import json from datasets import load_dataset import gradio as gr from huggingface_hub import HfApi, hf_hub_download from huggingface_hub.repocard import metadata_load import pandas as pd TASKS = [ "BitextMining", "Classification", "Clustering", "PairClassification", "Reranking", "Retrieval", "STS", "Summarization", ] TASK_LIST_CLASSIFICATION = [ "AmazonCounterfactualClassification (en)", "AmazonPolarityClassification", "AmazonReviewsClassification (en)", "Banking77Classification", "EmotionClassification", "ImdbClassification", "MassiveIntentClassification (en)", "MassiveScenarioClassification (en)", "MTOPDomainClassification (en)", "MTOPIntentClassification (en)", "ToxicConversationsClassification", "TweetSentimentExtractionClassification", ] TASK_LIST_CLASSIFICATION_NORM = [x.replace(" (en)", "") for x in TASK_LIST_CLASSIFICATION] TASK_LIST_CLUSTERING = [ "ArxivClusteringP2P", "ArxivClusteringS2S", "BiorxivClusteringP2P", "BiorxivClusteringS2S", "MedrxivClusteringP2P", "MedrxivClusteringS2S", "RedditClustering", "RedditClusteringP2P", "StackExchangeClustering", "StackExchangeClusteringP2P", "TwentyNewsgroupsClustering", ] TASK_LIST_PAIR_CLASSIFICATION = [ "SprintDuplicateQuestions", "TwitterSemEval2015", "TwitterURLCorpus", ] TASK_LIST_RERANKING = [ "AskUbuntuDupQuestions", "MindSmallReranking", "SciDocsRR", "StackOverflowDupQuestions", ] TASK_LIST_RETRIEVAL = [ "ArguAna", "ClimateFEVER", "CQADupstackRetrieval", "DBPedia", "FEVER", "FiQA2018", "HotpotQA", "MSMARCO", "NFCorpus", "NQ", "QuoraRetrieval", "SCIDOCS", "SciFact", "Touche2020", "TRECCOVID", ] TASK_LIST_RETRIEVAL_NORM = TASK_LIST_RETRIEVAL + ["CQADupstackAndroidRetrieval", "CQADupstackEnglishRetrieval", "CQADupstackGamingRetrieval", "CQADupstackGisRetrieval", "CQADupstackMathematicaRetrieval", "CQADupstackPhysicsRetrieval", "CQADupstackProgrammersRetrieval", "CQADupstackStatsRetrieval", "CQADupstackTexRetrieval", "CQADupstackUnixRetrieval", "CQADupstackWebmastersRetrieval", "CQADupstackWordpressRetrieval" ] TASK_LIST_STS = [ "BIOSSES", "SICK-R", "STS12", "STS13", "STS14", "STS15", "STS16", "STS17 (en-en)", "STS22 (en)", "STSBenchmark", ] TASK_LIST_STS_NORM = [x.replace(" (en)", "").replace(" (en-en)", "") for x in TASK_LIST_STS] TASK_LIST_SUMMARIZATION = [ "SummEval", ] TASK_LIST_EN = TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS + TASK_LIST_SUMMARIZATION TASK_TO_METRIC = { "BitextMining": "f1", "Clustering": "v_measure", "Classification": "accuracy", "PairClassification": "cos_sim_ap", "Reranking": "map", "Retrieval": "ndcg_at_10", "STS": "cos_sim_spearman", "Summarization": "cos_sim_spearman", } 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]}' ) # Models without metadata, thus we cannot fetch their results naturally EXTERNAL_MODELS = [ "LASER2", "LaBSE", "all-MiniLM-L12-v2", "all-MiniLM-L6-v2", "all-mpnet-base-v2", "allenai-specter", "bert-base-uncased", "contriever-base-msmarco", "glove.6B.300d", "gtr-t5-base", "gtr-t5-large", "gtr-t5-xl", "gtr-t5-xxl", "komninos", "msmarco-bert-co-condensor", "paraphrase-multilingual-MiniLM-L12-v2", "paraphrase-multilingual-mpnet-base-v2", "sentence-t5-base", "sentence-t5-large", "sentence-t5-xl", "sentence-t5-xxl", "sup-simcse-bert-base-uncased", "text-embedding-ada-002", "text-similarity-ada-001", "text-similarity-babbage-001", "text-similarity-curie-001", "text-similarity-davinci-001", "text-search-ada-001", "text-search-babbage-001", "text-search-curie-001", "text-search-davinci-001", "unsup-simcse-bert-base-uncased", ] EXTERNAL_MODEL_TO_LINK = { "LASER2": "https://github.com/facebookresearch/LASER", "text-embedding-ada-002": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models", "text-similarity-ada-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models", "text-similarity-babbage-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models", "text-similarity-curie-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models", "text-similarity-davinci-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models", "text-search-ada-doc-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models", "text-search-ada-query-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models", "text-search-ada-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models", "text-search-curie-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models", "text-search-babbage-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models", "text-search-davinci-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models", "LaBSE": "https://huggingface.co/sentence-transformers/LaBSE", "sentence-t5-xxl": "https://huggingface.co/sentence-transformers/sentence-t5-xxl", "sentence-t5-xl": "https://huggingface.co/sentence-transformers/sentence-t5-xl", "sentence-t5-large": "https://huggingface.co/sentence-transformers/sentence-t5-large", "sentence-t5-base": "https://huggingface.co/sentence-transformers/sentence-t5-base", "gtr-t5-xxl": "https://huggingface.co/sentence-transformers/gtr-t5-xxl", "gtr-t5-xl": "https://huggingface.co/sentence-transformers/gtr-t5-xl", "gtr-t5-large": "https://huggingface.co/sentence-transformers/gtr-t5-large", "gtr-t5-base": "https://huggingface.co/sentence-transformers/gtr-t5-base", "gtr-t5-xxl": "https://huggingface.co/sentence-transformers/gtr-t5-xxl", "gtr-t5-xl": "https://huggingface.co/sentence-transformers/gtr-t5-xl", "gtr-t5-large": "https://huggingface.co/sentence-transformers/gtr-t5-large", "gtr-t5-base": "https://huggingface.co/sentence-transformers/gtr-t5-base", "bert-base-uncased": "https://huggingface.co/bert-base-uncased", "allenai-specter": "https://huggingface.co/sentence-transformers/allenai-specter", "allenai-specter": "https://huggingface.co/sentence-transformers/allenai-specter", "unsup-simcse-bert-base-uncased": "https://huggingface.co/princeton-nlp/unsup-simcse-bert-base-uncased", "sup-simcse-bert-base-uncased": "https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased", "komninos": "https://huggingface.co/sentence-transformers/average_word_embeddings_komninos", "glove.6B.300d": "https://huggingface.co/sentence-transformers/average_word_embeddings_glove.6B.300d", "msmarco-bert-co-condensor": "https://huggingface.co/sentence-transformers/msmarco-bert-co-condensor", "all-MiniLM-L12-v2": "https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2", "all-MiniLM-L6-v2": "https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2", "all-mpnet-base-v2": "https://huggingface.co/sentence-transformers/all-mpnet-base-v2", "paraphrase-multilingual-mpnet-base-v2": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "paraphrase-multilingual-MiniLM-L12-v2": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", } EXTERNAL_MODEL_TO_DIM = { "LASER2": 1024, "LaBSE": 768, "all-MiniLM-L12-v2": 384, "all-MiniLM-L6-v2": 384, "all-mpnet-base-v2": 768, "allenai-specter": 768, "bert-base-uncased": 768, "contriever-base-msmarco": 768, "glove.6B.300d": 300, "gtr-t5-base": 768, "gtr-t5-large": 768, "gtr-t5-xl": 768, "gtr-t5-xxl": 768, "komninos": 300, "msmarco-bert-co-condensor": 768, "paraphrase-multilingual-MiniLM-L12-v2": 384, "paraphrase-multilingual-mpnet-base-v2": 768, "sentence-t5-base": 768, "sentence-t5-large": 768, "sentence-t5-xl": 768, "sentence-t5-xxl": 768, "sup-simcse-bert-base-uncased": 768, "text-embedding-ada-002": 1536, "text-similarity-ada-001": 1024, "text-similarity-babbage-001": 2048, "text-similarity-curie-001": 4096, "text-similarity-davinci-001": 12288, "text-search-ada-doc-001": 1024, "text-search-ada-query-001": 1024, "text-search-ada-001": 1024, "text-search-babbage-001": 2048, "text-search-curie-001": 4096, "text-search-davinci-001": 12288, "unsup-simcse-bert-base-uncased": 768, } MODELS_TO_SKIP = { "baseplate/instructor-large-1", # Duplicate "radames/e5-large", # Duplicate } EXTERNAL_MODEL_RESULTS = {model: {k: {v: []} for k, v in TASK_TO_METRIC.items()} for model in EXTERNAL_MODELS} 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 add_task(examples): # Could be added to the dataset loading script instead if examples["mteb_dataset_name"] in TASK_LIST_CLASSIFICATION_NORM: examples["mteb_task"] = "Classification" elif examples["mteb_dataset_name"] in TASK_LIST_CLUSTERING: examples["mteb_task"] = "Clustering" elif examples["mteb_dataset_name"] in TASK_LIST_PAIR_CLASSIFICATION: examples["mteb_task"] = "PairClassification" elif examples["mteb_dataset_name"] in TASK_LIST_RERANKING: examples["mteb_task"] = "Reranking" elif examples["mteb_dataset_name"] in TASK_LIST_RETRIEVAL_NORM: examples["mteb_task"] = "Retrieval" elif examples["mteb_dataset_name"] in TASK_LIST_STS_NORM: examples["mteb_task"] = "STS" elif examples["mteb_dataset_name"] in TASK_LIST_SUMMARIZATION: examples["mteb_task"] = "Summarization" else: examples["mteb_task"] = "BitextMining" return examples for model in EXTERNAL_MODELS: ds = load_dataset("mteb/results", model, download_mode='force_redownload', verification_mode="no_checks") # 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, "https://huggingface.co/spaces/mteb/leaderboard"))} # 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}) def get_emb_dim(model): filenames = [sib.rfilename for sib in model.siblings] dim = "" if "1_Pooling/config.json" in filenames: st_config_path = hf_hub_download(model.modelId, filename="1_Pooling/config.json") dim = json.load(open(st_config_path)).get("word_embedding_dimension", "") elif "2_Pooling/config.json" in filenames: st_config_path = hf_hub_download(model.modelId, filename="2_Pooling/config.json") dim = json.load(open(st_config_path)).get("word_embedding_dimension", "") elif "config.json" in filenames: config_path = hf_hub_download(model.modelId, filename="config.json") config = json.load(open(config_path)) if "hidden_dim" in config: dim = config["hidden_dim"] elif "hidden_size" in config: dim = config["hidden_size"] elif "d_model" in config: dim = config["d_model"] return dim def get_mteb_data(tasks=["Clustering"], langs=[], fillna=True, add_emb_dim=False, task_to_metric=TASK_TO_METRIC): api = HfApi() 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 = [res for task in tasks for res in EXTERNAL_MODEL_RESULTS[model][task][task_to_metric[task]]] if 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["Embedding Dimensions"] = EXTERNAL_MODEL_TO_DIM.get(model, "") df_list.append(res) for model in models: if model.modelId in MODELS_TO_SKIP: continue readme_path = hf_hub_download(model.modelId, filename="README.md") meta = metadata_load(readme_path) # 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 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) if add_emb_dim: out["Embedding Dimensions"] = get_emb_dim(model) df_list.append(out) df = pd.DataFrame(df_list) # Put 'Model' column first cols = sorted(list(df.columns)) cols.insert(0, cols.pop(cols.index("Model"))) df = df[cols] if fillna: df.fillna("", inplace=True) return df def get_mteb_average(): global DATA_OVERALL, DATA_CLASSIFICATION_EN, DATA_CLUSTERING, DATA_PAIR_CLASSIFICATION, DATA_RERANKING, DATA_RETRIEVAL, DATA_STS_EN, DATA_SUMMARIZATION, NUM_SCORES DATA_OVERALL = get_mteb_data( tasks=[ "Classification", "Clustering", "PairClassification", "Reranking", "Retrieval", "STS", "Summarization", ], langs=["en", "en-en"], fillna=False, add_emb_dim=True, ) # Approximation (Missing Bitext Mining & including some nans) NUM_SCORES = DATA_OVERALL.shape[0] * DATA_OVERALL.shape[1] # Debugging: # DATA_OVERALL.to_csv("overall.csv") DATA_OVERALL.insert(1, f"Average ({len(TASK_LIST_EN)} datasets)", DATA_OVERALL[TASK_LIST_EN].mean(axis=1, skipna=False)) DATA_OVERALL.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION)} datasets)", DATA_OVERALL[TASK_LIST_CLASSIFICATION].mean(axis=1, skipna=False)) DATA_OVERALL.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING)} datasets)", DATA_OVERALL[TASK_LIST_CLUSTERING].mean(axis=1, skipna=False)) DATA_OVERALL.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION)} datasets)", DATA_OVERALL[TASK_LIST_PAIR_CLASSIFICATION].mean(axis=1, skipna=False)) DATA_OVERALL.insert(5, f"Reranking Average ({len(TASK_LIST_RERANKING)} datasets)", DATA_OVERALL[TASK_LIST_RERANKING].mean(axis=1, skipna=False)) DATA_OVERALL.insert(6, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL)} datasets)", DATA_OVERALL[TASK_LIST_RETRIEVAL].mean(axis=1, skipna=False)) DATA_OVERALL.insert(7, f"STS Average ({len(TASK_LIST_STS)} datasets)", DATA_OVERALL[TASK_LIST_STS].mean(axis=1, skipna=False)) DATA_OVERALL.insert(8, f"Summarization Average ({len(TASK_LIST_SUMMARIZATION)} dataset)", DATA_OVERALL[TASK_LIST_SUMMARIZATION].mean(axis=1, skipna=False)) DATA_OVERALL.sort_values(f"Average ({len(TASK_LIST_EN)} 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) # Fill NaN after averaging DATA_OVERALL.fillna("", inplace=True) DATA_CLASSIFICATION_EN = DATA_OVERALL[["Model"] + TASK_LIST_CLASSIFICATION] DATA_CLUSTERING = DATA_OVERALL[["Model"] + TASK_LIST_CLUSTERING] DATA_PAIR_CLASSIFICATION = DATA_OVERALL[["Model"] + TASK_LIST_PAIR_CLASSIFICATION] DATA_RERANKING = DATA_OVERALL[["Model"] + TASK_LIST_RERANKING] DATA_RETRIEVAL = DATA_OVERALL[["Model"] + TASK_LIST_RETRIEVAL] DATA_STS_EN = DATA_OVERALL[["Model"] + TASK_LIST_STS] DATA_SUMMARIZATION = DATA_OVERALL[["Model"] + TASK_LIST_SUMMARIZATION] DATA_OVERALL = DATA_OVERALL[["Rank", "Model", "Embedding Dimensions", f"Average ({len(TASK_LIST_EN)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION)} datasets)", f"Reranking Average ({len(TASK_LIST_RERANKING)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL)} datasets)", f"STS Average ({len(TASK_LIST_STS)} datasets)", f"Summarization Average ({len(TASK_LIST_SUMMARIZATION)} dataset)"]] return DATA_OVERALL get_mteb_average() block = gr.Blocks() with block: gr.Markdown(f""" Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the MTEB GitHub repository 🤗 - **Total Datasets**: 58 - **Total Languages**: 112 - **Total Scores**: >{NUM_SCORES} - **Total Models**: {len(DATA_OVERALL)} """) with gr.Tabs(): with gr.TabItem("Overall"): with gr.Row(): gr.Markdown(""" **Overall MTEB English leaderboard 🔮** - **Metric:** Various, refer to task tabs - **Languages:** English, refer to task tabs for others """) with gr.Row(): data_overall = gr.components.Dataframe( DATA_OVERALL, datatype=["number", "markdown"] + ["number"] * len(DATA_OVERALL.columns), type="pandas", wrap=True, ) with gr.Row(): data_run = gr.Button("Refresh") data_run.click(get_mteb_average, inputs=None, outputs=data_overall) with gr.TabItem("Bitext Mining"): with gr.Row(): gr.Markdown(""" **Bitext Mining Leaderboard 🎌** - **Metric:** [F1](https://huggingface.co/spaces/evaluate-metric/f1) - **Languages:** 117 """) with gr.Row(): data_bitext_mining = gr.components.Dataframe( datatype=["markdown"] + ["number"] * 500, # hack when we don't know how many columns type="pandas", ) with gr.Row(): data_run = gr.Button("Refresh") task_bitext_mining = gr.Variable(value=["BitextMining"]) data_run.click( get_mteb_data, inputs=[task_bitext_mining], outputs=data_bitext_mining, ) with gr.TabItem("Classification"): with gr.TabItem("English"): with gr.Row(): gr.Markdown(""" **Classification Leaderboard ❤️** - **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) - **Languages:** English """) with gr.Row(): data_classification_en = gr.components.Dataframe( DATA_CLASSIFICATION_EN, datatype=["markdown"] + ["number"] * len(DATA_CLASSIFICATION_EN.columns), type="pandas", ) with gr.Row(): data_run_classification_en = gr.Button("Refresh") task_classification_en = gr.Variable(value=["Classification"]) lang_classification_en = gr.Variable(value=["en"]) data_run_classification_en.click( get_mteb_data, inputs=[ task_classification_en, lang_classification_en, ], outputs=data_classification_en, ) with gr.TabItem("Multilingual"): with gr.Row(): gr.Markdown(""" **Classification Multilingual Leaderboard 💜💚💙** - **Metric:** [Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) - **Languages:** 51 """) with gr.Row(): data_classification = gr.components.Dataframe( datatype=["markdown"] + ["number"] * 200, # hack when we don't know how many columns type="pandas", ) with gr.Row(): data_run = gr.Button("Refresh") task_classification = gr.Variable(value=["Classification"]) data_run.click( get_mteb_data, inputs=[task_classification], outputs=data_classification, ) with gr.TabItem("Clustering"): with gr.Row(): gr.Markdown(""" **Clustering Leaderboard ✨** - **Metric:** Validity Measure (v_measure) - **Languages:** English """) with gr.Row(): data_clustering = gr.components.Dataframe( DATA_CLUSTERING, datatype=["markdown"] + ["number"] * len(DATA_CLUSTERING.columns), type="pandas", ) with gr.Row(): data_run = gr.Button("Refresh") task_clustering = gr.Variable(value=["Clustering"]) data_run.click( get_mteb_data, inputs=[task_clustering], outputs=data_clustering, ) with gr.TabItem("Pair Classification"): with gr.Row(): gr.Markdown(""" **Pair Classification Leaderboard 🎭** - **Metric:** Average Precision based on Cosine Similarities (cos_sim_ap) - **Languages:** English """) with gr.Row(): data_pair_classification = gr.components.Dataframe( DATA_PAIR_CLASSIFICATION, datatype=["markdown"] + ["number"] * len(DATA_PAIR_CLASSIFICATION.columns), type="pandas", ) with gr.Row(): data_run = gr.Button("Refresh") task_pair_classification = gr.Variable(value=["PairClassification"]) data_run.click( get_mteb_data, inputs=[task_pair_classification], outputs=data_pair_classification, ) with gr.TabItem("Retrieval"): with gr.Row(): gr.Markdown(""" **Retrieval Leaderboard 🔎** - **Metric:** Normalized Discounted Cumulative Gain @ k (ndcg_at_10) - **Languages:** English """) with gr.Row(): data_retrieval = gr.components.Dataframe( DATA_RETRIEVAL, # Add support for more columns than existing as a buffer for CQADupstack & other Retrieval tasks (e.g. MSMARCOv2) datatype=["markdown"] + ["number"] * len(DATA_RETRIEVAL.columns) * 2, type="pandas", ) with gr.Row(): data_run = gr.Button("Refresh") task_retrieval = gr.Variable(value=["Retrieval"]) data_run.click( get_mteb_data, inputs=[task_retrieval], outputs=data_retrieval ) with gr.TabItem("Reranking"): with gr.Row(): gr.Markdown(""" **Reranking Leaderboard 🥈** - **Metric:** Mean Average Precision (MAP) - **Languages:** English """) with gr.Row(): data_reranking = gr.components.Dataframe( DATA_RERANKING, datatype=["markdown"] + ["number"] * len(DATA_RERANKING.columns), type="pandas", ) with gr.Row(): data_run = gr.Button("Refresh") task_reranking = gr.Variable(value=["Reranking"]) metric_reranking = gr.Variable(value="map") data_run.click( get_mteb_data, inputs=[task_reranking], outputs=data_reranking ) with gr.TabItem("STS"): with gr.TabItem("English"): with gr.Row(): gr.Markdown(""" **STS Leaderboard 🤖** - **Metric:** Spearman correlation based on cosine similarity - **Languages:** English """) with gr.Row(): data_sts_en = gr.components.Dataframe( DATA_STS_EN, datatype=["markdown"] + ["number"] * len(DATA_STS_EN.columns), type="pandas", ) with gr.Row(): data_run_sts_en = gr.Button("Refresh") task_sts_en = gr.Variable(value=["STS"]) lang_sts_en = gr.Variable(value=["en", "en-en"]) data_run_sts_en.click( get_mteb_data, inputs=[task_sts_en, lang_sts_en], outputs=data_sts_en, ) with gr.TabItem("Multilingual"): with gr.Row(): gr.Markdown(""" **STS Multilingual Leaderboard 👽** - **Metric:** Spearman correlation based on cosine similarity - **Languages:** Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Russian, Spanish """) with gr.Row(): data_sts = gr.components.Dataframe( datatype=["markdown"] + ["number"] * 100, # hack when we don't know how many columns type="pandas", ) with gr.Row(): data_run = gr.Button("Refresh") task_sts = gr.Variable(value=["STS"]) data_run.click(get_mteb_data, inputs=[task_sts], outputs=data_sts) with gr.TabItem("Summarization"): with gr.Row(): gr.Markdown(""" **Summarization Leaderboard 📜** - **Metric:** Spearman correlation based on cosine similarity - **Languages:** English """) with gr.Row(): data_summarization = gr.components.Dataframe( DATA_SUMMARIZATION, datatype=["markdown"] + ["number"] * 2, type="pandas", ) with gr.Row(): data_run = gr.Button("Refresh") task_summarization = gr.Variable(value=["Summarization"]) data_run.click( get_mteb_data, inputs=[task_summarization], outputs=data_summarization, ) gr.Markdown(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} } ``` """) # Running the function on page load in addition to when the button is clicked # This is optional - If deactivated the data created loaded at "Build time" is shown like for Overall tab block.load(get_mteb_data, inputs=[task_bitext_mining], outputs=data_bitext_mining) block.load(get_mteb_data, inputs=[task_classification_en, lang_classification_en], outputs=data_classification_en) block.load(get_mteb_data, inputs=[task_classification], outputs=data_classification) block.load(get_mteb_data, inputs=[task_clustering], outputs=data_clustering) block.load(get_mteb_data, inputs=[task_pair_classification], outputs=data_pair_classification) block.load(get_mteb_data, inputs=[task_retrieval], outputs=data_retrieval) block.load(get_mteb_data, inputs=[task_reranking], outputs=data_reranking) block.load(get_mteb_data, inputs=[task_sts_en, lang_sts_en], outputs=data_sts_en) block.load(get_mteb_data, inputs=[task_sts], outputs=data_sts) block.load(get_mteb_data, inputs=[task_summarization], outputs=data_summarization) block.launch() # Possible changes: # Could check if tasks are valid (Currently users could just invent new tasks - similar for languages) # Could make it load in the background without the Gradio logo closer to the Deep RL space # 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/