Add external models

app.py

@@ -1,3 +1,4 @@
+from datasets import load_dataset
 import gradio as gr
 import pandas as pd
 from huggingface_hub import HfApi, hf_hub_download
@@ -29,6 +30,8 @@ TASK_LIST_CLASSIFICATION = [
     "TweetSentimentExtractionClassification",
 ]
 
+TASK_LIST_CLASSIFICATION_NORM = [x.replace(" (en)", "") for x in TASK_LIST_CLASSIFICATION]
+
 TASK_LIST_CLUSTERING = [
     "ArxivClusteringP2P",
     "ArxivClusteringS2S",
@@ -74,6 +77,20 @@ TASK_LIST_RETRIEVAL = [
     "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",
@@ -87,6 +104,7 @@ TASK_LIST_STS = [
     "STSBenchmark",
 ]
 
+TASK_LIST_STS_NORM = [x.replace(" (en)", "").replace(" (en-en)", "") for x in TASK_LIST_STS]
 
 TASK_LIST_SUMMARIZATION = [
     "SummEval",
@@ -105,19 +123,107 @@ TASK_TO_METRIC = {
     "Summarization": "cos_sim_spearman",
 }
 
-def make_clickable_model(model_name):
+def make_clickable_model(model_name, link=None):
     # Remove user from model name
-    model_name_show = " ".join(model_name.split("/")[1:])
-    link = "https://huggingface.co/" + model_name
+    model_name = model_name.split("/")[-1]
+    if link is None:
+        link = "https://huggingface.co/" + model_name
     return (
-        f'<a target="_blank" style="text-decoration: underline" href="{link}">{model_name_show}</a>'
+        f'<a target="_blank" style="text-decoration: underline" href="{link}">{model_name}</a>'
     )
 
+# 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",
+    "sgpt-bloom-1b3-nli",
+    "sgpt-bloom-7b1-msmarco",
+    "sgpt-nli-bloom-1b3",
+    "sup-simcse-bert-base-uncased",
+#    "text-similarity-ada-001",
+    "unsup-simcse-bert-base-uncased",
+]
+EXTERNAL_MODEL_TO_LINK = {
+    "LASER2": "https://github.com/facebookresearch/LASER",
+    "text-similarity-ada-001": "https://beta.openai.com/docs/guides/embeddings/types-of-embedding-models",
+}
+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)
+    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/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_mteb_data(tasks=["Clustering"], langs=[], cast_to_str=True, 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:
+            df_list.append(res)
+    
     for model in models:
         readme_path = hf_hub_download(model.modelId, filename="README.md")
         meta = metadata_load(readme_path)
@@ -154,8 +260,8 @@ def get_mteb_data(tasks=["Clustering"], langs=[], cast_to_str=True, task_to_metr
         return df.astype(str) # Cast to str as Gradio does not accept floats
     return df
 
-def get_mteb_average(get_all_avgs=False):
-    global DATA_OVERALL, DATA_CLASSIFICATION_EN, DATA_CLUSTERING, DATA_PAIR_CLASSIFICATION, DATA_RERANKING, DATA_RETRIEVAL, DATA_STS_EN, DATA_SUMMARIZATION
+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",
@@ -169,6 +275,11 @@ def get_mteb_average(get_all_avgs=False):
         langs=["en", "en-en"],
         cast_to_str=False
     )
+    # Approximation (Missing Bitext Mining & including some nans)
+    NUM_SCORES = DATA_OVERALL.shape[0] * DATA_OVERALL.shape[1]
+
+    print("OVERALLDATA", DATA_OVERALL)
+    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))
@@ -204,7 +315,7 @@ with block:
     gr.Markdown(f"""
     Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the <a href="https://github.com/embeddings-benchmark/mteb#leaderboard" target="_blank" style="text-decoration: underline">MTEB GitHub repository</a> 🤗
 
-    - **Total Scores**: TODO
+    - **Total Scores**: >{NUM_SCORES}
     - **Total Models**: {len(DATA_OVERALL)}
     - **Total Users**: TODO
     """)
@@ -232,7 +343,7 @@ with block:
                     gr.Markdown("""
                     **Bitext Mining Leaderboard 🎌**
                     
-                    - **Metric:** Accuracy (accuracy)
+                    - **Metric:** F1 (f1)
                     - **Languages:** 117
                     """)
             with gr.Row():
@@ -242,7 +353,7 @@ with block:
                 )
             with gr.Row():
                 data_run = gr.Button("Refresh")
-                task_bitext_mining = gr.Variable(value="BitextMining")
+                task_bitext_mining = gr.Variable(value=["BitextMining"])
                 data_run.click(
                     get_mteb_data,
                     inputs=[task_bitext_mining],
@@ -265,7 +376,7 @@ with block:
                     )
                 with gr.Row():
                     data_run_classification_en = gr.Button("Refresh")
-                    task_classification_en = gr.Variable(value="Classification")
+                    task_classification_en = gr.Variable(value=["Classification"])
                     lang_classification_en = gr.Variable(value=["en"])
                     data_run_classification_en.click(
                         get_mteb_data,
@@ -285,12 +396,12 @@ with block:
                     """)
                 with gr.Row():
                     data_classification = gr.components.Dataframe(
-                        datatype=["markdown"] * 500, # hack when we don't know how many columns
+                        datatype=["markdown"] * 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")
+                    task_classification = gr.Variable(value=["Classification"])
                     data_run.click(
                         get_mteb_data,
                         inputs=[task_classification],
@@ -307,13 +418,12 @@ with block:
             with gr.Row():
                 data_clustering = gr.components.Dataframe(
                     DATA_CLUSTERING,
-                    datatype="markdown",
+                    datatype=["markdown"] * len(DATA_CLUSTERING.columns) * 2,
                     type="pandas",
-                    col_count=(len(DATA_CLUSTERING.columns), "fixed"),
                 )
             with gr.Row():
                 data_run = gr.Button("Refresh")
-                task_clustering = gr.Variable(value="Clustering")
+                task_clustering = gr.Variable(value=["Clustering"])
                 data_run.click(
                     get_mteb_data,
                     inputs=[task_clustering],
@@ -330,13 +440,12 @@ with block:
             with gr.Row():
                 data_pair_classification = gr.components.Dataframe(
                     DATA_PAIR_CLASSIFICATION,
-                    datatype="markdown",
+                    datatype=["markdown"] * len(DATA_PAIR_CLASSIFICATION.columns) * 2,
                     type="pandas",
-                    col_count=(len(DATA_PAIR_CLASSIFICATION.columns), "fixed"),
                 )
             with gr.Row():
                 data_run = gr.Button("Refresh")
-                task_pair_classification = gr.Variable(value="PairClassification")
+                task_pair_classification = gr.Variable(value=["PairClassification"])
                 data_run.click(
                     get_mteb_data,
                     inputs=[task_pair_classification],
@@ -358,7 +467,7 @@ with block:
                 )
             with gr.Row():
                 data_run = gr.Button("Refresh")
-                task_retrieval = gr.Variable(value="Retrieval")
+                task_retrieval = gr.Variable(value=["Retrieval"])
                 data_run.click(
                     get_mteb_data, inputs=[task_retrieval], outputs=data_retrieval
                 )
@@ -373,13 +482,12 @@ with block:
             with gr.Row():
                 data_reranking = gr.components.Dataframe(
                     DATA_RERANKING,
-                    datatype="markdown",
+                    datatype=["markdown"] * len(DATA_RERANKING.columns) * 2,
                     type="pandas",
-                    col_count=(len(DATA_RERANKING.columns), "fixed"),
                 )
             with gr.Row():
                 data_run = gr.Button("Refresh")
-                task_reranking = gr.Variable(value="Reranking")
+                task_reranking = gr.Variable(value=["Reranking"])
                 metric_reranking = gr.Variable(value="map")
                 data_run.click(
                     get_mteb_data, inputs=[task_reranking], outputs=data_reranking
@@ -396,15 +504,14 @@ with block:
                 with gr.Row():
                     data_sts_en = gr.components.Dataframe(
                         DATA_STS_EN,
-                        datatype="markdown",
+                        datatype=["markdown"] * len(DATA_STS_EN.columns) * 2,
                         type="pandas",
-                        col_count=(len(DATA_STS_EN.columns), "fixed"),
                     )
                 with gr.Row():
-                    data_run_en = gr.Button("Refresh")
-                    task_sts_en = gr.Variable(value="STS")
+                    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.click(
+                    data_run_sts_en.click(
                         get_mteb_data,
                         inputs=[task_sts_en, lang_sts_en],
                         outputs=data_sts_en,
@@ -424,7 +531,7 @@ with block:
                     )
                 with gr.Row():
                     data_run = gr.Button("Refresh")
-                    task_sts = gr.Variable(value="STS")
+                    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():
@@ -436,14 +543,13 @@ with block:
                 """)
             with gr.Row():
                 data_summarization = gr.components.Dataframe(
-                    DATA_SUMMARIZATION,
+                    DATA_SUMMARIZATION * len(DATA_SUMMARIZATION.columns) * 2,
                     datatype="markdown",
                     type="pandas",
-                    col_count=(len(DATA_SUMMARIZATION.columns), "fixed"),
                 )
             with gr.Row():
                 data_run = gr.Button("Refresh")
-                task_summarization = gr.Variable(value="Summarization")
+                task_summarization = gr.Variable(value=["Summarization"])
                 data_run.click(
                     get_mteb_data,
                     inputs=[task_summarization],

results/LASER2/AmazonCounterfactualClassification.json

@@ -0,0 +1,43 @@
+{
+    "dataset_version": null,
+    "mteb_version": "0.0.2",
+    "test": {
+        "de": {
+            "accuracy": 0.6781584582441113,
+            "accuracy_stderr": 0.060279798858073545,
+            "ap": 0.8036240553535807,
+            "ap_stderr": 0.03476499899077643,
+            "f1": 0.6628493463277175,
+            "f1_stderr": 0.05804533112556245,
+            "main_score": 0.6781584582441113
+        },
+        "en": {
+            "accuracy": 0.7683582089552239,
+            "accuracy_stderr": 0.03737785483516161,
+            "ap": 0.40076479274021654,
+            "ap_stderr": 0.05081532982471566,
+            "f1": 0.70787800776529,
+            "f1_stderr": 0.03884967003850526,
+            "main_score": 0.7683582089552239
+        },
+        "en-ext": {
+            "accuracy": 0.7616941529235383,
+            "accuracy_stderr": 0.05609726317155699,
+            "ap": 0.23620239901382217,
+            "ap_stderr": 0.055900376704944924,
+            "f1": 0.6259005944326002,
+            "f1_stderr": 0.057023255773266515,
+            "main_score": 0.7616941529235383
+        },
+        "evaluation_time": 162.63,
+        "ja": {
+            "accuracy": 0.6875802997858672,
+            "accuracy_stderr": 0.057291619728276316,
+            "ap": 0.18157282477398815,
+            "ap_stderr": 0.0359805625991896,
+            "f1": 0.5601658468471795,
+            "f1_stderr": 0.047780178480722454,
+            "main_score": 0.6875802997858672
+        }
+    }
+}

results/LASER2/AmazonPolarityClassification.json

@@ -0,0 +1,14 @@
+{
+    "dataset_version": null,
+    "mteb_version": "0.0.2",
+    "test": {
+        "accuracy": 0.6100945,
+        "accuracy_stderr": 0.032754584770685165,
+        "ap": 0.5701234744666878,
+        "ap_stderr": 0.023665879113998516,
+        "evaluation_time": 169.16,
+        "f1": 0.6049258458477238,
+        "f1_stderr": 0.03882387073720782,
+        "main_score": 0.6100945
+    }
+}

results/LASER2/AmazonReviewsClassification.json

@@ -0,0 +1,49 @@
+{
+    "dataset_version": null,
+    "mteb_version": "0.0.2",
+    "test": {
+        "de": {
+            "accuracy": 0.31068,
+            "accuracy_stderr": 0.030799766232879097,
+            "f1": 0.2938071341251565,
+            "f1_stderr": 0.03301311313116498,
+            "main_score": 0.31068
+        },
+        "en": {
+            "accuracy": 0.2871,
+            "accuracy_stderr": 0.031948740194254914,
+            "f1": 0.2763831660571802,
+            "f1_stderr": 0.03216710195455939,
+            "main_score": 0.2871
+        },
+        "es": {
+            "accuracy": 0.32724000000000003,
+            "accuracy_stderr": 0.015933311018115466,
+            "f1": 0.310782596824498,
+            "f1_stderr": 0.022188814588153163,
+            "main_score": 0.32724000000000003
+        },
+        "evaluation_time": 295.02,
+        "fr": {
+            "accuracy": 0.31116,
+            "accuracy_stderr": 0.030352304690089024,
+            "f1": 0.2995469284574527,
+            "f1_stderr": 0.03141580285250744,
+            "main_score": 0.31116
+        },
+        "ja": {
+            "accuracy": 0.28935999999999995,
+            "accuracy_stderr": 0.024075680675735834,
+            "f1": 0.2818735717046802,
+            "f1_stderr": 0.023753772760779744,
+            "main_score": 0.28935999999999995
+        },
+        "zh": {
+            "accuracy": 0.30892000000000003,
+            "accuracy_stderr": 0.02032696730946355,
+            "f1": 0.2990186813313857,
+            "f1_stderr": 0.021581437215568936,
+            "main_score": 0.30892000000000003
+        }
+    }
+}

results/LASER2/ArguAna.json

@@ -0,0 +1,31 @@
+{
+    "test": {
+        "evaluation_time": 62.18,
+        "map_at_1": 0.06117,
+        "map_at_10": 0.10455,
+        "map_at_100": 0.11002,
+        "map_at_1000": 0.11084,
+        "map_at_3": 0.09187,
+        "map_at_5": 0.09834,
+        "ndcg_at_1": 0.06117,
+        "ndcg_at_10": 0.12856,
+        "ndcg_at_100": 0.1601,
+        "ndcg_at_1000": 0.18712,
+        "ndcg_at_3": 0.10188,
+        "ndcg_at_5": 0.11358,
+        "precision_at_1": 0.06117,
+        "precision_at_10": 0.02055,
+        "precision_at_100": 0.00365,
+        "precision_at_1000": 0.00059,
+        "precision_at_3": 0.04362,
+        "precision_at_5": 0.03186,
+        "recall_at_1": 0.06117,
+        "recall_at_10": 0.20555,
+        "recall_at_100": 0.36486,
+        "recall_at_1000": 0.58962,
+        "recall_at_3": 0.13087,
+        "recall_at_5": 0.15932
+    },
+    "dataset_version": null,
+    "mteb_version": "0.0.2"
+}

results/LASER2/ArxivClusteringP2P.json

@@ -0,0 +1,9 @@
+{
+    "test": {
+        "evaluation_time": 3687.79,
+        "v_measure": 0.1776823856238192,
+        "v_measure_std": 0.15680242731305624
+    },
+    "dataset_version": null,
+    "mteb_version": "0.0.2"
+}

results/LASER2/ArxivClusteringS2S.json

@@ -0,0 +1,9 @@
+{
+    "test": {
+        "evaluation_time": 398.92,
+        "v_measure": 0.1239260518556585,
+        "v_measure_std": 0.16362867463758127
+    },
+    "dataset_version": null,
+    "mteb_version": "0.0.2"
+}

results/LASER2/AskUbuntuDupQuestions.json

@@ -0,0 +1,9 @@
+{
+    "test": {
+        "evaluation_time": 12.12,
+        "map": 0.4898595820868522,
+        "mrr": 0.6257276964340676
+    },
+    "dataset_version": null,
+    "mteb_version": "0.0.2"
+}

results/LASER2/BIOSSES.json

@@ -0,0 +1,19 @@
+{
+    "test": {
+        "cos_sim": {
+            "pearson": 0.6212936580597783,
+            "spearman": 0.6200864182463187
+        },
+        "euclidean": {
+            "pearson": 0.6471589936856987,
+            "spearman": 0.68256374544906
+        },
+        "evaluation_time": 10.33,
+        "manhattan": {
+            "pearson": 0.66773509051844,
+            "spearman": 0.7192212072202181
+        }
+    },
+    "dataset_version": null,
+    "mteb_version": "0.0.2"
+}

results/LASER2/BUCC.json

@@ -0,0 +1,31 @@
+{
+    "dataset_version": null,
+    "mteb_version": "0.0.2",
+    "test": {
+        "de-en": {
+            "accuracy": 0.9926931106471816,
+            "f1": 0.9921016005567153,
+            "precision": 0.9918058455114822,
+            "recall": 0.9926931106471816
+        },
+        "evaluation_time": 642.77,
+        "fr-en": {
+            "accuracy": 0.985362095531587,
+            "f1": 0.9838946364370093,
+            "precision": 0.9831609068897205,
+            "recall": 0.985362095531587
+        },
+        "ru-en": {
+            "accuracy": 0.9774160027710426,
+            "f1": 0.9762152176423046,
+            "precision": 0.9756148250779356,
+            "recall": 0.9774160027710426
+        },
+        "zh-en": {
+            "accuracy": 0.9778830963665087,
+            "f1": 0.9770054414604177,
+            "precision": 0.9765666140073723,
+            "recall": 0.9778830963665087
+        }
+    }
+}

results/LASER2/Banking77Classification.json

@@ -0,0 +1,12 @@
+{
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results/LASER2/BiorxivClusteringP2P.json

@@ -0,0 +1,9 @@
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results/LASER2/BiorxivClusteringS2S.json

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+{
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results/LASER2/CQADupstackAndroidRetrieval.json

@@ -0,0 +1,31 @@
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results/LASER2/CQADupstackEnglishRetrieval.json

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results/LASER2/CQADupstackGamingRetrieval.json

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results/LASER2/CQADupstackGisRetrieval.json

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results/LASER2/CQADupstackMathematicaRetrieval.json

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results/LASER2/CQADupstackPhysicsRetrieval.json

@@ -0,0 +1,31 @@
+{
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results/LASER2/CQADupstackProgrammersRetrieval.json

@@ -0,0 +1,31 @@
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+}

results/LASER2/CQADupstackRetrieval.json

@@ -0,0 +1,31 @@
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results/LASER2/CQADupstackStatsRetrieval.json

@@ -0,0 +1,31 @@
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+}

results/LASER2/CQADupstackTexRetrieval.json

@@ -0,0 +1,31 @@
+{
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+}

results/LASER2/CQADupstackUnixRetrieval.json

@@ -0,0 +1,31 @@
+{
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results/LASER2/CQADupstackWebmastersRetrieval.json

@@ -0,0 +1,31 @@
+{
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+}

results/LASER2/CQADupstackWordpressRetrieval.json

@@ -0,0 +1,31 @@
+{
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results/LASER2/ClimateFEVER.json

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+{
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+}

results/LASER2/DBPedia.json

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+{
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+}

results/LASER2/EmotionClassification.json

@@ -0,0 +1,12 @@
+{
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+}

results/LASER2/FEVER.json

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+{
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results/LASER2/FiQA2018.json

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results/LASER2/HotpotQA.json

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results/LASER2/ImdbClassification.json

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results/LASER2/MSMARCO.json

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results/LASER2/MTOPDomainClassification.json

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results/LASER2/MTOPIntentClassification.json

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results/LASER2/MassiveIntentClassification.json

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results/LASER2/MedrxivClusteringP2P.json

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results/LASER2/MedrxivClusteringS2S.json

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results/LASER2/MindSmallReranking.json

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+    "dataset_version": null,
+    "mteb_version": "0.0.2"
+}

results/LASER2/QuoraRetrieval.json

@@ -0,0 +1,31 @@
+{
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+        "map_at_1": 0.54965,
+        "map_at_10": 0.66201,
+        "map_at_100": 0.6704,
+        "map_at_1000": 0.6709,
+        "map_at_3": 0.63478,
+        "map_at_5": 0.65101,
+        "ndcg_at_1": 0.6354,
+        "ndcg_at_10": 0.71145,
+        "ndcg_at_100": 0.74023,
+        "ndcg_at_1000": 0.7486,
+        "ndcg_at_3": 0.67488,
+        "ndcg_at_5": 0.69306,
+        "precision_at_1": 0.6354,
+        "precision_at_10": 0.10798,
+        "precision_at_100": 0.01348,
+        "precision_at_1000": 0.00149,
+        "precision_at_3": 0.29257,
+        "precision_at_5": 0.19412,
+        "recall_at_1": 0.54965,
+        "recall_at_10": 0.80505,
+        "recall_at_100": 0.92322,
+        "recall_at_1000": 0.97766,
+        "recall_at_3": 0.69938,
+        "recall_at_5": 0.7496
+    },
+    "dataset_version": null,
+    "mteb_version": "0.0.2"
+}

results/LASER2/RedditClustering.json

@@ -0,0 +1,9 @@
+{
+    "test": {
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+        "v_measure": 0.0996202584007419,
+        "v_measure_std": 0.013494785830352516
+    },
+    "dataset_version": null,
+    "mteb_version": "0.0.2"
+}

results/LASER2/RedditClusteringP2P.json

@@ -0,0 +1,9 @@
+{
+    "dataset_version": null,
+    "mteb_version": "0.0.2",
+    "test": {
+        "evaluation_time": 1784.6,
+        "v_measure": 0.264185580282609,
+        "v_measure_std": 0.08501487346178989
+    }
+}

results/LASER2/SCIDOCS.json

@@ -0,0 +1,31 @@
+{
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+        "map_at_1": 0.003,
+        "map_at_10": 0.00387,
+        "map_at_100": 0.00459,
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+        "ndcg_at_100": 0.01565,
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+        "ndcg_at_3": 0.00787,
+        "ndcg_at_5": 0.00647,
+        "precision_at_1": 0.015,
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+        "precision_at_100": 0.00162,
+        "precision_at_1000": 0.00098,
+        "precision_at_3": 0.006,
+        "precision_at_5": 0.0046,
+        "recall_at_1": 0.003,
+        "recall_at_10": 0.00718,
+        "recall_at_100": 0.03288,
+        "recall_at_1000": 0.19957,
+        "recall_at_3": 0.00373,
+        "recall_at_5": 0.00473
+    },
+    "dataset_version": null,
+    "mteb_version": "0.0.2"
+}

results/LASER2/SICK-R.json

@@ -0,0 +1,19 @@
+{
+    "test": {
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+            "spearman": 0.6285761430269268
+        },
+        "euclidean": {
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+            "spearman": 0.5964342697857706
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+        "manhattan": {
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+            "spearman": 0.5531149921833667
+        }
+    },
+    "dataset_version": null,
+    "mteb_version": "0.0.2"
+}

results/LASER2/STS12.json

@@ -0,0 +1,19 @@
+{
+    "test": {
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+            "spearman": 0.6260375600516247
+        },
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+            "spearman": 0.5909106889528086
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+            "spearman": 0.5570403794622609
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+    "dataset_version": null,
+    "mteb_version": "0.0.2"
+}