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A Hindi benchmark is more than a folder of Hindi text uploaded to the Hugging Face Hub. It is a reproducible evaluation resource: a clearly defined task, legally usable data, documented annotation decisions, fixed splits, and metrics that reveal where models succeed or fail.

For Indian-language AI, this work matters because aggregate multilingual scores can hide important differences in script handling, code-mixing, dialect coverage, cultural context, and safety performance. A well-designed Hindi benchmark gives model builders a dependable target and gives researchers a fair way to compare systems.

1. Define the benchmark before collecting data

Start with one evaluation question. Avoid creating a broad “Hindi dataset” with unclear use cases. A focused benchmark is easier to annotate, audit, and maintain.

Possible tasks include:

• Text classification: sentiment, intent, topic, toxicity, or misinformation.
• Question answering: extractive, multiple-choice, or open-ended questions grounded in Hindi passages.
• Information extraction: named entities, dates, locations, organisations, or government-service fields.
• Summarisation: short summaries of news, public information, or long-form Hindi documents.
• Translation: Hindi paired with English or another Indian language, with clear domain and direction.
• Instruction following: prompts that test factuality, reasoning, refusal behaviour, and response quality.

Write a short benchmark specification containing the target users, task definition, input and output format, intended domain, expected failure modes, and primary metrics. If the benchmark is intended for Indian language LLM evaluation, define how it complements existing tests rather than duplicating them.

2. Set Hindi coverage and sampling rules

Hindi is not uniform across all users or settings. Decide what your benchmark represents and state what it does not represent. Useful coverage dimensions include:

• Devanagari text, Romanised Hindi, and mixed-script input.
• Formal, conversational, educational, administrative, and technical registers.
• Code-mixed Hindi-English expressions common in digital communication.
• Regional variation, while avoiding unsupported claims that a small sample represents every dialect.
• Urban and rural contexts, age groups, and different levels of digital literacy.
• Domains such as health, agriculture, finance, education, and public services.

Use a sampling plan instead of collecting only what is easiest to find. Record source, date, domain, script, region when available, and any filtering applied. This metadata helps users diagnose performance rather than treating one score as a complete measure of Hindi capability.

3. Handle copyright, consent, and privacy first

Do not scrape first and investigate permissions later. Every example needs a traceable provenance record. Check whether the source permits redistribution, whether attribution is required, and whether the licence is compatible with your intended Hub release.

For user-generated or conversational data, remove phone numbers, email addresses, government identifiers, precise addresses, and other personal information. Establish an exclusion policy for sensitive content. If people are annotating private or potentially harmful material, document consent, access controls, compensation, and escalation procedures.

A benchmark can be openly accessible while its raw source data cannot be redistributed. In that case, publish hashes, identifiers, transformation scripts, or a controlled-access process instead of uploading restricted text. This is particularly important for datasets intended to support training LLMs on Indian datasets, where downstream users may assume that “public on Hugging Face” means unrestricted commercial reuse.

4. Design annotation and quality control

Create an annotation guide before large-scale labelling begins. Define edge cases with Hindi examples, including spelling variation, punctuation, honorifics, sarcasm, code-mixing, ambiguous words, and references that require cultural context.

Use a pilot round with multiple annotators. Measure agreement, review disagreements, and revise the instructions before expanding. For subjective tasks, preserve multiple labels or annotator rationales where appropriate rather than forcing false certainty into one label.

Track at least:

• Annotator IDs in a private project log, with public release of only safe aggregate information.
• Number of annotators per example.
• Adjudication rules and unresolved cases.
• Class balance and missing labels.
• Quality checks, gold examples, and rejected items.

Keep a held-out test set that annotators and benchmark developers do not repeatedly inspect. Otherwise, the benchmark gradually becomes optimised for its own test cases.

5. Prevent leakage and build meaningful splits

Randomly splitting Hindi text is often insufficient. Near-duplicate articles, translated versions, repeated prompts, and documents from the same source can appear in both training and test sets.

Use deduplication at the document and semantic level where possible. Consider splits by source, time period, author, topic, or domain. For a temporal benchmark, publish the collection window and freeze the test set. For classification, check that labels are not revealed by filenames, templates, or metadata.

A strong benchmark should include a development set for iteration and a protected test set for final reporting. If you publish a public test set, explain the risk of overfitting and consider a server-side evaluation protocol for high-stakes comparisons.

6. Choose a clear schema and validate it

Prefer a schema that is machine-readable and easy to inspect. A typical example might contain:

{
  "id": "hi_000001",
  "text": "यह उदाहरण है।",
  "label": "informational",
  "domain": "public_services",
  "script": "Devanagari",
  "source": "licensed_collection",
  "language": "hin"
}

Use stable IDs, explicit label names, and consistent data types. Avoid storing ambiguous values such as “yes/no/maybe” in the same column as numeric scores. Validate required fields, label vocabulary, Unicode encoding, and duplicate IDs before release.

Parquet is usually efficient for larger datasets, while JSONL is convenient for inspection and version control. Include a dataset card with the task, language, domains, collection process, licence, limitations, known risks, intended uses, and citation. A Hindi benchmark should also document normalisation decisions, such as whether zero-width characters, nukta forms, punctuation, and Romanised text were preserved.

7. Load and publish the dataset on Hugging Face

Install the required libraries and authenticate with a Hugging Face token that has permission to create or update the repository:

pip install -U datasets huggingface_hub
huggingface-cli login

Create a dataset from local files or a Python object, inspect the features, and push a versioned repository:

from datasets import Dataset, DatasetDict

examples = {
    "id": ["hi_000001", "hi_000002"],
    "text": ["यह उदाहरण है।", "आप कैसे हैं?"],
    "label": ["informational", "greeting"],
}

train = Dataset.from_dict(examples)
valid = Dataset.from_dict({
    "id": ["hi_000003"],
    "text": ["यह सत्यापन उदाहरण है।"],
    "label": ["informational"],
})

benchmark = DatasetDict({"train": train, "validation": valid})
benchmark.push_to_hub("your-org/hindi-benchmark")

Before publishing, run a clean-room download test in a new environment. Confirm that users can load the repository with load_dataset, that the dataset card renders correctly, and that all referenced files are present. Use semantic release tags or Hub commits for changes, and maintain a changelog describing removed, corrected, or newly added examples.

8. Report baseline results and limitations

A benchmark is far more useful when it includes simple baselines. Report results from at least one traditional or lightweight baseline and one contemporary multilingual or Hindi-capable model. Record model version, prompt format, decoding settings, context length, hardware where relevant, and whether Romanisation or normalisation was applied.

Choose metrics that match the task. Accuracy alone can mislead on imbalanced labels; use macro-F1 where appropriate. For generation, combine automatic measures with human evaluation for factuality, relevance, fluency, and harmful outputs. Break down results by script, domain, class, and difficulty when the sample size supports it.

Be explicit about limits. A benchmark may contain regional bias, annotation disagreement, source concentration, or outdated facts. It should not be presented as a complete measure of Hindi intelligence or social representation. For broader multilingual comparisons, the methodology in benchmarking multilingual LLMs in India offers useful questions around comparability and reporting.

9. Maintain the benchmark responsibly

Assign ownership for issue review, licence questions, and data corrections. Publish a contribution process, but do not accept unverified additions directly into the test set. Keep immutable versions for published results and release new versions when changes materially affect scores.

Useful maintenance checks include:

• Broken or inaccessible source references.
• Newly discovered personal information.
• Duplicate or contaminated examples.
• Label drift and class imbalance.
• Model-generation contamination after publication.
• Changes in domain language and public-service terminology.

If the project targets broader low-resource language infrastructure, compare your governance choices with work on low-resource language datasets for AI training in India. The objective is not simply a larger dataset; it is a resource that Hindi researchers and builders can trust, reproduce, and improve.

Final checklist

Before releasing your Hindi benchmark on Hugging Face, confirm that you have:

• Defined one clear task and evaluation protocol.
• Recorded provenance, permissions, and licence compatibility.
• Removed or protected personal and sensitive information.
• Tested annotation guidelines and measured agreement.
• Deduplicated data and designed leakage-resistant splits.
• Validated the schema and included Hindi-specific metadata.
• Published a complete dataset card, citation, and version history.
• Added baselines, subgroup analysis, and known limitations.
• Tested loading and downloading from a clean environment.

A carefully scoped benchmark can become shared infrastructure for Hindi NLP. A loosely documented upload is unlikely to produce reliable comparisons or safe downstream use.