Skip to content

Document 10: Implementation Roadmap

Overview

This document provides a comprehensive implementation roadmap for the Indonesia-MTEB project, including phases, timeline, resource requirements, technical specifications, team structure, risk management, and deployment strategy. It serves as the practical guide for transforming the research vision into a production-ready benchmark.

1. Project Timeline Overview

1.1 Gantt Chart Summary

Phase 1: Foundation              Month 1-2    ████████
Phase 2: Data Aggregation        Month 2-4        ████████████
Phase 3: Translation Pipeline     Month 3-6            ████████████████████
Phase 4: AI Generation            Month 5-7                ██████████████
Phase 5: Validation               Month 6-8                    ████████████████
Phase 6: Integration              Month 8-9                        ████████
Phase 7: Benchmark                Month 9-10                            ████████
Phase 8: Publication              Month 10-12                               ████████████████

1.2 Phase Summary

Phase Duration Key Deliverables Dependencies
1. Foundation 2 months Infrastructure, licenses, source datasets None
2. Aggregation 2 months 50+ existing datasets formatted Phase 1
3. Translation 3 months Translated MTEB datasets Phase 1
4. AI Generation 2 months Novel datasets for gaps Phase 1
5. Validation 2 months Quality validation, kept ratios Phases 2-4
6. Integration 1 month MTEB PR, HuggingFace upload Phase 5
7. Benchmark 1 month Model evaluation results Phase 6
8. Publication 3 months Paper submitted, arXiv preprint Phase 7

2. Phase 1: Foundation (Months 1-2)

2.1 Objectives

  • Set up development infrastructure
  • Secure all necessary licenses
  • Establish data pipeline architecture
  • Set up quality assurance protocols

2.2 Infrastructure Setup

2.2.1 Development Environment

# Repository structure
indonesia-mteb/
├── data/
│   ├── raw/           # Source datasets
│   ├── processed/     # Aggregated datasets
│   ├── translated/    # Machine-translated datasets
│   ├── generated/     # AI-generated datasets
│   └── validated/     # Final validated datasets
├── code/
│   ├── aggregation/   # Dataset aggregation scripts
│   ├── translation/   # Translation pipeline
│   ├── generation/    # AI dataset generation
│   ├── validation/    # Quality validation
│   └── evaluation/    # Benchmark evaluation
├── configs/
│   ├── datasets.yaml  # Dataset configurations
│   ├── models.yaml    # Model configurations
│   └── evaluation.yaml # Evaluation configurations
├── docs/
│   ├── dataset_cards/ # HuggingFace README templates
│   └── licenses/      # License tracking
└── tests/
    ├── unit/          # Unit tests
    ├── integration/   # Integration tests
    └── quality/       # Quality assurance tests

2.2.2 Cloud Infrastructure

Service Purpose Est. Cost
GPU Compute Translation, validation $500-1000/month
Storage Dataset storage (S3/GCS) $50-100/month
HuggingFace Dataset hosting Free - $20/month
GitHub Code repository Free
CI/CD Automated testing Free

2.3 License Acquisition

# License tracking template
LICENSE_TRACKING = {
    "dataset_name": {
        "source": "Original dataset URL",
        "license": "CC-BY-4.0",
        "attribution": "Citation string",
        "restrictions": ["ShareAlike", "NC"],
        "compatible": True,
        "derivative_license": "CC-BY-4.0"
    }
}

# Required licenses to track:
# - IndoNLU datasets (MIT License)
# - NusaCrowd datasets (varies)
# - SEACrowd datasets (varies)
# - MTEB original datasets (varies)
# - Machine translation models

2.4 Deliverables

  • Repository initialized with structure
  • GPU compute resources provisioned
  • License tracking spreadsheet created
  • Development environment documented
  • CI/CD pipeline configured

3. Phase 2: Data Aggregation (Months 2-4)

3.1 Objectives

  • Identify and catalog 50+ existing Indonesian datasets
  • Convert datasets to MTEB format
  • Create dataset cards for each
  • Upload to HuggingFace staging

3.2 Dataset Inventory

Task Category Existing Datasets Target Gap
Classification 15+ 15+ 0
Clustering 0 5+ 5+
Pair Classification 2+ 3+ 1-2
Reranking 0 3+ 3+
Retrieval 5+ 15+ 10+
STS 3+ 5+ 2-3
Summarization 5+ 5+ 0
Instruction Following 0 3+ 3+

3.3 Aggregation Pipeline

# Dataset aggregation framework
class DatasetAggregator:
    """
    Aggregates existing Indonesian datasets into MTEB format.
    """
    def __init__(self, source_dir: str, output_dir: str):
        self.source_dir = source_dir
        self.output_dir = output_dir
        self.formatters = {
            "classification": self.format_classification,
            "retrieval": self.format_retrieval,
            "sts": self.format_sts,
            # ... other formatters
        }

    def format_classification(self, raw_data: dict) -> dict:
        """Convert raw classification data to MTEB format."""
        return {
            "texts": raw_data["text"],
            "labels": raw_data["label"],
            "split": raw_data.get("split", "test")
        }

    def validate_mteb_compatibility(self, dataset: dict) -> bool:
        """Validate dataset matches MTEB schema."""
        required_fields = ["texts", "labels"]
        return all(field in dataset for field in required_fields)

    def create_dataset_card(self, dataset_name: str,
                           metadata: dict) -> str:
        """Generate HuggingFace dataset card."""
        # Returns README.md content with YAML metadata

3.4 Deliverables

  • 50+ datasets aggregated
  • All datasets in MTEB format
  • Dataset cards created for each
  • Aggregation report with statistics

4. Phase 3: Translation Pipeline (Months 3-6)

4.1 Objectives

  • Implement 3-stage translation pipeline
  • Translate all target MTEB datasets
  • Validate translation quality
  • Calculate kept ratios by task

4.2 Translation Infrastructure

# Translation pipeline implementation
class TranslationPipeline:
    """
    3-stage translation pipeline adapted from VN-MTEB.
    """
    def __init__(self, config: dict):
        # Stage 1: Language detection
        self.language_detector = Qwen2_5_3B_Instruct()

        # Stage 2: Translation
        self.translator = TranslateGemma_12B()

        # Stage 3: Validation
        self.semantic_model = gte_Qwen2_7B_instruct()
        self.llm_judge = Llama_SEA_LION_v4_70B_IT()

    def translate_dataset(self, source_data: list,
                         task_type: str) -> dict:
        """
        Translate dataset with 3-stage quality control.
        """
        # Stage 1: Language detection
        english_samples = self.detect_english(source_data)

        # Stage 2: Translation
        translated = self.batch_translate(english_samples)

        # Stage 3: Validation
        validated = self.validate_translation(
            english_samples,
            translated,
            task_type
        )

        return {
            "original": english_samples,
            "translated": validated["texts"],
            "kept_indices": validated["kept_indices"],
            "kept_ratio": len(validated["texts"]) / len(english_samples)
        }

    def validate_translation(self, source: list, translated: list,
                            task_type: str) -> dict:
        """
        3-step validation:
        1. Language contamination check
        2. Semantic similarity threshold
        3. LLM-as-a-judge scoring
        """
        # Step 1: Language check
        lang_valid = self.check_indonesian(translated)

        # Step 2: Semantic similarity
        sem_valid = self.semantic_similarity_filter(
            source, translated,
            threshold=THRESHOLDS[task_type]
        )

        # Step 3: LLM-as-a-judge
        llm_valid = self.llm_judge_scoring(
            source, translated,
            criteria=CULTURAL_CRITERIA
        )

        # Combine results
        kept_indices = self.combine_validations(
            lang_valid, sem_valid, llm_valid
        )

        return {
            "texts": [translated[i] for i in kept_indices],
            "kept_indices": kept_indices
        }

4.3 Threshold Configuration

Task Type Semantic Threshold LLM Judge Threshold Expected Kept Ratio
Classification ≥0.75 ≥3.5/5.0 70-75%
Clustering ≥0.75 ≥3.5/5.0 70-75%
Pair Classification ≥0.75 ≥3.5/5.0 65-70%
Reranking ≥0.70 ≥3.0/5.0 60-65%
Retrieval ≥0.75 ≥3.5/5.0 65-70%
STS ≥0.80 ≥4.0/5.0 55-60%
Summarization ≥0.70 ≥3.0/5.0 60-65%
Instruction Following ≥0.75 ≥3.5/5.0 65-70%

4.4 Resource Estimation

Metric Value Notes
Total tokens to translate ~500M tokens Based on MTEB EN datasets
Translation speed ~3,800 tokens/sec With 4x H100 GPUs
Estimated time ~28 days 675 hours compute time
GPU hours required ~2,700 H100 hours Including validation
Estimated cost $5,000-10,000 Cloud GPU costs

4.5 Deliverables

  • Translation pipeline implemented
  • All target datasets translated
  • Kept ratios calculated and documented
  • Translation quality report generated

5. Phase 4: AI Dataset Generation (Months 5-7)

5.1 Objectives

  • Generate novel datasets for gap tasks
  • Validate AI-generated data quality
  • Create documentation for generated datasets

5.2 Target Tasks for AI Generation

Task Reason for AI Generation Sample Size Target
Clustering No existing Indonesian clustering datasets 5 datasets, 100K+ samples each
Reranking Limited Indonesian reranking data 3 datasets, 10K+ samples each
Instruction Following Novel task for Indonesian 3 datasets, 5K+ samples each

5.3 Generation Pipeline

# AI dataset generation framework
class AIDatasetGenerator:
    """
    Generate synthetic datasets for tasks with limited Indonesian data.
    """
    def __init__(self, llm, embedder):
        self.llm = llm  # Llama-SEA-LION-v4-70B-IT
        self.embedder = embedder  # gte-Qwen2-7B-instruct

    def generate_clustering_data(self, domain: str,
                                 n_samples: int) -> dict:
        """
        Generate clustering dataset for Indonesian text.

        Strategy: Use LLM to generate diverse Indonesian texts
        within specific domains, then validate semantic diversity.
        """
        prompt = f"""
        Generate {n_samples} diverse Indonesian texts about {domain}.
        Each text should be 2-4 sentences long.
        Cover different aspects and subtopics within {domain}.
        Ensure natural Indonesian phrasing.

        Output format: JSON list with "text" field.
        """

        raw_texts = self.llm.generate(prompt, temperature=0.8)

        # Validate diversity
        validated = self.validate_diversity(raw_texts)

        return {
            "texts": validated,
            "domain": domain,
            "generated_by": "AI"
        }

    def generate_reranking_data(self, query: str,
                                n_docs: int) -> dict:
        """
        Generate reranking dataset.

        Strategy: Generate query-document pairs with
        varying relevance levels.
        """
        prompt = f"""
        Given the query: "{query}"

        Generate {n_docs} Indonesian documents.
        - 30% highly relevant
        - 40% somewhat relevant
        - 30% not relevant

        Output format: JSON with "query", "documents", "relevance" fields
        """

        raw_data = self.llm.generate(prompt, temperature=0.7)

        # Validate relevance discrimination
        validated = self.validate_relevance(raw_data)

        return validated

    def validate_diversity(self, texts: list) -> list:
        """
        Validate semantic diversity of generated texts.
        Remove near-duplicates using embedding similarity.
        """
        embeddings = self.embedder.encode(texts)
        diverse = self.diversity_filter(embeddings, threshold=0.85)
        return [texts[i] for i in diverse]

    def validate_relevance(self, data: dict) -> dict:
        """
        Validate relevance labels using LLM-as-a-judge.
        """
        reviewed = self.llm_judge.review_relevance(data)
        return reviewed

5.4 Quality Validation

# AI-generated data validation
def validate_ai_dataset(dataset: dict, task_type: str) -> dict:
    """
    Comprehensive validation of AI-generated datasets.
    """
    results = {
        "diversity_score": measure_diversity(dataset["texts"]),
        "fluency_score": measure_fluency(dataset["texts"]),
        "label_quality": measure_label_quality(dataset),
        "human_sample": human_check_sample(dataset, n=100)
    }

    # Pass validation if:
    # - Diversity score ≥ 0.7
    # - Fluency score ≥ 0.8
    # - Label quality F1 ≥ 0.85
    # - Human agreement ≥ 0.8

    passed = all([
        results["diversity_score"] >= 0.7,
        results["fluency_score"] >= 0.8,
        results["label_quality"]["f1"] >= 0.85,
        results["human_sample"]["agreement"] >= 0.8
    ])

    return {"passed": passed, "details": results}

5.5 Deliverables

  • 5 clustering datasets generated
  • 3 reranking datasets generated
  • 3 instruction-following datasets generated
  • Validation reports for all generated datasets

6. Phase 5: Validation (Months 6-8)

6.1 Objectives

  • Implement cultural term preservation validation
  • Implement code-mixing detection
  • Implement register preservation validation
  • Create comprehensive quality report

6.2 Cultural Validation Framework

# Cultural term preservation validation
CULTURAL_TERMS = {
    # Social concepts
    "gotong_royong", "pancasila", "rukun", "siskamling", "musyawarah",
    # Religious/cultural
    "lebaran", "puasa", "halal_bil_halal", "nyepi", "waisak", "galungan",
    # Culinary
    "warung", "nasi_goreng", "rendang", "sate", "bakso", "gado_gado",
    # Arts/crafts
    "batik", "wayang", "gamelan", "keris", "ikat", "songket",
    # Geographic/identity
    "merantau", "kampung", "desa", "kos", "rumah_tinggi"
}

def validate_cultural_preservation(dataset: dict) -> dict:
    """
    Validate preservation of Indonesian cultural terms.
    """
    source_texts = dataset.get("source", [])
    translated_texts = dataset.get("translated", [])

    results = []
    for src, trans in zip(source_texts, translated_texts):
        source_terms = [t for t in CULTURAL_TERMS if t in src.lower()]
        trans_terms = [t for t in source_terms if t in trans.lower()]

        if source_terms:
            preservation_rate = len(trans_terms) / len(source_terms)
            results.append({
                "source": src,
                "translated": trans,
                "source_terms": source_terms,
                "preserved_terms": trans_terms,
                "preservation_rate": preservation_rate
            })

    overall_rate = sum(r["preservation_rate"] for r in results) / len(results) if results else 1.0

    return {
        "overall_preservation_rate": overall_rate,
        "term_level_results": results,
        "passes_threshold": overall_rate >= 0.9
    }

6.3 Code-Mixing Validation

# Code-mixing detection for Indonesian-English
def detect_code_mixing(text: str) -> dict:
    """
    Detect Indonesian-English code-mixing in text.
    """
    # Word-level language identification
    tokens = word_tokenize(text)
    lang_ids = [detect_language_word(t) for t in tokens]

    # Count switches
    switches = sum(1 for i in range(1, len(lang_ids))
                   if lang_ids[i] != lang_ids[i-1])

    # Calculate mixing ratio
    en_ratio = sum(1 for l in lang_ids if l == "en") / len(lang_ids)

    return {
        "has_code_mixing": switches > 0,
        "switch_count": switches,
        "english_ratio": en_ratio,
        "dominant_lang": max(set(lang_ids), key=lang_ids.count)
    }

def validate_code_mixing_dataset(dataset: dict) -> dict:
    """
    Validate code-mixing annotations in dataset.
    """
    results = [detect_code_mixing(text) for text in dataset["texts"]]

    mixing_stats = {
        "total_samples": len(dataset["texts"]),
        "code_mixed_samples": sum(r["has_code_mixing"] for r in results),
        "avg_switches": sum(r["switch_count"] for r in results) / len(results),
        "avg_english_ratio": sum(r["english_ratio"] for r in results) / len(results)
    }

    return mixing_stats

6.4 Deliverables

  • Cultural preservation report
  • Code-mixing analysis report
  • Register preservation analysis
  • Comprehensive validation documentation

7. Phase 6: Integration (Months 8-9)

7.1 Objectives

  • Create HuggingFace organization
  • Upload all datasets with proper cards
  • Submit MTEB integration PR
  • Create evaluation scripts

7.2 HuggingFace Organization Setup

# Organization structure
huggingface.co/indonesia-mteb/
├── datasets/
│   ├── classification/
│   │   ├── indo-sentiment/
│   │   ├── indo-emotion/
│   │   └── ...
│   ├── clustering/
│   ├── reranking/
│   ├── retrieval/
│   ├── sts/
│   ├── summarization/
│   └── instruction-following/
└── models/  # For future Indonesian embedding models

7.3 Dataset Upload Script

# Automated dataset upload to HuggingFace
from huggingface_hub import HfApi, Repository

def upload_dataset_to_huggingface(
    dataset_path: str,
    dataset_name: str,
    organization: str = "indonesia-mteb"
):
    """
    Upload validated dataset to HuggingFace.
    """
    api = HfApi()

    # Create repository
    repo_id = f"{organization}/{dataset_name}"
    api.create_repo(repo_id, repo_type="dataset", exist_ok=True)

    # Upload files
    api.upload_folder(
        repo_id=repo_id,
        folder_path=dataset_path,
        repo_type="dataset"
    )

    return repo_id

# Batch upload all datasets
def upload_all_datasets(validated_dir: str):
    """
    Upload all validated datasets to HuggingFace.
    """
    datasets = os.listdir(validated_dir)

    for dataset_name in datasets:
        dataset_path = os.path.join(validated_dir, dataset_name)
        upload_dataset_to_huggingface(dataset_path, dataset_name)
        print(f"Uploaded: {dataset_name}")

7.4 MTEB Integration PR

# MTEB dataset loader template
# File: mteb/indonesia_mteb/__init__.py

from . import (
    IndoSentimentClassification,
    IndoClustering,
    IndoReranking,
    # ... all datasets
)

__all__ = [
    "IndoSentimentClassification",
    "IndoClustering",
    "IndoReranking",
    # ...
]

# File: mteb/indonesia_mteb/indo_sentiment.py

from mteb.abstasks.AbsTask import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata

class IndoSentimentClassification(AbsTaskClassification):
    metadata = TaskMetadata(
        name="IndoSentimentClassification",
        description="Indonesian sentiment classification dataset",
        reference="https://huggingface.co/datasets/indonesia-mteb/indo-sentiment",
        dataset={
            "path": "indonesia-mteb/indo-sentiment",
            "revision": "main"
        },
        type="Classification",
        category="s2s",
        eval_splits=["test"],
        eval_langs=["id-ID"],
        main_score="accuracy",
        date=("2024-01-01", "2024-12-31"),
        form=["written"],
        domains=["Social", "Reviews"],
        task_subtypes=["Sentiment classification"],
        license="CC-BY-4.0",
        annotations_creators="human-verified",
        dialect=[],
        sample_creation="found",
        bibtex_citation="""@dataset{indo_sentiment_2024,
            title={Indonesian Sentiment Classification},
            author={Indonesia-MTEB Team},
            year={2024}
        }"""
    )

7.5 Deliverables

  • HuggingFace organization created
  • All datasets uploaded with proper cards
  • MTEB PR submitted
  • Integration documentation completed

8. Phase 7: Benchmark (Months 9-10)

8.1 Objectives

  • Select baseline models
  • Run comprehensive benchmark evaluation
  • Generate analysis and insights
  • Create leaderboard

8.2 Model Selection

Model Type Models to Evaluate Reasoning
Multilingual (APE) bge-m3, m-e5-large, gte-multilingual Baseline comparison
Multilingual (RoPE) e5-mistral-7b, gte-Qwen2-7B State-of-the-art
Instruct-tuned m-e5-large-instruct, bge-large-instruct Instruction following
Indonesian-specialized IndoBERT, SEA-LION variants Local comparison

8.3 Evaluation Script

# Comprehensive benchmark evaluation
import mteb
from sentence_transformers import SentenceTransformer

class IndonesiaMTEBBenchmark:
    """
    Benchmark models on Indonesia-MTEB datasets.
    """
    def __init__(self):
        self.evaluation = mteb.MTEB(tasks=mteb.get_indonesian_tasks())
        self.results = {}

    def evaluate_model(self, model_name: str, model_path: str):
        """
        Evaluate a single model on all Indonesia-MTEB tasks.
        """
        model = SentenceTransformer(model_path)

        results = self.evaluation.run(
            model,
            output_folder=f"results/{model_name}",
            eval_splits=["test"],
            batch_size=32,
            verbosity=2
        )

        self.results[model_name] = results
        return results

    def generate_leaderboard(self) -> dict:
        """
        Generate leaderboard from evaluation results.
        """
        leaderboard = {}

        for model_name, results in self.results.items():
            scores = {}
            for task, task_results in results.items():
                scores[task] = task_results.get("main_score", 0)

            leaderboard[model_name] = {
                "scores": scores,
                "average": sum(scores.values()) / len(scores)
            }

        # Sort by average score
        sorted_leaderboard = dict(
            sorted(leaderboard.items(),
                   key=lambda x: x[1]["average"],
                   reverse=True)
        )

        return sorted_leaderboard

    def compare_architectures(self) -> dict:
        """
        Compare APE vs RoPE vs Instruct-tuned performance.
        """
        comparison = {
            "APE": [],
            "RoPE": [],
            "Instruct": []
        }

        for model_name, results in self.results.items():
            arch_type = classify_architecture(model_name)
            avg_score = sum(r.get("main_score", 0)
                           for r in results.values()) / len(results)
            comparison[arch_type].append(avg_score)

        return {
            arch: scores and sum(scores) / len(scores)
            for arch, scores in comparison.items()
        }

8.4 Deliverables

  • 18 models evaluated
  • Leaderboard generated
  • Architecture comparison analysis
  • Performance insights documented

9. Phase 8: Publication (Months 10-12)

9.1 Objectives

  • Write and submit conference paper
  • Release arXiv preprint
  • Present at workshops
  • Engage community

9.2 Paper Writing Timeline

Week Task Deliverable
1-2 Outline & Abstract Paper structure
3-4 Introduction & Related Work Sections 1-2
5-6 Methodology Section 3
7-8 Experiments & Results Sections 4-5
9 Discussion & Conclusion Sections 6-7
10 Ethics & Broader Impact Sections A-B
11 Internal Review Revised draft
12 ARR Submission Submitted paper

9.3 Community Engagement Plan

# Community engagement checklist
COMMUNITY_TASKS = [
    # Pre-submission
    ("Create GitHub repository", "Month 10"),
    ("Post arXiv preprint", "Month 10"),
    ("Write blog post", "Month 10"),
    ("Social media announcement", "Month 10"),

    # Post-submission
    ("Submit to workshop", "Month 11"),
    ("Create demo/explanation", "Month 11"),
    ("Reach out to Indonesian NLP community", "Month 11"),
    ("Submit MTEB integration PR", "Month 11"),

    # Post-acceptance
    ("Prepare presentation", "Month 12"),
    ("Release code and data", "Month 12"),
    ("Create tutorial notebooks", "Month 12")
]

9.4 Deliverables

  • Conference paper submitted
  • arXiv preprint released
  • GitHub repository public
  • Community engagement initiated

10. Resource Requirements

10.1 Team Structure

Role Responsibilities FTE
Principal Investigator Overall direction, paper writing 0.5
Project Lead Day-to-day management, coordination 1.0
ML Engineer Translation pipeline, validation 1.0
Data Engineer Aggregation, formatting 1.0
Backend Developer Infrastructure, integration 0.5
Research Assistant Literature review, testing 0.5
Indonesian Linguist Cultural validation, annotation 0.5

Total: 5 FTE

10.2 Compute Requirements

Phase GPU Type GPU Hours Est. Cost
Translation 4x H100 2,700 $5,000
Validation 4x A100 500 $500
Benchmark 4x A100 800 $800
Total - 4,000 $6,300

10.3 Budget Summary

Category Item Cost (USD)
Compute GPU hours $6,300
Storage Cloud storage (1 year) $600
Personnel 5 FTE × 12 months $150,000
Contingency 10% buffer $15,700
Total $172,600

11. Risk Management

11.1 Risk Register

Risk Probability Impact Mitigation
Translation quality too low Medium High Use better models (TranslateGemma-12B), lower thresholds
MTEB PR rejected Low Medium Early engagement with maintainers, independent release
Insufficient compute Low High Secure funding allocation, use spot instances
License issues Low High Comprehensive tracking, legal review
Team turnover Medium Medium Documentation, knowledge transfer
Competition (SEA-BED) Low Low Differentiate with cultural framework

11.2 Contingency Plans

If translation kept ratios < 50%: - Increase LLM-as-judge threshold - Use human annotation for critical samples - Consider alternative translation models

If MTEB integration delayed: - Release independent evaluation framework - Create compatibility layer - Community fork if necessary

If budget insufficient: - Prioritize core tasks (Classification, Retrieval, STS) - Use smaller GPU clusters - Seek additional funding

12. Quality Assurance

12.1 Testing Strategy

# Test suite for Indonesia-MTEB
class TestIndonesiaMTEB:
    """
    Comprehensive test suite for all components.
    """
    def test_dataset_format(self):
        """Test all datasets match MTEB format."""
        for dataset in DATASETS:
            assert validate_mteb_format(dataset)

    def test_translation_quality(self):
        """Test translation quality metrics."""
        for task in TASKS:
            assert kept_ratio(task) >= MIN_KEPT_RATIOS[task]

    def test_cultural_preservation(self):
        """Test cultural term preservation."""
        for dataset in TRANSLATED_DATASETS:
            assert cultural_preservation(dataset) >= 0.9

    def test_mteb_integration(self):
        """Test MTEB framework integration."""
        for task in MTEB_TASKS:
            assert mteb.run_evaluation(task, MODEL)

    def test_reproducibility(self):
        """Test result reproducibility."""
        results1 = run_benchmark(MODEL, SEED=42)
        results2 = run_benchmark(MODEL, SEED=42)
        assert results1 == results2

12.2 Validation Checklist

Each dataset must pass:

  • Format validation (MTEB schema)
  • Language validation (Indonesian only)
  • Quality validation (semantic similarity)
  • Cultural validation (term preservation)
  • License validation (compatible license)
  • Documentation validation (complete README)
  • Accessibility validation (HuggingFace loadable)

13. Success Criteria

13.1 Phase-Based Milestones

Phase Success Criteria
Foundation Infrastructure operational, licenses secured
Aggregation 50+ datasets in MTEB format
Translation ≥60% average kept ratio across tasks
AI Generation All generated datasets pass validation
Validation Cultural preservation ≥90%
Integration All datasets on HuggingFace
Benchmark 18 models evaluated
Publication Paper submitted to tier-1 venue

13.2 Overall Project Success

The Indonesia-MTEB project will be considered successful if:

  1. ✅ Coverage: All 8 MTEB task categories have Indonesian datasets
  2. ✅ Quality: Average kept ratio ≥65% across translated datasets
  3. ✅ Integration: Successfully integrated into MTEB leaderboard
  4. ✅ Community: At least 5 external uses/citations within 6 months
  5. ✅ Publication: Paper accepted at tier-1 NLP venue

14. Maintenance Plan

14.1 Long-Term Maintenance

Activity Frequency Responsibility
Dataset updates Quarterly Project Lead
Bug fixes As needed ML Engineer
New model evaluation Monthly Community + Team
Documentation updates As needed Research Assistant
Community support Ongoing Project Lead

14.2 Version Control

v1.0.0 (2026): Initial release with 50-100 datasets
v1.1.0 (2026): Add regional language tasks
v1.2.0 (2027): Add new datasets from community
v2.0.0 (2027): Major expansion with new task types

15. Summary

15.1 Implementation Timeline

Month 1-2:   Foundation setup
Month 2-4:   Dataset aggregation
Month 3-6:   Translation pipeline
Month 5-7:   AI dataset generation
Month 6-8:   Validation and quality control
Month 8-9:   MTEB integration
Month 9-10:  Benchmark evaluation
Month 10-12: Paper submission and publication

15.2 Key Deliverables

Phase Deliverable
Foundation Infrastructure, licenses, environment
Aggregation 50+ existing datasets formatted
Translation 40+ translated datasets
Generation 11 novel AI-generated datasets
Validation Quality validation reports
Integration HuggingFace release, MTEB PR
Benchmark Leaderboard, analysis
Publication Conference paper, arXiv preprint

15.3 Resource Summary

Resource Quantity
Team 5 FTE
Duration 12 months
Compute 4,000 GPU hours
Budget $172,600
Datasets 50-100+
Models Evaluated 18

This roadmap provides the practical foundation for transforming Indonesia-MTEB from vision to reality, ensuring all phases are properly planned, resourced, and executed.