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Hugging Face is useful for more than hosting model weights. A well-structured repository can make an Indian-language benchmark easy to inspect, reproduce, compare, and reuse. This matters especially for Indic NLP, where scripts, code-mixed text, dialect variation, tokenisation choices, and uneven dataset sizes can materially affect results.

This guide explains how to publish benchmark outputs as a Hugging Face dataset repository, how to document the evaluation, and how to avoid common mistakes that make results difficult to trust. It complements the practical concerns covered in this guide to low-resource Indic natural language processing, particularly around data quality and language coverage.

Decide what you are publishing

Before creating a repository, define the unit of publication. You may be sharing:

• A leaderboard-style table containing model names, tasks, languages, metrics, and scores.
• Per-example predictions, labels, and optional confidence scores.
• Aggregated results from several datasets or evaluation suites.
• Evaluation scripts and configuration files needed to reproduce the scores.
• A benchmark dataset itself, if you have permission to redistribute it.

Do not upload sensitive test examples, personal data, copyrighted material, or restricted benchmark content simply because the evaluation produced it. If the underlying test set cannot be redistributed, publish aggregate results and code that lets authorised users reproduce them locally.

For multilingual work, use explicit language identifiers such as hin for Hindi, ben for Bengali, and tam for Tamil. Record script, region, dialect, and code-mixing status where relevant. “Indian languages” is not precise enough for a useful comparison.

Design a repository that others can understand

Create a Dataset repository on Hugging Face, rather than treating the project as an unlabelled file dump. A practical structure is:

indic-benchmark-results/
├── README.md
├── results.csv
├── results.json
├── predictions/
│   └── model_task_language.jsonl
├── configs/
│   └── evaluation.yaml
├── scripts/
│   └── evaluate.py
└── LICENSE

Use results.csv for quick inspection and results.json or JSON Lines for machine-readable access. Keep one row per model-task-language-split combination. Useful columns include:

• model_id and model_revision
• task and dataset_id
• language and script
• split
• metric and score
• num_examples
• prompt_template or evaluation configuration
• timestamp

Pin model revisions or commit hashes. A model’s main branch can change after publication, making an otherwise identical rerun produce different results.

Prepare metadata before uploading

The README is the repository’s evaluation card. It should answer five questions quickly:

1. What was evaluated? Name the task, datasets, languages, scripts, and splits.
2. How was it evaluated? State preprocessing, prompting, decoding, normalisation, and metric implementations.
3. What do the numbers mean? Define every metric and indicate whether higher or lower is better.
4. What are the limitations? Document sample size, dialect coverage, contamination checks, translation artefacts, and known failure modes.
5. Can someone reproduce it? Provide commands, dependency versions, configuration files, and model revisions.

For example, distinguish exact-match accuracy from normalised accuracy. Explain whether punctuation, whitespace, Unicode variants, spelling variants, or transliterated text were normalised. This is essential for Indic scripts, where Unicode representation and tokenisation can affect apparent performance.

Add a metadata block to the README where appropriate. For a dataset repository, include the language tags, task tags, license, dataset source, and citation. Never claim a permissive license for material whose original license does not allow redistribution.

Upload with the Hugging Face Hub client

Install the current Hub client and authenticate using a personal access token with the minimum required permissions:

pip install -U huggingface_hub datasets
hf auth login

Create the repository from the command line or the web interface:

hf repo create your-username/indic-benchmark-results --repo-type dataset

Then upload the prepared directory:

hf upload \
  your-username/indic-benchmark-results \
  ./indic-benchmark-results \
  --repo-type dataset

You can also upload individual files programmatically:

from huggingface_hub import HfApi

api = HfApi()
api.upload_file(
    path_or_fileobj="results.json",
    path_in_repo="results.json",
    repo_id="your-username/indic-benchmark-results",
    repo_type="dataset",
)

For large prediction files, use Git LFS where required and avoid committing generated artefacts that are not needed for verification. Keep tokens out of scripts, notebooks, and public logs.

Make results loadable and testable

Check that tabular files load without silent type conversion or encoding errors:

from datasets import load_dataset

data = load_dataset(
    "json",
    data_files="https://huggingface.co/datasets/your-username/indic-benchmark-results/resolve/main/results.json",
)
print(data)

Validate required fields before uploading. Confirm that scores are numeric, language codes are consistent, splits are valid, and the number of predictions matches the number of examples. Include a small, non-sensitive fixture for testing if the full evaluation set cannot be shared.

Report uncertainty where it is meaningful. For small regional or dialectal test sets, a single decimal score can imply more confidence than the sample supports. Include sample counts and, where practical, bootstrap confidence intervals or results across multiple seeds.

Document fair comparisons

A leaderboard is only useful when its comparisons are controlled. State whether models received the same prompt, context, examples, temperature, maximum token budget, and stopping rules. Separate zero-shot, few-shot, supervised, and translated evaluation settings.

Avoid collapsing all Indian languages into one average unless you also show per-language results. Macro-averages prevent high-resource languages from dominating, while weighted averages may reflect deployment volume; publish both definitions when relevant. For code-mixed and transliterated inputs, report them as separate conditions rather than hiding them inside a broad language label.

If the benchmark is intended for a product, connect the published numbers to deployment constraints such as latency, quantisation, memory, and inference cost. Builders working on Indian open-source AI projects can then compare not only quality but also practical trade-offs.

Common problems and fixes

• Repository is difficult to discover: Add language, task, and dataset tags, a clear title, and a descriptive README.
• Scores cannot be reproduced: Pin model revisions, dependency versions, prompts, and evaluation scripts.
• Hindi or regional text appears corrupted: Preserve UTF-8, test Devanagari and other scripts after loading, and check Unicode normalisation.
• Metrics disagree with published numbers: Record the exact metric implementation and normalisation rules.
• Private data was exposed: Remove it immediately, rotate credentials, and review repository history; changing visibility alone may not erase leaked content.
• Large files fail to upload: Use Git LFS or Hub upload commands and publish compressed, documented artefacts where appropriate.
• License is unclear: Link to the original dataset terms and explain precisely what your repository redistributes.

Publication checklist

Before sharing the repository, verify that:

• Every result has a model revision, dataset split, language, metric, and sample count.
• The README explains preprocessing, prompts, limitations, and licensing.
• Files load successfully through the Hub and locally from a clean environment.
• Results are separated by language, script, task, and evaluation condition.
• Sensitive or restricted examples are not publicly exposed.
• A citation, contact method, and changelog are included.

Publishing benchmark results this way turns a score table into a durable research artefact. It gives Indian researchers and builders a transparent baseline for improving models, evaluating open-source vision-language models for Indian languages, and building systems that work beyond English and Hindi-first assumptions.