How to Benchmark a Malayalam Model on IndicGLUE with Hugging Face

Indic-language models should be evaluated on evidence, not only on a few hand-picked examples. For Malayalam, that means testing performance across representative tasks, recording the exact model and preprocessing configuration, and examining errors that aggregate scores can hide. IndicGLUE provides a useful evaluation framework, while Hugging Face supplies the tooling needed to load datasets, run inference, and report metrics.

This guide explains how to benchmark a Malayalam model on IndicGLUE using Hugging Face. The examples focus on encoder-style models fine-tuned for classification, but the workflow also applies to multilingual checkpoints and custom Malayalam models. Dataset configurations and column names can change, so verify the current IndicGLUE card and task documentation before running a final report.

What IndicGLUE measures

IndicGLUE is a collection of Indian-language NLP tasks rather than one Malayalam test set. Depending on the task, evaluation may cover sentiment or topic classification, natural-language inference, named entity recognition, question answering, and related language-understanding problems. Each task can use different input fields, label formats, and metrics.

Before writing code, create an evaluation plan:

Select the IndicGLUE tasks relevant to your product or research question.
Confirm that Malayalam is available for each selected task.
Record the dataset revision, split, language field, and label mapping.
Decide whether you are evaluating a base checkpoint, a fine-tuned checkpoint, or a prompting setup.
Keep a held-out development or test procedure separate from any data used during training.

For broader comparisons, pair this workflow with guidance on benchmarking NLP models for Telugu and Sanskrit. The same discipline—task-level reporting, consistent splits, and language-specific error analysis—applies across Indic evaluations.

Set up a reproducible Hugging Face environment

Use a fresh virtual environment and pin the important packages. Hardware requirements depend on sequence length and model size; a CPU is sufficient for a small smoke test, while a GPU makes full evaluation substantially faster.

python -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install "transformers>=4.40" "datasets>=2.18" evaluate accelerate scikit-learn pandas

Log the following before evaluation:

Python, PyTorch, Transformers, Datasets, and Evaluate versions
Model repository and commit or revision
IndicGLUE dataset revision
Tokenizer settings and maximum sequence length
Device, batch size, and random seed

This metadata matters because tokenizer updates, dataset revisions, and truncation choices can change results. If your model is intended for constrained deployment, also measure latency and memory; the workflow described in the AI model optimization for mobile devices guide is useful after quality benchmarking.

Load the model and inspect the dataset

Start by loading the tokenizer and task-appropriate model head. A sequence-classification checkpoint cannot be evaluated directly on token classification or extractive question answering without the corresponding architecture and preprocessing.

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "your-org/your-malayalam-checkpoint"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

# Confirm the current dataset name and Malayalam configuration in its Hub card.
data = load_dataset("indic_glue", "your_task_config")
print(data)
print(data["validation"].column_names)
print(data["validation"][0])

Do not assume that load_dataset("indic_glue", "malayalam") is valid. IndicGLUE is organised by task and configuration, and the Malayalam examples may be represented by a language column rather than a Malayalam-only configuration. Inspect the dataset card, then filter explicitly when required:

malayalam = {
    split: ds.filter(lambda row: row["language"] == "ml")
    for split, ds in data.items()
}

Use the dataset’s documented language code and field names. Also check for empty splits, duplicate examples, missing labels, and unexpected Unicode or script variants before evaluation.

Tokenize according to the task

For single-sentence classification, tokenization may look like this:

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

tokenized = malayalam["validation"].map(
    tokenize_batch,
    batched=True,
    remove_columns=["text"],
)

For sentence-pair tasks, pass both text fields in the correct order:

def tokenize_pairs(batch):
    return tokenizer(
        batch["premise"],
        batch["hypothesis"],
        truncation=True,
        max_length=256,
    )

Named entity recognition requires is_split_into_words=True when examples are supplied as word lists, followed by label alignment using word_ids(). Question answering requires offset mappings and start/end span conversion. Reusing a single text-column function across all tasks is a common source of invalid scores.

Run evaluation with task-appropriate metrics

For classification, Hugging Face’s evaluate package or scikit-learn can calculate accuracy and macro F1. Macro F1 is particularly important when Malayalam labels are imbalanced because it gives each class equal weight.

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-eval",
    per_device_eval_batch_size=32,
    report_to="none",
    seed=42,
)

trainer = Trainer(
    model=model,
    args=args,
    eval_dataset=tokenized,
    processing_class=tokenizer,
    compute_metrics=compute_metrics,
)

results = trainer.evaluate()
print(results)

Use the metric specified by the task when publishing a benchmark. For NER, report entity-level precision, recall, and F1 rather than token accuracy. For question answering, report exact match and token-level F1. Do not compare scores from different preprocessing rules as if they came from the same experiment.

Make the benchmark trustworthy

A single aggregate number is not enough. Run at least three checks:

Baseline comparison: evaluate a multilingual or Malayalam baseline with the same code and split.
Seed or run stability: repeat fine-tuning, if applicable, and report mean and variation across runs.
Slice analysis: break results down by class, sentence length, genre, and available Malayalam subgroups.

Preserve raw predictions, labels, example IDs, and configuration files. Build a confusion matrix for classification and manually inspect false positives and false negatives. Look for issues such as code-mixed Malayalam-English text, spelling variation, named entities, transliterated Malayalam, punctuation, and long-context truncation.

If your model is generative rather than encoder-based, do not force it into a sequence-classification head simply to obtain a score. Use deterministic decoding, task-specific output parsing, and clearly documented normalization. For local inference workflows, the guide to deploying large language models locally offers useful operational considerations.

Report results for other builders

A useful Malayalam IndicGLUE report should include:

Model name, parameter count, tokenizer, and training data description
IndicGLUE task, Malayalam filtering method, split, and dataset revision
Maximum sequence length, truncation policy, batch size, and device
Primary metric plus accuracy, macro F1, or other supporting metrics
Baseline results and run-to-run variation
Known data contamination, licensing, and limitations
Representative failure cases, with sensitive text handled responsibly

Do not claim that a high benchmark score proves production readiness. Malayalam applications may face domain shift, dialect differences, noisy user input, and code-mixing that IndicGLUE does not fully represent. A small, consented, domain-specific test set should complement the benchmark before deployment.

Common failure modes

Dataset loading fails: check the current Hub repository, configuration name, access requirements, and revision. Dataset APIs and configuration names can change.

Labels do not match the model head: inspect model.config.id2label, dataset label names, and the training label mapping. Reorder labels only with an explicit documented mapping.

Results look unusually strong: check train-test overlap, language filtering, duplicate rows, and whether labels accidentally remain in the model input.

GPU memory runs out: lower the evaluation batch size, use dynamic padding, shorten the maximum length only if justified, or evaluate in smaller batches.

Scores cannot be compared: align the task, split, language subset, tokenizer, normalization, and metric implementation before drawing conclusions.

A careful IndicGLUE evaluation gives Malayalam model builders a defensible baseline and a practical map of what to improve next. Treat the benchmark as one part of a broader evaluation suite, then use the error analysis—not just the leaderboard score—to guide data collection, fine-tuning, and deployment.

Apply for AI Grants India