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'{model_name.split("/")[-1]}' ) 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", "")))) if dim == "" or seq == "": raise Exception(f"Could not find dim or seq for model {model.modelId}") # 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': 'BornholmBitextMining',}) 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: # The except clause triggers on gated repos, we can use external metadata for those try: 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)) except: name_without_org = model.modelId.split("/")[-1] # EXTERNAL_MODEL_TO_SIZE[name_without_org] refers to millions of parameters, so for memory usage # we multiply by 1e6 to get just the number of parameters, then by 4 to get the number of bytes # given fp32 precision (4 bytes per float), then divide by 1024**3 to get the number of GB MODEL_INFOS[model.modelId]["dim_seq_size"] = ( EXTERNAL_MODEL_TO_DIM.get(name_without_org, ""), EXTERNAL_MODEL_TO_SEQLEN.get(name_without_org, ""), EXTERNAL_MODEL_TO_SIZE.get(name_without_org, ""), round(EXTERNAL_MODEL_TO_SIZE[name_without_org] * 1e6 * 4 / 1024**3, 2) if name_without_org in EXTERNAL_MODEL_TO_SIZE else "", ) out["Embedding Dimensions"], out["Max Tokens"], out["Model Size (Million Parameters)"], out["Memory Usage (GB, fp32)"] = tuple(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("(.+)", 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 MTEB GitHub repository 🤗 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/