Add Farsi(Persian) MTEB - FaMTEB

.github/pull_request_template.md

@@ -1,11 +1,18 @@
-<!-- Description of the PR goes here -->
+<!-- If you are submitting a dataset or a model for the model registry, please use the corresponding checklists below. Otherwise, feel free to remove them. -->
 
-### Checklist
+<!-- add additional description, question etc. related to the new dataset -->
 
-- [ ] My model has a model sheet, report, or similar
-- [ ] My model has a reference implementation in [`mteb/models/model_implementations/`](https://github.com/embeddings-benchmark/mteb/tree/main/mteb/models/model_implementations), this can be as an API. Instruction on how to add a model can be found [here](https://embeddings-benchmark.github.io/mteb/contributing/adding_a_model/) 
-  - [ ] No, but there is an existing PR ___
-- [ ] The results submitted are obtained using the reference implementation
-- [ ] My model is available, either as a publicly accessible API or publicly on e.g., Huggingface
-- [ ] I *solemnly swear* that for all results submitted I have not trained on the evaluation dataset including training splits. If I have, I have disclosed it clearly.
 
+## Checklist
+<!-- Please do not delete this -->
+
+- [ ] Run tests locally to make sure nothing is broken using `make test`. 
+- [ ] Run the results files checker `make pre-push`.
+
+### Adding a model checklist
+<!-- 
+When adding a model to the model registry
+see also https://github.com/embeddings-benchmark/mteb/blob/main/docs/reproducible_workflow.md
+-->
+
+- [ ] I have added model implementation to `mteb/models/` [directory](https://github.com/embeddings-benchmark/mteb/tree/main/mteb/models). Instruction to add a model can be found [here](https://github.com/embeddings-benchmark/mteb/blob/main/docs/reproducible_workflow.md) in the following PR ____

.github/workflows/generate_cached_results.yml

@@ -1,105 +0,0 @@
-name: Generate Cached Results
-
-on:
-  push:
-    branches: [main]
-  # Allow manual trigger for testing
-  workflow_dispatch:
-
-jobs:
-  generate-cache:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Free disk space
-        run: |
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf /opt/ghc
-          sudo rm -rf /usr/local/share/boost
-          docker system prune -af
-
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          token: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.10'
-          cache: 'pip'
-
-      - name: Install dependencies
-        run: |
-           pip install git+https://github.com/embeddings-benchmark/mteb.git
-
-      - name: Generate cached results
-        run: |
-          python scripts/generate_cached_results.py
-        env:
-          PYTHONUNBUFFERED: 1
-
-      - name: Configure Git
-        run: |
-          git config --global user.name "github-actions[bot]"
-          git config --global user.email "github-actions[bot]@users.noreply.github.com"
-
-      - name: Update cached-data branch
-        env:
-          GIT_LFS_SKIP_SMUDGE: "1"
-        run: |
-          # Check if __cached_results.json.gz was created
-          if [ ! -f "__cached_results.json.gz" ]; then
-            echo "❌ Cached results file not found"
-            exit 1
-          fi
-
-          # Get file size for logging
-          FILE_SIZE=$(stat -f%z __cached_results.json.gz 2>/dev/null || stat -c%s __cached_results.json.gz)
-          echo "📦 Generated cache file: $(echo "scale=1; $FILE_SIZE/1024/1024" | bc -l)MB"
-
-          # Move the generated file outside repository before rebuilding branch
-          CACHE_TMP_PATH="$RUNNER_TEMP/__cached_results.json.gz"
-          mv __cached_results.json.gz "$CACHE_TMP_PATH"
-          README_TMP_PATH="$RUNNER_TEMP/cached-data-README.md"
-
-          # Preserve README.md from cached-data branch (if it exists) without modifications
-          if git ls-remote --exit-code --heads origin cached-data >/dev/null 2>&1; then
-            git fetch origin cached-data:refs/remotes/origin/cached-data
-            if git cat-file -e origin/cached-data:README.md 2>/dev/null; then
-              git show origin/cached-data:README.md > "$README_TMP_PATH"
-            fi
-          fi
-
-          # Rebuild cached-data as a fresh orphan branch so history always has one commit
-          git checkout --orphan cached-data
-          git rm -rf . 2>/dev/null || true
-
-          # Restore and stage cache file
-          mv "$CACHE_TMP_PATH" __cached_results.json.gz
-          git add __cached_results.json.gz
-
-          # Restore preserved README.md exactly as it was in cached-data
-          if [ -f "$README_TMP_PATH" ]; then
-            cp "$README_TMP_PATH" README.md
-            git add README.md
-          fi
-
-          # Commit with timestamp and file size
-          TIMESTAMP=$(date -u '+%Y-%m-%d %H:%M:%S UTC')
-          COMMIT_MSG="Update cached results - $TIMESTAMP ($(echo "scale=1; $FILE_SIZE/1024/1024" | bc -l)MB)"
-          git commit -m "$COMMIT_MSG"
-
-          # Force push keeps remote branch as a single-commit history
-          git push origin cached-data --force
-          echo "✅ Successfully replaced cached-data branch with a single commit"
-
-      - name: Report status
-        if: always()
-        run: |
-          if [ -f "__cached_results.json.gz" ]; then
-            FILE_SIZE=$(stat -f%z __cached_results.json.gz 2>/dev/null || stat -c%s __cached_results.json.gz)
-            echo "✅ Workflow completed. Cache file size: $(echo "scale=1; $FILE_SIZE/1024/1024" | bc -l)MB"
-          else
-            echo "❌ Workflow failed - no cache file generated"
-          fi

.github/workflows/model-results-comparison.yaml

@@ -1,89 +0,0 @@
-name: Model Results Comparison
-
-on:
-  pull_request_target:
-    types: [opened, synchronize, edited]
-    paths:
-      - 'results/**/*.json'
-  workflow_dispatch:
-    inputs:
-      reference_models:
-        description: 'Space-separated list of reference models for comparison'
-        required: true
-        type: string
-        default: 'intfloat/multilingual-e5-large google/gemini-embedding-001'
-      pull_request_number:
-        description: 'The pull request number to comment on (required if triggered manually)'
-        required: false
-        type: string
-
-permissions:
-  contents: read
-  pull-requests: write
-
-jobs:
-  compare-results:
-    runs-on: ubuntu-latest
-
-    steps:
-    - name: Free disk space
-      run: |
-        sudo rm -rf /usr/share/dotnet
-        sudo rm -rf /opt/ghc
-        sudo rm -rf /usr/local/share/boost
-        docker system prune -af
-
-    - name: Checkout code
-      uses: actions/checkout@v4
-      with:
-        # IMPORTANT: For pull_request_target, check out the PR branch explicitly
-        ref: ${{ github.event.pull_request.head.sha }}
-        fetch-depth: 0
-
-    - name: Fetch origin main
-      run: git fetch origin main
-
-    - name: Install uv and set the Python ${{ matrix.python-version }} version
-      uses: astral-sh/setup-uv@v7
-      with:
-        python-version: ${{ matrix.python-version }}
-
-    - name: Install dependencies
-      shell: bash
-      run: |
-        make install-for-tests
-
-    - name: Generate model comparison
-      env:
-        REFERENCE_MODELS: ${{ github.event.inputs.reference_models || 'intfloat/multilingual-e5-large google/gemini-embedding-001' }}
-      run: |
-        uv run python scripts/create_pr_results_comment.py --reference-models $REFERENCE_MODELS --output model-comparison.md
-
-    - name: Upload comparison report
-      uses: actions/upload-artifact@v4
-      with:
-        name: model-comparison
-        path: model-comparison.md
-
-    - name: Determine PR Number
-      id: pr_info
-      run: |
-        if [ "${{ github.event_name }}" == "pull_request_target" ]; then
-          echo "pr_number=${{ github.event.number }}" >> $GITHUB_OUTPUT
-        elif [ "${{ github.event_name }}" == "workflow_dispatch" ] && [ -n "${{ github.event.inputs.pull_request_number }}" ]; then
-          echo "pr_number=${{ github.event.inputs.pull_request_number }}" >> $GITHUB_OUTPUT
-        else
-          echo "pr_number=" >> $GITHUB_OUTPUT
-        fi
-
-    - name: Post PR comment
-      if: steps.pr_info.outputs.pr_number != ''
-      env:
-        GITHUB_TOKEN: ${{ github.token }}
-      run: |
-        echo "Truncating model-comparison.md to fit GitHub API limit..."
-        head -c 65300 model-comparison.md > model-comparison-truncated.md
-        echo -e "\n\n---\n**Note:** Content truncated due to GitHub API limits. See the full report in the workflow artifacts." >> model-comparison-truncated.md
-        mv model-comparison-truncated.md model-comparison.md
-
-        gh pr comment ${{ steps.pr_info.outputs.pr_number }} --body-file model-comparison.md --create-if-none --edit-last

.github/workflows/stale_pr.yml

@@ -1,20 +0,0 @@
-name: Label and Close stale PRs
-on:
-  schedule:
-    - cron: '30 1 * * *' # runs daily at 1:30 AM
-jobs:
-  stale:
-    runs-on: ubuntu-latest
-    permissions:
-      actions: write
-      issues: write
-      pull-requests: write
-    steps:
-      - uses: actions/stale@v9.1.0
-        with:
-          stale-pr-label: 'stale'
-          stale-issue-label: 'stale'
-          days-before-pr-stale: 14
-          days-before-pr-close: 7
-          stale-pr-message: 'This pull request has been automatically marked as stale due to inactivity.'
-          close-pr-message: 'This pull request has been automatically closed due to inactivity.'

.github/workflows/test.yml

@@ -2,6 +2,7 @@
 # 1) install Python dependencies
 # 2) run make test
 
+
 name: Test
 on:
   push:
@@ -15,24 +16,13 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest] #, macos-latest, windows-latest]
-        python-version: ["3.10"]
+        python-version: ["3.9"]
     steps:
-      - name: Free disk space
-        run: |
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf /opt/ghc
-          sudo rm -rf /usr/local/share/boost
-          docker system prune -af
-
       - uses: actions/checkout@v3
-        with:
-          fetch-depth: 0 # to allow us to examine the git diff
-
-      - name: Install uv and set the Python ${{ matrix.python-version }} version
-        uses: astral-sh/setup-uv@v7
-        with:
-          python-version: ${{ matrix.python-version }}
 
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        
       - name: Install dependencies
         shell: bash
         run: |
@@ -40,7 +30,5 @@ jobs:
 
       - name: Run tests
         shell: bash
-        env:
-          PR_BASE_SHA: ${{ github.event.pull_request.base.sha }}
         run: |
           make test

.gitignore

@@ -20,9 +20,3 @@ tmp.py
 
 # UV
 uv.lock
-
-build/
-results.egg-info/
-
-# codecarbon
-.codecarbon.lock

README.md

@@ -5,7 +5,8 @@ submission_name: MTEB
 ---
 
 > [!NOTE]  
-> Previously, it was possible to submit model results to MTEB by adding them to the metadata of the model card on huggingface. However, this is no longer possible as we want to ensure that we can match the results with the model implementation. If you want to add your model, please follow the [guide](https://embeddings-benchmark.github.io/mteb/contributing/adding_a_model/) on how to do so.
+> Previously it was possible to submit models results to MTEB by adding the results to the model metadata. This is no longer an option as we want to ensure high quality metadata.
+
 
 This repository contains the results of the embedding benchmark evaluated using the package `mteb`.
 

load_external.py

@@ -0,0 +1,253 @@
+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
+
+
+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]:
+    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 = 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()

makefile

@@ -1,11 +1,12 @@
 install-for-tests:
 	@echo "--- Installing dependencies for tests ---"
-	uv sync --group dev --group pr-comment
+	pip install pip --upgrade
+	pip install .
 
 test:
 	@echo "--- Running tests ---"
-	uv run --no-sync pytest
+	pytest
 
 pre-push:
 	@echo "--- Running pre-push commands ---"
-	python reduce_large_json_files.py
+	python reduce_large_json_files.py

pyproject.toml

@@ -3,21 +3,5 @@ name = "results"
 version = "0.1.0"
 description = "The result repository for mteb"
 readme = "README.md"
-requires-python = ">=3.10"
-dependencies = [
-    "mteb>=2.0",
-]
-
-[dependency-groups]
-dev = [
-    "pytest>=8.3.4",
-]
-lint = [
-    "ruff>=0.14.9",
-]
-pr-comment = [
-    "tabulate>=0.9.0",
-]
-
-[tool.uv.sources]
-mteb = { git = "https://github.com/embeddings-benchmark/mteb" }
+requires-python = ">=3.9"
+dependencies = ["mteb[dev]>=1.13.0"]

results.py

@@ -84,7 +84,6 @@ EVAL_LANGS = [
     "est-eng",
     "eus-eng",
     "fa",
-    "fas-Arab",
     "fao-eng",
     "fi",
     "fin-eng",
@@ -241,7 +240,6 @@ VALIDATION_SPLIT = [
     "LEMBSummScreenFDRetrieval",
     "MSMARCO",
     "MSMARCO-PL",
-    "MSMARCO-Fa",
     "MultilingualSentiment",
     "Ocnli",
     "TNews",
@@ -257,7 +255,6 @@ DEV_SPLIT = [
     "MMarcoRetrieval",
     "MSMARCO",
     "MSMARCO-PL",
-    "MSMARCO-Fa",
     "T2Reranking",
     "T2Retrieval",
     "VideoRetrieval",

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/AmazonCounterfactualVNClassification.json

@@ -1,95 +0,0 @@
-{
-  "dataset_revision": "b48bc27d383cfca5b6a47135a52390fa5f66b253",
-  "task_name": "AmazonCounterfactualVNClassification",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "accuracy": 0.619742,
-        "f1": 0.561867,
-        "f1_weighted": 0.660984,
-        "ap": 0.257592,
-        "ap_weighted": 0.257592,
-        "scores_per_experiment": [
-          {
-            "accuracy": 0.587983,
-            "f1": 0.555485,
-            "f1_weighted": 0.631829,
-            "ap": 0.276246,
-            "ap_weighted": 0.276246
-          },
-          {
-            "accuracy": 0.736052,
-            "f1": 0.655812,
-            "f1_weighted": 0.761372,
-            "ap": 0.318313,
-            "ap_weighted": 0.318313
-          },
-          {
-            "accuracy": 0.652361,
-            "f1": 0.594521,
-            "f1_weighted": 0.691796,
-            "ap": 0.280129,
-            "ap_weighted": 0.280129
-          },
-          {
-            "accuracy": 0.51073,
-            "f1": 0.482958,
-            "f1_weighted": 0.559073,
-            "ap": 0.224991,
-            "ap_weighted": 0.224991
-          },
-          {
-            "accuracy": 0.622318,
-            "f1": 0.539112,
-            "f1_weighted": 0.6635,
-            "ap": 0.219724,
-            "ap_weighted": 0.219724
-          },
-          {
-            "accuracy": 0.583691,
-            "f1": 0.53557,
-            "f1_weighted": 0.630528,
-            "ap": 0.241011,
-            "ap_weighted": 0.241011
-          },
-          {
-            "accuracy": 0.690987,
-            "f1": 0.626203,
-            "f1_weighted": 0.725049,
-            "ap": 0.302755,
-            "ap_weighted": 0.302755
-          },
-          {
-            "accuracy": 0.594421,
-            "f1": 0.549602,
-            "f1_weighted": 0.639849,
-            "ap": 0.255337,
-            "ap_weighted": 0.255337
-          },
-          {
-            "accuracy": 0.618026,
-            "f1": 0.556143,
-            "f1_weighted": 0.661415,
-            "ap": 0.244741,
-            "ap_weighted": 0.244741
-          },
-          {
-            "accuracy": 0.600858,
-            "f1": 0.523266,
-            "f1_weighted": 0.645433,
-            "ap": 0.212676,
-            "ap_weighted": 0.212676
-          }
-        ],
-        "main_score": 0.619742,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 1.7541632652282715,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/AmazonPolarityVNClassification.json

@@ -1,95 +0,0 @@
-{
-  "dataset_revision": "4e9a0d6e6bd97ab32f23c50c043d751eed2a5f8a",
-  "task_name": "AmazonPolarityVNClassification",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "accuracy": 0.887758,
-        "f1": 0.887555,
-        "f1_weighted": 0.88755,
-        "ap": 0.85126,
-        "ap_weighted": 0.85126,
-        "scores_per_experiment": [
-          {
-            "accuracy": 0.887756,
-            "f1": 0.887564,
-            "f1_weighted": 0.88753,
-            "ap": 0.86203,
-            "ap_weighted": 0.86203
-          },
-          {
-            "accuracy": 0.886623,
-            "f1": 0.886583,
-            "f1_weighted": 0.886599,
-            "ap": 0.840929,
-            "ap_weighted": 0.840929
-          },
-          {
-            "accuracy": 0.887771,
-            "f1": 0.887457,
-            "f1_weighted": 0.887413,
-            "ap": 0.86644,
-            "ap_weighted": 0.86644
-          },
-          {
-            "accuracy": 0.879607,
-            "f1": 0.879353,
-            "f1_weighted": 0.879312,
-            "ap": 0.852779,
-            "ap_weighted": 0.852779
-          },
-          {
-            "accuracy": 0.894125,
-            "f1": 0.894014,
-            "f1_weighted": 0.893989,
-            "ap": 0.86726,
-            "ap_weighted": 0.86726
-          },
-          {
-            "accuracy": 0.870804,
-            "f1": 0.870803,
-            "f1_weighted": 0.870801,
-            "ap": 0.827334,
-            "ap_weighted": 0.827334
-          },
-          {
-            "accuracy": 0.896821,
-            "f1": 0.896763,
-            "f1_weighted": 0.896744,
-            "ap": 0.86776,
-            "ap_weighted": 0.86776
-          },
-          {
-            "accuracy": 0.901696,
-            "f1": 0.901691,
-            "f1_weighted": 0.901696,
-            "ap": 0.863676,
-            "ap_weighted": 0.863676
-          },
-          {
-            "accuracy": 0.90081,
-            "f1": 0.900772,
-            "f1_weighted": 0.900787,
-            "ap": 0.858558,
-            "ap_weighted": 0.858558
-          },
-          {
-            "accuracy": 0.871574,
-            "f1": 0.870546,
-            "f1_weighted": 0.870631,
-            "ap": 0.805833,
-            "ap_weighted": 0.805833
-          }
-        ],
-        "main_score": 0.887758,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 1072.2194910049438,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/AmazonReviewsVNClassification.json

@@ -1,73 +0,0 @@
-{
-  "dataset_revision": "27da94deb6d4f44af789a3d70750fa506b79f189",
-  "task_name": "AmazonReviewsVNClassification",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "accuracy": 0.444801,
-        "f1": 0.426392,
-        "f1_weighted": 0.432322,
-        "scores_per_experiment": [
-          {
-            "accuracy": 0.450058,
-            "f1": 0.42758,
-            "f1_weighted": 0.434702
-          },
-          {
-            "accuracy": 0.470502,
-            "f1": 0.448032,
-            "f1_weighted": 0.455157
-          },
-          {
-            "accuracy": 0.436624,
-            "f1": 0.42657,
-            "f1_weighted": 0.430991
-          },
-          {
-            "accuracy": 0.451811,
-            "f1": 0.435001,
-            "f1_weighted": 0.441204
-          },
-          {
-            "accuracy": 0.462325,
-            "f1": 0.433835,
-            "f1_weighted": 0.440433
-          },
-          {
-            "accuracy": 0.439836,
-            "f1": 0.413124,
-            "f1_weighted": 0.419804
-          },
-          {
-            "accuracy": 0.418808,
-            "f1": 0.415917,
-            "f1_weighted": 0.420269
-          },
-          {
-            "accuracy": 0.463785,
-            "f1": 0.457309,
-            "f1_weighted": 0.461625
-          },
-          {
-            "accuracy": 0.439252,
-            "f1": 0.428232,
-            "f1_weighted": 0.433021
-          },
-          {
-            "accuracy": 0.415012,
-            "f1": 0.378321,
-            "f1_weighted": 0.386011
-          }
-        ],
-        "main_score": 0.444801,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 11.328449964523315,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/ArguAna-VN.json

@@ -1,158 +0,0 @@
-{
-  "dataset_revision": "2a5133a05d7430e6f353497b1624a6e73148105b",
-  "task_name": "ArguAna-VN",
-  "mteb_version": "1.38.39",
-  "scores": {
-    "test": [
-      {
-        "ndcg_at_1": 0.00154,
-        "ndcg_at_3": 0.25091,
-        "ndcg_at_5": 0.3202,
-        "ndcg_at_10": 0.37932,
-        "ndcg_at_20": 0.40551,
-        "ndcg_at_100": 0.42317,
-        "ndcg_at_1000": 0.42594,
-        "map_at_1": 0.00154,
-        "map_at_3": 0.18906,
-        "map_at_5": 0.22748,
-        "map_at_10": 0.25228,
-        "map_at_20": 0.25969,
-        "map_at_100": 0.26227,
-        "map_at_1000": 0.26239,
-        "recall_at_1": 0.00154,
-        "recall_at_3": 0.43012,
-        "recall_at_5": 0.59846,
-        "recall_at_10": 0.77915,
-        "recall_at_20": 0.88108,
-        "recall_at_100": 0.97452,
-        "recall_at_1000": 0.99537,
-        "precision_at_1": 0.00154,
-        "precision_at_3": 0.14337,
-        "precision_at_5": 0.11969,
-        "precision_at_10": 0.07792,
-        "precision_at_20": 0.04405,
-        "precision_at_100": 0.00975,
-        "precision_at_1000": 0.001,
-        "mrr_at_1": 0.001544,
-        "mrr_at_3": 0.18906,
-        "mrr_at_5": 0.227477,
-        "mrr_at_10": 0.252276,
-        "mrr_at_20": 0.259688,
-        "mrr_at_100": 0.262271,
-        "mrr_at_1000": 0.262394,
-        "nauc_ndcg_at_1_max": -0.15791,
-        "nauc_ndcg_at_1_std": -0.309824,
-        "nauc_ndcg_at_1_diff1": -0.573698,
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-        "nauc_ndcg_at_5_max": 0.051704,
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-        "nauc_mrr_at_1000_max": 0.05229,
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-        "main_score": 0.37932,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 56.45738124847412,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/AskUbuntuDupQuestions-VN.json

@@ -1,26 +0,0 @@
-{
-  "dataset_revision": "5cfaa5c07252d30c37302bfc056f0d85884971a1",
-  "task_name": "AskUbuntuDupQuestions-VN",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "map": 0.618748,
-        "mrr": 0.729745,
-        "nAUC_map_max": 0.263989,
-        "nAUC_map_std": 0.233967,
-        "nAUC_map_diff1": 0.170011,
-        "nAUC_mrr_max": 0.350929,
-        "nAUC_mrr_std": 0.29905,
-        "nAUC_mrr_diff1": 0.207218,
-        "main_score": 0.618748,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 6.192819356918335,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/BIOSSES-VN.json

@@ -1,26 +0,0 @@
-{
-  "dataset_revision": "1dae4a6df91c0852680cd4ab48c8c1d8a9ed49b2",
-  "task_name": "BIOSSES-VN",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "pearson": 0.799628,
-        "spearman": 0.781415,
-        "cosine_pearson": 0.799628,
-        "cosine_spearman": 0.781415,
-        "manhattan_pearson": 0.777069,
-        "manhattan_spearman": 0.777819,
-        "euclidean_pearson": 0.780537,
-        "euclidean_spearman": 0.781415,
-        "main_score": 0.781415,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 0.6975255012512207,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/Banking77VNClassification.json

@@ -1,73 +0,0 @@
-{
-  "dataset_revision": "42541b07c25a49604be129fba6d70b752be229c1",
-  "task_name": "Banking77VNClassification",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "accuracy": 0.792094,
-        "f1": 0.777269,
-        "f1_weighted": 0.78413,
-        "scores_per_experiment": [
-          {
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-            "f1": 0.766683,
-            "f1_weighted": 0.774623
-          },
-          {
-            "accuracy": 0.795627,
-            "f1": 0.776761,
-            "f1_weighted": 0.784439
-          },
-          {
-            "accuracy": 0.796468,
-            "f1": 0.781336,
-            "f1_weighted": 0.787568
-          },
-          {
-            "accuracy": 0.799832,
-            "f1": 0.791148,
-            "f1_weighted": 0.79406
-          },
-          {
-            "accuracy": 0.77418,
-            "f1": 0.752046,
-            "f1_weighted": 0.764798
-          },
-          {
-            "accuracy": 0.785114,
-            "f1": 0.775827,
-            "f1_weighted": 0.77941
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackAndroid-VN.json

@@ -1,54 +0,0 @@
-{
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-    "mteb_dataset_name": "CQADupstackAndroid-VN",
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackGis-VN.json

@@ -1,54 +0,0 @@
-{
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackMathematica-VN.json

@@ -1,54 +0,0 @@
-{
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackPhysics-VN.json

@@ -1,54 +0,0 @@
-{
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackProgrammers-VN.json

@@ -1,54 +0,0 @@
-{
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackStats-VN.json

@@ -1,54 +0,0 @@
-{
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackTex-VN.json

@@ -1,54 +0,0 @@
-{
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackUnix-VN.json

@@ -1,54 +0,0 @@
-{
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-    "mteb_dataset_name": "CQADupstackUnix-VN",
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-    "task_name": "CQADupstackUnix-VN",
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackWebmasters-VN.json

@@ -1,54 +0,0 @@
-{
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-    "mteb_dataset_name": "CQADupstackWebmasters-VN",
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-    "task_name": "CQADupstackWebmasters-VN",
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackWordpress-VN.json

@@ -1,54 +0,0 @@
-{
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-    "mteb_dataset_name": "CQADupstackWordpress-VN",
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-    "task_name": "CQADupstackWordpress-VN",
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/ClimateFEVER-VN.json

@@ -1,54 +0,0 @@
-{
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/DBPedia-VN.json

@@ -1,54 +0,0 @@
-{
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/EmotionVNClassification.json

@@ -1,137 +0,0 @@
-{
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-  "task_name": "EmotionVNClassification",
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/FEVER-VN.json

@@ -1,54 +0,0 @@
-{
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/FiQA2018-VN.json

@@ -1,54 +0,0 @@
-{
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/GreenNodeTableMarkdownRetrieval.json

@@ -1,158 +0,0 @@
-{
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/HotpotQA-VN.json

@@ -1,54 +0,0 @@
-{
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-    "mteb_dataset_name": "HotpotQA-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "HotpotQA-VN",
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/ImdbVNClassification.json

@@ -1,95 +0,0 @@
-{
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-  "task_name": "ImdbVNClassification",
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/MSMARCO-VN.json

@@ -1,54 +0,0 @@
-{
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-    "task_name": "MSMARCO-VN",
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/MTOPDomainVNClassification.json

@@ -1,73 +0,0 @@
-{
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-  "task_name": "MTOPDomainVNClassification",
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/MTOPIntentVNClassification.json

@@ -1,73 +0,0 @@
-{
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-  "task_name": "MTOPIntentVNClassification",
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/MassiveIntentVNClassification.json

@@ -1,73 +0,0 @@
-{
-  "dataset_revision": "35c7ced69f958dbbaa24f792db4a9250e461866d",
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/MassiveScenarioVNClassification.json

@@ -1,73 +0,0 @@
-{
-  "dataset_revision": "a82e282d9f5aec1a8cf7d868ce40f70669c16b89",
-  "task_name": "MassiveScenarioVNClassification",
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/NFCorpus-VN.json

@@ -1,54 +0,0 @@
-{
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-    "mteb_dataset_name": "NFCorpus-VN",
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-    "task_name": "NFCorpus-VN",
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results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/NQ-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "40a6d7f343b9c9f4855a426d8c431ad5f8aaf56b",
-    "mteb_dataset_name": "NQ-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "NQ-VN",
-    "scores": {
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-                "precision_at_1000": 0.00115,
-                "precision_at_20": 0.04271,
-                "precision_at_3": 0.16298,
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-                "recall_at_1": 0.22333,
-                "recall_at_10": 0.62671,
-                "recall_at_100": 0.86105,
-                "recall_at_1000": 0.95798,
-                "recall_at_20": 0.72228,
-                "recall_at_3": 0.42149,
-                "recall_at_5": 0.51241,
-                "hf_subset": "default",
-                "main_score": 0.42611,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/Quora-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "3363d81e41b67c1032bf3b234882a03d271e2289",
-    "mteb_dataset_name": "Quora-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "Quora-VN",
-    "scores": {
-        "test": [
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-                "map_at_1": 0.42135,
-                "map_at_10": 0.54669,
-                "map_at_100": 0.55931,
-                "map_at_1000": 0.55994,
-                "map_at_20": 0.55423,
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-                "map_at_5": 0.53213,
-                "mrr_at_1": 0.49979,
-                "mrr_at_10": 0.59915,
-                "mrr_at_100": 0.60569,
-                "mrr_at_1000": 0.60589,
-                "mrr_at_20": 0.60345,
-                "mrr_at_3": 0.57472,
-                "mrr_at_5": 0.58942,
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-                "ndcg_at_100": 0.65467,
-                "ndcg_at_1000": 0.66382,
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-                "precision_at_100": 0.01399,
-                "precision_at_1000": 0.00154,
-                "precision_at_20": 0.05794,
-                "precision_at_3": 0.2526,
-                "precision_at_5": 0.1746,
-                "recall_at_1": 0.42135,
-                "recall_at_10": 0.74453,
-                "recall_at_100": 0.92748,
-                "recall_at_1000": 0.98654,
-                "recall_at_20": 0.81644,
-                "recall_at_3": 0.59369,
-                "recall_at_5": 0.66411,
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-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/RedditClustering-VN.json

@@ -1,32 +0,0 @@
-{
-  "dataset_revision": "7f7d4097979633181b2df3f73905218f74c4665d",
-  "task_name": "RedditClustering-VN",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
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-        "v_measure": 0.438902,
-        "v_measure_std": 0.044274,
-        "v_measures": [
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-          0.5316,
-          0.399559,
-          0.425985,
-          0.427061,
-          0.446164,
-          0.512027,
-          0.424049,
-          0.426086,
-          0.40386
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-        "main_score": 0.438902,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 125.31211185455322,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/RedditClusteringP2P-VN.json

@@ -1,32 +0,0 @@
-{
-  "dataset_revision": "841856dcb82496f1f2f59356e4798ce15baeb200",
-  "task_name": "RedditClusteringP2P-VN",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "v_measure": 0.561615,
-        "v_measure_std": 0.118839,
-        "v_measures": [
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-          0.611291,
-          0.601361,
-          0.345424,
-          0.620675,
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-          0.315689,
-          0.660052,
-          0.610597,
-          0.672515
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-        "main_score": 0.561615,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 1739.9407813549042,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/SCIDOCS-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "724cddfa9d328a193f303a0a9b7789468ac79f26",
-    "mteb_dataset_name": "SCIDOCS-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "SCIDOCS-VN",
-    "scores": {
-        "test": [
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-                "map_at_10": 0.07617,
-                "map_at_100": 0.08657,
-                "map_at_1000": 0.08887,
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-                "map_at_3": 0.06048,
-                "map_at_5": 0.06859,
-                "mrr_at_1": 0.1825,
-                "mrr_at_10": 0.2542,
-                "mrr_at_100": 0.26523,
-                "mrr_at_1000": 0.26606,
-                "mrr_at_20": 0.25994,
-                "mrr_at_3": 0.23333,
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-                "ndcg_at_1000": 0.23392,
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-                "recall_at_100": 0.28675,
-                "recall_at_1000": 0.5355,
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-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/SICK-R-VN.json

@@ -1,26 +0,0 @@
-{
-  "dataset_revision": "bc89f0401983c456b609f7fb324278f346b2cccf",
-  "task_name": "SICK-R-VN",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "pearson": 0.80717,
-        "spearman": 0.771026,
-        "cosine_pearson": 0.80717,
-        "cosine_spearman": 0.771062,
-        "manhattan_pearson": 0.768392,
-        "manhattan_spearman": 0.770402,
-        "euclidean_pearson": 0.768774,
-        "euclidean_spearman": 0.771054,
-        "main_score": 0.771062,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 25.608262062072754,
-  "kg_co2_emissions": null
-}