leaderboard / app.py
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from functools import reduce
import re
import gradio as gr
import pandas as pd
from envs import REPO_ID
from refresh import BOARDS_CONFIG, TASKS, TASKS_CONFIG, TASK_DESCRIPTIONS, PRETTY_NAMES, load_results, make_clickable_model
from refresh import PROPRIETARY_MODELS, SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS, CROSS_ENCODERS, BI_ENCODERS, INSTRUCT_MODELS, NOINSTRUCT_MODELS, EXTERNAL_MODEL_TO_LINK
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
}
INSTRUCT_MODELS = {
make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
for model in INSTRUCT_MODELS
}
NOINSTRUCT_MODELS = {
make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
for model in NOINSTRUCT_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
}
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
# 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
- desc: [optional] The description of the leaderboard
- data: The data for the leaderboard
"""
# No more refreshing manually, happens daily
# 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
# def get_refresh_overall_function(tasks):
# return lambda: get_mteb_average(tasks)[0]
# load in the pre-calculated `all_data_tasks` and `boards_data`
print(f"Loading pre-calculated data....")
all_data_tasks = load_results("all_data_tasks")
boards_data = load_results("boards_data")
#### Caclulate Metadata
# Exact, add all non-nan integer values for every dataset
NUM_SCORES = 0
DATASETS = []
MODELS = []
# LANGUAGES = []
for d in all_data_tasks:
if isinstance(d, list) and len(d) == 0:
continue
# 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))
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)
metric = board_config.get("metric", None)
desc = board_config.get("desc", 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": get_refresh_overall_function(board_config["tasks"]),
"credits": credits,
"metric": metric,
"desc": desc,
})
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,
"metric": metric,
"desc": desc,
})
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",
"Uses Instructions",
"No Instructions",
]
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.lower().str.contains(query.lower()))
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))
elif model_type == "Uses Instructions":
masks.append(df["Model"].isin(INSTRUCT_MODELS))
elif model_type == "No Instructions":
masks.append(df["Model"].isin(NOINSTRUCT_MODELS))
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. Also check out [MTEB Arena](https://huggingface.co/spaces/mteb/arena) ⚔️
""")
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"],
scale=3,
)
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)
specific_metric = metric
if item.get("metric", None) is not None:
specific_metric = item['metric']
with gr.Row():
gr.Markdown(f"""
{item['description']}
- **Metric:** {specific_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 ''}
{"- **Description:** " + item['desc'] if ("desc" in item and item["desc"] 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()
# Add model names here so the mteb/leaderboard space shows up on their model page
# from envs import MODEL_META
# print("','".join(MODEL_META["models_to_skip"]))
# print("','".join(list(MODEL_META['model_meta'].keys())))
# print("','".join([x['link'].split("co/")[-1] for x in MODEL_META['model_meta'].values() if (x.get('link', None)) and ("huggingface.co" in x['link'])]))
# from envs import API; print("','".join([x.modelId for x in list(API.list_models(filter="mteb")) if x.modelId not in UNUSED]))
UNUSED = ['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','nadeem1362/mxbai-embed-large-v1-Q4_K_M-GGUF','agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF','ekorman-strive/bge-large-en-v1.5','raghavlight/SE_v1','liddlefish/privacyembeddingv2_bge_small','ahmet1338/finetuned_embedder','radia/snowflake-arctic-embed-l-Q4_K_M-GGUF','GregorBiswanger/GritLM-7B-Q4_K_M-GGUF','powermove72/GritLM-7B-Q4_K_M-GGUF','sunzx0810/gte-Qwen2-7B-instruct-Q5_K_M-GGUF','nazimali/gte-Qwen2-7B-instruct-Q6_K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# 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/