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Why Kannada evaluation needs a careful benchmark

A Kannada model can post a strong aggregate score and still fail on the text people use: code-mixed Kannada-English, spelling variation, dialectal vocabulary, informal messaging, transliterated Kannada, or text extracted from noisy documents. IndicGlue is useful because it puts Indic-language tasks into a comparable evaluation framework, but the framework does not remove the need for sound data design.

Treat IndicGlue as one layer of an evaluation strategy. Use its standard tasks for repeatable comparison, then add a Kannada-specific test set that reflects your product. This is the same principle used in a broader multi-stage LLM pipeline for developers: separate benchmark measurement, targeted testing, and production validation instead of relying on one score.

What IndicGlue gives you

IndicGlue is an Indic-language benchmark covering multiple NLP tasks and languages, including Kannada, depending on the version and task configuration available in the project repository. It can help you:

• Compare models on the same task and split.
• Reuse published datasets and task definitions.
• Standardise preprocessing and evaluation commands.
• Track whether fine-tuning improves a target capability or merely overfits.
• Produce results that other researchers and engineering teams can reproduce.

Before installing anything, verify the current repository, package name, supported tasks, Kannada availability, dataset licences, and expected model interface. Tooling and command names can change, so do not assume that an example command from an older blog post still works in 2026.

Set up a reproducible evaluation environment

Create an isolated Python environment and record the exact versions used for the run:

python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
# Install the package and dependencies specified by the IndicGlue repository

If the project is distributed through GitHub rather than PyPI, follow its official installation instructions and pin the commit or release tag. Keep a small environment.yml, requirements.txt, or lockfile with your experiment. Also capture:

• Python, PyTorch, Transformers, CUDA, and IndicGlue versions.
• Model name, revision, tokenizer, and fine-tuning checkpoint.
• Dataset version, split, language code, and preprocessing script.
• Hardware, batch size, maximum sequence length, and random seed.
• Evaluation date and the command used to launch the run.

Run a smoke test on a small sample before a full benchmark. It should confirm that the tokenizer accepts Kannada Unicode, the labels map correctly, the model returns the expected output shape, and the evaluator can write results.

Prepare Kannada data without damaging the signal

Unicode handling is a common source of misleading results. Normalise text consistently, but do not silently remove characters that carry linguistic meaning. Inspect Kannada combining marks, punctuation, numerals, emojis, zero-width characters, and whitespace before choosing a normalisation policy.

Build a data audit with at least these checks:

• Missing, duplicated, or conflicting labels.
• Unexpected language content and Kannada-English code-switching.
• Very short and unusually long examples.
• Duplicate text across training and evaluation splits.
• Transliteration written in Latin script versus Kannada script.
• Personal or sensitive information that should not enter an external benchmark workflow.

Do not edit the official test set after seeing model predictions. If you need a corrected or domain-specific version, create a new documented test set and report both results. For a production system, pair benchmark data with a continuously refreshed evaluation set, much like the checks recommended for LLM application performance monitoring in India.

Select the right task and metric

Map your use case to the benchmark task before choosing a metric:

• Text classification: report accuracy for balanced labels, but prefer macro-F1 when classes are uneven. Include per-class precision, recall, and a confusion matrix.
• Named entity recognition: use entity-level precision, recall, and F1. Token-level accuracy can hide poor boundary detection.
• Natural language inference or pair classification: report macro-F1 and accuracy, along with results by label.
• Question answering: use the metric defined by the dataset, commonly exact match and token-level F1. Check how Kannada tokenisation affects the score.
• Generation or translation: BLEU can support comparison, but add chrF, semantic review, and human assessment. Do not treat one automatic score as a complete quality judgment.

Report confidence intervals or variation across multiple seeds when practical. A one-point improvement may not be meaningful if it falls within run-to-run noise. For a fair comparison, keep the evaluation protocol fixed and change one major variable at a time.

Run IndicGlue and validate the output

The exact command depends on the IndicGlue release and task runner. Start with the repository's help output and documentation rather than copying an unverified command:

python <evaluation_entrypoint>.py --help

A typical workflow is:

1. Select the Kannada task and official evaluation split.
2. Point the runner to a compatible pretrained or fine-tuned model.
3. Set the tokenizer, batch size, sequence length, device, and seed.
4. Run a small sample, then launch the complete evaluation.
5. Save raw predictions, labels, configuration, logs, and aggregate metrics.
6. Re-run the same command from a clean environment to confirm reproducibility.

Inspect the output, not just the headline score. Check the number of evaluated examples, skipped rows, label distribution, truncation count, failed batches, and whether predictions align with the original examples. A suspiciously high score can indicate leakage; a suspiciously low score can come from an incorrect label map or tokenizer mismatch.

Go beyond the benchmark score

Create a Kannada error taxonomy and sample failures from every major category. Useful categories include negation, honorifics, named entities, spelling variants, dialectal forms, sarcasm, code-mixing, long-context truncation, and ambiguous morphology. Compare errors by category and user segment rather than reviewing only the worst examples.

For a serious deployment, add challenge slices such as Bengaluru code-mixed text, rural or dialectal content, social-media spelling, OCR output, and low-resource domains. Measure latency, memory use, throughput, and failure rate alongside quality. Teams building repeatable high-performance AI pipelines should store these results as versioned artefacts so every model release can be compared against the same baseline.

Human review remains important for Kannada. Use bilingual reviewers who understand the task and define an adjudication process for disagreement. For generative systems, assess factuality, instruction following, toxicity, and whether the response preserves Kannada meaning rather than merely producing fluent-looking text.

A practical reporting template

Publish a compact evaluation table containing:

• Model and checkpoint.
• Kannada task, dataset release, and split.
• Preprocessing and tokenizer details.
• Main metric plus per-class or slice metrics.
• Number of examples and excluded rows.
• Mean and variation across seeds, where available.
• Hardware, latency, and inference settings.
• Known limitations and representative errors.

If IndicGlue is only one component of your evaluation stack, say so clearly. For retrieval-augmented applications, benchmark retrieval and answer quality separately using the methods in how to evaluate RAG pipelines. For Python-heavy experiments, maintain a small, tested evaluation harness and use dependable Python libraries for high-performance NLP rather than embedding ad hoc transformations in notebooks.

Common problems and fixes

• Installation fails: use the repository's pinned dependency versions and isolate the environment.
• Kannada text appears corrupted: inspect UTF-8 handling, Unicode normalisation, fonts, and terminal output.
• Labels do not match: print the label map and compare it with the dataset schema before scoring.
• Results change unexpectedly: fix seeds, versions, splits, and preprocessing; then repeat the run.
• The score is high but users complain: add domain, dialect, code-mixed, and long-tail test slices.
• Evaluation is too slow: use batching and an appropriate device, but confirm that optimisation does not alter outputs or truncate inputs.

FAQ

Can IndicGlue alone prove that a Kannada model is production-ready? No. It provides standardised evidence, not complete product validation. Add representative data, human review, safety tests, and operational measurements.

Should I report accuracy or F1? Report the metric required by the task, then add macro-F1 and per-class results when class imbalance or uneven performance matters.

How often should I rerun the benchmark? Run it for every material model or preprocessing change, and include it in release checks. Refresh the custom Kannada test set as user language and product domains change.

What is the most important first step? Confirm the exact IndicGlue release, Kannada task configuration, dataset split, and model interface. Reproducibility starts before the first command is executed.