Apply for AI Grants India

Benchmarking Kannada language models requires more than running a single accuracy command. Kannada has distinct script, morphology, tokenisation, transliteration, and code-mixing challenges, so a useful evaluation must control the data, model configuration, metrics, and reporting format. Hugging Face provides the tooling; IndicGenBench provides a task-oriented evaluation setting.

This guide shows how to build a reproducible workflow for Kannada evaluation. Before running anything, verify the official IndicGenBench repository or dataset card for the current configuration, task names, splits, licences, and supported metrics. Dataset identifiers and column names can change, so treat the examples below as a template rather than an assumed API contract.

What you are actually benchmarking

IndicGenBench-style evaluations may cover generation, translation, summarisation, question answering, instruction following, or other language tasks. A classification workflow using AutoModelForSequenceClassification is appropriate only when the selected Kannada task has labelled classes. For open-ended generation, use a causal or seq2seq model and task-specific generation metrics instead.

Define the evaluation question first:

• Model comparison: Which model performs better on the same Kannada test set?
• Prompt comparison: Which instruction or prompt format produces more reliable answers?
• Fine-tuning impact: Does Kannada adaptation improve results without harming other languages?
• Deployment readiness: Does quality remain acceptable within your latency and memory budget?

For broader context on selecting datasets and interpreting results, review this Indian language LLM benchmark datasets guide and the practical framework for benchmarking multilingual LLMs in India.

1. Create a reproducible Hugging Face environment

Use a virtual environment and record package versions. GPU evaluation is strongly recommended for larger models, but a CPU can work for small checkpoints or a reduced smoke test.

python -m venv .venv
source .venv/bin/activate
pip install -U torch transformers datasets evaluate accelerate sentencepiece sacrebleu

On Windows, activate the environment with .venv\\Scripts\\activate. Pin versions in requirements.txt after confirming that the IndicGenBench code works with them. Also record the GPU type, CUDA version, batch size, sequence length, and decoding settings. These details affect both speed and output quality.

Set seeds where possible, but do not treat a seed as a guarantee of identical generation across hardware. Save the exact model revision and dataset revision used for each run.

2. Locate and inspect the Kannada configuration

Do not assume that load_dataset("indicgenbench", "kannada_sentiment") is the correct identifier. Find the official dataset name and configuration, then inspect its structure:

from datasets import load_dataset

# Replace these placeholders with the identifiers in the official dataset card.
dataset = load_dataset("<indicgenbench-dataset>", "<kannada-config>")
print(dataset)
print(dataset["test"].column_names)
print(dataset["test"][0])

Check the following before evaluation:

• Kannada text is actually written in Kannada script, unless the task explicitly includes transliteration.
• The test split is held out and has no accidental overlap with training data.
• Prompts, references, labels, and metadata are in the expected columns.
• Empty examples, duplicated rows, malformed Unicode, and unexpected languages are removed or reported.
• The licence permits your intended research or commercial use.

Preserve the original test set. If you normalise Unicode or strip whitespace, save the transformation script and report it. Do not silently translate Kannada into English before scoring a Kannada task; that changes the evaluation question.

3. Select a model and match the task head

Choose a checkpoint with documented Indian-language or Kannada coverage. Possible starting points include multilingual encoder models for classification and instruction-tuned or seq2seq models for generation. Confirm the model card’s training data, supported languages, licence, context window, and known limitations.

For classification:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "<model-id>"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
model = AutoModelForSequenceClassification.from_pretrained(
    model_name,
    num_labels=<number-of-labels>,
)

For generation, load the architecture that matches the checkpoint—for example, AutoModelForCausalLM or AutoModelForSeq2SeqLM. Avoid forcing a classification head onto a generative checkpoint merely because the Hugging Face interface is convenient.

Tokenizer behaviour deserves special attention. Compare token counts for Kannada script, English, and code-mixed examples. Excessive fragmentation can reduce effective context and increase inference cost. If your application will receive Romanised Kannada, evaluate it as a separate condition rather than mixing it into the native-script score.

4. Run a smoke test before the full benchmark

First evaluate a small sample to catch schema, padding, device, and generation errors:

from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model=model_name,
    tokenizer=tokenizer,
    device_map="auto",
)

sample = dataset["test"][0]
print(sample)
# Construct the prompt according to the task specification.

For generation, keep decoding settings explicit. Record max_new_tokens, temperature, top-p, beam settings, repetition penalty, and stop conditions. Greedy decoding and sampling are different experiments; do not combine their results in one leaderboard row.

5. Evaluate with task-appropriate metrics

Use the official IndicGenBench evaluation script whenever one is provided. It may handle Kannada-specific normalisation, multiple references, exact prompt formatting, or specialised human checks. If you implement metrics yourself, document every preprocessing step.

Typical choices include:

• Classification: accuracy, macro-F1, per-class precision and recall.
• Translation: BLEU, chrF, and human assessment for adequacy and fluency.
• Summarisation: ROUGE or similar overlap measures, supplemented by factuality and coverage checks.
• Question answering: exact match or token-level F1, with manual review of acceptable Kannada variants.
• Instruction following: rubric-based human evaluation, constraint success, safety, and factuality.

Macro-F1 is often more informative than accuracy when Kannada test labels are imbalanced. For generative tasks, automatic scores should not be treated as a complete quality judgement: spelling variants, inflection, word order, and legitimate paraphrases can be penalised by surface-overlap metrics.

6. Build a transparent results table

Report more than one aggregate number. A useful Kannada benchmark record includes:

• Model name, revision, quantisation, and fine-tuning status.
• Dataset and configuration revision, split, and sample count.
• Prompt template and language of the instructions.
• Tokenizer, maximum input length, and truncation policy.
• Decoding parameters and number of runs.
• Overall and per-category scores, plus confidence intervals where practical.
• Runtime, peak memory, hardware, and cost per example.
• Error categories such as script confusion, hallucination, untranslated text, and code-mixing failures.

Keep predictions and references in a versioned file so that errors can be audited. A result without the exact prompt and checkpoint is difficult to reproduce and should not be presented as a definitive model ranking.

Common failure modes

Wrong dataset configuration: The loader succeeds but returns a different task or language. Always print the configuration, columns, and first examples.

Hidden English leakage: Prompts, labels, or references may contain English instructions that make the task easier than intended. Report the prompt language and test native Kannada instructions separately.

Inconsistent normalisation: Applying Unicode normalisation to predictions but not references can distort scores. Use one documented policy.

Truncation: Long Kannada inputs can be cut silently. Measure the proportion of truncated examples and run a longer-context condition if relevant.

Overfitting to the benchmark: Do not tune repeatedly on the public test set. Use a development split or a separate Kannada evaluation set for iteration.

Teams building downstream products can connect benchmark findings to practical interfaces; for example, the workflow in building a Kannada WhatsApp chatbot with a small language model highlights deployment constraints that aggregate scores do not capture.

A sensible 2026 reporting standard

As of 2026, a credible Kannada benchmark report should include the official task definition, reproducible code, dataset and model revisions, native-script and code-mixed conditions where relevant, confidence or variance estimates, and qualitative error analysis. Compare models under identical compute and decoding budgets, then separate quality from efficiency rather than collapsing both into one score.

If you also evaluate Telugu, Sanskrit, or other Indic languages, use the same reporting template while preserving language-specific analyses. The methods in benchmarking NLP models for Telugu and Sanskrit can help structure cross-language comparisons without hiding Kannada’s distinct failure modes.

A strong benchmark is not simply a leaderboard number. It is an auditable measurement of what a model can do in Kannada, under clearly stated conditions, with enough evidence for another Indian-language team to reproduce and challenge the result.