How to Use Hugging Face to Benchmark Malayalam on IndicGenBench

Why benchmark Malayalam carefully?

Benchmarking Malayalam models is not just a matter of loading a checkpoint and recording one score. Malayalam has a rich inflectional system, a distinctive script, frequent code-mixing, and meaningful variation between formal, conversational, transliterated, and regional usage. A model can perform well on one carefully curated test set while failing on real inputs from government services, education, search, or customer support.

Hugging Face provides the tooling to load models, datasets, tokenizers, and evaluation pipelines. IndicGenBench provides a shared basis for testing generative performance across Indian languages. Used together, they help you compare models consistently and identify where Malayalam performance breaks down. For wider context, pair this workflow with the Indian language LLM benchmark datasets guide and the practical framework for benchmarking multilingual LLMs in India.

Before you start: verify the benchmark release

Do not assume that a dataset configuration, split name, or task schema is unchanged. Before writing code:

Open the official IndicGenBench repository or Hugging Face dataset page.
Confirm the exact dataset identifier and Malayalam configuration name.
Record the release or commit hash, task names, split definitions, and evaluation scripts.
Check the licence and whether test data may be used for prompting, fine-tuning, or only final evaluation.
Read the task-specific instructions: generative tasks may require exact prompt templates and constrained decoding.

The original example—load_dataset('indicgenbench', 'malayalam')—may not work if the benchmark is published under a different namespace or uses task-level configurations. Treat the repository documentation as authoritative rather than copying an unverified identifier.

Set up a reproducible environment

Use Python 3.10 or newer where possible, and pin the libraries used for the run. A basic environment might include:

python -m venv .venv
source .venv/bin/activate        # Windows: .venv\Scripts\activate
python -m pip install --upgrade pip
pip install "transformers>=4.40" "datasets>=2.18" evaluate accelerate sentencepiece

Add PyTorch according to your operating system and CUDA version. Save the environment after installation:

pip freeze > requirements-lock.txt

For a credible comparison, also record the GPU or CPU, precision (float32, float16, or bfloat16), batch size, maximum input length, generation parameters, and model revision. These details can change both speed and results.

Choose a model that actually supports Malayalam

Start with a tokenizer and model that include Malayalam vocabulary and have documented multilingual coverage. bert-base-multilingual-cased can be a useful classification baseline, but it is an encoder model and is not suitable for every IndicGenBench generation task. For text generation, select an instruction-tuned or causal language model that supports Malayalam, then test its tokenizer before running the full benchmark.

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "YOUR_MALAYALAM_CAPABLE_MODEL"
tokenizer = AutoTokenizer.from_pretrained(model_id, revision="main")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto"
)

sample = "കേരളത്തിലെ കാലാവസ്ഥയെക്കുറിച്ച് ഒരു വാക്യം എഴുതുക."
encoded = tokenizer(sample, return_tensors="pt")
print(tokenizer.decode(encoded["input_ids"][0]))

Inspect tokenisation on Malayalam words, punctuation, numerals, and mixed-script text. Excessive fragmentation can indicate that a model will need more computation and may struggle with spelling or morphology. For extraction-oriented projects, Malayalam OCR and document pipelines may need separate validation; the Malayalam document extraction guide covers that adjacent problem.

Load and inspect IndicGenBench

Once you have confirmed the official identifier, load the benchmark and inspect its structure instead of guessing column names:

from datasets import load_dataset

benchmark = load_dataset("OFFICIAL_DATASET_ID", "OFFICIAL_MALAYALAM_CONFIG")
print(benchmark)
for split, data in benchmark.items():
    print(split, data.column_names, len(data))
    print(data[0])

Look for the input, reference, label, task, and metadata fields. Keep the benchmark untouched. If you need a development subset, create it from the training or validation split and preserve the official test set for one final run. Never tune prompts or decoding settings repeatedly against the test set and then report the resulting score as unbiased.

Match the evaluation method to the task

IndicGenBench may contain several task types, and they should not all be evaluated with one Trainer configuration.

Classification: use a task-appropriate classification head and report accuracy plus macro-F1. Macro-F1 matters when Malayalam labels are imbalanced.
Generation: generate with a fixed prompt template and fixed decoding settings. Compare outputs with the benchmark’s approved metric and normalisation rules.
Translation or rewriting: report the specified automatic score, but also inspect adequacy, fluency, named entities, numbers, and honorifics.
Question answering or structured output: verify exact-match rules, whitespace handling, and whether multiple valid answers are accepted.

For an encoder classifier, a minimal evaluation pattern is:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "xlm-roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=2)

You must adapt num_labels, input columns, labels, and metrics to the actual task. Do not attach a classification head to a generative benchmark simply because the Hugging Face Trainer API is convenient.

Make generation reproducible

For generative evaluations, set the model to evaluation mode and disable sampling unless the benchmark explicitly requires it. Keep prompts identical across models and save every raw prediction.

import torch

def generate_one(text, max_new_tokens=128):
    prompt = text
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.inference_mode():
        output = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            do_sample=False,
            num_beams=1,
            pad_token_id=tokenizer.eos_token_id,
        )
    return tokenizer.decode(output[0], skip_special_tokens=True)

Use the benchmark’s prompt format, context limit, and stopping rules. If you change temperature, top_p, beam search, or maximum output length, treat it as a new experimental condition. Store predictions in JSONL with the model revision, example ID, prompt version, and run configuration.

Report results beyond one number

A useful Malayalam benchmark report should include:

The model name, revision, parameter count, and tokenizer.
Dataset version, Malayalam configuration, split, and number of examples.
Exact prompt or preprocessing code.
Hardware, software versions, batch size, and precision.
Task-specific metrics and any normalisation applied.
Runtime, memory use, and failed or truncated examples.
Results by task, category, length, and script or code-mixing condition where metadata allows.

Compare systems with the same test examples and evaluation script. If you run multiple seeds or prompt variants, report the mean and spread rather than selecting the best run. For cross-language comparisons, consult work on benchmarking NLP models for Telugu and Sanskrit, but avoid treating scores across different datasets as directly interchangeable.

Diagnose Malayalam-specific errors

Automatic metrics are a starting point. Sample errors systematically and label them: script corruption, spelling variation, tokenisation failure, untranslated English, dropped negation, incorrect case or tense, hallucinated facts, poor handling of names and numbers, and culturally unsuitable phrasing. Separate model failures from reference-quality problems; a single reference may penalise a valid Malayalam answer.

For production decisions, create a small human review set with Malayalam-fluent evaluators. Use a clear rubric for factuality, completeness, fluency, register, and safety. This is especially important for public-facing Indian-language systems, where a high aggregate score can conceal failures in minority domains or informal speech.

A practical final checklist

Before publishing your result, confirm that you have:

Used the official IndicGenBench Malayalam configuration and evaluation procedure.
Kept the test set isolated from tuning.
Validated Malayalam tokenisation and prompt formatting.
Pinned model, dataset, and library versions.
Saved raw outputs and failed examples.
Reported task-level metrics, not only an overall average.
Added qualitative error analysis and limitations.

This workflow turns Hugging Face from a model download interface into a reproducible evaluation stack. It also produces evidence that Indian AI teams can use to decide whether a model is ready for Malayalam research, fine-tuning, or deployment.

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