka-ai/m3e-base
+    seq_len: 512
+    size: 102
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  m3e-large:
+    link: https://huggingface.co/moka-ai/m3e-large
+    seq_len: 512
+    size: 102
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  mistral-7b-instruct-v0.2:
+    link: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
+    seq_len: 4096
+    size: 7240
+    dim: -1
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  mistral-embed:
+    link: https://docs.mistral.ai/guides/embeddings
+    seq_len: null
+    size: null
+    dim: 1024
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  monobert-large-msmarco:
+    link: https://huggingface.co/castorini/monobert-large-msmarco
+    seq_len: 512
+    size: 770
+    dim: -1
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  monot5-3b-msmarco-10k:
+    link: https://huggingface.co/castorini/monot5-3b-msmarco-10k
+    seq_len: 512
+    size: 2480
+    dim: -1
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  monot5-base-msmarco-10k:
+    link: https://huggingface.co/castorini/monot5-base-msmarco-10k
+    seq_len: 512
+    size: 220
+    dim: -1
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  msmarco-bert-co-condensor:
+    link: https://huggingface.co/sentence-transformers/msmarco-bert-co-condensor
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  multi-qa-MiniLM-L6-cos-v1:
+    link: https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1
+    seq_len: 512
+    size: 23
+    dim: 384
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  multilingual-e5-base:
+    link: https://huggingface.co/intfloat/multilingual-e5-base
+    seq_len: 514
+    size: 278
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  multilingual-e5-large:
+    link: https://huggingface.co/intfloat/multilingual-e5-large
+    seq_len: 514
+    size: 560
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  multilingual-e5-small:
+    link: https://huggingface.co/intfloat/multilingual-e5-small
+    seq_len: 512
+    size: 118
+    dim: 384
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  nb-bert-base:
+    link: https://huggingface.co/NbAiLab/nb-bert-base
+    seq_len: 512
+    size: 179
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  nb-bert-large:
+    link: https://huggingface.co/NbAiLab/nb-bert-large
+    seq_len: 512
+    size: 355
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  nomic-embed-text-v1.5-128:
+    link: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
+    seq_len: 8192
+    size: 138
+    dim: 128
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  nomic-embed-text-v1.5-256:
+    link: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
+    seq_len: 8192
+    size: 138
+    dim: 256
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  nomic-embed-text-v1.5-512:
+    link: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
+    seq_len: 8192
+    size: 138
+    dim: 512
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  nomic-embed-text-v1.5-64:
+    link: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
+    seq_len: 8192
+    size: 138
+    dim: 64
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  norbert3-base:
+    link: https://huggingface.co/ltg/norbert3-base
+    seq_len: 512
+    size: 131
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  norbert3-large:
+    link: https://huggingface.co/ltg/norbert3-large
+    seq_len: 512
+    size: 368
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  paraphrase-multilingual-MiniLM-L12-v2:
+    link: https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+    seq_len: 512
+    size: 118
+    dim: 384
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  paraphrase-multilingual-mpnet-base-v2:
+    link: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+    seq_len: 514
+    size: 278
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  sentence-bert-swedish-cased:
+    link: https://huggingface.co/KBLab/sentence-bert-swedish-cased
+    seq_len: 512
+    size: 125
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  sentence-camembert-base:
+    link: https://huggingface.co/dangvantuan/sentence-camembert-base
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  sentence-camembert-large:
+    link: https://huggingface.co/dangvantuan/sentence-camembert-large
+    seq_len: 512
+    size: 337
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  sentence-croissant-llm-base:
+    link: https://huggingface.co/Wissam42/sentence-croissant-llm-base
+    seq_len: 2048
+    size: 1280
+    dim: 2048
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  sentence-t5-base:
+    link: https://huggingface.co/sentence-transformers/sentence-t5-base
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  sentence-t5-large:
+    link: https://huggingface.co/sentence-transformers/sentence-t5-large
+    seq_len: 512
+    size: 168
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  sentence-t5-xl:
+    link: https://huggingface.co/sentence-transformers/sentence-t5-xl
+    seq_len: 512
+    size: 1240
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  sentence-t5-xxl:
+    link: https://huggingface.co/sentence-transformers/sentence-t5-xxl
+    seq_len: 512
+    size: 4865
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  silver-retriever-base-v1:
+    link: https://huggingface.co/ipipan/silver-retriever-base-v1
+    seq_len: 514
+    size: 125
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  st-polish-paraphrase-from-distilroberta:
+    link: https://huggingface.co/sdadas/st-polish-paraphrase-from-distilroberta
+    seq_len: 514
+    size: 125
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  st-polish-paraphrase-from-mpnet:
+    link: https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet
+    seq_len: 514
+    size: 125
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  sup-simcse-bert-base-uncased:
+    link: https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  text-embedding-3-large:
+    link: https://openai.com/blog/new-embedding-models-and-api-updates
+    seq_len: 8191
+    size: null
+    dim: 3072
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  text-embedding-3-large-256:
+    link: https://openai.com/blog/new-embedding-models-and-api-updates
+    seq_len: 8191
+    size: null
+    dim: 256
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  text-embedding-3-small:
+    link: https://openai.com/blog/new-embedding-models-and-api-updates
+    seq_len: 8191
+    size: null
+    dim: 1536
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  text-embedding-ada-002:
+    link: https://openai.com/blog/new-and-improved-embedding-model
+    seq_len: 8191
+    size: null
+    dim: 1536
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  text-search-ada-001:
+    link: https://openai.com/blog/introducing-text-and-code-embeddings
+    seq_len: 2046
+    size: null
+    dim: 1024
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  text-search-ada-doc-001:
+    link: https://openai.com/blog/introducing-text-and-code-embeddings
+    seq_len: 2046
+    size: null
+    dim: 1024
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  text-search-ada-query-001:
+    link: https://openai.com/blog/introducing-text-and-code-embeddings
+    seq_len: 2046
+    size: null
+    dim: 1024
+    is_external: false
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  text-search-babbage-001:
+    link: https://openai.com/blog/introducing-text-and-code-embeddings
+    seq_len: 2046
+    size: null
+    dim: 2048
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  text-search-curie-001:
+    link: https://openai.com/blog/introducing-text-and-code-embeddings
+    seq_len: 2046
+    size: null
+    dim: 4096
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  text-search-davinci-001:
+    link: https://openai.com/blog/introducing-text-and-code-embeddings
+    seq_len: 2046
+    size: null
+    dim: 12288
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  text-similarity-ada-001:
+    link: https://openai.com/blog/introducing-text-and-code-embeddings
+    seq_len: 2046
+    size: null
+    dim: 1024
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  text-similarity-babbage-001:
+    link: https://openai.com/blog/introducing-text-and-code-embeddings
+    seq_len: 2046
+    size: null
+    dim: 2048
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  text-similarity-curie-001:
+    link: https://openai.com/blog/introducing-text-and-code-embeddings
+    seq_len: 2046
+    size: null
+    dim: 4096
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  text-similarity-davinci-001:
+    link: https://openai.com/blog/introducing-text-and-code-embeddings
+    seq_len: 2046
+    size: null
+    dim: 12288
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  tart-dual-contriever-msmarco:
+    link: https://huggingface.co/orionweller/tart-dual-contriever-msmarco
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  tart-full-flan-t5-xl:
+    link: https://huggingface.co/facebook/tart-full-flan-t5-xl
+    seq_len: 512
+    size: 2480
+    dim: -1
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  text2vec-base-chinese:
+    link: https://huggingface.co/shibing624/text2vec-base-chinese
+    seq_len: 512
+    size: 102
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  text2vec-base-multilingual:
+    link: null
+    seq_len: null
+    size: null
+    dim: null
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  text2vec-large-chinese:
+    link: https://huggingface.co/GanymedeNil/text2vec-large-chinese
+    seq_len: 512
+    size: 326
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  titan-embed-text-v1:
+    link: https://docs.aws.amazon.com/bedrock/latest/userguide/embeddings.html
+    seq_len: 8000
+    size: null
+    dim: 1536
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  udever-bloom-1b1:
+    link: https://huggingface.co/izhx/udever-bloom-1b1
+    seq_len: 2048
+    size: null
+    dim: 1536
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  udever-bloom-560m:
+    link: https://huggingface.co/izhx/udever-bloom-560m
+    seq_len: 2048
+    size: null
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  universal-sentence-encoder-multilingual-3:
+    link: https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-3
+    seq_len: 512
+    size: null
+    dim: 512
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  universal-sentence-encoder-multilingual-large-3:
+    link: https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-large-3
+    seq_len: 512
+    size: null
+    dim: 512
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  unsup-simcse-bert-base-uncased:
+    link: https://huggingface.co/princeton-nlp/unsup-simcse-bert-base-uncased
+    seq_len: 512
+    size: 110
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  use-cmlm-multilingual:
+    link: https://huggingface.co/sentence-transformers/use-cmlm-multilingual
+    seq_len: 512
+    size: 472
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  voyage-2:
+    link: https://docs.voyageai.com/embeddings/
+    seq_len: 1024
+    size: null
+    dim: 1024
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  voyage-code-2:
+    link: https://docs.voyageai.com/embeddings/
+    seq_len: 16000
+    size: null
+    dim: 1536
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  voyage-large-2-instruct:
+    link: https://docs.voyageai.com/embeddings/
+    seq_len: 16000
+    size: null
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  voyage-law-2:
+    link: https://docs.voyageai.com/embeddings/
+    seq_len: 4000
+    size: null
+    dim: 1024
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  voyage-lite-01-instruct:
+    link: https://docs.voyageai.com/embeddings/
+    seq_len: 4000
+    size: null
+    dim: 1024
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  voyage-lite-02-instruct:
+    link: https://docs.voyageai.com/embeddings/
+    seq_len: 4000
+    size: 1220
+    dim: 1024
+    is_external: true
+    is_proprietary: true
+    is_sentence_transformers_compatible: false
+  xlm-roberta-base:
+    link: https://huggingface.co/xlm-roberta-base
+    seq_len: 514
+    size: 279
+    dim: 768
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+  xlm-roberta-large:
+    link: https://huggingface.co/xlm-roberta-large
+    seq_len: 514
+    size: 560
+    dim: 1024
+    is_external: true
+    is_proprietary: false
+    is_sentence_transformers_compatible: true
+models_to_skip:
+- michaelfeil/ct2fast-e5-large-v2
+- McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse
+- newsrx/instructor-xl
+- sionic-ai/sionic-ai-v1
+- lsf1000/bge-evaluation
+- Intel/bge-small-en-v1.5-sst2
+- newsrx/instructor-xl-newsrx
+- McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-unsup-simcse
+- McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse
+- davidpeer/gte-small
+- goldenrooster/multilingual-e5-large
+- kozistr/fused-large-en
+- mixamrepijey/instructor-small
+- McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised
+- DecisionOptimizationSystem/DeepFeatEmbeddingLargeContext
+- Intel/bge-base-en-v1.5-sst2-int8-dynamic
+- morgendigital/multilingual-e5-large-quantized
+- BAAI/bge-small-en
+- ggrn/e5-small-v2
+- vectoriseai/gte-small
+- giulio98/placeholder
+- odunola/UAE-Large-VI
+- vectoriseai/e5-large-v2
+- gruber/e5-small-v2-ggml
+- Severian/nomic
+- arcdev/e5-mistral-7b-instruct
+- mlx-community/multilingual-e5-base-mlx
+- michaelfeil/ct2fast-bge-base-en-v1.5
+- Intel/bge-small-en-v1.5-sst2-int8-static
+- jncraton/stella-base-en-v2-ct2-int8
+- vectoriseai/multilingual-e5-large
+- rlsChapters/Chapters-SFR-Embedding-Mistral
+- arcdev/SFR-Embedding-Mistral
+- McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised
+- McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised
+- vectoriseai/gte-base
+- mixamrepijey/instructor-models
+- GovCompete/e5-large-v2
+- ef-zulla/e5-multi-sml-torch
+- khoa-klaytn/bge-small-en-v1.5-angle
+- krilecy/e5-mistral-7b-instruct
+- vectoriseai/bge-base-en-v1.5
+- vectoriseai/instructor-base
+- jingyeom/korean_embedding_model
+- rizki/bgr-tf
+- barisaydin/bge-base-en
+- jamesgpt1/zzz
+- Malmuk1/e5-large-v2_Sharded
+- vectoriseai/ember-v1
+- Consensus/instructor-base
+- barisaydin/bge-small-en
+- barisaydin/gte-base
+- woody72/multilingual-e5-base
+- Einas/einas_ashkar
+- michaelfeil/ct2fast-bge-large-en-v1.5
+- vectoriseai/bge-small-en-v1.5
+- iampanda/Test
+- cherubhao/yogamodel
+- ieasybooks/multilingual-e5-large-onnx
+- jncraton/e5-small-v2-ct2-int8
+- radames/e5-large
+- khoa-klaytn/bge-base-en-v1.5-angle
+- Intel/bge-base-en-v1.5-sst2-int8-static
+- vectoriseai/e5-large
+- TitanML/jina-v2-base-en-embed
+- Koat/gte-tiny
+- binqiangliu/EmbeddingModlebgelargeENv1.5
+- beademiguelperez/sentence-transformers-multilingual-e5-small
+- sionic-ai/sionic-ai-v2
+- jamesdborin/jina-v2-base-en-embed
+- maiyad/multilingual-e5-small
+- dmlls/all-mpnet-base-v2
+- odunola/e5-base-v2
+- vectoriseai/bge-large-en-v1.5
+- vectoriseai/bge-small-en
+- karrar-alwaili/UAE-Large-V1
+- t12e/instructor-base
+- Frazic/udever-bloom-3b-sentence
+- Geolumina/instructor-xl
+- hsikchi/dump
+- recipe/embeddings
+- michaelfeil/ct2fast-bge-small-en-v1.5
+- ildodeltaRule/multilingual-e5-large
+- shubham-bgi/UAE-Large
+- BAAI/bge-large-en
+- michaelfeil/ct2fast-e5-small-v2
+- cgldo/semanticClone
+- barisaydin/gte-small
+- aident-ai/bge-base-en-onnx
+- jamesgpt1/english-large-v1
+- michaelfeil/ct2fast-e5-small
+- baseplate/instructor-large-1
+- newsrx/instructor-large
+- Narsil/bge-base-en
+- michaelfeil/ct2fast-e5-large
+- mlx-community/multilingual-e5-small-mlx
+- lightbird-ai/nomic
+- MaziyarPanahi/GritLM-8x7B-GGUF
+- newsrx/instructor-large-newsrx
+- dhairya0907/thenlper-get-large
+- barisaydin/bge-large-en
+- jncraton/bge-small-en-ct2-int8
+- retrainai/instructor-xl
+- BAAI/bge-base-en
+- gentlebowl/instructor-large-safetensors
+- d0rj/e5-large-en-ru
+- atian-chapters/Chapters-SFR-Embedding-Mistral
+- Intel/bge-base-en-v1.5-sts-int8-static
+- Intel/bge-base-en-v1.5-sts-int8-dynamic
+- jncraton/GIST-small-Embedding-v0-ct2-int8
+- jncraton/gte-tiny-ct2-int8
+- d0rj/e5-small-en-ru
+- vectoriseai/e5-small-v2
+- SmartComponents/bge-micro-v2
+- michaelfeil/ct2fast-gte-base
+- vectoriseai/e5-base-v2
+- Intel/bge-base-en-v1.5-sst2
+- McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised
+- Research2NLP/electrical_stella
+- weakit-v/bge-base-en-v1.5-onnx
+- GovCompete/instructor-xl
+- barisaydin/text2vec-base-multilingual
+- Intel/bge-small-en-v1.5-sst2-int8-dynamic
+- jncraton/gte-small-ct2-int8
+- d0rj/e5-base-en-ru
+- barisaydin/gte-large
+- fresha/e5-large-v2-endpoint
+- vectoriseai/instructor-large
+- Severian/embed
+- vectoriseai/e5-base
+- mlx-community/multilingual-e5-large-mlx
+- vectoriseai/gte-large
+- anttip/ct2fast-e5-small-v2-hfie
+- michaelfeil/ct2fast-gte-large
+- gizmo-ai/Cohere-embed-multilingual-v3.0
+- McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse
+- Kenknight1999/tungdd7_ft_e5
+- joteqwork/new_gsev0
+- vantagediscovery/jina-embeddings-v2-base-en
+- vantagediscovery/nomic-embed-text-v1
+- vantagediscovery/nomic-embed-text-v1.5
+- srikanthmalla/hkunlp-instructor-xl
+- afrideva/GIST-all-MiniLM-L6-v2-GGUF
+cross_encoders:
+- FollowIR-7B
+- flan-t5-base
+- flan-t5-large
+- monobert-large-msmarco
+- monot5-3b-msmarco-10k
+- monot5-base-msmarco-10k
+- llama-2-7b-chat
+- mistral-7b-instruct-v0.2
+- tart-full-flan-t5-xl

requirements.txt

@@ -0,0 +1,5 @@
+gradio
+datasets
+pandas
+huggingface_hub
+tqdm

utils/model_size.py

@@ -0,0 +1,43 @@
+import json
+import re
+from huggingface_hub.hf_api import ModelInfo, get_safetensors_metadata, model_info as get_model_info, get_hf_file_metadata, hf_hub_url
+from huggingface_hub import hf_hub_download
+
+# Map model IDs to the number of bytes used for one parameter. So, 4 bytes for fp32, 2 bytes for fp16, etc.
+# By default, we assume that the model is stored in fp32.
+KNOWN_BYTES_PER_PARAM = {
+    "dwzhu/e5-base-4k": 2,
+}
+
+def get_model_parameters_memory(model_info: ModelInfo):
+    '''Get the size of the model in million of parameters.'''
+    try:
+        safetensors = get_safetensors_metadata(model_info.id)
+        num_parameters = sum(safetensors.parameter_count.values())
+        return round(num_parameters / 1e6), round(num_parameters * 4 / 1024**3, 2)
+    except Exception as e:
+        pass
+
+    filenames = [sib.rfilename for sib in model_info.siblings]
+    if "pytorch_model.bin" in filenames:
+        url = hf_hub_url(model_info.id, filename="pytorch_model.bin")
+        meta = get_hf_file_metadata(url)
+        bytes_per_param = KNOWN_BYTES_PER_PARAM.get(model_info.id, 4)
+        num_params = round(meta.size / bytes_per_param / 1e6)
+        size_gb = round(meta.size * (4 / bytes_per_param) / 1024**3, 2)
+        return num_params, size_gb
+    
+    if "pytorch_model.bin.index.json" in filenames:
+        index_path = hf_hub_download(model_info.id, filename="pytorch_model.bin.index.json")
+        """
+        {
+        "metadata": {
+            "total_size": 28272820224
+        },....
+        """
+        size = json.load(open(index_path))
+        bytes_per_param = KNOWN_BYTES_PER_PARAM.get(model_info.id, 4)
+        if ("metadata" in size) and ("total_size" in size["metadata"]):
+            return round(size["metadata"]["total_size"] / bytes_per_param / 1e6), round(size["metadata"]["total_size"] / 1024**3, 2)
+
+    return None, None