Why a Kannada benchmark needs deliberate design
A Kannada benchmark should do more than collect Kannada text. It must measure a clearly defined capability, represent the language’s real-world variation, and make results comparable across models. Kannada AI evaluation is especially sensitive to script variation, transliteration, code-mixing with English, dialect differences, spelling inconsistencies, and domain imbalance.
Start by writing a one-page benchmark specification before collecting data. Define:
- Task: classification, question answering, summarisation, translation, named-entity recognition, safety, or instruction following.
- Input and output: specify the fields, label set, answer format, and maximum length.
- Target users: researchers, public-sector teams, education platforms, or commercial builders.
- Evaluation metric: accuracy, macro-F1, exact match, chrF, BLEU, ROUGE, or a human-rated rubric.
- Data boundaries: domains, dialects, time period, content categories, and excluded material.
For broader context, compare your design with Indian-language LLM benchmark datasets and the practical considerations in low-resource language datasets for AI training in India.
Choose a task and construct a testable dataset
Avoid making the first release an oversized collection of loosely related examples. A smaller, carefully controlled benchmark is more useful than a large noisy corpus. For example, a Kannada sentiment dataset should define whether labels represent sentiment, emotion, stance, or customer-service intent. These are different tasks and should not be mixed under one label.
Create a data schema before annotation. A simple JSONL record might contain:
{
"id": "kn_sent_000001",
"text": "ಈ ಸೇವೆ ತುಂಬಾ ಉತ್ತಮವಾಗಿದೆ.",
"label": "positive",
"domain": "consumer_services",
"source_type": "licensed_web_text",
"annotator_agreement": 1.0
}Keep provenance fields separate from model inputs. Include source, licence, collection date, annotator notes, and quality flags in metadata rather than exposing information that could leak the answer. Assign stable IDs so problematic records can be removed or corrected without breaking version history.
Collect Kannada data responsibly
Use sources you can legally redistribute. Suitable options may include openly licensed text, commissioned writing, public-domain material, synthetic prompts reviewed by native speakers, and consented contributions. Do not assume that text being publicly visible means it can be republished in a dataset.
For every source, record:
- Copyright holder or source organisation.
- Licence and redistribution permissions.
- Collection method and date.
- Original URL where permitted.
- Whether personal or sensitive information may be present.
Web scraping requires particular care. Respect terms of service, robots directives, rate limits, and takedown requests. Filter phone numbers, email addresses, government IDs, precise addresses, and other personal data before publication. For a broader sourcing strategy, see this guide to open-source AI datasets for India.
Balance the dataset across Kannada script, formal and conversational registers, urban and rural contexts, and relevant domains. If transliterated Kannada or code-mixed Kannada-English is in scope, label it explicitly rather than silently combining it with standard Kannada.
Clean and normalise without destroying meaning
Kannada preprocessing should be conservative. Unicode normalisation can resolve equivalent representations, but aggressive rewriting may erase legitimate spelling, punctuation, emphasis, or dialect information.
A robust cleaning pipeline should:
1. Validate UTF-8 and apply a documented Unicode normalisation policy.
2. Remove boilerplate, duplicate pages, broken markup, and accidental extraction artefacts.
3. Detect language and flag Kannada-English code-mixed records.
4. Identify near-duplicates across train, validation, and test splits.
5. Preserve original text in a restricted audit file when redistribution permits.
6. Record each transformation in a versioned script.
Do not lowercase Kannada text as a default step. Kannada has no direct equivalent of English case handling, and punctuation, numerals, whitespace, and zero-width characters may affect tokenisation. Test preprocessing with actual Kannada examples and publish the rules in the dataset repository.
Annotate with native-speaker review
Annotation guidelines should include positive and negative examples, edge cases, escalation rules, and a decision tree for ambiguous items. Use at least two independent annotators for a meaningful subset. Report agreement using an appropriate statistic, such as Cohen’s kappa or Krippendorff’s alpha, while also explaining disagreements in plain language.
Recruit reviewers who understand Kannada grammar and usage in the target domain. For medical, legal, financial, or safety data, add subject-matter review. Machine-generated labels can accelerate triage, but they should not be treated as ground truth without human verification.
Create a separate challenge set for difficult cases: dialectal wording, spelling variation, sarcasm, code-mixing, long context, culturally specific references, and ambiguous intent. Keep this set hidden from training and document how it was assembled.
Split the data to prevent leakage
Random splitting is not always valid. Near-duplicate articles, translated versions, conversation threads, or records from the same source can appear in multiple splits and inflate scores. Prefer group-based splits by source, document, user, or topic where appropriate.
Use three partitions:
- Training: available for model development.
- Validation: used for tuning and model selection.
- Test: held back for final reporting.
For a small benchmark, publish fixed split files and a seed. For sensitive or competitive evaluations, keep test labels private and provide a submission script. Check whether benchmark prompts or labels have already entered popular model training data; contamination does not make the dataset useless, but it must be disclosed.
Build and publish the dataset on Hugging Face
Install the required libraries and authenticate with a write-enabled token:
pip install datasets huggingface_hub
huggingface-cli loginLoad local files, inspect the schema, and push a versioned dataset repository:
from datasets import load_dataset
files = {
"train": "data/train.jsonl",
"validation": "data/validation.jsonl",
"test": "data/test.jsonl",
}
dataset = load_dataset("json", data_files=files)
dataset.push_to_hub("your-org/kannada-benchmark")Use a clear repository name and semantic release tags such as v1.0.0. Add a complete dataset card covering the task, intended use, limitations, licence, sources, collection process, annotation protocol, demographic or domain gaps, personal-data controls, known contamination risks, and citation details. Include a small sample, but do not expose restricted or sensitive records.
Validate the public repository from a clean environment. Confirm that users can load each split, that labels match the declared feature schema, and that the licence is visible. A README is not a substitute for machine-readable metadata.
Evaluate models fairly
Publish a baseline before claiming progress. Use at least one multilingual model and one Kannada-capable model where available, with the same preprocessing and prompt format. Report aggregate scores alongside per-domain, per-label, and challenge-set results. Macro-F1 is often more informative than accuracy when classes are imbalanced.
For generative tasks, automated metrics should be supplemented with native-speaker evaluation. Define a rubric for factuality, relevance, fluency, script correctness, harmful content, and instruction adherence. Report the number of raters, adjudication process, and uncertainty or confidence intervals where feasible.
Benchmarking should be reproducible: publish evaluation code, model versions, decoding settings, seeds, and hardware constraints. The same discipline used in benchmarking multilingual LLMs in India applies to Kannada-specific work.
Maintain the benchmark after release
Treat the first upload as a release, not the finish line. Track issues, accept corrections through pull requests, maintain a changelog, and never silently alter an existing test set. If a record is removed for licensing or privacy reasons, document the change and increment the version.
A strong Kannada benchmark gives builders a dependable way to compare models, identify failures, and justify investment in local-language systems. It also creates an evidence base for training LLMs on Indian datasets, provided that its limitations remain visible.
Frequently asked questions
How large should the dataset be?
There is no universal minimum. For classification, prioritise label balance and annotation quality; for generative evaluation, prioritise task coverage and carefully reviewed references. A few thousand high-quality examples can be valuable when the task is narrow and the test set is protected.
Should I include transliterated Kannada?
Only if it reflects your intended use. Keep native Kannada, Latin-script Kannada, and code-mixed text distinguishable through metadata or separate configurations so scores remain interpretable.
Can I use synthetic Kannada data?
Yes, but label it clearly and review it with native speakers. Synthetic examples can improve coverage, but they may reproduce model bias, unnatural phrasing, or benchmark artefacts.
What licence should I use?
Use the licence that matches every source and contributor agreement. If sources have incompatible restrictions, do not merge them into a single redistributable release without legal review.
How can I get feedback?
Share the repository with Kannada NLP researchers, native-speaker reviewers, and downstream builders. Make issues easy to file and publish a roadmap for corrections, new domains, and challenge sets.
Apply for AI Grants India
If you are building Kannada or other Indian-language AI infrastructure, apply to AI Grants India for potential funding, mentorship, and ecosystem support.