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

@@ -1,149 +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: ${{ secrets.GITHUB_TOKEN }}
-          lfs: true
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.10'
-          cache: 'pip'
-
-      - name: Install dependencies
-        run: |
-           pip install git+https://github.com/embeddings-benchmark/mteb.git
-
-      - name: Generate cached results
-        run: |
-          python scripts/generate_cached_results.py
-        env:
-          PYTHONUNBUFFERED: 1
-
-      - name: Install Git LFS
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y git-lfs
-          git lfs install
-          # Explicitly pull LFS files to work around checkout action issues
-          git lfs pull || true
-
-      - name: Configure Git
-        run: |
-          git config --global user.name "github-actions[bot]"
-          git config --global user.email "github-actions[bot]@users.noreply.github.com"
-
-      - name: Update cached-data branch
-        run: |
-          # Check if __cached_results.json.gz was created
-          if [ ! -f "__cached_results.json.gz" ]; then
-            echo "❌ Cached results file not found"
-            exit 1
-          fi
-
-          # Get file size for logging
-          FILE_SIZE=$(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"
-
-          # Temporarily move the cache file to avoid checkout conflicts
-          if [ -f "__cached_results.json.gz" ]; then
-            mv __cached_results.json.gz __cached_results.json.gz.tmp
-          fi
-
-          # Check if cached-data branch exists
-          if git show-ref --verify --quiet refs/remotes/origin/cached-data; then
-            echo "📋 Switching to existing cached-data branch"
-            git checkout cached-data
-            git pull origin cached-data
-            # Ensure LFS files are available after branch checkout
-            git lfs pull || true
-          else
-            echo "🆕 Creating new cached-data branch"
-            git checkout --orphan cached-data
-            # Remove all files from staging area when creating orphan branch
-            git rm -rf . 2>/dev/null || true
-          fi
-
-          # Restore the cache file
-          if [ -f "__cached_results.json.gz.tmp" ]; then
-            mv __cached_results.json.gz.tmp __cached_results.json.gz
-          fi
-
-          # Setup Git LFS tracking for the cache file (if not already tracked)
-          if ! git lfs ls-files | grep -q "__cached_results.json.gz"; then
-            git lfs track "__cached_results.json.gz"
-          fi
-
-          # Ensure we only have the files we want
-          # Remove all tracked files except README.md and .gitattributes
-          if [ -f "README.md" ]; then
-            git ls-files | grep -v "README.md" | grep -v ".gitattributes" | xargs -r git rm 2>/dev/null || true
-          else
-            git ls-files | grep -v ".gitattributes" | xargs -r git rm 2>/dev/null || true
-          fi
-
-          # Add the cached results file (will be tracked by LFS)
-          git add __cached_results.json.gz
-
-          # Preserve README.md if it exists in the working directory
-          if [ -f "README.md" ]; then
-            git add README.md
-          fi
-
-          # Add .gitattributes file (required for LFS tracking)
-          if [ -f ".gitattributes" ]; then
-            git add .gitattributes
-          fi
-
-          # Check if there are changes to commit
-          if git diff --staged --quiet; then
-            echo "✅ No changes in cached results, skipping commit"
-          else
-            # Verify we're not committing too many files (safety check)
-            STAGED_FILES=$(git diff --staged --name-only | wc -l)
-            if [ "$STAGED_FILES" -gt 10 ]; then
-              echo "❌ ERROR: Too many files staged ($STAGED_FILES). Expected only 1-3 files (__cached_results.json.gz, README.md, .gitattributes)."
-              echo "Staged files:"
-              git diff --staged --name-only
-              exit 1
-            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"
-
-            # Push to remote (LFS will handle the large file automatically)
-            git push origin cached-data
-            echo "✅ Successfully updated cached-data branch"
-          fi
-
-      - 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

@@ -1,82 +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: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version: '3.10'
-
-    - name: Install dependencies
-      run: |
-        pip install git+https://github.com/embeddings-benchmark/mteb.git tabulate
-
-    - name: Generate model comparison
-      env:
-        REFERENCE_MODELS: ${{ github.event.inputs.reference_models || 'intfloat/multilingual-e5-large google/gemini-embedding-001' }}
-      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: 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:
@@ -15,22 +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: Setup Python ${{ matrix.python-version }}
         uses: actions/setup-python@v4
-
+        
       - name: Install dependencies
         shell: bash
         run: |
@@ -38,7 +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

@@ -1,7 +1,7 @@
 install-for-tests:
 	@echo "--- Installing dependencies for tests ---"
 	pip install pip --upgrade
-	pip install . --group dev
+	pip install .
 
 test:
 	@echo "--- Running tests ---"
@@ -9,4 +9,4 @@ test:
 
 pre-push:
 	@echo "--- Running pre-push commands ---"
-	python reduce_large_json_files.py
+	python reduce_large_json_files.py

pyproject.toml

@@ -3,16 +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",
-]
+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,
-        "nauc_ndcg_at_3_max": 0.050466,
-        "nauc_ndcg_at_3_std": -0.047425,
-        "nauc_ndcg_at_3_diff1": -0.053972,
-        "nauc_ndcg_at_5_max": 0.051704,
-        "nauc_ndcg_at_5_std": -0.034976,
-        "nauc_ndcg_at_5_diff1": -0.067507,
-        "nauc_ndcg_at_10_max": 0.051058,
-        "nauc_ndcg_at_10_std": 0.002573,
-        "nauc_ndcg_at_10_diff1": -0.115774,
-        "nauc_ndcg_at_20_max": 0.060309,
-        "nauc_ndcg_at_20_std": -0.002941,
-        "nauc_ndcg_at_20_diff1": -0.112784,
-        "nauc_ndcg_at_100_max": 0.064026,
-        "nauc_ndcg_at_100_std": -0.001192,
-        "nauc_ndcg_at_100_diff1": -0.102481,
-        "nauc_ndcg_at_1000_max": 0.055098,
-        "nauc_ndcg_at_1000_std": -0.009017,
-        "nauc_ndcg_at_1000_diff1": -0.099237,
-        "nauc_map_at_1_max": -0.15791,
-        "nauc_map_at_1_std": -0.309824,
-        "nauc_map_at_1_diff1": -0.573698,
-        "nauc_map_at_3_max": 0.049703,
-        "nauc_map_at_3_std": -0.048378,
-        "nauc_map_at_3_diff1": -0.054478,
-        "nauc_map_at_5_max": 0.049706,
-        "nauc_map_at_5_std": -0.041634,
-        "nauc_map_at_5_diff1": -0.062748,
-        "nauc_map_at_10_max": 0.049333,
-        "nauc_map_at_10_std": -0.023585,
-        "nauc_map_at_10_diff1": -0.08462,
-        "nauc_map_at_20_max": 0.052529,
-        "nauc_map_at_20_std": -0.026038,
-        "nauc_map_at_20_diff1": -0.082893,
-        "nauc_map_at_100_max": 0.052676,
-        "nauc_map_at_100_std": -0.025392,
-        "nauc_map_at_100_diff1": -0.081601,
-        "nauc_map_at_1000_max": 0.05229,
-        "nauc_map_at_1000_std": -0.025648,
-        "nauc_map_at_1000_diff1": -0.081523,
-        "nauc_recall_at_1_max": -0.15791,
-        "nauc_recall_at_1_std": -0.309824,
-        "nauc_recall_at_1_diff1": -0.573698,
-        "nauc_recall_at_3_max": 0.051543,
-        "nauc_recall_at_3_std": -0.046082,
-        "nauc_recall_at_3_diff1": -0.053542,
-        "nauc_recall_at_5_max": 0.055409,
-        "nauc_recall_at_5_std": -0.022324,
-        "nauc_recall_at_5_diff1": -0.077261,
-        "nauc_recall_at_10_max": 0.055321,
-        "nauc_recall_at_10_std": 0.07644,
-        "nauc_recall_at_10_diff1": -0.207529,
-        "nauc_recall_at_20_max": 0.098502,
-        "nauc_recall_at_20_std": 0.101036,
-        "nauc_recall_at_20_diff1": -0.255702,
-        "nauc_recall_at_100_max": 0.346732,
-        "nauc_recall_at_100_std": 0.450867,
-        "nauc_recall_at_100_diff1": -0.448661,
-        "nauc_recall_at_1000_max": -0.18723,
-        "nauc_recall_at_1000_std": 0.764955,
-        "nauc_recall_at_1000_diff1": -1.251363,
-        "nauc_precision_at_1_max": -0.15791,
-        "nauc_precision_at_1_std": -0.309824,
-        "nauc_precision_at_1_diff1": -0.573698,
-        "nauc_precision_at_3_max": 0.051543,
-        "nauc_precision_at_3_std": -0.046082,
-        "nauc_precision_at_3_diff1": -0.053542,
-        "nauc_precision_at_5_max": 0.055409,
-        "nauc_precision_at_5_std": -0.022324,
-        "nauc_precision_at_5_diff1": -0.077261,
-        "nauc_precision_at_10_max": 0.055321,
-        "nauc_precision_at_10_std": 0.07644,
-        "nauc_precision_at_10_diff1": -0.207529,
-        "nauc_precision_at_20_max": 0.098502,
-        "nauc_precision_at_20_std": 0.101036,
-        "nauc_precision_at_20_diff1": -0.255702,
-        "nauc_precision_at_100_max": 0.346732,
-        "nauc_precision_at_100_std": 0.450867,
-        "nauc_precision_at_100_diff1": -0.448661,
-        "nauc_precision_at_1000_max": -0.18723,
-        "nauc_precision_at_1000_std": 0.764955,
-        "nauc_precision_at_1000_diff1": -1.251363,
-        "nauc_mrr_at_1_max": -0.15791,
-        "nauc_mrr_at_1_std": -0.309824,
-        "nauc_mrr_at_1_diff1": -0.573698,
-        "nauc_mrr_at_3_max": 0.049703,
-        "nauc_mrr_at_3_std": -0.048378,
-        "nauc_mrr_at_3_diff1": -0.054478,
-        "nauc_mrr_at_5_max": 0.049706,
-        "nauc_mrr_at_5_std": -0.041634,
-        "nauc_mrr_at_5_diff1": -0.062748,
-        "nauc_mrr_at_10_max": 0.049333,
-        "nauc_mrr_at_10_std": -0.023585,
-        "nauc_mrr_at_10_diff1": -0.08462,
-        "nauc_mrr_at_20_max": 0.052529,
-        "nauc_mrr_at_20_std": -0.026038,
-        "nauc_mrr_at_20_diff1": -0.082893,
-        "nauc_mrr_at_100_max": 0.052676,
-        "nauc_mrr_at_100_std": -0.025392,
-        "nauc_mrr_at_100_diff1": -0.081601,
-        "nauc_mrr_at_1000_max": 0.05229,
-        "nauc_mrr_at_1000_std": -0.025648,
-        "nauc_mrr_at_1000_diff1": -0.081523,
-        "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": [
-          {
-            "accuracy": 0.784693,
-            "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
-          },
-          {
-            "accuracy": 0.799832,
-            "f1": 0.785842,
-            "f1_weighted": 0.792011
-          },
-          {
-            "accuracy": 0.801514,
-            "f1": 0.788659,
-            "f1_weighted": 0.795154
-          },
-          {
-            "accuracy": 0.80614,
-            "f1": 0.795751,
-            "f1_weighted": 0.800618
-          },
-          {
-            "accuracy": 0.777544,
-            "f1": 0.758633,
-            "f1_weighted": 0.768624
-          }
-        ],
-        "main_score": 0.792094,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 12.62467908859253,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackAndroid-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "4a022e7213ccc05ee970a176abd0293b3a0a2da0",
-    "mteb_dataset_name": "CQADupstackAndroid-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "CQADupstackAndroid-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 44.89,
-                "map_at_1": 0.26147,
-                "map_at_10": 0.35997,
-                "map_at_100": 0.3737,
-                "map_at_1000": 0.37509,
-                "map_at_20": 0.36694,
-                "map_at_3": 0.32705,
-                "map_at_5": 0.34598,
-                "mrr_at_1": 0.32474,
-                "mrr_at_10": 0.41319,
-                "mrr_at_100": 0.42197,
-                "mrr_at_1000": 0.42238,
-                "mrr_at_20": 0.41806,
-                "mrr_at_3": 0.38431,
-                "mrr_at_5": 0.4008,
-                "ndcg_at_1": 0.32474,
-                "ndcg_at_10": 0.41928,
-                "ndcg_at_100": 0.47686,
-                "ndcg_at_1000": 0.49942,
-                "ndcg_at_20": 0.43829,
-                "ndcg_at_3": 0.36824,
-                "ndcg_at_5": 0.39274,
-                "precision_at_1": 0.32474,
-                "precision_at_10": 0.08041,
-                "precision_at_100": 0.01424,
-                "precision_at_1000": 0.00203,
-                "precision_at_20": 0.04828,
-                "precision_at_3": 0.17468,
-                "precision_at_5": 0.12921,
-                "recall_at_1": 0.26147,
-                "recall_at_10": 0.53732,
-                "recall_at_100": 0.7876,
-                "recall_at_1000": 0.93133,
-                "recall_at_20": 0.60459,
-                "recall_at_3": 0.38587,
-                "recall_at_5": 0.4548,
-                "hf_subset": "default",
-                "main_score": 0.41928,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackGis-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "755156d548a8288efdb29b80bad302750ab33977",
-    "mteb_dataset_name": "CQADupstackGis-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "CQADupstackGis-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 199.51,
-                "map_at_1": 0.20556,
-                "map_at_10": 0.2769,
-                "map_at_100": 0.28656,
-                "map_at_1000": 0.28748,
-                "map_at_20": 0.28257,
-                "map_at_3": 0.25622,
-                "map_at_5": 0.26698,
-                "mrr_at_1": 0.22308,
-                "mrr_at_10": 0.2932,
-                "mrr_at_100": 0.30271,
-                "mrr_at_1000": 0.3034,
-                "mrr_at_20": 0.29919,
-                "mrr_at_3": 0.27256,
-                "mrr_at_5": 0.28372,
-                "ndcg_at_1": 0.22308,
-                "ndcg_at_10": 0.31907,
-                "ndcg_at_100": 0.36814,
-                "ndcg_at_1000": 0.39334,
-                "ndcg_at_20": 0.33973,
-                "ndcg_at_3": 0.27709,
-                "ndcg_at_5": 0.29583,
-                "precision_at_1": 0.22308,
-                "precision_at_10": 0.05015,
-                "precision_at_100": 0.00785,
-                "precision_at_1000": 0.00105,
-                "precision_at_20": 0.02969,
-                "precision_at_3": 0.11949,
-                "precision_at_5": 0.08308,
-                "recall_at_1": 0.20556,
-                "recall_at_10": 0.43315,
-                "recall_at_100": 0.66017,
-                "recall_at_1000": 0.85128,
-                "recall_at_20": 0.51194,
-                "recall_at_3": 0.31881,
-                "recall_at_5": 0.36374,
-                "hf_subset": "default",
-                "main_score": 0.31907,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackMathematica-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "d0cc9b60ba66faa3fb21cb9a54ef969af548b312",
-    "mteb_dataset_name": "CQADupstackMathematica-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "CQADupstackMathematica-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 111.08,
-                "map_at_1": 0.11446,
-                "map_at_10": 0.17372,
-                "map_at_100": 0.18428,
-                "map_at_1000": 0.18553,
-                "map_at_20": 0.17952,
-                "map_at_3": 0.15416,
-                "map_at_5": 0.16377,
-                "mrr_at_1": 0.14499,
-                "mrr_at_10": 0.20717,
-                "mrr_at_100": 0.21714,
-                "mrr_at_1000": 0.21797,
-                "mrr_at_20": 0.21261,
-                "mrr_at_3": 0.18799,
-                "mrr_at_5": 0.19737,
-                "ndcg_at_1": 0.14499,
-                "ndcg_at_10": 0.21439,
-                "ndcg_at_100": 0.26912,
-                "ndcg_at_1000": 0.30206,
-                "ndcg_at_20": 0.23465,
-                "ndcg_at_3": 0.17684,
-                "ndcg_at_5": 0.19131,
-                "precision_at_1": 0.14499,
-                "precision_at_10": 0.04009,
-                "precision_at_100": 0.00755,
-                "precision_at_1000": 0.00117,
-                "precision_at_20": 0.02516,
-                "precision_at_3": 0.08529,
-                "precision_at_5": 0.06226,
-                "recall_at_1": 0.11446,
-                "recall_at_10": 0.30977,
-                "recall_at_100": 0.55949,
-                "recall_at_1000": 0.8006,
-                "recall_at_20": 0.38463,
-                "recall_at_3": 0.20025,
-                "recall_at_5": 0.2377,
-                "hf_subset": "default",
-                "main_score": 0.21439,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackPhysics-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "8b6b68b59933cc72985f674f76c80a678c27d6be",
-    "mteb_dataset_name": "CQADupstackPhysics-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "CQADupstackPhysics-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 190.76,
-                "map_at_1": 0.21216,
-                "map_at_10": 0.29794,
-                "map_at_100": 0.31226,
-                "map_at_1000": 0.31362,
-                "map_at_20": 0.30679,
-                "map_at_3": 0.26528,
-                "map_at_5": 0.28309,
-                "mrr_at_1": 0.27629,
-                "mrr_at_10": 0.36405,
-                "mrr_at_100": 0.37387,
-                "mrr_at_1000": 0.37455,
-                "mrr_at_20": 0.37036,
-                "mrr_at_3": 0.34158,
-                "mrr_at_5": 0.35354,
-                "ndcg_at_1": 0.27629,
-                "ndcg_at_10": 0.35518,
-                "ndcg_at_100": 0.4152,
-                "ndcg_at_1000": 0.44354,
-                "ndcg_at_20": 0.38148,
-                "ndcg_at_3": 0.30811,
-                "ndcg_at_5": 0.32809,
-                "precision_at_1": 0.27629,
-                "precision_at_10": 0.07072,
-                "precision_at_100": 0.01196,
-                "precision_at_1000": 0.00166,
-                "precision_at_20": 0.04381,
-                "precision_at_3": 0.15258,
-                "precision_at_5": 0.11134,
-                "recall_at_1": 0.21216,
-                "recall_at_10": 0.46074,
-                "recall_at_100": 0.71265,
-                "recall_at_1000": 0.9034,
-                "recall_at_20": 0.55265,
-                "recall_at_3": 0.32342,
-                "recall_at_5": 0.38045,
-                "hf_subset": "default",
-                "main_score": 0.35518,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackProgrammers-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "1a628c4e61f71ffdb7707d6d4024d25cfe68215a",
-    "mteb_dataset_name": "CQADupstackProgrammers-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "CQADupstackProgrammers-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 166.63,
-                "map_at_1": 0.19431,
-                "map_at_10": 0.27708,
-                "map_at_100": 0.2901,
-                "map_at_1000": 0.29131,
-                "map_at_20": 0.28417,
-                "map_at_3": 0.25104,
-                "map_at_5": 0.26674,
-                "mrr_at_1": 0.23328,
-                "mrr_at_10": 0.31688,
-                "mrr_at_100": 0.32747,
-                "mrr_at_1000": 0.32809,
-                "mrr_at_20": 0.32311,
-                "mrr_at_3": 0.29201,
-                "mrr_at_5": 0.30685,
-                "ndcg_at_1": 0.23328,
-                "ndcg_at_10": 0.3271,
-                "ndcg_at_100": 0.38812,
-                "ndcg_at_1000": 0.41372,
-                "ndcg_at_20": 0.35064,
-                "ndcg_at_3": 0.28162,
-                "ndcg_at_5": 0.30513,
-                "precision_at_1": 0.23328,
-                "precision_at_10": 0.06101,
-                "precision_at_100": 0.01064,
-                "precision_at_1000": 0.00145,
-                "precision_at_20": 0.03695,
-                "precision_at_3": 0.13431,
-                "precision_at_5": 0.09886,
-                "recall_at_1": 0.19431,
-                "recall_at_10": 0.43628,
-                "recall_at_100": 0.70198,
-                "recall_at_1000": 0.87329,
-                "recall_at_20": 0.52302,
-                "recall_at_3": 0.31336,
-                "recall_at_5": 0.37245,
-                "hf_subset": "default",
-                "main_score": 0.3271,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackStats-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "6b8164f3af61f3bb7728724229ba36213fb46c25",
-    "mteb_dataset_name": "CQADupstackStats-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "CQADupstackStats-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 234.79,
-                "map_at_1": 0.16556,
-                "map_at_10": 0.22848,
-                "map_at_100": 0.23963,
-                "map_at_1000": 0.24038,
-                "map_at_20": 0.23554,
-                "map_at_3": 0.20422,
-                "map_at_5": 0.21873,
-                "mrr_at_1": 0.18874,
-                "mrr_at_10": 0.2507,
-                "mrr_at_100": 0.26037,
-                "mrr_at_1000": 0.26091,
-                "mrr_at_20": 0.25655,
-                "mrr_at_3": 0.22572,
-                "mrr_at_5": 0.24078,
-                "ndcg_at_1": 0.18874,
-                "ndcg_at_10": 0.26859,
-                "ndcg_at_100": 0.32125,
-                "ndcg_at_1000": 0.34208,
-                "ndcg_at_20": 0.29217,
-                "ndcg_at_3": 0.22063,
-                "ndcg_at_5": 0.2463,
-                "precision_at_1": 0.18874,
-                "precision_at_10": 0.04603,
-                "precision_at_100": 0.00768,
-                "precision_at_1000": 0.00102,
-                "precision_at_20": 0.02881,
-                "precision_at_3": 0.09492,
-                "precision_at_5": 0.07219,
-                "recall_at_1": 0.16556,
-                "recall_at_10": 0.3683,
-                "recall_at_100": 0.60969,
-                "recall_at_1000": 0.76575,
-                "recall_at_20": 0.45836,
-                "recall_at_3": 0.24264,
-                "recall_at_5": 0.30273,
-                "hf_subset": "default",
-                "main_score": 0.26859,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackTex-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "aec43e5ae40451526528b3fc80dd5983ec388e21",
-    "mteb_dataset_name": "CQADupstackTex-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "CQADupstackTex-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 490.61,
-                "map_at_1": 0.14818,
-                "map_at_10": 0.20724,
-                "map_at_100": 0.21763,
-                "map_at_1000": 0.2188,
-                "map_at_20": 0.21326,
-                "map_at_3": 0.18685,
-                "map_at_5": 0.19831,
-                "mrr_at_1": 0.18582,
-                "mrr_at_10": 0.24641,
-                "mrr_at_100": 0.2559,
-                "mrr_at_1000": 0.25671,
-                "mrr_at_20": 0.25211,
-                "mrr_at_3": 0.2268,
-                "mrr_at_5": 0.23826,
-                "ndcg_at_1": 0.18582,
-                "ndcg_at_10": 0.24781,
-                "ndcg_at_100": 0.29847,
-                "ndcg_at_1000": 0.32928,
-                "ndcg_at_20": 0.26771,
-                "ndcg_at_3": 0.21233,
-                "ndcg_at_5": 0.22894,
-                "precision_at_1": 0.18582,
-                "precision_at_10": 0.04523,
-                "precision_at_100": 0.00827,
-                "precision_at_1000": 0.00126,
-                "precision_at_20": 0.02799,
-                "precision_at_3": 0.10025,
-                "precision_at_5": 0.07364,
-                "recall_at_1": 0.14818,
-                "recall_at_10": 0.33285,
-                "recall_at_100": 0.56243,
-                "recall_at_1000": 0.78623,
-                "recall_at_20": 0.40797,
-                "recall_at_3": 0.22972,
-                "recall_at_5": 0.27396,
-                "hf_subset": "default",
-                "main_score": 0.24781,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackUnix-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "f8b884697871cb38901139f2435c273135f83a3f",
-    "mteb_dataset_name": "CQADupstackUnix-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "CQADupstackUnix-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 248.55,
-                "map_at_1": 0.22331,
-                "map_at_10": 0.29694,
-                "map_at_100": 0.30728,
-                "map_at_1000": 0.30838,
-                "map_at_20": 0.30234,
-                "map_at_3": 0.27118,
-                "map_at_5": 0.28451,
-                "mrr_at_1": 0.26481,
-                "mrr_at_10": 0.33574,
-                "mrr_at_100": 0.34457,
-                "mrr_at_1000": 0.34526,
-                "mrr_at_20": 0.34089,
-                "mrr_at_3": 0.31177,
-                "mrr_at_5": 0.32465,
-                "ndcg_at_1": 0.26481,
-                "ndcg_at_10": 0.34515,
-                "ndcg_at_100": 0.39566,
-                "ndcg_at_1000": 0.42141,
-                "ndcg_at_20": 0.36389,
-                "ndcg_at_3": 0.29735,
-                "ndcg_at_5": 0.31759,
-                "precision_at_1": 0.26481,
-                "precision_at_10": 0.05925,
-                "precision_at_100": 0.00946,
-                "precision_at_1000": 0.00128,
-                "precision_at_20": 0.03458,
-                "precision_at_3": 0.13301,
-                "precision_at_5": 0.09432,
-                "recall_at_1": 0.22331,
-                "recall_at_10": 0.45476,
-                "recall_at_100": 0.67928,
-                "recall_at_1000": 0.86037,
-                "recall_at_20": 0.52341,
-                "recall_at_3": 0.32383,
-                "recall_at_5": 0.37485,
-                "hf_subset": "default",
-                "main_score": 0.34515,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackWebmasters-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "482d6e560d977b137e435d33379c5a8049e70e8d",
-    "mteb_dataset_name": "CQADupstackWebmasters-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "CQADupstackWebmasters-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 73.56,
-                "map_at_1": 0.18814,
-                "map_at_10": 0.26309,
-                "map_at_100": 0.27633,
-                "map_at_1000": 0.2786,
-                "map_at_20": 0.26934,
-                "map_at_3": 0.23491,
-                "map_at_5": 0.25194,
-                "mrr_at_1": 0.225,
-                "mrr_at_10": 0.30061,
-                "mrr_at_100": 0.31017,
-                "mrr_at_1000": 0.31098,
-                "mrr_at_20": 0.30585,
-                "mrr_at_3": 0.27083,
-                "mrr_at_5": 0.29083,
-                "ndcg_at_1": 0.225,
-                "ndcg_at_10": 0.31666,
-                "ndcg_at_100": 0.36984,
-                "ndcg_at_1000": 0.40278,
-                "ndcg_at_20": 0.33293,
-                "ndcg_at_3": 0.26741,
-                "ndcg_at_5": 0.29431,
-                "precision_at_1": 0.225,
-                "precision_at_10": 0.062,
-                "precision_at_100": 0.01322,
-                "precision_at_1000": 0.00234,
-                "precision_at_20": 0.039,
-                "precision_at_3": 0.125,
-                "precision_at_5": 0.097,
-                "recall_at_1": 0.18814,
-                "recall_at_10": 0.42675,
-                "recall_at_100": 0.66891,
-                "recall_at_1000": 0.88531,
-                "recall_at_20": 0.49,
-                "recall_at_3": 0.28062,
-                "recall_at_5": 0.35206,
-                "hf_subset": "default",
-                "main_score": 0.31666,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/CQADupstackWordpress-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "2230f80e1baf42aa005731ca86577621c566fcd7",
-    "mteb_dataset_name": "CQADupstackWordpress-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "CQADupstackWordpress-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 283.45,
-                "map_at_1": 0.13534,
-                "map_at_10": 0.20314,
-                "map_at_100": 0.21388,
-                "map_at_1000": 0.21491,
-                "map_at_20": 0.20936,
-                "map_at_3": 0.18317,
-                "map_at_5": 0.19319,
-                "mrr_at_1": 0.15183,
-                "mrr_at_10": 0.22404,
-                "mrr_at_100": 0.23378,
-                "mrr_at_1000": 0.23451,
-                "mrr_at_20": 0.22996,
-                "mrr_at_3": 0.20201,
-                "mrr_at_5": 0.21366,
-                "ndcg_at_1": 0.15183,
-                "ndcg_at_10": 0.24744,
-                "ndcg_at_100": 0.29839,
-                "ndcg_at_1000": 0.32591,
-                "ndcg_at_20": 0.2689,
-                "ndcg_at_3": 0.20599,
-                "ndcg_at_5": 0.22338,
-                "precision_at_1": 0.15183,
-                "precision_at_10": 0.04162,
-                "precision_at_100": 0.0073,
-                "precision_at_1000": 0.00109,
-                "precision_at_20": 0.02592,
-                "precision_at_3": 0.09511,
-                "precision_at_5": 0.06702,
-                "recall_at_1": 0.13534,
-                "recall_at_10": 0.36562,
-                "recall_at_100": 0.59327,
-                "recall_at_1000": 0.80055,
-                "recall_at_20": 0.44546,
-                "recall_at_3": 0.24908,
-                "recall_at_5": 0.29228,
-                "hf_subset": "default",
-                "main_score": 0.24744,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/ClimateFEVER-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "42328bf787e17b1ad1a88be4f5e87ea9fb668511",
-    "mteb_dataset_name": "ClimateFEVER-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "ClimateFEVER-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 16316.19,
-                "map_at_1": 0.04967,
-                "map_at_10": 0.08868,
-                "map_at_100": 0.09661,
-                "map_at_1000": 0.0982,
-                "map_at_20": 0.09205,
-                "map_at_3": 0.07457,
-                "map_at_5": 0.08228,
-                "mrr_at_1": 0.11353,
-                "mrr_at_10": 0.18208,
-                "mrr_at_100": 0.19152,
-                "mrr_at_1000": 0.1925,
-                "mrr_at_20": 0.18658,
-                "mrr_at_3": 0.15849,
-                "mrr_at_5": 0.17107,
-                "ndcg_at_1": 0.11353,
-                "ndcg_at_10": 0.13249,
-                "ndcg_at_100": 0.17814,
-                "ndcg_at_1000": 0.21712,
-                "ndcg_at_20": 0.14482,
-                "ndcg_at_3": 0.10497,
-                "ndcg_at_5": 0.1154,
-                "precision_at_1": 0.11353,
-                "precision_at_10": 0.04223,
-                "precision_at_100": 0.00927,
-                "precision_at_1000": 0.00164,
-                "precision_at_20": 0.02643,
-                "precision_at_3": 0.08053,
-                "precision_at_5": 0.06267,
-                "recall_at_1": 0.04967,
-                "recall_at_10": 0.16757,
-                "recall_at_100": 0.33663,
-                "recall_at_1000": 0.5625,
-                "recall_at_20": 0.20262,
-                "recall_at_3": 0.09955,
-                "recall_at_5": 0.12846,
-                "hf_subset": "default",
-                "main_score": 0.13249,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/DBPedia-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "c3e20179fbcee16217ef9461a14a54b7faca9b63",
-    "mteb_dataset_name": "DBPedia-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "DBPedia-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 4419.42,
-                "map_at_1": 0.07486,
-                "map_at_10": 0.15539,
-                "map_at_100": 0.21047,
-                "map_at_1000": 0.22274,
-                "map_at_20": 0.17743,
-                "map_at_3": 0.11208,
-                "map_at_5": 0.13146,
-                "mrr_at_1": 0.53106,
-                "mrr_at_10": 0.6375,
-                "mrr_at_100": 0.64307,
-                "mrr_at_1000": 0.64321,
-                "mrr_at_20": 0.64143,
-                "mrr_at_3": 0.61439,
-                "mrr_at_5": 0.62805,
-                "ndcg_at_1": 0.43634,
-                "ndcg_at_10": 0.34204,
-                "ndcg_at_100": 0.38478,
-                "ndcg_at_1000": 0.45509,
-                "ndcg_at_20": 0.34051,
-                "ndcg_at_3": 0.37097,
-                "ndcg_at_5": 0.35581,
-                "precision_at_1": 0.53106,
-                "precision_at_10": 0.26553,
-                "precision_at_100": 0.08469,
-                "precision_at_1000": 0.01695,
-                "precision_at_20": 0.2028,
-                "precision_at_3": 0.39648,
-                "precision_at_5": 0.34472,
-                "recall_at_1": 0.07486,
-                "recall_at_10": 0.21316,
-                "recall_at_100": 0.44686,
-                "recall_at_1000": 0.67695,
-                "recall_at_20": 0.27749,
-                "recall_at_3": 0.12701,
-                "recall_at_5": 0.16045,
-                "hf_subset": "default",
-                "main_score": 0.34204,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/EmotionVNClassification.json

@@ -1,137 +0,0 @@
-{
-  "dataset_revision": "797a93c0dd755ebf5818fbf54d0e0a024df9216d",
-  "task_name": "EmotionVNClassification",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "validation": [
-      {
-        "accuracy": 0.44375,
-        "f1": 0.390648,
-        "f1_weighted": 0.463387,
-        "scores_per_experiment": [
-          {
-            "accuracy": 0.472656,
-            "f1": 0.404343,
-            "f1_weighted": 0.498363
-          },
-          {
-            "accuracy": 0.43125,
-            "f1": 0.354818,
-            "f1_weighted": 0.446879
-          },
-          {
-            "accuracy": 0.473438,
-            "f1": 0.402721,
-            "f1_weighted": 0.496464
-          },
-          {
-            "accuracy": 0.422656,
-            "f1": 0.384927,
-            "f1_weighted": 0.440412
-          },
-          {
-            "accuracy": 0.461719,
-            "f1": 0.414061,
-            "f1_weighted": 0.476512
-          },
-          {
-            "accuracy": 0.405469,
-            "f1": 0.381572,
-            "f1_weighted": 0.421386
-          },
-          {
-            "accuracy": 0.478906,
-            "f1": 0.391987,
-            "f1_weighted": 0.487998
-          },
-          {
-            "accuracy": 0.396875,
-            "f1": 0.378969,
-            "f1_weighted": 0.422998
-          },
-          {
-            "accuracy": 0.463281,
-            "f1": 0.413623,
-            "f1_weighted": 0.487997
-          },
-          {
-            "accuracy": 0.43125,
-            "f1": 0.379461,
-            "f1_weighted": 0.454856
-          }
-        ],
-        "main_score": 0.44375,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ],
-    "test": [
-      {
-        "accuracy": 0.446822,
-        "f1": 0.380728,
-        "f1_weighted": 0.468056,
-        "scores_per_experiment": [
-          {
-            "accuracy": 0.481395,
-            "f1": 0.401385,
-            "f1_weighted": 0.508444
-          },
-          {
-            "accuracy": 0.432558,
-            "f1": 0.352439,
-            "f1_weighted": 0.446656
-          },
-          {
-            "accuracy": 0.475969,
-            "f1": 0.394022,
-            "f1_weighted": 0.49485
-          },
-          {
-            "accuracy": 0.413953,
-            "f1": 0.362915,
-            "f1_weighted": 0.439652
-          },
-          {
-            "accuracy": 0.455039,
-            "f1": 0.399925,
-            "f1_weighted": 0.475066
-          },
-          {
-            "accuracy": 0.397674,
-            "f1": 0.362269,
-            "f1_weighted": 0.412897
-          },
-          {
-            "accuracy": 0.490698,
-            "f1": 0.389544,
-            "f1_weighted": 0.498402
-          },
-          {
-            "accuracy": 0.402326,
-            "f1": 0.361925,
-            "f1_weighted": 0.434939
-          },
-          {
-            "accuracy": 0.473643,
-            "f1": 0.405323,
-            "f1_weighted": 0.496273
-          },
-          {
-            "accuracy": 0.444961,
-            "f1": 0.377529,
-            "f1_weighted": 0.473386
-          }
-        ],
-        "main_score": 0.446822,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 16.90190815925598,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/FEVER-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "a543dd8b98aed3603110c01d26db05ba39b87d49",
-    "mteb_dataset_name": "FEVER-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "FEVER-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 7374.25,
-                "map_at_1": 0.31332,
-                "map_at_10": 0.42736,
-                "map_at_100": 0.43506,
-                "map_at_1000": 0.43552,
-                "map_at_20": 0.432,
-                "map_at_3": 0.3997,
-                "map_at_5": 0.41616,
-                "mrr_at_1": 0.33676,
-                "mrr_at_10": 0.45185,
-                "mrr_at_100": 0.45905,
-                "mrr_at_1000": 0.45943,
-                "mrr_at_20": 0.45621,
-                "mrr_at_3": 0.42454,
-                "mrr_at_5": 0.44056,
-                "ndcg_at_1": 0.33676,
-                "ndcg_at_10": 0.4881,
-                "ndcg_at_100": 0.52495,
-                "ndcg_at_1000": 0.53726,
-                "ndcg_at_20": 0.50418,
-                "ndcg_at_3": 0.43293,
-                "ndcg_at_5": 0.46137,
-                "precision_at_1": 0.33676,
-                "precision_at_10": 0.0715,
-                "precision_at_100": 0.00916,
-                "precision_at_1000": 0.00104,
-                "precision_at_20": 0.03934,
-                "precision_at_3": 0.1826,
-                "precision_at_5": 0.12488,
-                "recall_at_1": 0.31332,
-                "recall_at_10": 0.6531,
-                "recall_at_100": 0.8205,
-                "recall_at_1000": 0.91405,
-                "recall_at_20": 0.71409,
-                "recall_at_3": 0.50359,
-                "recall_at_5": 0.57214,
-                "hf_subset": "default",
-                "main_score": 0.4881,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/FiQA2018-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "6c3cdf6f102151dbbbbc1d2cf999b305eba44dae",
-    "mteb_dataset_name": "FiQA2018-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "FiQA2018-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 149.94,
-                "map_at_1": 0.15915,
-                "map_at_10": 0.23933,
-                "map_at_100": 0.25351,
-                "map_at_1000": 0.25561,
-                "map_at_20": 0.24686,
-                "map_at_3": 0.21014,
-                "map_at_5": 0.22637,
-                "mrr_at_1": 0.29024,
-                "mrr_at_10": 0.3643,
-                "mrr_at_100": 0.37292,
-                "mrr_at_1000": 0.37358,
-                "mrr_at_20": 0.36883,
-                "mrr_at_3": 0.34301,
-                "mrr_at_5": 0.35396,
-                "ndcg_at_1": 0.29024,
-                "ndcg_at_10": 0.29939,
-                "ndcg_at_100": 0.35992,
-                "ndcg_at_1000": 0.39932,
-                "ndcg_at_20": 0.32076,
-                "ndcg_at_3": 0.27282,
-                "ndcg_at_5": 0.27997,
-                "precision_at_1": 0.29024,
-                "precision_at_10": 0.08153,
-                "precision_at_100": 0.01478,
-                "precision_at_1000": 0.00216,
-                "precision_at_20": 0.05013,
-                "precision_at_3": 0.17326,
-                "precision_at_5": 0.12823,
-                "recall_at_1": 0.15915,
-                "recall_at_10": 0.35196,
-                "recall_at_100": 0.57857,
-                "recall_at_1000": 0.81862,
-                "recall_at_20": 0.41776,
-                "recall_at_3": 0.24858,
-                "recall_at_5": 0.29204,
-                "hf_subset": "default",
-                "main_score": 0.29939,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/GreenNodeTableMarkdownRetrieval.json

@@ -1,158 +0,0 @@
-{
-  "dataset_revision": "d86a4dad9fd7c70359f617d86984395ea89be1c5",
-  "task_name": "GreenNodeTableMarkdownRetrieval",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "ndcg_at_1": 0.30846,
-        "ndcg_at_3": 0.36243,
-        "ndcg_at_5": 0.37957,
-        "ndcg_at_10": 0.39719,
-        "ndcg_at_20": 0.412,
-        "ndcg_at_100": 0.43624,
-        "ndcg_at_1000": 0.45896,
-        "map_at_1": 0.30846,
-        "map_at_3": 0.3491,
-        "map_at_5": 0.35861,
-        "map_at_10": 0.36591,
-        "map_at_20": 0.36997,
-        "map_at_100": 0.3732,
-        "map_at_1000": 0.37392,
-        "recall_at_1": 0.30846,
-        "recall_at_3": 0.40102,
-        "recall_at_5": 0.44265,
-        "recall_at_10": 0.49702,
-        "recall_at_20": 0.55553,
-        "recall_at_100": 0.68819,
-        "recall_at_1000": 0.87447,
-        "precision_at_1": 0.30846,
-        "precision_at_3": 0.13367,
-        "precision_at_5": 0.08853,
-        "precision_at_10": 0.0497,
-        "precision_at_20": 0.02778,
-        "precision_at_100": 0.00688,
-        "precision_at_1000": 0.00087,
-        "mrr_at_1": 0.308457,
-        "mrr_at_3": 0.349096,
-        "mrr_at_5": 0.35861,
-        "mrr_at_10": 0.365907,
-        "mrr_at_20": 0.369975,
-        "mrr_at_100": 0.373196,
-        "mrr_at_1000": 0.373924,
-        "nauc_ndcg_at_1_max": 0.139786,
-        "nauc_ndcg_at_1_std": -0.039386,
-        "nauc_ndcg_at_1_diff1": 0.733187,
-        "nauc_ndcg_at_3_max": 0.09619,
-        "nauc_ndcg_at_3_std": -0.017705,
-        "nauc_ndcg_at_3_diff1": 0.676034,
-        "nauc_ndcg_at_5_max": 0.08391,
-        "nauc_ndcg_at_5_std": -0.006374,
-        "nauc_ndcg_at_5_diff1": 0.667409,
-        "nauc_ndcg_at_10_max": 0.071841,
-        "nauc_ndcg_at_10_std": 0.004193,
-        "nauc_ndcg_at_10_diff1": 0.657513,
-        "nauc_ndcg_at_20_max": 0.06409,
-        "nauc_ndcg_at_20_std": 0.014012,
-        "nauc_ndcg_at_20_diff1": 0.653131,
-        "nauc_ndcg_at_100_max": 0.059821,
-        "nauc_ndcg_at_100_std": 0.028492,
-        "nauc_ndcg_at_100_diff1": 0.649499,
-        "nauc_ndcg_at_1000_max": 0.066935,
-        "nauc_ndcg_at_1000_std": 0.028327,
-        "nauc_ndcg_at_1000_diff1": 0.655572,
-        "nauc_map_at_1_max": 0.139786,
-        "nauc_map_at_1_std": -0.039386,
-        "nauc_map_at_1_diff1": 0.733187,
-        "nauc_map_at_3_max": 0.106754,
-        "nauc_map_at_3_std": -0.023135,
-        "nauc_map_at_3_diff1": 0.689956,
-        "nauc_map_at_5_max": 0.099963,
-        "nauc_map_at_5_std": -0.016825,
-        "nauc_map_at_5_diff1": 0.685233,
-        "nauc_map_at_10_max": 0.09492,
-        "nauc_map_at_10_std": -0.012515,
-        "nauc_map_at_10_diff1": 0.681245,
-        "nauc_map_at_20_max": 0.092859,
-        "nauc_map_at_20_std": -0.00995,
-        "nauc_map_at_20_diff1": 0.68014,
-        "nauc_map_at_100_max": 0.092295,
-        "nauc_map_at_100_std": -0.008175,
-        "nauc_map_at_100_diff1": 0.67967,
-        "nauc_map_at_1000_max": 0.092486,
-        "nauc_map_at_1000_std": -0.008127,
-        "nauc_map_at_1000_diff1": 0.679852,
-        "nauc_recall_at_1_max": 0.139786,
-        "nauc_recall_at_1_std": -0.039386,
-        "nauc_recall_at_1_diff1": 0.733187,
-        "nauc_recall_at_3_max": 0.066333,
-        "nauc_recall_at_3_std": -0.002289,
-        "nauc_recall_at_3_diff1": 0.636658,
-        "nauc_recall_at_5_max": 0.036325,
-        "nauc_recall_at_5_std": 0.025109,
-        "nauc_recall_at_5_diff1": 0.615216,
-        "nauc_recall_at_10_max": -0.001472,
-        "nauc_recall_at_10_std": 0.058617,
-        "nauc_recall_at_10_diff1": 0.583252,
-        "nauc_recall_at_20_max": -0.037741,
-        "nauc_recall_at_20_std": 0.102985,
-        "nauc_recall_at_20_diff1": 0.560586,
-        "nauc_recall_at_100_max": -0.089231,
-        "nauc_recall_at_100_std": 0.224089,
-        "nauc_recall_at_100_diff1": 0.515154,
-        "nauc_recall_at_1000_max": -0.101471,
-        "nauc_recall_at_1000_std": 0.430692,
-        "nauc_recall_at_1000_diff1": 0.493278,
-        "nauc_precision_at_1_max": 0.139786,
-        "nauc_precision_at_1_std": -0.039386,
-        "nauc_precision_at_1_diff1": 0.733187,
-        "nauc_precision_at_3_max": 0.066333,
-        "nauc_precision_at_3_std": -0.002289,
-        "nauc_precision_at_3_diff1": 0.636658,
-        "nauc_precision_at_5_max": 0.036325,
-        "nauc_precision_at_5_std": 0.025109,
-        "nauc_precision_at_5_diff1": 0.615216,
-        "nauc_precision_at_10_max": -0.001472,
-        "nauc_precision_at_10_std": 0.058617,
-        "nauc_precision_at_10_diff1": 0.583252,
-        "nauc_precision_at_20_max": -0.037741,
-        "nauc_precision_at_20_std": 0.102985,
-        "nauc_precision_at_20_diff1": 0.560586,
-        "nauc_precision_at_100_max": -0.089231,
-        "nauc_precision_at_100_std": 0.224089,
-        "nauc_precision_at_100_diff1": 0.515154,
-        "nauc_precision_at_1000_max": -0.101471,
-        "nauc_precision_at_1000_std": 0.430692,
-        "nauc_precision_at_1000_diff1": 0.493278,
-        "nauc_mrr_at_1_max": 0.139786,
-        "nauc_mrr_at_1_std": -0.039386,
-        "nauc_mrr_at_1_diff1": 0.733187,
-        "nauc_mrr_at_3_max": 0.106754,
-        "nauc_mrr_at_3_std": -0.023135,
-        "nauc_mrr_at_3_diff1": 0.689956,
-        "nauc_mrr_at_5_max": 0.099963,
-        "nauc_mrr_at_5_std": -0.016825,
-        "nauc_mrr_at_5_diff1": 0.685233,
-        "nauc_mrr_at_10_max": 0.09492,
-        "nauc_mrr_at_10_std": -0.012515,
-        "nauc_mrr_at_10_diff1": 0.681245,
-        "nauc_mrr_at_20_max": 0.092859,
-        "nauc_mrr_at_20_std": -0.00995,
-        "nauc_mrr_at_20_diff1": 0.68014,
-        "nauc_mrr_at_100_max": 0.092295,
-        "nauc_mrr_at_100_std": -0.008175,
-        "nauc_mrr_at_100_diff1": 0.67967,
-        "nauc_mrr_at_1000_max": 0.092486,
-        "nauc_mrr_at_1000_std": -0.008127,
-        "nauc_mrr_at_1000_diff1": 0.679852,
-        "main_score": 0.39719,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 716.343478679657,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/HotpotQA-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "8a5220c7af5084f0d5d2afeb74f9c2b41b759ff0",
-    "mteb_dataset_name": "HotpotQA-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "HotpotQA-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 13567.89,
-                "map_at_1": 0.39226,
-                "map_at_10": 0.6178,
-                "map_at_100": 0.62675,
-                "map_at_1000": 0.62738,
-                "map_at_20": 0.6231,
-                "map_at_3": 0.58315,
-                "map_at_5": 0.60581,
-                "mrr_at_1": 0.78453,
-                "mrr_at_10": 0.84187,
-                "mrr_at_100": 0.84371,
-                "mrr_at_1000": 0.84377,
-                "mrr_at_20": 0.8431,
-                "mrr_at_3": 0.8326,
-                "mrr_at_5": 0.83842,
-                "ndcg_at_1": 0.78453,
-                "ndcg_at_10": 0.70074,
-                "ndcg_at_100": 0.73145,
-                "ndcg_at_1000": 0.74324,
-                "ndcg_at_20": 0.71379,
-                "ndcg_at_3": 0.65249,
-                "ndcg_at_5": 0.6806,
-                "precision_at_1": 0.78453,
-                "precision_at_10": 0.14613,
-                "precision_at_100": 0.01701,
-                "precision_at_1000": 0.00186,
-                "precision_at_20": 0.07725,
-                "precision_at_3": 0.4166,
-                "precision_at_5": 0.27211,
-                "recall_at_1": 0.39226,
-                "recall_at_10": 0.73066,
-                "recall_at_100": 0.85026,
-                "recall_at_1000": 0.92794,
-                "recall_at_20": 0.7725,
-                "recall_at_3": 0.6249,
-                "recall_at_5": 0.68027,
-                "hf_subset": "default",
-                "main_score": 0.70074,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/ImdbVNClassification.json

@@ -1,95 +0,0 @@
-{
-  "dataset_revision": "0dccb383ee26c90c99d03c8674cf40de642f099a",
-  "task_name": "ImdbVNClassification",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "accuracy": 0.830619,
-        "f1": 0.830218,
-        "f1_weighted": 0.830237,
-        "ap": 0.775496,
-        "ap_weighted": 0.775496,
-        "scores_per_experiment": [
-          {
-            "accuracy": 0.845523,
-            "f1": 0.845006,
-            "f1_weighted": 0.844953,
-            "ap": 0.811288,
-            "ap_weighted": 0.811288
-          },
-          {
-            "accuracy": 0.812101,
-            "f1": 0.810477,
-            "f1_weighted": 0.810581,
-            "ap": 0.740588,
-            "ap_weighted": 0.740588
-          },
-          {
-            "accuracy": 0.837824,
-            "f1": 0.83732,
-            "f1_weighted": 0.837373,
-            "ap": 0.77416,
-            "ap_weighted": 0.77416
-          },
-          {
-            "accuracy": 0.860559,
-            "f1": 0.860469,
-            "f1_weighted": 0.860448,
-            "ap": 0.820455,
-            "ap_weighted": 0.820455
-          },
-          {
-            "accuracy": 0.839409,
-            "f1": 0.839408,
-            "f1_weighted": 0.839411,
-            "ap": 0.787281,
-            "ap_weighted": 0.787281
-          },
-          {
-            "accuracy": 0.816856,
-            "f1": 0.816719,
-            "f1_weighted": 0.81669,
-            "ap": 0.767843,
-            "ap_weighted": 0.767843
-          },
-          {
-            "accuracy": 0.81826,
-            "f1": 0.817479,
-            "f1_weighted": 0.81755,
-            "ap": 0.751218,
-            "ap_weighted": 0.751218
-          },
-          {
-            "accuracy": 0.826729,
-            "f1": 0.82647,
-            "f1_weighted": 0.82651,
-            "ap": 0.765153,
-            "ap_weighted": 0.765153
-          },
-          {
-            "accuracy": 0.816403,
-            "f1": 0.816309,
-            "f1_weighted": 0.816333,
-            "ap": 0.756783,
-            "ap_weighted": 0.756783
-          },
-          {
-            "accuracy": 0.832526,
-            "f1": 0.832525,
-            "f1_weighted": 0.832524,
-            "ap": 0.780192,
-            "ap_weighted": 0.780192
-          }
-        ],
-        "main_score": 0.830619,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 162.1266748905182,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/MSMARCO-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "85d1ad4cc9070b8d019d65f5af1631a2ab91e294",
-    "mteb_dataset_name": "MSMARCO-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "MSMARCO-VN",
-    "scores": {
-        "dev": [
-            {
-                "evaluation_time": 22839.72,
-                "map_at_1": 0.14118,
-                "map_at_10": 0.24393,
-                "map_at_100": 0.25563,
-                "map_at_1000": 0.25629,
-                "map_at_20": 0.25091,
-                "map_at_3": 0.20812,
-                "map_at_5": 0.22766,
-                "mrr_at_1": 0.14551,
-                "mrr_at_10": 0.24818,
-                "mrr_at_100": 0.25954,
-                "mrr_at_1000": 0.26016,
-                "mrr_at_20": 0.25497,
-                "mrr_at_3": 0.21241,
-                "mrr_at_5": 0.23198,
-                "ndcg_at_1": 0.14551,
-                "ndcg_at_10": 0.30497,
-                "ndcg_at_100": 0.36328,
-                "ndcg_at_1000": 0.38075,
-                "ndcg_at_20": 0.33003,
-                "ndcg_at_3": 0.2309,
-                "ndcg_at_5": 0.26603,
-                "precision_at_1": 0.14551,
-                "precision_at_10": 0.05166,
-                "precision_at_100": 0.00808,
-                "precision_at_1000": 0.00095,
-                "precision_at_20": 0.03099,
-                "precision_at_3": 0.10149,
-                "precision_at_5": 0.07867,
-                "recall_at_1": 0.14118,
-                "recall_at_10": 0.49585,
-                "recall_at_100": 0.76965,
-                "recall_at_1000": 0.90623,
-                "recall_at_20": 0.59377,
-                "recall_at_3": 0.29241,
-                "recall_at_5": 0.37722,
-                "hf_subset": "default",
-                "main_score": 0.30497,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/MTOPDomainVNClassification.json

@@ -1,73 +0,0 @@
-{
-  "dataset_revision": "6e1ec8c54c018151c77472d94b1c0765230cf6ca",
-  "task_name": "MTOPDomainVNClassification",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "accuracy": 0.855346,
-        "f1": 0.853271,
-        "f1_weighted": 0.854398,
-        "scores_per_experiment": [
-          {
-            "accuracy": 0.841622,
-            "f1": 0.833588,
-            "f1_weighted": 0.841051
-          },
-          {
-            "accuracy": 0.845535,
-            "f1": 0.84299,
-            "f1_weighted": 0.845127
-          },
-          {
-            "accuracy": 0.860658,
-            "f1": 0.858643,
-            "f1_weighted": 0.859362
-          },
-          {
-            "accuracy": 0.865097,
-            "f1": 0.860371,
-            "f1_weighted": 0.864139
-          },
-          {
-            "accuracy": 0.85351,
-            "f1": 0.85694,
-            "f1_weighted": 0.852418
-          },
-          {
-            "accuracy": 0.851704,
-            "f1": 0.850781,
-            "f1_weighted": 0.850459
-          },
-          {
-            "accuracy": 0.867279,
-            "f1": 0.863823,
-            "f1_weighted": 0.865732
-          },
-          {
-            "accuracy": 0.868558,
-            "f1": 0.862947,
-            "f1_weighted": 0.866173
-          },
-          {
-            "accuracy": 0.828681,
-            "f1": 0.837955,
-            "f1_weighted": 0.829348
-          },
-          {
-            "accuracy": 0.870815,
-            "f1": 0.864666,
-            "f1_weighted": 0.870169
-          }
-        ],
-        "main_score": 0.855346,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 9.854939937591553,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/MTOPIntentVNClassification.json

@@ -1,73 +0,0 @@
-{
-  "dataset_revision": "c4e81a5c9a813a0142d905e261e5a446cc6fbc4a",
-  "task_name": "MTOPIntentVNClassification",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "accuracy": 0.580114,
-        "f1": 0.394653,
-        "f1_weighted": 0.619897,
-        "scores_per_experiment": [
-          {
-            "accuracy": 0.558573,
-            "f1": 0.394329,
-            "f1_weighted": 0.598478
-          },
-          {
-            "accuracy": 0.591528,
-            "f1": 0.401524,
-            "f1_weighted": 0.634855
-          },
-          {
-            "accuracy": 0.580618,
-            "f1": 0.387417,
-            "f1_weighted": 0.616344
-          },
-          {
-            "accuracy": 0.601685,
-            "f1": 0.407789,
-            "f1_weighted": 0.648841
-          },
-          {
-            "accuracy": 0.569408,
-            "f1": 0.394753,
-            "f1_weighted": 0.606008
-          },
-          {
-            "accuracy": 0.578963,
-            "f1": 0.379034,
-            "f1_weighted": 0.616886
-          },
-          {
-            "accuracy": 0.58739,
-            "f1": 0.400041,
-            "f1_weighted": 0.622262
-          },
-          {
-            "accuracy": 0.568731,
-            "f1": 0.392647,
-            "f1_weighted": 0.610166
-          },
-          {
-            "accuracy": 0.562862,
-            "f1": 0.393473,
-            "f1_weighted": 0.60385
-          },
-          {
-            "accuracy": 0.601384,
-            "f1": 0.395527,
-            "f1_weighted": 0.64128
-          }
-        ],
-        "main_score": 0.580114,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 16.95852541923523,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/MassiveIntentVNClassification.json

@@ -1,73 +0,0 @@
-{
-  "dataset_revision": "35c7ced69f958dbbaa24f792db4a9250e461866d",
-  "task_name": "MassiveIntentVNClassification",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "accuracy": 0.67741,
-        "f1": 0.643766,
-        "f1_weighted": 0.665189,
-        "scores_per_experiment": [
-          {
-            "accuracy": 0.679933,
-            "f1": 0.670853,
-            "f1_weighted": 0.66368
-          },
-          {
-            "accuracy": 0.705717,
-            "f1": 0.65224,
-            "f1_weighted": 0.698288
-          },
-          {
-            "accuracy": 0.675448,
-            "f1": 0.627179,
-            "f1_weighted": 0.662605
-          },
-          {
-            "accuracy": 0.707399,
-            "f1": 0.675465,
-            "f1_weighted": 0.699657
-          },
-          {
-            "accuracy": 0.659193,
-            "f1": 0.624411,
-            "f1_weighted": 0.641452
-          },
-          {
-            "accuracy": 0.661996,
-            "f1": 0.611837,
-            "f1_weighted": 0.654907
-          },
-          {
-            "accuracy": 0.679372,
-            "f1": 0.646968,
-            "f1_weighted": 0.669935
-          },
-          {
-            "accuracy": 0.665359,
-            "f1": 0.646588,
-            "f1_weighted": 0.653284
-          },
-          {
-            "accuracy": 0.648543,
-            "f1": 0.624194,
-            "f1_weighted": 0.628283
-          },
-          {
-            "accuracy": 0.691143,
-            "f1": 0.657925,
-            "f1_weighted": 0.679795
-          }
-        ],
-        "main_score": 0.67741,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 8.183109998703003,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/MassiveScenarioVNClassification.json

@@ -1,73 +0,0 @@
-{
-  "dataset_revision": "a82e282d9f5aec1a8cf7d868ce40f70669c16b89",
-  "task_name": "MassiveScenarioVNClassification",
-  "mteb_version": "1.38.41",
-  "scores": {
-    "test": [
-      {
-        "accuracy": 0.728523,
-        "f1": 0.701865,
-        "f1_weighted": 0.724309,
-        "scores_per_experiment": [
-          {
-            "accuracy": 0.74217,
-            "f1": 0.721876,
-            "f1_weighted": 0.741215
-          },
-          {
-            "accuracy": 0.746085,
-            "f1": 0.724708,
-            "f1_weighted": 0.744061
-          },
-          {
-            "accuracy": 0.742729,
-            "f1": 0.715626,
-            "f1_weighted": 0.740294
-          },
-          {
-            "accuracy": 0.725951,
-            "f1": 0.704168,
-            "f1_weighted": 0.71827
-          },
-          {
-            "accuracy": 0.705257,
-            "f1": 0.665866,
-            "f1_weighted": 0.696431
-          },
-          {
-            "accuracy": 0.722595,
-            "f1": 0.691101,
-            "f1_weighted": 0.719705
-          },
-          {
-            "accuracy": 0.716443,
-            "f1": 0.689115,
-            "f1_weighted": 0.708131
-          },
-          {
-            "accuracy": 0.71085,
-            "f1": 0.69065,
-            "f1_weighted": 0.710364
-          },
-          {
-            "accuracy": 0.739374,
-            "f1": 0.711083,
-            "f1_weighted": 0.736213
-          },
-          {
-            "accuracy": 0.733781,
-            "f1": 0.704461,
-            "f1_weighted": 0.728405
-          }
-        ],
-        "main_score": 0.728523,
-        "hf_subset": "default",
-        "languages": [
-          "vie-Latn"
-        ]
-      }
-    ]
-  },
-  "evaluation_time": 5.51207423210144,
-  "kg_co2_emissions": null
-}

results/AITeamVN__Vietnamese_Embedding/fcbbb905e6c3757d421aaa5db6fd7c53d038f6fb/NFCorpus-VN.json

@@ -1,54 +0,0 @@
-{
-    "dataset_revision": "a13d72fbb859be3dc19ab669d1ec9510407d2dcd",
-    "mteb_dataset_name": "NFCorpus-VN",
-    "mteb_version": "1.38.41",
-    "task_name": "NFCorpus-VN",
-    "scores": {
-        "test": [
-            {
-                "evaluation_time": 19.28,
-                "map_at_1": 0.03605,
-                "map_at_10": 0.08866,
-                "map_at_100": 0.10878,
-                "map_at_1000": 0.11959,
-                "map_at_20": 0.09686,
-                "map_at_3": 0.06829,
-                "map_at_5": 0.07966,
-                "mrr_at_1": 0.31928,
-                "mrr_at_10": 0.41437,
-                "mrr_at_100": 0.42156,
-                "mrr_at_1000": 0.422,
-                "mrr_at_20": 0.41738,
-                "mrr_at_3": 0.39056,
-                "mrr_at_5": 0.3993,
-                "ndcg_at_1": 0.3012,
-                "ndcg_at_10": 0.25389,
-                "ndcg_at_100": 0.23107,
-                "ndcg_at_1000": 0.31251,
-                "ndcg_at_20": 0.23925,
-                "ndcg_at_3": 0.28632,
-                "ndcg_at_5": 0.27126,
-                "precision_at_1": 0.31928,
-                "precision_at_10": 0.18855,
-                "precision_at_100": 0.06163,
-                "precision_at_1000": 0.01931,
-                "precision_at_20": 0.14217,
-                "precision_at_3": 0.2751,
-                "precision_at_5": 0.23494,
-                "recall_at_1": 0.03605,
-                "recall_at_10": 0.12335,
-                "recall_at_100": 0.24212,
-                "recall_at_1000": 0.52674,
-                "recall_at_20": 0.14914,
-                "recall_at_3": 0.0801,
-                "recall_at_5": 0.09947,
-                "hf_subset": "default",
-                "main_score": 0.25389,
-                "languages": [
-                    "vie-Latn"
-                ]
-            }
-        ]
-    },
-    "evaluation_time": null
-}

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": {
-        "test": [
-            {
-                "evaluation_time": 8244.07,
-                "map_at_1": 0.22333,
-                "map_at_10": 0.35419,
-                "map_at_100": 0.36617,
-                "map_at_1000": 0.36666,
-                "map_at_20": 0.36191,
-                "map_at_3": 0.31386,
-                "map_at_5": 0.3369,
-                "mrr_at_1": 0.25158,
-                "mrr_at_10": 0.37521,
-                "mrr_at_100": 0.38499,
-                "mrr_at_1000": 0.38535,
-                "mrr_at_20": 0.38163,
-                "mrr_at_3": 0.33939,
-                "mrr_at_5": 0.36031,
-                "ndcg_at_1": 0.25158,
-                "ndcg_at_10": 0.42611,
-                "ndcg_at_100": 0.47922,
-                "ndcg_at_1000": 0.49185,
-                "ndcg_at_20": 0.45171,
-                "ndcg_at_3": 0.34799,
-                "ndcg_at_5": 0.38727,
-                "precision_at_1": 0.25158,
-                "precision_at_10": 0.07363,
-                "precision_at_100": 0.01035,
-                "precision_at_1000": 0.00115,
-                "precision_at_20": 0.04271,
-                "precision_at_3": 0.16298,
-                "precision_at_5": 0.11979,
-                "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": [
-            {
-                "evaluation_time": 1180.22,
-                "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,
-                "map_at_3": 0.51137,
-                "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,
-                "ndcg_at_1": 0.49979,
-                "ndcg_at_10": 0.61001,
-                "ndcg_at_100": 0.65467,
-                "ndcg_at_1000": 0.66382,
-                "ndcg_at_20": 0.63036,
-                "ndcg_at_3": 0.55641,
-                "ndcg_at_5": 0.58314,
-                "precision_at_1": 0.49979,
-                "precision_at_10": 0.10162,
-                "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,
-                "hf_subset": "default",
-                "main_score": 0.61001,
-                "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": [
-      {
-        "v_measure": 0.438902,
-        "v_measure_std": 0.044274,
-        "v_measures": [
-          0.39263,
-          0.5316,
-          0.399559,
-          0.425985,
-          0.427061,
-          0.446164,
-          0.512027,
-          0.424049,
-          0.426086,
-          0.40386
-        ],
-        "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": [
-          0.605948,
-          0.611291,
-          0.601361,
-          0.345424,
-          0.620675,
-          0.572602,
-          0.315689,
-          0.660052,
-          0.610597,
-          0.672515
-        ],
-        "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": [
-            {
-                "evaluation_time": 96.99,
-                "map_at_1": 0.03771,
-                "map_at_10": 0.07617,
-                "map_at_100": 0.08657,
-                "map_at_1000": 0.08887,
-                "map_at_20": 0.08085,
-                "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,
-                "mrr_at_5": 0.24621,
-                "ndcg_at_1": 0.1825,
-                "ndcg_at_10": 0.13034,
-                "ndcg_at_100": 0.18324,
-                "ndcg_at_1000": 0.23392,
-                "ndcg_at_20": 0.14611,
-                "ndcg_at_3": 0.13596,
-                "ndcg_at_5": 0.11389,
-                "precision_at_1": 0.1825,
-                "precision_at_10": 0.062,
-                "precision_at_100": 0.01407,
-                "precision_at_1000": 0.00263,
-                "precision_at_20": 0.04013,
-                "precision_at_3": 0.1225,
-                "precision_at_5": 0.094,
-                "recall_at_1": 0.03771,
-                "recall_at_10": 0.12642,
-                "recall_at_100": 0.28675,
-                "recall_at_1000": 0.5355,
-                "recall_at_20": 0.16375,
-                "recall_at_3": 0.07496,
-                "recall_at_5": 0.09571,
-                "hf_subset": "default",
-                "main_score": 0.13034,
-                "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
-}