How to Benchmark Punjabi Translation on FLORES with Hugging Face

Punjabi translation benchmarks need more than a single BLEU score. Punjabi is written primarily in Gurmukhi in India and Shahmukhi in Pakistan, while many real deployments must also handle code-switching, named entities, informal spelling, and domain-specific vocabulary. A credible benchmark should therefore make the language direction, script, model version, decoding settings, metrics, and evaluation data explicit.

This guide shows how to benchmark Punjabi translation on FLORES using Hugging Face in a way that is reproducible and useful for production decisions. The same workflow can support English–Punjabi, Punjabi–English, or comparisons across Indian-language systems. For broader dataset selection, see this Indian language LLM benchmark datasets guide.

Define the benchmark before writing code

Start with a short evaluation specification. Record:

Direction: English to Punjabi (eng_Latn → pan_Guru) or Punjabi to English (pan_Guru → eng_Latn).
Script: Gurmukhi for Indian Punjabi; do not silently mix Gurmukhi and Shahmukhi references.
Model scope: pretrained, fine-tuned, or instruction-tuned systems.
Dataset split: FLORES-200 dev or devtest, with the exact revision pinned.
Hardware and decoding: GPU type, batch size, beam count, length penalty, and maximum input length.
Output policy: whether Unicode normalization, punctuation cleanup, or transliteration is applied.

FLORES is valuable because it provides professionally translated, multilingual evaluation sentences with consistent content across languages. It is not a complete proxy for conversational Punjabi, social-media language, government forms, or speech transcripts. Treat it as a controlled test set, then add a small, permissioned in-domain set before making deployment claims.

Set up a reproducible Hugging Face environment

Use a fresh virtual environment and pin the main packages. The current Hugging Face workflow generally uses evaluate rather than the older datasets.load_metric API.

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

Load FLORES-200 with the language configuration used by your installed dataset version. Configuration names may change, so inspect available configurations if loading fails.

from datasets import load_dataset

flores = load_dataset("facebook/flores", "eng_Latn-pan_Guru")
print(flores)
print(flores["devtest"][0])

Some repositories expose separate source and target language configurations. Confirm the schema rather than assuming fields such as sentence or translation. Save the dataset revision and a checksum in your experiment log.

Choose and load a Punjabi-capable model

A model must support the exact direction and script you are testing. MarianMT checkpoints can be useful baselines, but coverage varies. NLLB-family models are often a stronger multilingual comparison point when the required FLORES language codes are supported. Always inspect the model card for training data, licences, language coverage, and known limitations.

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "facebook/nllb-200-distilled-600M"
tokenizer = AutoTokenizer.from_pretrained(model_id, src_lang="eng_Latn")
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
model.eval()

For English-to-Punjabi generation, force the target language token where the model requires it:

import torch

def translate_batch(texts):
    inputs = tokenizer(
        texts, return_tensors="pt", padding=True, truncation=True,
        max_length=512
    )
    with torch.inference_mode():
        outputs = model.generate(
            **inputs,
            forced_bos_token_id=tokenizer.convert_tokens_to_ids("pan_Guru"),
            num_beams=5,
            max_new_tokens=256,
        )
    return tokenizer.batch_decode(outputs, skip_special_tokens=True)

Use batched inference, not one sentence at a time. It is faster, makes throughput measurable, and reduces accidental differences between runs.

Prepare FLORES references correctly

Inspect one record and map the fields explicitly. A common structure contains language-specific columns, but the exact layout depends on the dataset release.

split = flores["devtest"]
source = split["sentence_eng_Latn"]
reference = split["sentence_pan_Guru"]
predictions = translate_batch(source)

If your loaded dataset uses a different schema, identify the relevant columns with split.column_names. Do not evaluate against a mismatched language, script, or split. Keep source sentences and references in a versioned JSONL file so another researcher can reproduce the run.

Before scoring, check for:

Empty predictions or truncated outputs.
Accidental Latin-script Punjabi.
Copying of the English source.
Repeated phrases and hallucinated content.
Invalid Unicode or unexpected control characters.

Avoid aggressive normalization. Punjabi punctuation and spacing can affect metrics, and a preprocessing rule that improves one score may hide real errors. Report the exact normalization script alongside results.

Score with complementary metrics

No automatic metric captures Punjabi translation quality alone. Use at least one surface-overlap metric and one semantic metric, then add human review.

import evaluate

sacrebleu = evaluate.load("sacrebleu")
chrf = evaluate.load("chrf")

bleu_result = sacrebleu.compute(
    predictions=predictions,
    references=[[x] for x in reference]
)
chrf_result = chrf.compute(
    predictions=predictions,
    references=reference
)

print({"sacrebleu": bleu_result["score"], "chrf": chrf_result["score"]})

Report SacreBLEU, chrF++, and, where practical, a calibrated metric such as COMET or a multilingual quality estimator. BLEU is sensitive to tokenisation and exact wording; chrF is often more informative for morphologically rich or orthographically variable outputs. A higher score is not automatically better if the system changes meaning, drops negation, or mishandles names.

For a broader comparison methodology, use this practical framework for benchmarking multilingual LLMs in India. If your project compares Punjabi with Telugu or Sanskrit, the NLP benchmarking guide for Telugu and Sanskrit provides a useful cross-language structure.

Add human evaluation for Punjabi-specific failures

Sample outputs from the full test set using a fixed random seed, and ask at least two qualified reviewers to rate them. Reviewers should understand Punjabi and the source language; for Indian deployments, specify whether Gurmukhi conventions and regional usage are acceptable.

Use a 1–5 scale for:

Adequacy: Is the source meaning preserved?
Fluency: Is the Punjabi grammatical and natural?
Terminology: Are names, public-sector terms, and technical words handled correctly?
Script and spelling: Is Gurmukhi consistent and readable?
Safety: Are negation, medical advice, legal conditions, and quantities preserved?

Track inter-rater disagreement rather than averaging it away. Categorise errors such as omissions, additions, mistranslation, word-order problems, code-switching, and named-entity corruption. This error taxonomy is usually more actionable than a small metric difference.

Make results comparable and production-relevant

Publish a compact experiment table containing model ID, revision, FLORES split, language direction, decoding settings, preprocessing, scores, runtime, and hardware. Include confidence intervals where possible by bootstrap resampling sentences. Benchmark more than one checkpoint and run each configuration consistently.

FLORES should be the controlled baseline, not the entire launch gate. Add a separate evaluation set covering Indian government terminology, education, agriculture, health, customer support, and user-generated Punjabi. Obtain consent and remove personal information. If the product will support speech or video, pair this text benchmark with an evaluation of the full pipeline; relevant design considerations appear in AI translation platforms for Indian regional languages.

Common mistakes to avoid

Loading a generic flores configuration without verifying the language pair.
Using train as though FLORES were a conventional training corpus.
Reporting BLEU without its tokenisation and preprocessing details.
Comparing Gurmukhi output with Shahmukhi references.
Fine-tuning on devtest and then presenting the score as an unbiased benchmark.
Treating automatic scores as evidence of safety or factual correctness.
Failing to record model revisions, which makes later comparisons unreliable.

FAQ

Which FLORES split should I use?

Use devtest for the headline evaluation when you are not tuning on it. Use dev for development and keep it separate from final reporting.

Is BLEU enough for Punjabi?

No. Pair SacreBLEU with chrF or a semantic metric, and perform human review focused on meaning, script, terminology, and named entities.

Should I fine-tune on FLORES?

Usually not for a final benchmark. Fine-tuning on the evaluation material contaminates the result. Use separate training or in-domain data, then evaluate on untouched FLORES and a private test set.

What should an Indian AI team report?

Report language direction, script, dataset revision, model checkpoint, decoding parameters, metrics, human-evaluation protocol, latency, and representative error categories. This makes the benchmark useful for both research and procurement decisions.

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