How to Benchmark a Telugu Model on IndicGLUE with Hugging Face

IndicGLUE is useful only when you treat it as a task-specific evaluation suite, not as a single score. To benchmark a Telugu model on IndicGLUE using Hugging Face, you need to identify the exact Telugu task, load the matching dataset configuration, attach the correct model head, and report metrics under reproducible conditions.

This guide focuses on encoder-style Hugging Face models—such as Telugu BERT or multilingual models fine-tuned for classification, natural language inference, or sequence labelling. Generation tasks require a different evaluation pipeline and should not be forced into a classification template. For wider context on comparable Indian-language evaluations, see this guide to benchmarking NLP models for Telugu and Sanskrit.

What IndicGLUE measures

IndicGLUE is a benchmark collection for Indian-language natural language processing. Its tasks can cover areas such as:

Text classification, including sentiment or topic labels
Natural language inference, where a model predicts the relationship between two sentences
Named entity recognition, evaluated at token or entity level
Question answering or other task-specific formats, depending on the release and configuration

Before writing code, check the current dataset card, configuration names, language fields, label definitions, and licence. Dataset APIs and split names may change, so avoid assuming that a package called indicglue or a generic IndicGlue() class is the official access method. In many Hugging Face workflows, the reliable starting point is datasets.load_dataset() with the dataset identifier documented by the benchmark maintainers.

Prerequisites and environment

Use an isolated environment and record package versions. A practical CPU or GPU setup for 2026 is:

python -m venv .venv
source .venv/bin/activate
pip install -U torch transformers datasets evaluate accelerate scikit-learn seqeval

On Windows, activate the environment with .venv\\Scripts\\activate. Install a CUDA-compatible PyTorch build if you are using an NVIDIA GPU. Then capture the environment before running the benchmark:

pip freeze > requirements-lock.txt
python -c "import torch; print(torch.__version__, torch.cuda.is_available())"

You will need:

A Telugu-capable checkpoint available locally or on the Hugging Face Hub
The correct IndicGLUE dataset configuration
A task-specific model head, such as AutoModelForSequenceClassification or AutoModelForTokenClassification
A fixed evaluation protocol, including maximum sequence length, batch size, and preprocessing rules

If your checkpoint is too large for the available hardware, test quantisation or other deployment techniques separately; AI model optimisation for mobile devices is relevant when the target is an on-device Telugu application. Do not compare an aggressively quantised model with a full-precision model without clearly labelling the difference.

Load the Telugu checkpoint and dataset

Replace the placeholder names below with the exact identifiers from the model and dataset cards:

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "YOUR_TELUGU_CHECKPOINT"
dataset_id = "YOUR_INDICGLUE_DATASET_ID"
config_name = "YOUR_TELUGU_CONFIG"

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
dataset = load_dataset(dataset_id, config_name)

label_names = dataset["train"].features["label"].names
model = AutoModelForSequenceClassification.from_pretrained(
    model_id,
    num_labels=len(label_names),
    id2label={i: name for i, name in enumerate(label_names)},
    label2id={name: i for i, name in enumerate(label_names)},
)

This example assumes a single-text classification task with a text column and integer label column. IndicGLUE tasks may instead use fields such as premise, hypothesis, sentence1, sentence2, or token-level labels. Inspect the schema before tokenisation:

print(dataset)
print(dataset["train"].column_names)
print(dataset["train"][0])

For paired inputs, pass both fields to the tokenizer. For NER, use AutoModelForTokenClassification and align word-level labels with subword tokens using word_ids(). A sequence-classification head cannot produce valid NER results merely because the base encoder supports Telugu.

Tokenise without leaking test information

Use the benchmark's prescribed splits. Do not merge test data into training, select hyperparameters on the test set, or normalise examples using statistics calculated from the full corpus.

from transformers import DataCollatorWithPadding

text_column = "text"

def tokenize_batch(batch):
    return tokenizer(
        batch[text_column],
        truncation=True,
        max_length=256,
    )

tokenized = dataset.map(tokenize_batch, batched=True)
collator = DataCollatorWithPadding(tokenizer=tokenizer)

For Telugu, preserve the original Unicode text unless the benchmark explicitly requires normalisation. Record whether punctuation, whitespace, numerals, emojis, or code-mixed English were changed. These details can materially affect results on social-media and conversational data.

Run evaluation with the correct metric

For classification, use Trainer and a metric function that matches the task. Accuracy alone can hide poor performance on minority classes, so report macro-F1 when class balance is uneven.

import numpy as np
import evaluate
from transformers import TrainingArguments, Trainer

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"],
    }

args = TrainingArguments(
    output_dir="./indicglue-telugu-results",
    per_device_eval_batch_size=32,
    report_to="none",
    seed=42,
)

trainer = Trainer(
    model=model,
    args=args,
    eval_dataset=tokenized["test"],
    tokenizer=tokenizer,
    data_collator=collator,
    compute_metrics=compute_metrics,
)

results = trainer.evaluate()
print(results)

If the benchmark provides an official scorer, prefer it over a locally improvised metric implementation. For NER, use entity-level precision, recall, and F1 with the required BIO or IOB2 interpretation. For question answering, use exact match and token-level F1. For generation, report the benchmark's specified metric and include human or error analysis where automatic scores are insufficient.

Make the benchmark reproducible

A credible result is more than a number. Record:

Model and tokenizer revision or commit hash
IndicGLUE dataset version, configuration, and split
Python, PyTorch, Transformers, and Datasets versions
Random seed and hardware
Maximum sequence length and truncation policy
Batch size, precision mode, and number of evaluation examples
Label mapping and preprocessing decisions

Run at least three seeds when the result will support a paper, product decision, or grant claim. Report the mean and standard deviation rather than presenting the most favourable run. Include a confusion matrix for classification and inspect errors involving spelling variants, transliterated Telugu, dialects, named entities, and Telugu-English code-mixing.

For a model intended to serve Indian-language users across modalities, benchmark text performance separately from vision-language capability; resources on open-source vision-language models for Indian languages address a different evaluation problem.

Common mistakes to avoid

Loading AutoModel and treating hidden states as class logits
Using a Telugu tokenizer with an incompatible model checkpoint
Reporting weighted-F1 when the benchmark requires macro-F1
Evaluating on the training split by accident
Silently dropping examples that fail tokenisation
Comparing scores from different IndicGLUE versions
Claiming broad Telugu understanding from one narrow task

How to report the result

Use a compact table with the task, split, model revision, seed count, metric, and score. State whether the checkpoint was pretrained, fine-tuned on IndicGLUE, or evaluated zero-shot. If you fine-tuned the model, keep a held-out development set for decisions and reserve the official test split for the final run. Report limitations, especially domain mismatch and coverage of Telugu dialects.

The strongest benchmark reports combine the score with qualitative evidence: representative successes, systematic failures, and a clear account of what changed between model versions. That makes the evaluation useful to other Indian-language builders—not just a number on a leaderboard.

FAQ

Can I benchmark any Hugging Face Telugu model?
Only if its architecture and tokenizer support the IndicGLUE task. Base encoders need the appropriate task head or fine-tuning checkpoint.

Should I use accuracy or F1?
Follow the benchmark specification. For imbalanced classification, include macro-F1 alongside accuracy; for NER, use entity-level F1.

Can I run the evaluation on a CPU?
Yes, but reduce the batch size and expect slower inference. Keep the same preprocessing and maximum length when comparing CPU and GPU results.

Is a high IndicGLUE score proof that a model is production-ready?
No. Add domain-specific tests, dialect and code-mixing checks, latency measurements, safety evaluation, and human review before deployment.

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