init

README.md

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+---
+benchmark: mteb
+type: evaluation
+submission_name: MTEB
+---

remove_ablations.py

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+import glob
+import os
+import json
+import tqdm
+
+
+for file in tqdm.tqdm(glob.glob("results/*/*.json")):
+    print(file)
+    if "Instruction" not in file:
+        continue
+    with open(file, "r") as fin:
+        data = json.load(fin)
+        if "individual" in data["test"]:
+            del data["test"]["individual"]
+        
+    with open(file, "w") as fout:
+        json.dump(data, fout, indent=2)
+

results.py

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+"""MTEB Results"""
+
+import json
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """@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}
+}
+"""
+
+_DESCRIPTION = """Results on MTEB"""
+
+URL = "https://huggingface.co/datasets/mteb/results/resolve/main/paths.json"
+VERSION = datasets.Version("1.0.1")
+EVAL_LANGS = ['af', 'afr-eng', 'am', "amh", 'amh-eng', 'ang-eng', 'ar', 'ar-ar', 'ara-eng', 'arq-eng', 'arz-eng', 'ast-eng', 'awa-eng', 'az', 'aze-eng', 'bel-eng', 'ben-eng', 'ber-eng', 'bn', 'bos-eng', 'bre-eng', 'bul-eng', 'cat-eng', 'cbk-eng', 'ceb-eng', 'ces-eng', 'cha-eng', 'cmn-eng', 'cor-eng', 'csb-eng', 'cy', 'cym-eng', 'da', 'dan-eng', 'de', 'de-fr', 'de-pl', 'deu-eng', 'dsb-eng', 'dtp-eng', 'el', 'ell-eng', 'en', 'en-ar', 'en-de', 'en-en', 'en-tr', 'eng', 'epo-eng', 'es', 'es-en', 'es-es', 'es-it', 'est-eng', 'eus-eng', 'fa', 'fao-eng', 'fi', 'fin-eng', 'fr', 'fr-en', 'fr-pl', 'fra', 'fra-eng', 'fry-eng', 'gla-eng', 'gle-eng', 'glg-eng', 'gsw-eng', 'hau', 'he', 'heb-eng', 'hi', 'hin-eng', 'hrv-eng', 'hsb-eng', 'hu', 'hun-eng', 'hy', 'hye-eng', 'ibo', 'id', 'ido-eng', 'ile-eng', 'ina-eng', 'ind-eng', 'is', 'isl-eng', 'it', 'it-en', 'ita-eng', 'ja', 'jav-eng', 'jpn-eng', 'jv', 'ka', 'kab-eng', 'kat-eng', 'kaz-eng', 'khm-eng', 'km', 'kn', 'ko', 'ko-ko', 'kor-eng', 'kur-eng', 'kzj-eng', 'lat-eng', 'lfn-eng', 'lit-eng', 'lin', 'lug', 'lv', 'lvs-eng', 'mal-eng', 'mar-eng', 'max-eng', 'mhr-eng', 'mkd-eng', 'ml', 'mn', 'mon-eng', 'ms', 'my', 'nb', 'nds-eng', 'nl', 'nl-ende-en', 'nld-eng', 'nno-eng', 'nob-eng', 'nov-eng', 'oci-eng', 'orm', 'orv-eng', 'pam-eng', 'pcm', 'pes-eng', 'pl', 'pl-en', 'pms-eng', 'pol-eng', 'por-eng', 'pt', 'ro', 'ron-eng', 'ru', 'run', 'rus-eng', 'sl', 'slk-eng', 'slv-eng', 'spa-eng', 'sna', 'som', 'sq', 'sqi-eng', 'srp-eng', 'sv', 'sw', 'swa', 'swe-eng', 'swg-eng', 'swh-eng', 'ta', 'tam-eng', 'tat-eng', 'te', 'tel-eng', 'tgl-eng', 'th', 'tha-eng', 'tir', 'tl', 'tr', 'tuk-eng', 'tur-eng', 'tzl-eng', 'uig-eng', 'ukr-eng', 'ur', 'urd-eng', 'uzb-eng', 'vi', 'vie-eng', 'war-eng', 'wuu-eng', 'xho', 'xho-eng', 'yid-eng', 'yor', 'yue-eng', 'zh', 'zh-CN', 'zh-TW', 'zh-en', 'zsm-eng']
+
+# v_measures key is somehow present in voyage-2-law results and is a list
+SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold", "v_measures"]
+
+# Use "train" split instead
+TRAIN_SPLIT = ["DanishPoliticalCommentsClassification"]
+# Use "validation" split instead
+VALIDATION_SPLIT = ["AFQMC", "Cmnli", "IFlyTek", "LEMBSummScreenFDRetrieval", "MSMARCO", "MSMARCO-PL", "MultilingualSentiment", "Ocnli", "TNews"]
+# Use "dev" split instead
+DEV_SPLIT = ["CmedqaRetrieval", "CovidRetrieval", "DuRetrieval", "EcomRetrieval", "MedicalRetrieval", "MMarcoReranking", "MMarcoRetrieval", "MSMARCO", "MSMARCO-PL", "T2Reranking", "T2Retrieval", "VideoRetrieval"]
+# Use "test.full" split
+TESTFULL_SPLIT = ["OpusparcusPC"]
+
+TEST_AVG_SPLIT = {
+    "LEMBNeedleRetrieval": ["test_256", "test_512", "test_1024", "test_2048", "test_4096", "test_8192", "test_16384", "test_32768"],
+    "LEMBPasskeyRetrieval": ["test_256", "test_512", "test_1024", "test_2048", "test_4096", "test_8192", "test_16384", "test_32768"],
+}
+
+MODELS = [
+    "Baichuan-text-embedding",
+    "Cohere-embed-english-v3.0",
+    "Cohere-embed-multilingual-light-v3.0",
+    "Cohere-embed-multilingual-v3.0",
+    "DanskBERT",
+    "FollowIR-7B",
+    "GritLM-7B",
+    "LASER2",
+    "LLM2Vec-Llama-2-supervised",
+    "LLM2Vec-Llama-2-unsupervised",
+    "LLM2Vec-Meta-Llama-3-supervised",
+    "LLM2Vec-Meta-Llama-3-unsupervised",
+    "LLM2Vec-Mistral-supervised",
+    "LLM2Vec-Mistral-unsupervised",
+    "LLM2Vec-Sheared-Llama-supervised",
+    "LLM2Vec-Sheared-Llama-unsupervised",
+    "LaBSE",
+    "OpenSearch-text-hybrid",
+    "all-MiniLM-L12-v2",
+    "all-MiniLM-L6-v2",
+    "all-mpnet-base-v2",
+    "allenai-specter",
+    "bert-base-10lang-cased",
+    "bert-base-15lang-cased",
+    "bert-base-25lang-cased",
+    "bert-base-multilingual-cased",
+    "bert-base-multilingual-uncased",
+    "bert-base-swedish-cased",
+    "bert-base-uncased",
+    "bge-base-en",
+    "bge-base-zh",
+    "bge-base-zh-v1.5",
+    "bge-large-en",
+    "bge-large-en-v1.5",
+    "bge-large-zh",
+    "bge-large-zh-noinstruct",
+    "bge-large-zh-v1.5",
+    "bge-m3",
+    "bge-small-zh",
+    "bge-small-zh-v1.5",
+    "bm25",
+    "camembert-base",
+    "camembert-large",
+    "contriever-base-msmarco",
+    "cross-en-de-roberta-sentence-transformer",
+    "dfm-encoder-large-v1",
+    "dfm-sentence-encoder-large-1",
+    "distilbert-base-25lang-cased",
+    "distilbert-base-en-fr-cased",
+    "distilbert-base-en-fr-es-pt-it-cased",
+    "distilbert-base-fr-cased",
+    "distilbert-base-uncased",
+    "distiluse-base-multilingual-cased-v2",
+    "e5-base",
+    "e5-base-4k",
+    "e5-base-v2",
+    "e5-large",
+    "e5-large-v2",
+    "e5-mistral-7b-instruct",
+    "e5-small",
+    "electra-small-nordic",
+    "electra-small-swedish-cased-discriminator",
+    "elser-v2",
+    "embedder-100p",
+    "facebook-dpr-ctx_encoder-multiset-base",
+    "flan-t5-base",
+    "flan-t5-large",
+    "flaubert_base_cased",
+    "flaubert_base_uncased",
+    "flaubert_large_cased",
+    "gbert-base",
+    "gbert-large",
+    "gelectra-base",
+    "gelectra-large",
+    "glove.6B.300d",
+    "google-gecko-256.text-embedding-preview-0409",
+    "google-gecko.text-embedding-preview-0409",
+    "gottbert-base",
+    "gte-Qwen1.5-7B-instruct",
+    "gtr-t5-base",
+    "gtr-t5-large",
+    "gtr-t5-xl",
+    "gtr-t5-xxl",
+    "herbert-base-retrieval-v2",
+    "instructor-base",
+    "instructor-xl",
+    "jina-embeddings-v2-base-en",
+    "komninos",
+    "llama-2-7b-chat",
+    "luotuo-bert-medium",
+    "m3e-base",
+    "m3e-large",
+    "mistral-7b-instruct-v0.2",
+    "mistral-embed",
+    "monobert-large-msmarco",
+    "monot5-3b-msmarco-10k",
+    "monot5-base-msmarco-10k",
+    "msmarco-bert-co-condensor",
+    "multi-qa-MiniLM-L6-cos-v1",
+    "multilingual-e5-base",
+    "multilingual-e5-large",
+    "multilingual-e5-small",
+    "nb-bert-base",
+    "nb-bert-large",
+    "nomic-embed-text-v1",
+    "nomic-embed-text-v1.5-128",
+    "nomic-embed-text-v1.5-256",
+    "nomic-embed-text-v1.5-512",
+    "nomic-embed-text-v1.5-64",
+    "norbert3-base",
+    "norbert3-large",
+    "paraphrase-multilingual-MiniLM-L12-v2",
+    "paraphrase-multilingual-mpnet-base-v2",
+    "sentence-bert-swedish-cased",
+    "sentence-camembert-base",
+    "sentence-camembert-large",
+    "sentence-croissant-llm-base",
+    "sentence-t5-base",
+    "sentence-t5-large",
+    "sentence-t5-xl",
+    "sentence-t5-xxl",
+    "sgpt-bloom-1b7-nli",
+    "sgpt-bloom-7b1-msmarco",
+    "silver-retriever-base-v1",
+    "st-polish-paraphrase-from-distilroberta",
+    "st-polish-paraphrase-from-mpnet",
+    "sup-simcse-bert-base-uncased",
+    "tart-dual-contriever-msmarco",
+    "tart-full-flan-t5-xl",
+    "text-embedding-3-large",
+    "text-embedding-3-large-256",
+    "text-embedding-3-small",
+    "text-embedding-ada-002",
+    "text-search-ada-001",
+    "text-search-ada-doc-001",
+    "text-search-babbage-001",
+    "text-search-curie-001",
+    "text-search-davinci-001",
+    "text-similarity-ada-001",
+    "text-similarity-babbage-001",
+    "text-similarity-curie-001",
+    "text-similarity-davinci-001",
+    "text2vec-base-chinese",
+    "text2vec-base-multilingual",
+    "text2vec-large-chinese",
+    "titan-embed-text-v1",
+    "udever-bloom-1b1",
+    "udever-bloom-560m",
+    "universal-sentence-encoder-multilingual-3",
+    "universal-sentence-encoder-multilingual-large-3",
+    "unsup-simcse-bert-base-uncased",
+    "use-cmlm-multilingual",
+    "voyage-2",
+    "voyage-code-2",
+    "voyage-large-2-instruct",
+    "voyage-law-2",
+    "voyage-lite-01-instruct",
+    "voyage-lite-02-instruct",
+    "voyage-multilingual-2",
+    "xlm-roberta-base",
+    "xlm-roberta-large",
+]
+
+
+# Needs to be run whenever new files are added
+def get_paths():
+    import collections, json, os
+    files = collections.defaultdict(list)
+    for model_dir in os.listdir("results"):
+        results_model_dir = os.path.join("results", model_dir)
+        if not os.path.isdir(results_model_dir):
+            print(f"Skipping {results_model_dir}")
+            continue
+        for res_file in os.listdir(results_model_dir):
+            if (res_file.endswith(".json")) and not(res_file.endswith("overall_results.json")):
+                results_model_file = os.path.join(results_model_dir, res_file)
+                files[model_dir].append(results_model_file)
+    with open("paths.json", "w") as f:
+        json.dump(files, f)
+    return files
+
+
+class MTEBResults(datasets.GeneratorBasedBuilder):
+    """MTEBResults"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=model,
+            description=f"{model} MTEB results",
+            version=VERSION,
+        )
+        for model in MODELS
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "mteb_dataset_name": datasets.Value("string"),
+                    "eval_language": datasets.Value("string"),
+                    "metric": datasets.Value("string"),
+                    "score": datasets.Value("float"),
+                }
+            ),
+            supervised_keys=None,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        path_file = dl_manager.download_and_extract(URL)
+        with open(path_file) as f:
+            files = json.load(f)
+
+        downloaded_files = dl_manager.download_and_extract(files[self.config.name])
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={'filepath': downloaded_files}
+            )
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info(f"Generating examples from {filepath}")
+        out = []
+
+        for path in filepath:
+            with open(path, encoding="utf-8") as f:
+                res_dict = json.load(f)
+                # Naming changed from mteb_dataset_name to task_name
+                ds_name = res_dict.get("mteb_dataset_name", res_dict.get("task_name"))
+                # New MTEB format uses scores
+                res_dict = res_dict.get("scores", res_dict)
+
+                split = "test"
+                if (ds_name in TRAIN_SPLIT) and ("train" in res_dict):
+                    split = "train"
+                elif (ds_name in VALIDATION_SPLIT) and ("validation" in res_dict):
+                    split = "validation"
+                elif (ds_name in DEV_SPLIT) and ("dev" in res_dict):
+                    split = "dev"
+                elif (ds_name in TESTFULL_SPLIT) and ("test.full" in res_dict):
+                    split = "test.full"
+                elif (ds_name in TEST_AVG_SPLIT):
+                    # Average splits
+                    res_dict["test_avg"] = {}
+                    for split in TEST_AVG_SPLIT[ds_name]:
+                        # Old MTEB format
+                        if isinstance(res_dict.get(split), dict):
+                            for k, v in res_dict.get(split, {}).items():
+                                v /= len(TEST_AVG_SPLIT[ds_name])
+                                if k not in res_dict["test_avg"]:
+                                    res_dict["test_avg"][k] = v
+                                else:
+                                    res_dict["test_avg"][k] += v
+                        # New MTEB format
+                        elif isinstance(res_dict.get(split), list):
+                            assert len(res_dict[split]) == 1, "Only single-lists supported for now"
+                            for k, v in res_dict[split][0].items():
+                                if not isinstance(v, float): continue
+                                v /= len(TEST_AVG_SPLIT[ds_name])
+                                if k not in res_dict["test_avg"]:
+                                    res_dict["test_avg"][k] = v
+                                else:
+                                    res_dict["test_avg"][k] += v
+                    split = "test_avg"
+                elif "test" not in res_dict:
+                    print(f"Skipping {ds_name} as split {split} not present.")
+                    continue
+                res_dict = res_dict.get(split)
+
+                ### New MTEB format ###
+                if isinstance(res_dict, list):
+                    for res in res_dict:
+                        lang = res.get("languages", [""])
+                        assert len(lang) == 1, "Only single-languages supported for now"
+                        lang = lang[0].replace("eng-Latn", "")
+                        for metric, score in res.items():
+                            if metric in SKIP_KEYS: continue
+                            out.append({
+                                "mteb_dataset_name": ds_name,
+                                "eval_language": lang,
+                                "metric": metric,
+                                "score": score * 100,
+                            })
+
+                ### Old MTEB format ###
+                else:
+                    is_multilingual = any(x in res_dict for x in EVAL_LANGS)
+                    langs = res_dict.keys() if is_multilingual else ["en"]
+                    for lang in langs:
+                        if lang in SKIP_KEYS: continue
+                        test_result_lang = res_dict.get(lang) if is_multilingual else res_dict
+                        for metric, score in test_result_lang.items():
+                            if not isinstance(score, dict):
+                                score = {metric: score}
+                            for sub_metric, sub_score in score.items():
+                                if any(x in sub_metric for x in SKIP_KEYS): continue
+                                if isinstance(sub_score, dict): continue
+                                out.append({
+                                    "mteb_dataset_name": ds_name,
+                                    "eval_language": lang if is_multilingual else "",
+                                    "metric": f"{metric}_{sub_metric}" if metric != sub_metric else metric,
+                                    "score": sub_score * 100,
+                                })
+        for idx, row in enumerate(sorted(out, key=lambda x: x["mteb_dataset_name"])):
+            yield idx, row

results/Baichuan-text-embedding/AFQMC.json

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+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "AFQMC",
+  "mteb_version": "1.1.0",
+  "validation": {
+    "cos_sim": {
+      "pearson": 0.4829609272631085,
+      "spearman": 0.5080031098340034
+    },
+    "euclidean": {
+      "pearson": 0.48915888167383914,
+      "spearman": 0.508003310876931
+    },
+    "evaluation_time": 5.52,
+    "manhattan": {
+      "pearson": 0.4883913003371612,
+      "spearman": 0.507119124081868
+    }
+  }
+}

results/Baichuan-text-embedding/ATEC.json

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+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "ATEC",
+  "mteb_version": "1.1.0",
+  "test": {
+    "cos_sim": {
+      "pearson": 0.5108024865980523,
+      "spearman": 0.5322524599077678
+    },
+    "euclidean": {
+      "pearson": 0.5495649374475876,
+      "spearman": 0.5322525387159475
+    },
+    "evaluation_time": 23.5,
+    "manhattan": {
+      "pearson": 0.5490069145550858,
+      "spearman": 0.5318577305140235
+    }
+  }
+}

results/Baichuan-text-embedding/AmazonReviewsClassification.json

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+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "AmazonReviewsClassification",
+  "mteb_version": "1.1.0",
+  "test": {
+    "evaluation_time": 37.19,
+    "zh": {
+      "accuracy": 0.48301999999999995,
+      "accuracy_stderr": 0.015009983344427814,
+      "f1": 0.4358151996593711,
+      "f1_stderr": 0.021935952504451846,
+      "main_score": 0.48301999999999995
+    }
+  },
+  "validation": {
+    "evaluation_time": 44.97,
+    "zh": {
+      "accuracy": 0.47596,
+      "accuracy_stderr": 0.013457131938121147,
+      "f1": 0.4297519925445886,
+      "f1_stderr": 0.022271859166647427,
+      "main_score": 0.47596
+    }
+  }
+}

results/Baichuan-text-embedding/BQ.json

@@ -0,0 +1,20 @@
+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "BQ",
+  "mteb_version": "1.1.0",
+  "test": {
+    "cos_sim": {
+      "pearson": 0.6382205659368678,
+      "spearman": 0.664875868977765
+    },
+    "euclidean": {
+      "pearson": 0.6469068710733963,
+      "spearman": 0.6648758689697865
+    },
+    "evaluation_time": 11.53,
+    "manhattan": {
+      "pearson": 0.6465254698872425,
+      "spearman": 0.664768204689053
+    }
+  }
+}

results/Baichuan-text-embedding/CLSClusteringP2P.json

@@ -0,0 +1,10 @@
+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "CLSClusteringP2P",
+  "mteb_version": "1.1.0",
+  "test": {
+    "evaluation_time": 842.9,
+    "v_measure": 0.6036843826092417,
+    "v_measure_std": 0.011663855200498223
+  }
+}

results/Baichuan-text-embedding/CLSClusteringS2S.json

@@ -0,0 +1,10 @@
+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "CLSClusteringS2S",
+  "mteb_version": "1.1.0",
+  "test": {
+    "evaluation_time": 99.78,
+    "v_measure": 0.5108781005084964,
+    "v_measure_std": 0.01733772998995446
+  }
+}

results/Baichuan-text-embedding/CMedQAv1.json

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+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "CMedQAv1",
+  "mteb_version": "1.1.0",
+  "test": {
+    "evaluation_time": 245.66,
+    "map": 0.8806336186199681,
+    "mrr": 0.9029142857142856
+  }
+}

results/Baichuan-text-embedding/CMedQAv2.json

@@ -0,0 +1,10 @@
+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "CMedQAv2",
+  "mteb_version": "1.1.0",
+  "test": {
+    "evaluation_time": 255.0,
+    "map": 0.8845694611153427,
+    "mrr": 0.9049976190476191
+  }
+}

results/Baichuan-text-embedding/CmedqaRetrieval.json

@@ -0,0 +1,38 @@
+{
+  "dataset_revision": null,
+  "dev": {
+    "evaluation_time": 293.85,
+    "map_at_1": 0.27486,
+    "map_at_10": 0.40762,
+    "map_at_100": 0.42651,
+    "map_at_1000": 0.42753,
+    "map_at_3": 0.36423,
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+}

results/Baichuan-text-embedding/Cmnli.json

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+}

results/Baichuan-text-embedding/CovidRetrieval.json

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+}

results/Baichuan-text-embedding/DuRetrieval.json

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results/Baichuan-text-embedding/EcomRetrieval.json

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+}

results/Baichuan-text-embedding/IFlyTek.json

@@ -0,0 +1,13 @@
+{
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results/Baichuan-text-embedding/JDReview.json

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results/Baichuan-text-embedding/LCQMC.json

@@ -0,0 +1,20 @@
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+}

results/Baichuan-text-embedding/MMarcoReranking.json

@@ -0,0 +1,10 @@
+{
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results/Baichuan-text-embedding/MMarcoRetrieval.json

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+  "mteb_dataset_name": "MMarcoRetrieval",
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+}

results/Baichuan-text-embedding/MassiveIntentClassification.json

@@ -0,0 +1,25 @@
+{
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+}

results/Baichuan-text-embedding/MassiveScenarioClassification.json

@@ -0,0 +1,25 @@
+{
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+  }
+}

results/Baichuan-text-embedding/MedicalRetrieval.json

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+{
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+    "recall_at_3": 0.631,
+    "recall_at_5": 0.654
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+  "mteb_dataset_name": "MedicalRetrieval",
+  "mteb_version": "1.1.0"
+}

results/Baichuan-text-embedding/MultilingualSentiment.json

@@ -0,0 +1,13 @@
+{
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+  "mteb_dataset_name": "MultilingualSentiment",
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+}

results/Baichuan-text-embedding/Ocnli.json

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+{
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+}

results/Baichuan-text-embedding/OnlineShopping.json

@@ -0,0 +1,15 @@
+{
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+  "mteb_dataset_name": "OnlineShopping",
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+}

results/Baichuan-text-embedding/PAWSX.json

@@ -0,0 +1,20 @@
+{
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+      "spearman": 0.47509647784531567
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+}

results/Baichuan-text-embedding/QBQTC.json

@@ -0,0 +1,20 @@
+{
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+  "mteb_dataset_name": "QBQTC",
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+      "spearman": 0.3990713604918495
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+  }
+}

results/Baichuan-text-embedding/STS22.json

@@ -0,0 +1,22 @@
+{
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+  "mteb_dataset_name": "STS22",
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+        "spearman": 0.6575028602582914
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+    }
+  }
+}

results/Baichuan-text-embedding/STSB.json

@@ -0,0 +1,20 @@
+{
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+  "mteb_dataset_name": "STSB",
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+  }
+}

results/Baichuan-text-embedding/T2Reranking.json

@@ -0,0 +1,10 @@
+{
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+  "mteb_dataset_name": "T2Reranking",
+  "mteb_version": "1.1.0",
+  "dev": {
+    "evaluation_time": 767.43,
+    "map": 0.6784944108203279,
+    "mrr": 0.7791862795671192
+  }
+}

results/Baichuan-text-embedding/T2Retrieval.json

@@ -0,0 +1,38 @@
+{
+  "dataset_revision": null,
+  "dev": {
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+    "map_at_1": 0.25021,
+    "map_at_10": 0.71544,
+    "map_at_100": 0.75456,
+    "map_at_1000": 0.75537,
+    "map_at_3": 0.49671,
+    "map_at_5": 0.61299,
+    "mrr_at_1": 0.85534,
+    "mrr_at_10": 0.89019,
+    "mrr_at_100": 0.89152,
+    "mrr_at_1000": 0.89158,
+    "mrr_at_3": 0.88413,
+    "mrr_at_5": 0.8879,
+    "ndcg_at_1": 0.85534,
+    "ndcg_at_10": 0.8011,
+    "ndcg_at_100": 0.84517,
+    "ndcg_at_1000": 0.85311,
+    "ndcg_at_3": 0.81212,
+    "ndcg_at_5": 0.79782,
+    "precision_at_1": 0.85534,
+    "precision_at_10": 0.40517,
+    "precision_at_100": 0.04961,
+    "precision_at_1000": 0.00514,
+    "precision_at_3": 0.71484,
+    "precision_at_5": 0.60081,
+    "recall_at_1": 0.25021,
+    "recall_at_10": 0.79721,
+    "recall_at_100": 0.93976,
+    "recall_at_1000": 0.98037,
+    "recall_at_3": 0.51869,
+    "recall_at_5": 0.65616
+  },
+  "mteb_dataset_name": "T2Retrieval",
+  "mteb_version": "1.1.0"
+}

results/Baichuan-text-embedding/TNews.json

@@ -0,0 +1,13 @@
+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "TNews",
+  "mteb_version": "1.1.0",
+  "validation": {
+    "accuracy": 0.5261899999999999,
+    "accuracy_stderr": 0.005332813516334514,
+    "evaluation_time": 80.03,
+    "f1": 0.5088699308193914,
+    "f1_stderr": 0.005808330018430932,
+    "main_score": 0.5261899999999999
+  }
+}

results/Baichuan-text-embedding/ThuNewsClusteringP2P.json

@@ -0,0 +1,10 @@
+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "ThuNewsClusteringP2P",
+  "mteb_version": "1.1.0",
+  "test": {
+    "evaluation_time": 684.88,
+    "v_measure": 0.582283860392516,
+    "v_measure_std": 0.02478622759991042
+  }
+}

results/Baichuan-text-embedding/ThuNewsClusteringS2S.json

@@ -0,0 +1,10 @@
+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "ThuNewsClusteringS2S",
+  "mteb_version": "1.1.0",
+  "test": {
+    "evaluation_time": 71.89,
+    "v_measure": 0.5782974132365385,
+    "v_measure_std": 0.013022585576314076
+  }
+}

results/Baichuan-text-embedding/VideoRetrieval.json

@@ -0,0 +1,38 @@
+{
+  "dataset_revision": null,
+  "dev": {
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+    "map_at_1": 0.606,
+    "map_at_10": 0.70041,
+    "map_at_100": 0.70481,
+    "map_at_1000": 0.70494,
+    "map_at_3": 0.682,
+    "map_at_5": 0.6925,
+    "mrr_at_1": 0.606,
+    "mrr_at_10": 0.70041,
+    "mrr_at_100": 0.70481,
+    "mrr_at_1000": 0.70494,
+    "mrr_at_3": 0.682,
+    "mrr_at_5": 0.6925,
+    "ndcg_at_1": 0.606,
+    "ndcg_at_10": 0.74282,
+    "ndcg_at_100": 0.7629,
+    "ndcg_at_1000": 0.76595,
+    "ndcg_at_3": 0.70476,
+    "ndcg_at_5": 0.72369,
+    "precision_at_1": 0.606,
+    "precision_at_10": 0.0875,
+    "precision_at_100": 0.00966,
+    "precision_at_1000": 0.00099,
+    "precision_at_3": 0.25667,
+    "precision_at_5": 0.1632,
+    "recall_at_1": 0.606,
+    "recall_at_10": 0.875,
+    "recall_at_100": 0.966,
+    "recall_at_1000": 0.989,
+    "recall_at_3": 0.77,
+    "recall_at_5": 0.816
+  },
+  "mteb_dataset_name": "VideoRetrieval",
+  "mteb_version": "1.1.0"
+}

results/Baichuan-text-embedding/Waimai.json

@@ -0,0 +1,15 @@
+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "Waimai",
+  "mteb_version": "1.1.0",
+  "test": {
+    "accuracy": 0.8877,
+    "accuracy_stderr": 0.005478138369920941,
+    "ap": 0.7410049925615304,
+    "ap_stderr": 0.01151741307800683,
+    "evaluation_time": 13.59,
+    "f1": 0.8718799117513892,
+    "f1_stderr": 0.005127107574743738,
+    "main_score": 0.8877
+  }
+}

results/Cohere-embed-english-v3.0/AILACasedocs.json

@@ -0,0 +1 @@
+{"test": {"ndcg_at_10": 0.31543}, "mteb_dataset_name": "AILACasedocs"}

results/Cohere-embed-english-v3.0/AILAStatutes.json

@@ -0,0 +1 @@
+{"test": {"ndcg_at_10": 0.27152}, "mteb_dataset_name": "AILAStatutes"}

results/Cohere-embed-english-v3.0/Core17InstructionRetrieval.json

@@ -0,0 +1,9 @@
+{
+  "dataset_revision": "e39ff896cf3efbbdeeb950e6bd7c79f266995b07",
+  "mteb_dataset_name": "Core17InstructionRetrieval",
+  "mteb_version": "1.7.32",
+  "test": {
+    "evaluation_time": 746.94,
+    "p-MRR": 0.028043926455402175
+  }
+}

results/Cohere-embed-english-v3.0/GerDaLIRSmall.json

@@ -0,0 +1 @@
+{"test": {"ndcg_at_10": 0.06047}, "mteb_dataset_name": "GerDaLIRSmall"}

results/Cohere-embed-english-v3.0/LeCaRDv2.json

@@ -0,0 +1 @@
+{"test": {"ndcg_at_10": 0.21017}, "mteb_dataset_name": "LeCaRDv2"}

results/Cohere-embed-english-v3.0/LegalBenchConsumerContractsQA.json

@@ -0,0 +1 @@
+{"test": {"ndcg_at_10": 0.7712}, "mteb_dataset_name": "LegalBenchConsumerContractsQA"}

results/Cohere-embed-english-v3.0/LegalBenchCorporateLobbying.json

@@ -0,0 +1 @@
+{"test": {"ndcg_at_10": 0.93681}, "mteb_dataset_name": "LegalBenchCorporateLobbying"}

results/Cohere-embed-english-v3.0/LegalQuAD.json

@@ -0,0 +1 @@
+{"test": {"ndcg_at_10": 0.26075}, "mteb_dataset_name": "LegalQuAD"}

results/Cohere-embed-english-v3.0/LegalSummarization.json

@@ -0,0 +1 @@
+{"test": {"ndcg_at_10": 0.61697}, "mteb_dataset_name": "LegalSummarization"}

results/Cohere-embed-english-v3.0/News21InstructionRetrieval.json

@@ -0,0 +1,9 @@
+{
+  "dataset_revision": "e0144086b45fe31ac125e9ac1a83b6a409bb6ca6",
+  "mteb_dataset_name": "News21InstructionRetrieval",
+  "mteb_version": "1.7.32",
+  "test": {
+    "evaluation_time": 1439.26,
+    "p-MRR": 0.002004465362643827
+  }
+}

results/Cohere-embed-english-v3.0/Robust04InstructionRetrieval.json

@@ -0,0 +1,9 @@
+{
+  "dataset_revision": "a5a1c4fe2bc528ac12e83f8cdf82178da85d2f1d",
+  "mteb_dataset_name": "Robust04InstructionRetrieval",
+  "mteb_version": "1.7.32",
+  "test": {
+    "evaluation_time": 2142.32,
+    "p-MRR": -0.036304526527805095
+  }
+}