Fetch models results from HF (#43)

* init

* updates from run

* download results

* remove models list

* update

* remove test ci bump

* fix tests

* small fixes

* small fixes

* change -1 to nan

* update paths

* get back removed files

* get back removed files

* rename mteb version name

* change evaluation time and mteb version to null

* fixes

* pre-release fix

load_external.py

@@ -0,0 +1,238 @@
+from __future__ import annotations
+
+import json
+import logging
+import math
+import re
+from pathlib import Path
+from typing import Any
+
+from huggingface_hub import HfApi, get_hf_file_metadata, hf_hub_download, hf_hub_url
+from huggingface_hub.errors import NotASafetensorsRepoError
+from huggingface_hub.hf_api import ModelInfo
+from huggingface_hub.repocard import metadata_load
+from mteb import ModelMeta, get_task
+
+API = HfApi()
+logger = logging.getLogger(__name__)
+
+
+library_mapping = {
+    "sentence-transformers": "Sentence Transformers",
+}
+
+
+def get_model_dir(model_id: str) -> Path:
+    external_result_dir = Path("results") / model_id.replace("/", "__") / "external"
+    return external_result_dir
+
+
+def simplify_dataset_name(name: str) -> str:
+    return name.replace("MTEB ", "").split()[0]
+
+
+def get_model_parameters_memory(model_info: ModelInfo) -> tuple[int| None, float|None]:
+    try:
+        safetensors = API.get_safetensors_metadata(model_info.id)
+        num_parameters = sum(safetensors.parameter_count.values())
+        return num_parameters, round(num_parameters * 4 / 1024 ** 3, 2)
+    except NotASafetensorsRepoError as e:
+        logger.info(f"Could not find SafeTensors metadata for {model_info.id}")
+
+    filenames = [sib.rfilename for sib in model_info.siblings]
+    if "pytorch_model.bin" in filenames:
+        url = hf_hub_url(model_info.id, filename="pytorch_model.bin")
+        meta = get_hf_file_metadata(url)
+        bytes_per_param = 4
+        num_params = round(meta.size / bytes_per_param)
+        size_gb = round(meta.size * (4 / bytes_per_param) / 1024 ** 3, 2)
+        return num_params, size_gb
+    if "pytorch_model.bin.index.json" in filenames:
+        index_path = hf_hub_download(model_info.id, filename="pytorch_model.bin.index.json")
+        size = json.load(open(index_path))
+        bytes_per_param = 4
+        if "metadata" in size and "total_size" in size["metadata"]:
+            return round(size["metadata"]["total_size"] / bytes_per_param), round(size["metadata"]["total_size"] / 1024 ** 3, 2)
+    logger.info(f"Could not find the model parameters for {model_info.id}")
+    return None, None
+
+
+def get_dim_seq_size(model: ModelInfo) -> tuple[str | None, str | None, int, float]:
+    siblings = model.siblings or []
+    filenames = [sib.rfilename for sib in siblings]
+    dim, seq = None, None
+    for filename in filenames:
+        if re.match(r"\d+_Pooling/config.json", filename):
+            st_config_path = hf_hub_download(model.id, filename=filename)
+            dim = json.load(open(st_config_path)).get("word_embedding_dimension", None)
+            break
+    for filename in filenames:
+        if re.match(r"\d+_Dense/config.json", filename):
+            st_config_path = hf_hub_download(model.id, filename=filename)
+            dim = json.load(open(st_config_path)).get("out_features", dim)
+    if "config.json" in filenames:
+        config_path = hf_hub_download(model.id, 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", None)))
+        seq = config.get("n_positions", config.get("max_position_embeddings", config.get("n_ctx", config.get("seq_length", None))))
+
+    parameters, memory = get_model_parameters_memory(model)
+    return dim, seq, parameters, memory
+
+
+def create_model_meta(model_info: ModelInfo) -> ModelMeta | None:
+    readme_path = hf_hub_download(model_info.id, filename="README.md", etag_timeout=30)
+    meta = metadata_load(readme_path)
+    dim, seq, parameters, memory = None, None, None, None
+    try:
+        dim, seq, parameters, memory = get_dim_seq_size(model_info)
+    except Exception as e:
+        logger.error(f"Error getting model parameters for {model_info.id}, {e}")
+
+    release_date = str(model_info.created_at.date()) if model_info.created_at else ""
+    library = [library_mapping[model_info.library_name]] if model_info.library_name in library_mapping else []
+    languages = meta.get("language", [])
+    if not isinstance(languages, list) and isinstance(languages, str):
+        languages = [languages]
+    # yaml transforms norwegian `no` to False
+    for i in range(len(languages)):
+        if languages[i] is False:
+            languages[i] = "no"
+
+    model_meta = ModelMeta(
+        name=model_info.id,
+        revision=model_info.sha,
+        release_date=release_date,
+        open_weights=True,
+        framework=library,
+        license=meta.get("license", None),
+        embed_dim=dim,
+        max_tokens=seq,
+        n_parameters=parameters,
+        languages=languages,
+    )
+    return model_meta
+
+
+def parse_readme(model_info: ModelInfo) -> dict[str, dict[str, Any]] | None:
+    model_id = model_info.id
+    try:
+        readme_path = hf_hub_download(model_info.id, filename="README.md", etag_timeout=30)
+    except Exception:
+        logger.warning(f"ERROR: Could not fetch metadata for {model_id}, trying again")
+        readme_path = hf_hub_download(model_id, filename="README.md", etag_timeout=30)
+    meta = metadata_load(readme_path)
+    if "model-index" not in meta:
+        logger.info(f"Could not find model-index in {model_id}")
+        return
+    model_index = meta["model-index"][0]
+    model_name_from_readme = model_index.get("name", None)
+    orgs = ["Alibaba-NLP", "HIT-TMG", "McGill-NLP", "Snowflake", "facebook", "jinaai", "nomic-ai"]
+    is_org = any([model_id.startswith(org) for org in orgs])
+    # There a lot of reuploads with tunes, quantization, etc. We only want the original model
+    # to prevent this most of the time we can check if the model name from the readme is the same as the model id
+    # but some orgs have a different naming in their readme
+    if model_name_from_readme and not model_info.id.endswith(model_name_from_readme) and not is_org:
+        logger.warning(f"Model name mismatch: {model_info.id} vs {model_name_from_readme}")
+        return
+    results = model_index.get("results", [])
+    model_results = {}
+    for result in results:
+        dataset = result["dataset"]
+        dataset_type = dataset["type"]  # type is repo of the dataset
+        if dataset_type not in model_results:
+            output_dict = {
+                "dataset_revision": dataset.get("revision", ""),
+                "task_name": simplify_dataset_name(dataset["name"]),
+                "evaluation_time": None,
+                "mteb_version": None,
+                "scores": {},
+            }
+        else:
+            output_dict = model_results[dataset_type]
+
+        try:
+            mteb_task = get_task(output_dict["task_name"])
+        except Exception:
+            logger.warning(f"Error getting task for {model_id} {output_dict['task_name']}")
+            continue
+
+        mteb_task_metadata = mteb_task.metadata
+        mteb_task_eval_languages = mteb_task_metadata.eval_langs
+
+        scores_dict = output_dict["scores"]
+        current_split = dataset["split"]
+        current_config = dataset.get("config", "")
+        cur_split_metrics = {
+            "hf_subset": current_config,
+            "languages": mteb_task_eval_languages if isinstance(mteb_task_eval_languages, list) else mteb_task_eval_languages.get(current_config, ["None"]),
+        }
+        for metric in result["metrics"]:
+            cur_split_metrics[metric["type"]] = metric["value"]
+
+        main_score_str = "main_score"
+        if main_score_str not in cur_split_metrics:
+            # old sts and sum_eval have cos_sim_pearson, but in model_meta cosine_spearman is main_score
+            for old_metric, new_metric in zip(["cos_sim_pearson", "cos_sim_spearman"], ["cosine_pearson", "cosine_spearman"]):
+                if old_metric in cur_split_metrics:
+                    cur_split_metrics[new_metric] = cur_split_metrics[old_metric]
+
+            if mteb_task.metadata.main_score not in cur_split_metrics:
+                logger.warning(f"Could not find main score for {model_id} {output_dict['task_name']}, mteb task {mteb_task.metadata.name}. Main score: {mteb_task.metadata.main_score}. Metrics: {cur_split_metrics}, result {result['metrics']}")
+                continue
+
+            cur_split_metrics[main_score_str] = cur_split_metrics.get(mteb_task.metadata.main_score, None)
+        split_metrics = scores_dict.get(current_split, [])
+        split_metrics.append(cur_split_metrics)
+        scores_dict[current_split] = split_metrics
+        model_results[dataset_type] = output_dict
+    return model_results
+
+
+def get_mteb_data() -> None:
+    models = sorted(list(API.list_models(filter="mteb", full=True)), key=lambda x: x.id)
+    # models = [model for model in models if model.id == "intfloat/multilingual-e5-large"]
+    for i, model_info in enumerate(models, start=1):
+        logger.info(f"[{i}/{len(models)}] Processing {model_info.id}")
+        model_path = get_model_dir(model_info.id)
+        if (model_path / "model_meta.json").exists() and len(list(model_path.glob("*.json"))) > 1:
+            logger.info(f"Model meta already exists for {model_info.id}")
+            continue
+        if model_info.id.lower().endswith("gguf"):
+            logger.info(f"Skipping {model_info.id} GGUF model")
+            continue
+
+        spam_users = ["ILKT", "fine-tuned", "mlx-community"]
+        is_spam = False
+        for spam_user in spam_users:
+            if model_info.id.startswith(spam_user):
+                logger.info(f"Skipping {model_info.id}")
+                is_spam = True
+                continue
+        if is_spam:
+            continue
+        model_meta = create_model_meta(model_info)
+        model_results = parse_readme(model_info)
+
+        if not model_meta or not model_results:
+            logger.warning(f"Could not get model meta or results for {model_info.id}")
+            continue
+
+        if not model_path.exists():
+            model_path.mkdir(parents=True, exist_ok=True)
+
+        model_meta_path = model_path / "model_meta.json"
+        with model_meta_path.open("w") as f:
+            json.dump(model_meta.model_dump(), f, indent=4)
+
+        for model_result in model_results:
+            task_name = model_results[model_result]["task_name"]
+            result_file = model_path / f"{task_name}.json"
+            with result_file.open("w") as f:
+                json.dump(model_results[model_result], f, indent=4)
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    get_mteb_data()

results.py

@@ -3,6 +3,8 @@
 from __future__ import annotations
 
 import json
+import os
+from pathlib import Path
 
 import datasets
 
@@ -290,250 +292,29 @@ TEST_AVG_SPLIT = {
     ],
 }
 
-MODELS = [
-    "Alibaba-NLP__gte-Qwen1.5-7B-instruct",
-    "Alibaba-NLP__gte-Qwen2-7B-instruct",
-    "BAAI__bge-base-en",
-    "BAAI__bge-base-en-v1.5",
-    "BAAI__bge-base-en-v1.5-instruct",
-    "BAAI__bge-base-zh",
-    "BAAI__bge-base-zh-v1.5",
-    "BAAI__bge-large-en",
-    "BAAI__bge-large-en-v1.5",
-    "BAAI__bge-large-en-v1.5-instruct",
-    "BAAI__bge-large-zh",
-    "BAAI__bge-large-zh-noinstruct",
-    "BAAI__bge-large-zh-v1.5",
-    "BAAI__bge-m3",
-    "BAAI__bge-m3-instruct",
-    "BAAI__bge-small-en-v1.5",
-    "BAAI__bge-small-en-v1.5-instruct",
-    "BAAI__bge-small-zh",
-    "BAAI__bge-small-zh-v1.5",
-    "Cohere__Cohere-embed-english-v3.0",
-    "Cohere__Cohere-embed-english-v3.0-instruct",
-    "Cohere__Cohere-embed-multilingual-light-v3.0",
-    "Cohere__Cohere-embed-multilingual-v3.0",
-    "DeepPavlov__distilrubert-small-cased-conversational",
-    "DeepPavlov__rubert-base-cased",
-    "DeepPavlov__rubert-base-cased-sentence",
-    "FacebookAI__xlm-roberta-base",
-    "FacebookAI__xlm-roberta-large",
-    "Geotrend__bert-base-10lang-cased",
-    "Geotrend__bert-base-15lang-cased",
-    "Geotrend__bert-base-25lang-cased",
-    "Geotrend__distilbert-base-25lang-cased",
-    "Geotrend__distilbert-base-en-fr-cased",
-    "Geotrend__distilbert-base-en-fr-es-pt-it-cased",
-    "Geotrend__distilbert-base-fr-cased",
-    "GritLM__GritLM-7B",
-    "GritLM__GritLM-7B-noinstruct",
-    "KBLab__electra-small-swedish-cased-discriminator",
-    "KBLab__sentence-bert-swedish-cased",
-    "KB__bert-base-swedish-cased",
-    "McGill-NLP__LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised",
-    "McGill-NLP__LLM2Vec-Llama-2-unsupervised",
-    "McGill-NLP__LLM2Vec-Meta-Llama-3-supervised",
-    "McGill-NLP__LLM2Vec-Meta-Llama-3-unsupervised",
-    "McGill-NLP__LLM2Vec-Mistral-supervised",
-    "McGill-NLP__LLM2Vec-Mistral-unsupervised",
-    "McGill-NLP__LLM2Vec-Sheared-Llama-supervised",
-    "McGill-NLP__LLM2Vec-Sheared-Llama-unsupervised",
-    "Muennighoff__SGPT-1.3B-weightedmean-msmarco-specb-bitfit",
-    "Muennighoff__SGPT-125M-weightedmean-msmarco-specb-bitfit",
-    "Muennighoff__SGPT-125M-weightedmean-msmarco-specb-bitfit-doc",
-    "Muennighoff__SGPT-125M-weightedmean-msmarco-specb-bitfit-que",
-    "Muennighoff__SGPT-125M-weightedmean-nli-bitfit",
-    "Muennighoff__SGPT-2.7B-weightedmean-msmarco-specb-bitfit",
-    "Muennighoff__SGPT-5.8B-weightedmean-msmarco-specb-bitfit",
-    "Muennighoff__SGPT-5.8B-weightedmean-msmarco-specb-bitfit-que",
-    "Muennighoff__SGPT-5.8B-weightedmean-nli-bitfit",
-    "NbAiLab__nb-bert-base",
-    "NbAiLab__nb-bert-large",
-    "Salesforce__SFR-Embedding-Mistral",
-    "T-Systems-onsite__cross-en-de-roberta-sentence-transformer",
-    "Wissam42__sentence-croissant-llm-base",
-    "ai-forever__sbert_large_mt_nlu_ru",
-    "ai-forever__sbert_large_nlu_ru",
-    "aliyun__OpenSearch-text-hybrid",
-    "almanach__camembert-base",
-    "almanach__camembert-large",
-    "amazon__titan-embed-text-v1",
-    "baichuan-ai__text-embedding",
-    "bigscience-data__sgpt-bloom-1b7-nli",
-    "bigscience-data__sgpt-bloom-7b1-msmarco",
-    "bm25",
-    "bm25s",
-    "castorini__monobert-large-msmarco",
-    "castorini__monot5-3b-msmarco-10k",
-    "castorini__monot5-base-msmarco-10k",
-    "chcaa__dfm-encoder-large-v1",
-    "cointegrated__LaBSE-en-ru",
-    "cointegrated__rubert-tiny",
-    "cointegrated__rubert-tiny2",
-    "dangvantuan__sentence-camembert-base",
-    "dangvantuan__sentence-camembert-large",
-    "deepfile__embedder-100p",
-    "deepset__gbert-base",
-    "deepset__gbert-large",
-    "deepset__gelectra-base",
-    "deepset__gelectra-large",
-    "deepvk__USER-base",
-    "deepvk__USER-bge-m3",
-    "deepvk__deberta-v1-base",
-    "distilbert__distilbert-base-uncased",
-    "dwzhu__e5-base-4k",
-    "elastic__elser-v2",
-    "facebook__contriever",
-    "facebook__contriever-instruct",
-    "facebook__dpr-ctx_encoder-multiset-base",
-    "facebook__dragon-plus-context-encoder",
-    "facebook__tart-full-flan-t5-xl",
-    "facebookresearch__LASER2",
-    "facebookresearch__dragon-plus",
-    "facebookresearch__dragon-plus-instruct",
-    "flaubert__flaubert_base_cased",
-    "flaubert__flaubert_base_uncased",
-    "flaubert__flaubert_large_cased",
-    "google-bert__bert-base-multilingual-cased",
-    "google-bert__bert-base-multilingual-uncased",
-    "google-bert__bert-base-uncased",
-    "google-gecko__text-embedding-004",
-    "google-gecko__text-embedding-004-256",
-    "google__flan-t5-base",
-    "google__flan-t5-large",
-    "hkunlp__instructor-base",
-    "hkunlp__instructor-large",
-    "hkunlp__instructor-xl",
-    "intfloat__e5-base",
-    "intfloat__e5-base-v2",
-    "intfloat__e5-large",
-    "intfloat__e5-large-v2",
-    "intfloat__e5-mistral-7b-instruct",
-    "intfloat__e5-mistral-7b-instruct-noinstruct",
-    "intfloat__e5-small",
-    "intfloat__e5-small-v2",
-    "intfloat__multilingual-e5-base",
-    "intfloat__multilingual-e5-large",
-    "intfloat__multilingual-e5-large-instruct",
-    "intfloat__multilingual-e5-small",
-    "ipipan__herbert-base-retrieval-v2",
-    "ipipan__silver-retriever-base-v1",
-    "izhx__udever-bloom-1b1",
-    "izhx__udever-bloom-560m",
-    "jhu-clsp__FollowIR-7B",
-    "jinaai__jina-embeddings-v2-base-en",
-    "jonfd__electra-small-nordic",
-    "ltg__norbert3-base",
-    "ltg__norbert3-large",
-    "meta-llama__llama-2-7b-chat",
-    "mistral__mistral-embed",
-    "mistralai__mistral-7b-instruct-v0.2",
-    "mixedbread-ai__mxbai-embed-large-v1",
-    "moka-ai__m3e-base",
-    "moka-ai__m3e-large",
-    "nomic-ai__nomic-embed-text-v1",
-    "nomic-ai__nomic-embed-text-v1.5-128",
-    "nomic-ai__nomic-embed-text-v1.5-256",
-    "nomic-ai__nomic-embed-text-v1.5-512",
-    "nomic-ai__nomic-embed-text-v1.5-64",
-    "nthakur__contriever-base-msmarco",
-    "openai__text-embedding-3-large",
-    "openai__text-embedding-3-large-256",
-    "openai__text-embedding-3-large-instruct",
-    "openai__text-embedding-3-small-instruct",
-    "openai__text-embedding-ada-002",
-    "openai__text-embedding-ada-002-instruct",
-    "openai__text-search-ada-001",
-    "openai__text-search-ada-doc-001",
-    "openai__text-search-babbage-001",
-    "openai__text-search-curie-001",
-    "openai__text-search-davinci-001",
-    "openai__text-similarity-ada-001",
-    "openai__text-similarity-babbage-001",
-    "openai__text-similarity-curie-001",
-    "openai__text-similarity-davinci-001",
-    "openai__text-embedding-3-small",
-    "orionweller__tart-dual-contriever-msmarco",
-    "princeton-nlp__sup-simcse-bert-base-uncased",
-    "princeton-nlp__unsup-simcse-bert-base-uncased",
-    "sdadas__st-polish-paraphrase-from-distilroberta",
-    "sdadas__st-polish-paraphrase-from-mpnet",
-    "sentence-transformers__LaBSE",
-    "sentence-transformers__all-MiniLM-L12-v2",
-    "sentence-transformers__all-MiniLM-L6-v2",
-    "sentence-transformers__all-MiniLM-L6-v2-instruct",
-    "sentence-transformers__all-mpnet-base-v2",
-    "sentence-transformers__all-mpnet-base-v2-instruct",
-    "sentence-transformers__allenai-specter",
-    "sentence-transformers__average_word_embeddings_glove.6B.300d",
-    "sentence-transformers__average_word_embeddings_komninos",
-    "sentence-transformers__distiluse-base-multilingual-cased-v2",
-    "sentence-transformers__gtr-t5-base",
-    "sentence-transformers__gtr-t5-large",
-    "sentence-transformers__gtr-t5-xl",
-    "sentence-transformers__gtr-t5-xxl",
-    "sentence-transformers__msmarco-bert-co-condensor",
-    "sentence-transformers__multi-qa-MiniLM-L6-cos-v1",
-    "sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2",
-    "sentence-transformers__paraphrase-multilingual-mpnet-base-v2",
-    "sentence-transformers__sentence-t5-base",
-    "sentence-transformers__sentence-t5-large",
-    "sentence-transformers__sentence-t5-xl",
-    "sentence-transformers__sentence-t5-xxl",
-    "sentence-transformers__use-cmlm-multilingual",
-    "sergeyzh__LaBSE-ru-turbo",
-    "sergeyzh__rubert-tiny-turbo",
-    "shibing624__text2vec-base-chinese",
-    "shibing624__text2vec-base-multilingual",
-    "shibing624__text2vec-large-chinese",
-    "silk-road__luotuo-bert-medium",
-    "uklfr__gottbert-base",
-    "vesteinn__DanskBERT",
-    "voyageai__voyage-2",
-    "voyageai__voyage-code-2",
-    "voyageai__voyage-large-2-instruct",
-    "voyageai__voyage-law-2",
-    "voyageai__voyage-lite-01-instruct",
-    "voyageai__voyage-lite-02-instruct",
-    "voyageai__voyage-multilingual-2",
-    "voyageai__voyage-3",
-    "voyageai__voyage-3-lite",
-    "vprelovac__universal-sentence-encoder-multilingual-3",
-    "vprelovac__universal-sentence-encoder-multilingual-large-3",
-]
-
-
-def get_model_for_current_dir(dir_name: str) -> str | None:
-    for model in MODELS:
-        if model == dir_name or ("__" in dir_name and dir_name.split("__")[1] == model):
-            return model
-    return None
-
+MODELS = sorted(list(set([str(file).split('/')[-1] for file in (Path(__file__).parent / "results").glob("*") if file.is_dir()])))
 
 # 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"):
+    for model_dir in MODELS:
         results_model_dir = os.path.join("results", model_dir)
         if not os.path.isdir(results_model_dir):
             print(f"Skipping {results_model_dir}")
             continue
-        model_name = get_model_for_current_dir(model_dir)
-        if model_name is None:
-            print(f"Skipping {model_dir} model dir")
-            continue
         for revision_folder in os.listdir(results_model_dir):
             if not os.path.isdir(os.path.join(results_model_dir, revision_folder)):
                 continue
+            if revision_folder == "external":
+                continue
             for res_file in os.listdir(os.path.join(results_model_dir, revision_folder)):
                 if (res_file.endswith(".json")) and not (
                     res_file.endswith(("overall_results.json", "model_meta.json"))
                 ):
                     results_model_file = os.path.join(results_model_dir, revision_folder, res_file)
-                    files[model_name].append(results_model_file)
+                    files[model_dir].append(results_model_file)
     with open("paths.json", "w") as f:
         json.dump(files, f, indent=2)
     return files

results/AbderrahmanSkiredj1__Arabic_text_embedding_for_sts/external/STS17.json

@@ -0,0 +1,23 @@
+{
+    "dataset_revision": "faeb762787bd10488a50c8b5be4a3b82e411949c",
+    "task_name": "STS17",
+    "evaluation_time": null,
+    "mteb_version": null,
+    "scores": {
+        "test": [
+            {
+                "hf_subset": "ar-ar",
+                "languages": [
+                    "ara-Arab"
+                ],
+                "cosine_pearson": 84.5661961200634,
+                "cosine_spearman": 85.04839486363485,
+                "euclidean_pearson": 83.06651812356505,
+                "euclidean_spearman": 84.64285835335832,
+                "main_score": 85.04839486363485,
+                "manhattan_pearson": 83.12273085900199,
+                "manhattan_spearman": 84.51258675131285
+            }
+        ]
+    }
+}

results/AbderrahmanSkiredj1__Arabic_text_embedding_for_sts/external/model_meta.json

@@ -0,0 +1,22 @@
+{
+    "name": "AbderrahmanSkiredj1/Arabic_text_embedding_for_sts",
+    "revision": "bbd8aaebafc25b38eaa96300313db0bbe0086d12",
+    "release_date": "2024-07-07",
+    "languages": [],
+    "loader": null,
+    "n_parameters": 135193344,
+    "memory_usage": null,
+    "max_tokens": 512,
+    "embed_dim": 768,
+    "license": null,
+    "open_weights": true,
+    "public_training_data": null,
+    "public_training_code": null,
+    "framework": [
+        "Sentence Transformers"
+    ],
+    "reference": null,
+    "similarity_fn_name": null,
+    "use_instructions": null,
+    "zero_shot_benchmarks": null
+}

results/AbderrahmanSkiredj1__arabic_text_embedding_sts_arabertv02_arabicnlitriplet/external/STS17.json

@@ -0,0 +1,23 @@
+{
+    "dataset_revision": "faeb762787bd10488a50c8b5be4a3b82e411949c",
+    "task_name": "STS17",
+    "evaluation_time": null,
+    "mteb_version": null,
+    "scores": {
+        "test": [
+            {
+                "hf_subset": "ar-ar",
+                "languages": [
+                    "ara-Arab"
+                ],
+                "cosine_pearson": 84.4552565985224,
+                "cosine_spearman": 84.95895784502326,
+                "euclidean_pearson": 82.78929391924628,
+                "euclidean_spearman": 84.30172209203968,
+                "main_score": 84.95895784502326,
+                "manhattan_pearson": 82.8787314198733,
+                "manhattan_spearman": 84.31971710672602
+            }
+        ]
+    }
+}

results/AbderrahmanSkiredj1__arabic_text_embedding_sts_arabertv02_arabicnlitriplet/external/model_meta.json

@@ -0,0 +1,22 @@
+{
+    "name": "AbderrahmanSkiredj1/arabic_text_embedding_sts_arabertv02_arabicnlitriplet",
+    "revision": "a920aa3e6222ce4a03a5799ecb6f1e6dea6e4c46",
+    "release_date": "2024-07-06",
+    "languages": [],
+    "loader": null,
+    "n_parameters": 135193344,
+    "memory_usage": null,
+    "max_tokens": 512,
+    "embed_dim": 768,
+    "license": null,
+    "open_weights": true,
+    "public_training_data": null,
+    "public_training_code": null,
+    "framework": [
+        "Sentence Transformers"
+    ],
+    "reference": null,
+    "similarity_fn_name": null,
+    "use_instructions": null,
+    "zero_shot_benchmarks": null
+}

results/AdrienB134__llm2vec-occiglot-mntp/external/AlloProfClusteringP2P.json

@@ -0,0 +1,2030 @@
+{
+    "dataset_revision": "392ba3f5bcc8c51f578786c1fc3dae648662cb9b",
+    "task_name": "AlloProfClusteringP2P",
+    "evaluation_time": null,
+    "mteb_version": null,
+    "scores": {
+        "test": [
+            {
+                "hf_subset": "default",
+                "languages": [
+                    "fra-Latn"
+                ],
+                "v_measure": 63.99706100543374,
+                "v_measures": [
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results/AdrienB134__llm2vec-occiglot-mntp/external/AlloprofReranking.json

@@ -0,0 +1,19 @@
+{
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results/AdrienB134__llm2vec-occiglot-mntp/external/BSARDRetrieval.json

@@ -0,0 +1,52 @@
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results/AdrienB134__llm2vec-occiglot-mntp/external/HALClusteringS2S.json

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results/AdrienB134__llm2vec-occiglot-mntp/external/MLSUMClusteringP2P.json

@@ -0,0 +1,2030 @@
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+            }
+        ]
+    }
+}

results/AdrienB134__llm2vec-occiglot-mntp/external/MintakaRetrieval.json

@@ -0,0 +1,52 @@
+{
+    "dataset_revision": "efa78cc2f74bbcd21eff2261f9e13aebe40b814e",
+    "task_name": "MintakaRetrieval",
+    "evaluation_time": null,
+    "mteb_version": null,
+    "scores": {
+        "test": [
+            {
+                "hf_subset": "fr",
+                "languages": [
+                    "fra-Latn"
+                ],
+                "map_at_1": 0.737,
+                "map_at_10": 1.248,
+                "map_at_100": 1.5350000000000001,
+                "map_at_1000": 1.727,
+                "map_at_20": 1.3259999999999998,
+                "map_at_3": 0.9690000000000001,
+                "map_at_5": 1.1119999999999999,
+                "mrr_at_1": 0.737,
+                "mrr_at_10": 1.248,
+                "mrr_at_100": 1.5350000000000001,
+                "mrr_at_1000": 1.727,
+                "mrr_at_20": 1.3259999999999998,
+                "mrr_at_3": 0.9690000000000001,
+                "mrr_at_5": 1.1119999999999999,
+                "ndcg_at_1": 0.737,
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results/AdrienB134__llm2vec-occiglot-mntp/external/OpusparcusPC.json

@@ -0,0 +1,40 @@
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+}

results/AdrienB134__llm2vec-occiglot-mntp/external/SICKFr.json

@@ -0,0 +1,25 @@
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results/AdrienB134__llm2vec-occiglot-mntp/external/STS22.json

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results/AdrienB134__llm2vec-occiglot-mntp/external/STSBenchmarkMultilingualSTS.json

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results/AdrienB134__llm2vec-occiglot-mntp/external/SummEvalFr.json

@@ -0,0 +1,23 @@
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results/AdrienB134__llm2vec-occiglot-mntp/external/SyntecReranking.json

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results/AdrienB134__llm2vec-occiglot-mntp/external/SyntecRetrieval.json

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results/AdrienB134__llm2vec-occiglot-mntp/external/XPQARetrieval.json

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results/AdrienB134__llm2vec-occiglot-mntp/external/model_meta.json

@@ -0,0 +1,22 @@
+{
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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/AFQMC.json

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+{
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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/ATEC.json

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+{
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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/AmazonCounterfactualClassification.json

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+{
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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/AmazonPolarityClassification.json

@@ -0,0 +1,20 @@
+{
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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/AmazonReviewsClassification.json

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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/ArguAna.json

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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/ArxivClusteringP2P.json

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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/ArxivClusteringS2S.json

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+{
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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/AskUbuntuDupQuestions.json

@@ -0,0 +1,19 @@
+{
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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/BIOSSES.json

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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/BQ.json

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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/Banking77Classification.json

@@ -0,0 +1,19 @@
+{
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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/BiorxivClusteringP2P.json

@@ -0,0 +1,18 @@
+{
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+}

results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/BiorxivClusteringS2S.json

@@ -0,0 +1,18 @@
+{
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results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/CLSClusteringP2P.json

@@ -0,0 +1,18 @@
+{
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+}

results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/CLSClusteringS2S.json

@@ -0,0 +1,18 @@
+{
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+}

results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/CQADupstackAndroidRetrieval.json

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+        ]
+    }
+}

results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/ClimateFEVER.json

@@ -0,0 +1,47 @@
+{
+    "dataset_revision": "47f2ac6acb640fc46020b02a5b59fdda04d39380",
+    "task_name": "ClimateFEVER",
+    "evaluation_time": null,
+    "mteb_version": null,
+    "scores": {
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+                "precision_at_1000": 0.269,
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+                "recall_at_1000": 90.098,
+                "recall_at_3": 33.343,
+                "recall_at_5": 40.744,
+                "main_score": 43.996
+            }
+        ]
+    }
+}

results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/CmedqaRetrieval.json

@@ -0,0 +1,47 @@
+{
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+    "task_name": "CmedqaRetrieval",
+    "evaluation_time": null,
+    "mteb_version": null,
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+                "recall_at_3": 38.047,
+                "recall_at_5": 45.08,
+                "main_score": 43.469
+            }
+        ]
+    }
+}

results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/Cmnli.json

@@ -0,0 +1,40 @@
+{
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+    "task_name": "Cmnli",
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+            }
+        ]
+    }
+}

results/Alibaba-NLP__gte-Qwen1.5-7B-instruct/external/CovidRetrieval.json

@@ -0,0 +1,47 @@
+{
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+    "task_name": "CovidRetrieval",
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+                "precision_at_10": 9.452,
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+                "precision_at_5": 17.682000000000002,
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+                "recall_at_10": 93.467,
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+                "recall_at_3": 83.14,
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+                "main_score": 80.87299999999999
+            }
+        ]
+    }
+}