Why this benchmark matters
A Kannada model should not be judged only by a single accuracy number or a few hand-picked examples. IndicGLUE provides a common evaluation framework for Indian-language NLP, while Hugging Face supplies the tooling to load data, fine-tune models, run evaluations, and publish reproducible results.
The important distinction is that IndicGLUE is a collection of task-specific datasets and metrics, not necessarily one dataset with a universal `kannada` configuration. Dataset names, language coverage, column names, splits, and label formats can change between releases. Treat the repository documentation and dataset card as the source of truth before writing your training script.
This guide covers a robust workflow for classification-style Kannada tasks, with notes for token classification and other IndicGLUE evaluations.
1. Define the task and evaluation protocol
Start by recording the exact task you intend to benchmark. Possible tasks include sentiment or topic classification, natural language inference, paraphrase detection, and named entity recognition. The task determines the model head, input format, and metric.
Before downloading data, write down:
- Dataset and configuration name.
- Kannada split or language identifier.
- Train, validation, and test availability.
- Number of labels and their meanings.
- Official metric and averaging method.
- Whether the benchmark expects a held-out test submission rather than local test labels.
For a fair comparison, keep the test set untouched. Use the training split for fine-tuning and the validation split for model selection. If you repeatedly inspect test results while changing hyperparameters, the test set becomes part of training and the reported score is optimistic.
Teams working across Indian languages can also compare this workflow with benchmarking NLP models for Telugu and Sanskrit, but do not assume that a metric or label schema transfers unchanged between languages.
2. Create a reproducible Hugging Face environment
Use a clean virtual environment and pin the main dependencies. The exact versions should match the environment used for your final run; the following is a reasonable starting point:
python -m venv .venv
source .venv/bin/activate
pip install -U "transformers" "datasets" "evaluate" "accelerate" "torch" "sentencepiece" "scikit-learn"Log the following for every experiment:
- Python, PyTorch, Transformers, Datasets, and CUDA versions.
- Model checkpoint and tokenizer revision.
- Dataset revision or commit hash.
- Random seed and hardware.
- Maximum sequence length, batch size, learning rate, and number of epochs.
Set seeds, but do not treat one seed as definitive evidence. Fine-tuning scores can vary substantially on smaller Kannada datasets. Run at least three seeds for a serious comparison and report the mean and standard deviation.
3. Load and inspect the correct IndicGLUE configuration
Do not rely on an assumed call such as load_dataset("indic_glue", "kannada"). The available configuration may identify the task rather than the language, or the benchmark may be distributed across separate dataset repositories. Inspect the dataset page and list available configurations first:
from datasets import get_dataset_config_names, load_dataset
repo_id = "<official-indicglue-dataset-repository>"
print(get_dataset_config_names(repo_id))
# Replace with the documented configuration.
ds = load_dataset(repo_id, "<task-or-language-config>")
print(ds)
for split in ds:
print(split, ds[split].column_names, ds[split].features)Check several rows manually. Identify the text fields, label field, language field, and whether examples contain one sentence or a sentence pair. Also inspect class counts:
from collections import Counter
train = ds["train"]
print(Counter(train["label"]))
print(train[0])If Kannada examples are stored alongside other languages, filter using the documented language code and verify the resulting counts. Preserve the original dataset for auditability; create a filtered copy rather than overwriting it.
4. Select a Kannada-capable model and tokenizer
Choose a checkpoint that has documented Kannada coverage and a tokenizer compatible with the script. IndicBERT-family checkpoints, multilingual encoders, and newer Indic-language models can all be valid candidates, but compare their tokenisation behaviour rather than selecting solely by model size.
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_id = "<kannada-capable-checkpoint>"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForSequenceClassification.from_pretrained(
model_id,
num_labels=num_labels,
ignore_mismatched_sizes=True,
)Measure how often Kannada text is split into unknown or unusually many subword tokens. A model with poor segmentation may need a longer context window and still lose information. For deployment constraints, review AI model optimization for mobile devices after establishing a reliable full-size baseline.
5. Tokenise according to the task
For single-sentence classification:
def tokenize(batch):
return tokenizer(
batch["text"],
truncation=True,
max_length=256,
)For sentence-pair tasks, pass both fields:
def tokenize_pairs(batch):
return tokenizer(
batch["sentence1"],
batch["sentence2"],
truncation=True,
max_length=256,
)Avoid unconditional padding="max_length" during preprocessing unless you have a specific reason. Dynamic padding through DataCollatorWithPadding generally reduces memory use. Rename the label column to labels if required by your Transformers version, and remove raw text columns only after confirming that the tokenised dataset is correct.
from transformers import DataCollatorWithPadding
encoded = ds.map(tokenize, batched=True)
collator = DataCollatorWithPadding(tokenizer=tokenizer)For NER, use AutoModelForTokenClassification and align word-level tags with subword tokens. Do not reuse a sequence-classification script for token-level IndicGLUE tasks.
6. Fine-tune with task-appropriate metrics
Accuracy can hide poor performance on imbalanced labels. Use the official metric and add macro-F1 when appropriate. A simple classification metric function is:
import evaluate
import numpy as np
accuracy = evaluate.load("accuracy")
f1 = evaluate.load("f1")
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return {
"accuracy": accuracy.compute(
predictions=predictions, references=labels
)["accuracy"],
"macro_f1": f1.compute(
predictions=predictions,
references=labels,
average="macro",
)["f1"],
}Configure evaluation and checkpoint selection explicitly. In current Transformers releases, eval_strategy is used; older versions may expect evaluation_strategy. Check your installed version rather than copying a setting blindly.
from transformers import TrainingArguments, Trainer
args = TrainingArguments(
output_dir="runs/kannada-indicglue",
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="macro_f1",
greater_is_better=True,
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=32,
num_train_epochs=3,
weight_decay=0.01,
warmup_ratio=0.1,
logging_steps=50,
report_to="none",
seed=42,
)
trainer = Trainer(
model=model,
args=args,
train_dataset=encoded["train"],
eval_dataset=encoded["validation"],
tokenizer=tokenizer,
data_collator=collator,
compute_metrics=compute_metrics,
)
trainer.train()If the benchmark has no public validation labels, use a documented split from the training data for model selection and reserve the official test procedure for the final run.
7. Evaluate beyond the headline score
Run the final evaluation once the configuration is frozen:
test_results = trainer.predict(encoded["test"])
print(test_results.metrics)Then produce a confusion matrix and per-class precision, recall, and F1. Inspect errors by category:
- Kannada spelling variation and informal transliteration.
- Code-mixed Kannada-English text.
- Long inputs truncated at the selected maximum length.
- Ambiguous labels or annotation disagreement.
- Names, locations, numerals, and dialect-specific vocabulary.
Compare against a majority-class baseline and, where useful, a multilingual baseline. A small absolute gain over a weak baseline may not justify a larger model or higher inference cost. If the model will serve a regional application, supplement IndicGLUE with an in-domain Kannada test set; benchmark scores alone do not establish production reliability.
8. Report results so others can reproduce them
A useful benchmark report includes the exact checkpoint, dataset revision, preprocessing, hyperparameters, seed count, mean and standard deviation, official metric, and compute details. Publish evaluation code and, where licensing permits, predictions or error categories.
Keep a clear distinction between fine-tuned benchmark performance and zero-shot or frozen-model performance. Do not compare them as if they measure the same capability. Likewise, do not compare scores from different dataset revisions without noting the change.
For teams building broader Indic-language systems, resources on open-source small language models for Hindi can help with model selection, while fine-tuning AI models for Marathi dialects offers useful lessons on linguistic variation and evaluation design.
Common mistakes to avoid
- Assuming every IndicGLUE release supports the same Kannada configuration.
- Training on validation or test examples through accidental dataset concatenation.
- Reporting only accuracy for an imbalanced task.
- Changing the tokenizer, truncation length, or label mapping between runs.
- Selecting the best checkpoint using the test score.
- Treating one random seed as a stable result.
- Publishing a score without the dataset and model revisions.
Conclusion
The reliable way to benchmark a Kannada model on IndicGLUE using Hugging Face is to make the evaluation contract explicit: verify the task and configuration, inspect Kannada-specific data, use a compatible tokenizer and model head, select checkpoints on validation data, report task-appropriate metrics across multiple seeds, and analyse errors beyond the aggregate score. This produces results that are easier to trust, reproduce, and translate into better Kannada NLP systems.
FAQ
Is IndicGLUE one dataset that I can always load with a Kannada configuration?
No. IndicGLUE releases and Hugging Face repositories can expose tasks, languages, or datasets differently. Inspect the official dataset card and available configurations before coding.
Which metric should I report?
Report the benchmark’s official metric first. Add macro-F1, per-class scores, and a confusion matrix when class balance or error costs make accuracy insufficient.
Should I fine-tune before benchmarking?
State clearly whether you are measuring zero-shot, frozen-model, or fine-tuned performance. Fine-tuning is appropriate for a supervised benchmark, but the protocol must be consistent across models.
How many runs are enough?
For a credible comparison, use at least three seeds and report mean and standard deviation. More runs may be needed for small or highly variable datasets.
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