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What this benchmark is—and what it is not

If you want to know how to use Hugging Face to benchmark Urdu on IndicGenBench, start by separating three activities: loading a model, running an evaluation, and interpreting language-specific results. Hugging Face supplies the model, tokenizer, datasets, and evaluation tooling. IndicGenBench supplies the benchmark protocol and Urdu task data. Your job is to connect them without changing the test conditions.

Do not treat one score as a complete verdict. Urdu performance can vary with script, spelling variation, tokenisation, formality, domain, and whether the model is being tested for classification, generation, translation, or question answering. For a broader view of Indian-language evaluation, compare this workflow with the Indian language LLM benchmark datasets guide and the practical framework for benchmarking multilingual LLMs in India.

1. Confirm the IndicGenBench task and protocol

Before installing anything, inspect the current IndicGenBench repository and documentation. Names of datasets, scripts, configuration files, and supported task types may change. Avoid relying on an assumed package such as indicgenbench or an invented Benchmarker class unless the project documentation confirms it.

Record these details in an experiment note:

• The exact IndicGenBench commit, release, or dataset revision.
• The Urdu task and split being evaluated.
• Whether the task is classification, sequence labelling, generation, translation, or another format.
• The official metric and any normalisation rules.
• The published baseline and its model checkpoint.
• Whether external training data or Urdu-specific adaptation is allowed.

This prevents accidental test-set tuning and makes your result comparable with other submissions. If you are evaluating several Indic languages, the workflow in benchmarking NLP models for Telugu and Sanskrit offers a useful comparison template.

2. Create a reproducible Hugging Face environment

Use a fresh virtual environment and pin the principal dependencies. A typical starting point is:

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

Install the benchmark’s own dependencies from its repository rather than guessing. If you are using a GPU, document the CUDA, PyTorch, and GPU versions. If you are using a hosted notebook, save the full package list and runtime details.

Set deterministic seeds where supported, but remember that exact reproducibility can still vary across hardware and generation kernels. Save:

• Model and tokenizer identifiers.
• Revision or commit hashes.
• Python and library versions.
• Hardware details and batch size.
• Maximum input and output lengths.
• Random seeds and decoding parameters.

For generation, use the benchmark’s specified decoding settings. Changing temperature, top-p, beam count, or maximum output length can make two apparently similar runs incomparable.

3. Select a model that matches the task

Choose a Hugging Face checkpoint based on the benchmark interface, not popularity alone. A masked-language model is not a drop-in replacement for a causal generator, and a sequence-classification checkpoint cannot be evaluated on a generation task without an appropriate head and prompt format.

Use the Hugging Face model card to check:

• Urdu coverage and script support.
• Training languages and known data limitations.
• Intended architecture and task head.
• License and commercial-use conditions.
• Context length and tokenizer behaviour.
• Whether fine-tuning or instruction tuning was used.

For an Urdu baseline, a multilingual model with documented Urdu support may be more defensible than a larger model with no Urdu evidence. If model selection is your main question, see what is the best small language model for Urdu, but keep model-size comparisons separate from benchmark-protocol comparisons.

Load the checkpoint with task-appropriate classes:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

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

For causal or sequence-to-sequence generation, use AutoModelForCausalLM or AutoModelForSeq2SeqLM instead. Match the model class to the task definition in IndicGenBench.

4. Load and inspect the Urdu data

Use the benchmark’s official loader or files. Do not silently substitute a similarly named Urdu dataset. After loading, inspect examples before inference:

• Confirm that Urdu text is stored as Unicode and displayed correctly.
• Check labels, references, and input fields for missing values.
• Verify that train, development, and test examples are not mixed.
• Look for duplicate or near-duplicate prompts.
• Preserve punctuation, diacritics, digits, and script unless the protocol specifies normalisation.

Urdu text may contain Arabic and Persian characters with visually similar Unicode forms. If you normalise text, apply the same documented procedure to predictions and references, and report it. Never remove difficult examples merely because they expose tokenizer or script weaknesses.

Tokenise with truncation settings that reflect the benchmark, not whatever fits conveniently on your GPU:

def tokenize(batch):
    return tokenizer(
        batch["text"],
        truncation=True,
        max_length=512,
    )

For paired inputs, pass both fields. For generation, retain the original prompt and reference so that post-processing can be audited.

5. Run the official evaluation path

Follow IndicGenBench’s documented command, configuration, or evaluation script. A generic Hugging Face loop is useful for debugging, but it is not automatically the official benchmark. The benchmark may apply label mapping, Urdu-specific normalisation, prompt templates, or task-specific aggregation that a custom loop misses.

For a classification-style evaluation, the core pattern resembles:

import numpy as np
from datasets import load_metric
from transformers import TrainingArguments, Trainer

# Use the metric and dataset preparation specified by IndicGenBench.
args = TrainingArguments(
    output_dir="runs/urdu-eval",
    per_device_eval_batch_size=16,
    report_to="none",
)

trainer = Trainer(model=model, args=args, tokenizer=tokenizer)
output = trainer.predict(tokenized_test_dataset)
predictions = np.argmax(output.predictions, axis=-1)

Replace the placeholder dataset and metric logic with the benchmark’s implementation. For generation, decode with skip_special_tokens=True, retain raw outputs, and calculate the official metric through the provided evaluator. Save predictions, references, and per-example metadata—not only the aggregate score.

6. Interpret Urdu results responsibly

Report the official metric first, followed by supporting measures where useful. Depending on the task, these may include accuracy, macro-F1, precision, recall, exact match, BLEU, chrF, or a semantic metric. Macro averages are particularly important when Urdu labels are imbalanced.

Add uncertainty where feasible:

• Use bootstrap confidence intervals for aggregate scores.
• Report results by category, domain, or difficulty if the benchmark provides those fields.
• Compare against the official baseline under identical settings.
• Separate zero-shot, few-shot, fine-tuned, and retrieval-augmented runs.

A small score difference may not be meaningful if it falls inside run-to-run variation. Conversely, a strong aggregate score can hide systematic failures on named entities, code-mixed Urdu, dialectal text, dates, or numerals.

7. Perform error analysis before making claims

Create a review sample of correct and incorrect predictions. Tag errors such as:

• Unicode or normalisation mismatch.
• Tokenisation failure or truncation.
• Confusion between Urdu and related scripts or languages.
• Negation, politeness, morphology, or agreement errors.
• Named entities, numbers, transliteration, and code-mixing.
• Hallucinated or incomplete generated content.

For generative tasks, compare exact output and meaning separately. Automatic metrics can penalise valid wording differences, while fluent output can still contain factual or safety-critical errors. If your product will serve Indian users across modalities, pair text evaluation with a dedicated guide on benchmarking speech-to-text accuracy in India.

8. Publish a result others can reproduce

A useful benchmark report includes the command or configuration, checkpoint revision, dataset revision, hardware, decoding settings, preprocessing, metric implementation, and known limitations. Uploading a model to Hugging Face is not enough: attach a model card that states Urdu coverage, evaluation data, and whether the benchmark test set influenced training or prompt design.

If the model will support a public-service, education, health, or financial workflow in India, add human review and domain-specific testing before deployment. Benchmark scores are evidence—not permission to ship.

Frequently asked questions

Can I use my own Urdu dataset? Yes, but label it as an auxiliary evaluation. Do not present it as an IndicGenBench result unless it follows the official task and split.

Should I fine-tune before benchmarking? Only if the experiment is explicitly a fine-tuned setting. Keep zero-shot, adapted, and fine-tuned results in separate tables.

Why do my scores differ from a published result? Check dataset revisions, tokenizer versions, preprocessing, label mapping, generation settings, and whether the reported result used a different checkpoint revision.

How should I compare two Urdu models? Hold the dataset, prompt, metric, decoding settings, hardware constraints, and evaluation script constant. Then inspect per-example errors rather than ranking models by one number.

Apply for AI Grants India

If your Urdu evaluation supports an India-focused research or product initiative, explore AI Grants India for potential funding and ecosystem support.