Punjabi evaluation needs more than loading a checkpoint and reporting one score. Script variation, spelling differences, code-mixing, regional usage, and uneven training data can all affect results. A useful benchmark run should therefore make the model, dataset version, prompt format, decoding settings, metrics, and hardware traceable.
This guide explains how to use Hugging Face to benchmark Punjabi on IndicGenBench. It is written for researchers and product teams evaluating open models, adapting Indic-language systems, or building Punjabi applications for India. For broader context, compare this workflow with the Indian language LLM benchmark datasets guide and the practical framework for benchmarking multilingual LLMs in India.
What you are actually measuring
IndicGenBench-style evaluation may include several generation tasks rather than a single classification test. Depending on the benchmark release, tasks can cover translation, summarisation, question answering, instruction following, and culturally or linguistically grounded generation. Confirm the current repository documentation and task configuration before writing a results table; dataset names, split names, and evaluation commands can change.
For Punjabi, document at least:
- Language and script: Gurmukhi Punjabi, Shahmukhi Punjabi, transliterated Punjabi, or a mixed setting.
- Task definition: input format, expected output, context window, and whether references are available.
- Model class: causal language model, encoder-decoder model, or task-specific encoder.
- Evaluation mode: zero-shot, few-shot, instruction-tuned, or fine-tuned.
- Data provenance: benchmark version, split, licence, and any filtering applied.
Do not compare scores produced under different scripts, prompts, or decoding settings as if they were equivalent. If your project also evaluates Telugu or Sanskrit, the same discipline applies to the benchmarking workflow for Telugu and Sanskrit.
Set up a reproducible Hugging Face environment
Use a fresh virtual environment and record package versions. GPU inference is strongly recommended for larger models, but CPU evaluation is possible for small checkpoints or a reduced development split.
python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
pip install torch transformers datasets evaluate accelerate sentencepiece sacrebleu rouge-scoreClone the official IndicGenBench repository or install its documented package, rather than assuming that a dataset identifier exists on the Hugging Face Hub:
git clone <official-indicgenbench-repository-url>
cd IndicGenBench
pip install -r requirements.txtThe placeholder above is intentional: use the official repository URL and release instructions for the version you are evaluating. Before running a full benchmark, save the following in a manifest:
- Git commit or release tag
- Hugging Face model ID and revision
- Python, PyTorch, Transformers, and CUDA versions
- GPU model and available memory
- Dataset hashes or revision IDs
- Random seed and generation parameters
This turns a one-off score into an experiment another researcher can reproduce.
Choose and load a Punjabi-capable model
Search the Hugging Face Hub for models whose cards explicitly document Punjabi or broader Indic-language coverage. Do not assume that a model labelled “multilingual” handles Punjabi equally well. Check its tokenizer, training languages, licence, context length, and intended use.
For a causal model, a safe starting pattern is:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "YOUR_PUNJABI_OR_MULTILINGUAL_MODEL"
tokenizer = AutoTokenizer.from_pretrained(model_id, revision="main")
model = AutoModelForCausalLM.from_pretrained(
model_id,
revision="main",
torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
device_map="auto" if torch.cuda.is_available() else None,
)
model.eval()Remove the accidental leading space before tokenizer if you copy this into a script. In production experiments, pin a commit hash rather than main, and use trust_remote_code=True only after reviewing the model repository. For encoder-decoder checkpoints, load AutoModelForSeq2SeqLM instead.
Inspect tokenisation before evaluating. Punjabi text that is fragmented into many subwords can be slower and less reliable than text represented efficiently. Compare token counts for clean Gurmukhi, punctuation-heavy examples, numerals, English-Punjabi code-mixing, and normalised versus unnormalised Unicode.
Load and validate the benchmark data
Use the exact IndicGenBench command for the selected task and Punjabi configuration. If the benchmark is supplied as local files, load it with datasets and preserve the original fields:
from datasets import load_dataset
# Replace with the dataset ID, configuration, and split documented by the release.
data = load_dataset("DATASET_ID", "punjabi", split="test")
print(data.column_names)
print(data[0])Before inference, validate that:
- Punjabi examples are actually in the requested split.
- Inputs and references are not empty or duplicated.
- Unicode is consistently normalised, preferably with an explicitly recorded policy.
- No training or development examples have leaked into the test set.
- Long contexts are not silently truncated.
- Any instructions, demonstrations, or metadata match the benchmark specification.
Keep the original test set immutable. Create a separate derived file for cleaned or transformed inputs, and report every transformation. This is especially important when evaluating models intended for public-facing Indian-language products.
Run generation with fixed settings
Generation settings can move scores substantially. Fix the prompt template, maximum new tokens, temperature, top-p, beam configuration, stop strings, and batch size where relevant. For deterministic comparison, use greedy decoding or a documented beam setup.
from transformers import pipeline
generator = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
batch_size=4,
)
prompt = "Translate the following text into Punjabi:\nEnglish: ...\nPunjabi:"
result = generator(
prompt,
max_new_tokens=128,
do_sample=False,
return_full_text=False,
)
print(result[0]["generated_text"])For a formal benchmark, prefer the benchmark’s own runner when one is provided. It may implement task-specific prompting, post-processing, language filtering, and metric calculation that a generic pipeline does not. Save raw predictions, prompts, timestamps, and failures—not only aggregate scores.
Select metrics that fit Punjabi generation
Metric choice must follow the task. BLEU and chrF can be useful for translation, while ROUGE may help summarisation; exact match and token-level F1 are more appropriate for some question-answering formats. For open-ended generation, automatic metrics alone are insufficient because valid Punjabi answers can differ from the reference wording.
Report:
- The primary metric required by IndicGenBench.
- At least one complementary metric, where meaningful.
- Number of evaluated examples and failed generations.
- Mean, median, and, where possible, confidence intervals from bootstrap resampling.
- Separate results by task, script, domain, and code-mixing condition.
Use the same normalisation policy for predictions and references, but never hide the raw outputs. Unicode normalisation, punctuation stripping, whitespace handling, and transliteration can improve apparent scores while removing meaningful errors. For multilingual evaluation, consult guidance on benchmarking LLM performance on Indian legal text when domain-specific terminology affects correctness.
Perform Punjabi-specific error analysis
A score tells you where to investigate, not what to fix. Sample errors by task and categorise them manually:
- Gurmukhi spelling and Unicode inconsistencies
- Incorrect gender, number, tense, or honorific agreement
- Hindi, Urdu, or English substitutions
- Named entities, dates, numerals, and technical terms
- Hallucinated facts or omitted clauses
- Literal translations that lose Punjabi meaning
- Unsafe, offensive, or culturally inappropriate completions
Create a small adjudicated set with at least two fluent Punjabi reviewers for high-impact use cases. Record whether disagreements arise from the reference itself, model output, or ambiguous phrasing. For speech products, pair text evaluation with the workflow for benchmarking speech-to-text accuracy in India.
Compare models without misleading yourself
Run every candidate against the same frozen data and configuration. Include a strong multilingual baseline, a Punjabi-focused checkpoint where available, and a simple prompting baseline. Avoid ranking models on a single aggregate number when task mixes differ.
A useful results table includes model revision, parameter scale, licence, prompt regime, Punjabi script, task scores, latency, peak memory, and failure rate. Also measure cost per 1,000 examples if the benchmark informs a production decision.
If performance is weak, test interventions separately: better prompting, retrieval, continued pre-training, supervised fine-tuning, or a Punjabi-specific tokenizer. Do not fine-tune on the test set, and do not report tuned results beside zero-shot results without labelling the distinction.
A practical reporting checklist
Before publishing or using the result internally, confirm that you have:
- Pinned model and dataset revisions
- Preserved raw predictions and prompts
- Reported Punjabi script and normalisation choices
- Used the official task definitions and splits
- Documented decoding and hardware settings
- Included uncertainty or per-task variation
- Reviewed representative errors with Punjabi speakers
- Checked licence, privacy, and data-use constraints
A careful Hugging Face and IndicGenBench workflow makes Punjabi evaluation more credible and more actionable. It helps teams distinguish tokenizer problems from data problems, benchmark gains from prompt effects, and leaderboard improvements from real progress for Punjabi users.