Ensure external does not have a revision (#107)

* removed gritLM7B external

generally we should not have external scores where the revision is specified (we have no garuantee that the scores are as specified)

* set external revision to "no_revision_available"

* added test to ensure formatting and no duplicate revisions

* use "external" as revision for external models

* removed null revisions

* strictly test for revision

* deleted load external

load_external.py

@@ -1,262 +0,0 @@
-from __future__ import annotations
-
-import json
-import logging
-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
-
-
-renamed_tasks = {
-    "NorwegianParliament": "NorwegianParliamentClassification",
-    "CMedQAv2": "CMedQAv2-reranking",
-    "CMedQAv1": "CMedQAv1-reranking",
-    "8TagsClustering": "EightTagsClustering",
-    "PPC": "PpcPC",
-    "PawsX": "PawsXParaphraseIdentification",
-}
-
-
-def simplify_dataset_name(name: str) -> str:
-    task_name = name.replace("MTEB ", "").split()[0]
-    return renamed_tasks.get(task_name, task_name)
-
-
-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, str | None]:
-    siblings = model.siblings or []
-    filenames = [sib.rfilename for sib in siblings]
-    dim, seq = None, None
-    similarity_fn_name = None
-    for filename in filenames:
-        if re.match(r"\d+_Pooling/config.json", filename):
-            st_config_path = hf_hub_download(model.id, filename=filename)
-            with open(st_config_path) as f:
-                pooling_config = json.load(f)
-            dim = pooling_config.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))))
-    if "config_sentence_transformers.json" in filenames:
-        st_config_path = hf_hub_download(model.id, filename="config_sentence_transformers.json")
-        with open(st_config_path) as f:
-            st_config = json.load(f)
-        similarity_fn_name = st_config.get("similarity_fn_name", None)
-    parameters, memory = get_model_parameters_memory(model)
-    return dim, seq, parameters, memory, similarity_fn_name
-
-
-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, similarity_fn_name = None, None, None, None, None
-    try:
-        dim, seq, parameters, memory, similarity_fn_name = 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"
-    datasets = meta.get("datasets", None)
-    if datasets is not None:
-        datasets = {
-            d: []
-            for d in datasets
-        }
-    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,
-        public_training_code=None,
-        public_training_data=None,
-        similarity_fn_name=similarity_fn_name,
-        use_instructions=None,
-        training_datasets=datasets,
-    )
-    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)
-
-    results = model_index.get("results", [])
-    model_results = {}
-    for result in results:
-        dataset = result["dataset"]
-        dataset_type = simplify_dataset_name(dataset["name"])
-
-        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"]:
-            if isinstance(metric["value"], (float, int)):
-                cur_split_metrics[metric["type"]] = metric["value"] / 100
-            else:
-                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 == "ai-forever/ru-en-RoSBERTa"]
-    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/AbderrahmanSkiredj1__Arabic_text_embedding_for_sts/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "AbderrahmanSkiredj1/Arabic_text_embedding_for_sts",
-    "revision": "bbd8aaebafc25b38eaa96300313db0bbe0086d12",
+    "revision": "external",
     "release_date": "2024-07-07",
     "languages": [],
     "loader": null,

results/AbderrahmanSkiredj1__arabic_text_embedding_sts_arabertv02_arabicnlitriplet/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "AbderrahmanSkiredj1/arabic_text_embedding_sts_arabertv02_arabicnlitriplet",
-    "revision": "a920aa3e6222ce4a03a5799ecb6f1e6dea6e4c46",
+    "revision": "external",
     "release_date": "2024-07-06",
     "languages": [],
     "loader": null,

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

@@ -1,6 +1,6 @@
 {
     "name": "AdrienB134/llm2vec-croissant-mntp",
-    "revision": "36d9180216ca362269b297c060af8c8b55e1c67a",
+    "revision": "external",
     "release_date": "2024-05-14",
     "languages": [
         "fr"

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

@@ -1,6 +1,6 @@
 {
     "name": "AdrienB134/llm2vec-occiglot-mntp",
-    "revision": "2bf0b636d03efd648ed8c0c1709a66e181716025",
+    "revision": "external",
     "release_date": "2024-05-14",
     "languages": [
         "fr"

results/Alibaba-NLP__gme-Qwen2-VL-2B-Instruct/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "Alibaba-NLP/gme-Qwen2-VL-2B-Instruct",
-    "revision": "cfeb66885b598de483cc04eb08c7d9da534d7afe",
+    "revision": "external",
     "release_date": "2024-12-21",
     "languages": [
         "en",

results/Alibaba-NLP__gme-Qwen2-VL-7B-Instruct/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "Alibaba-NLP/gme-Qwen2-VL-7B-Instruct",
-    "revision": "d42eca5a540526cfa982a349724b24b25c12a95e",
+    "revision": "external",
     "release_date": "2024-12-21",
     "languages": [
         "en",

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

@@ -1,6 +1,6 @@
 {
     "name": "Alibaba-NLP/gte-Qwen1.5-7B-instruct",
-    "revision": "e2f771ba1aba0666210c7fa446fdcf467fb49108",
+    "revision": "external",
     "release_date": "2024-04-20",
     "languages": [],
     "loader": null,

results/Alibaba-NLP__gte-Qwen2-1.5B-instruct/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "Alibaba-NLP/gte-Qwen2-1.5B-instruct",
-    "revision": "0d2ad8e1ac654a2b626e62154778a70868141208",
+    "revision": "external",
     "release_date": "2024-06-29",
     "languages": [],
     "loader": null,

results/Alibaba-NLP__gte-Qwen2-7B-instruct/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "Alibaba-NLP/gte-Qwen2-7B-instruct",
-    "revision": "132a598ddb894f1fada52b5f124a429c48fc7aa1",
+    "revision": "external",
     "release_date": "2024-06-15",
     "languages": [],
     "loader": null,

results/Alibaba-NLP__gte-base-en-v1.5/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "Alibaba-NLP/gte-base-en-v1.5",
-    "revision": "a829fd0e060bb84554da0dfd354d0de0f7712b7f",
+    "revision": "external",
     "release_date": "2024-04-20",
     "languages": [
         "en"

results/Alibaba-NLP__gte-large-en-v1.5/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "Alibaba-NLP/gte-large-en-v1.5",
-    "revision": "104333d6af6f97649377c2afbde10a7704870c7b",
+    "revision": "external",
     "release_date": "2024-04-20",
     "languages": [
         "en"

results/Alibaba-NLP__gte-multilingual-base/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "Alibaba-NLP/gte-multilingual-base",
-    "revision": "ca1791e0bcc104f6db161f27de1340241b13c5a4",
+    "revision": "external",
     "release_date": "2024-07-20",
     "languages": [
         "af",

results/Alignment-Lab-AI__e5-mistral-7b-instruct/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "Alignment-Lab-AI/e5-mistral-7b-instruct",
-    "revision": "07ead35d522c1368772a24fc1c4977114f140ba3",
+    "revision": "external",
     "release_date": "2024-12-17",
     "languages": [
         "en"

results/Amu__tao-8k/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "Amu/tao-8k",
-    "revision": "bc783a14a907aa520ebf978787d3d6513803de13",
+    "revision": "external",
     "release_date": "2023-11-04",
     "languages": [
         "zh"

results/Amu__tao/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "Amu/tao",
-    "revision": "06bf3750652bca5c532293030c79d0c8714aaf70",
+    "revision": "external",
     "release_date": "2023-10-18",
     "languages": [
         "zh"

results/BAAI__bge-base-en-v1.5/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BAAI/bge-base-en-v1.5",
-    "revision": "a5beb1e3e68b9ab74eb54cfd186867f64f240e1a",
+    "revision": "external",
     "release_date": "2023-09-11",
     "languages": [
         "en"

results/BAAI__bge-base-en/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BAAI/bge-base-en",
-    "revision": "b737bf5dcc6ee8bdc530531266b4804a5d77b5d8",
+    "revision": "external",
     "release_date": "2023-08-05",
     "languages": [
         "en"

results/BAAI__bge-en-icl/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BAAI/bge-en-icl",
-    "revision": "971c7e1445cc86656ca0bd85ed770b8675a40bb5",
+    "revision": "external",
     "release_date": "2024-07-25",
     "languages": [],
     "loader": null,

results/BAAI__bge-large-en-v1.5/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BAAI/bge-large-en-v1.5",
-    "revision": "d4aa6901d3a41ba39fb536a557fa166f842b0e09",
+    "revision": "external",
     "release_date": "2023-09-12",
     "languages": [
         "en"

results/BAAI__bge-large-en/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BAAI/bge-large-en",
-    "revision": "abe7d9d814b775ca171121fb03f394dc42974275",
+    "revision": "external",
     "release_date": "2023-08-02",
     "languages": [
         "en"

results/BAAI__bge-multilingual-gemma2/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BAAI/bge-multilingual-gemma2",
-    "revision": "992e13d8984fde2c31ef8a3cb2c038aeec513b8a",
+    "revision": "external",
     "release_date": "2024-07-25",
     "languages": [],
     "loader": null,

results/BAAI__bge-reranker-base/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BAAI/bge-reranker-base",
-    "revision": "2cfc18c9415c912f9d8155881c133215df768a70",
+    "revision": "external",
     "release_date": "2023-09-11",
     "languages": [
         "en",

results/BAAI__bge-reranker-large/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BAAI/bge-reranker-large",
-    "revision": "55611d7bca2a7133960a6d3b71e083071bbfc312",
+    "revision": "external",
     "release_date": "2023-09-12",
     "languages": [
         "en",

results/BAAI__bge-small-en-v1.5/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BAAI/bge-small-en-v1.5",
-    "revision": "5c38ec7c405ec4b44b94cc5a9bb96e735b38267a",
+    "revision": "external",
     "release_date": "2023-09-12",
     "languages": [
         "en"

results/BAAI__bge-small-en/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BAAI/bge-small-en",
-    "revision": "2275a7bdee235e9b4f01fa73aa60d3311983cfea",
+    "revision": "external",
     "release_date": "2023-08-05",
     "languages": [
         "en"

results/BASF-AI__nomic-embed-text-v1.5/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BASF-AI/nomic-embed-text-v1.5",
-    "revision": "cfa81d3c628e192dbb6bb323104ff444862204f1",
+    "revision": "external",
     "release_date": "2025-01-10",
     "languages": [
         "en"

results/BASF-AI__nomic-embed-text-v1/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BASF-AI/nomic-embed-text-v1",
-    "revision": "e88eaac23464e24935d785ffbd7bb3bd964c8092",
+    "revision": "external",
     "release_date": "2025-01-09",
     "languages": [
         "en"

results/BeastyZ__e5-R-mistral-7b/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BeastyZ/e5-R-mistral-7b",
-    "revision": "3f810a6a7fd220369ad248e3705cf13d71803602",
+    "revision": "external",
     "release_date": "2024-06-28",
     "languages": [
         "en"

results/BillSYZhang__gte-Qwen2-7B-instruct-Q4-mlx/external/model_meta.json

@@ -1,6 +1,6 @@
 {
     "name": "BillSYZhang/gte-Qwen2-7B-instruct-Q4-mlx",
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results/BookingCare__multilingual-e5-base-similarity-v1-onnx-quantized/external/model_meta.json

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results/ByteDance__ListConRanker/external/model_meta.json

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results/CAiRE__UniVaR-lambda-1/external/model_meta.json

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results/CAiRE__UniVaR-lambda-20/external/model_meta.json

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results/CAiRE__UniVaR-lambda-5/external/model_meta.json

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results/CAiRE__UniVaR-lambda-80/external/model_meta.json

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results/DMetaSoul__Dmeta-embedding-zh-small/external/model_meta.json

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results/DMetaSoul__Dmeta-embedding-zh/external/model_meta.json

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results/DMetaSoul__sbert-chinese-general-v1/external/model_meta.json

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results/DecisionOptimizationSystemProduction__DeepFeatTextEmbeddingLarge/external/model_meta.json

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results/DecisionOptimizationSystem__DeepFeatEmbeddingLargeContext/external/model_meta.json

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