leaderboard / app.py
Tom Aarsen
Prevent crash for empty task
cef3366
raw
history blame
81.6 kB
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
- 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)
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,
})
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,
})
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.
""")
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 ''}
""")
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-GGUF','nazimali/gte-Qwen2-7B-instruct-Q6_K-GGUF','fishbone64/gte-Qwen2-7B-instruct-Q8_0-GGUF','tobchef/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF','liddlefish/privacy_embedding_rag','liddlefish/privacy_embedding_rag_10k_tmp','liddlefish/privacy_embedding_bge_small_synthetic','mxs980/gte-Qwen2-1.5B-instruct-Q8_0-GGUF','leonn71/gte-Qwen2-1.5B-instruct-Q6_K-GGUF', 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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/