How to Benchmark Telugu Translation on FLORES with Hugging Face

What you are measuring

A useful Telugu translation benchmark should answer more than “what is the BLEU score?” It should show how accurately a model translates, how consistently it handles Telugu morphology and word order, how quickly it runs, and where it fails. FLORES-200 is a strong starting point because it offers professionally translated, multilingual evaluation data with stable language identifiers, including Telugu (tel_Latn in FLORES conventions).

Use the benchmark to compare models under identical conditions—not to claim that one score represents all Telugu use cases. FLORES sentences are valuable for controlled comparison, but production systems should also be tested on domain data such as government services, education, health, customer support, or media. For a broader India-focused evaluation plan, see this practical framework for benchmarking multilingual LLMs in India.

Choose the task and dataset split

Define the direction before writing code:

Telugu to English: tel_Latn → eng_Latn
English to Telugu: eng_Latn → tel_Latn
Bidirectional evaluation: run both directions separately and report them separately

Do not mix development and test data. Use the FLORES dev split for debugging and model selection, then reserve devtest for the final report. Record the dataset revision, language codes, model revision, decoding settings, hardware, and software versions. These details matter because tokenizer, checkpoint, and library changes can affect results.

FLORES data access can vary by datasets version and configuration. Inspect the available configurations rather than assuming that the older flores loading pattern will work unchanged. A typical setup is:

pip install -U datasets transformers evaluate sacrebleu sentencepiece accelerate

Then inspect the dataset builder and select the configuration that exposes FLORES-200 language pairs or language-specific text columns. Keep the exact loading command in your benchmark repository so another team can reproduce it.

Load a translation model with Hugging Face

Select a model that explicitly supports Telugu and the required direction. A MarianMT checkpoint may be a useful baseline, while multilingual encoder-decoder models can provide stronger coverage across Indian languages. Do not assume that a model advertised as multilingual performs equally well in Telugu; verify its tokenizer, language codes, and model card.

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "Helsinki-NLP/opus-mt-te-en"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

For multilingual models, set the target language correctly. Depending on the architecture, this may involve a forced beginning-of-sentence token, a target-language prefix, or a model-specific prompt. Check the model documentation and test a few known examples before running the full benchmark.

If you are comparing several Indian-language systems, pair this experiment with benchmarking NLP models for Telugu and Sanskrit. That comparison can reveal whether a model’s apparent Telugu strength reflects general multilingual quality or language-specific optimisation.

Generate translations consistently

Generation settings must be identical across models. Start with deterministic decoding so the result is repeatable:

import torch
from transformers import set_seed

set_seed(42)
model.eval()
model.to("cuda" if torch.cuda.is_available() else "cpu")

def translate_batch(texts, batch_size=16):
    device = next(model.parameters()).device
    outputs = []
    for start in range(0, len(texts), batch_size):
        batch = texts[start:start + batch_size]
        encoded = tokenizer(
            batch,
            return_tensors="pt",
            padding=True,
            truncation=True,
        ).to(device)
        with torch.inference_mode():
            generated = model.generate(
                **encoded,
                num_beams=5,
                max_new_tokens=256,
            )
        outputs.extend(tokenizer.batch_decode(generated, skip_special_tokens=True))
    return outputs

Keep source sentences, references, predictions, and metadata in a structured file such as JSONL or Parquet. Validate that every prediction aligns with exactly one reference. Empty outputs, duplicated sentences, truncation, and accidental source copying should be reported rather than silently removed.

Measure throughput and latency separately from quality. Record total wall-clock time, examples per second, peak GPU memory, batch size, and whether compilation or quantisation was enabled. A model with a marginally higher score may not be the right choice for a low-latency public service.

Score with SacreBLEU, chrF, and COMET

Use standardised metric implementations rather than hand-written BLEU code. SacreBLEU records the metric signature and applies consistent tokenisation, making comparisons easier. chrF is particularly useful for Telugu because character n-grams can capture partial matches despite inflection, spelling variation, and different word segmentation choices.

import evaluate

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

predictions = translate_batch(source_texts)
references = [[ref] for ref in reference_texts]

bleu_result = bleu.compute(
    predictions=predictions,
    references=references,
)
chrf_result = chrf.compute(
    predictions=predictions,
    references=reference_texts,
)

print({"BLEU": bleu_result["score"], "chrF": chrf_result["score"]})

For semantic adequacy, add COMET or another learned metric, but pin its model version and document the compute requirements. Learned metrics can be informative when wording differs from the reference, yet they are not a substitute for human review. Report confidence intervals where possible, using bootstrap resampling over sentences instead of presenting a score with false precision.

Analyse Telugu-specific errors

A score alone will not tell you whether a system is safe to deploy. Sample errors by category and direction:

Named entities: people, locations, organisations, schemes, and product names
Numbers and units: dates, currency, percentages, measurements, and phone numbers
Honorifics and register: formal, neutral, and conversational Telugu
Morphology: case markers, tense, aspect, agreement, and compound words
Word order: Telugu’s flexible ordering and long-distance dependencies
Ambiguity: omitted subjects, pronouns, and context-dependent meanings
Script and punctuation: Telugu Unicode normalisation, mixed English, and punctuation handling

Create a small human evaluation sheet with adequacy, fluency, terminology, and harmful-error flags. Two Telugu-fluent reviewers should independently rate a stratified sample, then reconcile disagreements. Include examples from Indian public-sector and consumer contexts rather than relying only on generic FLORES sentences. For specialist workflows, compare your system against high-precision AI translation approaches for linguistics professionals.

Publish a reproducible benchmark report

A credible report should include:

Model name, checkpoint revision, tokenizer, and language direction
FLORES configuration, split, dataset revision, and sentence count
Decoding parameters, batch size, hardware, and runtime
SacreBLEU, chrF, and any learned metric with versions
Results by sentence length or category where available
Human evaluation protocol and representative failure cases
Known limitations, including domain mismatch and reference bias

Avoid comparing scores copied from different papers unless their data, preprocessing, and metric signatures match. If your goal is deployment, add in-domain tests and monitor quality after release. Teams building products can also review AI translation platforms for Indian regional languages to understand operational concerns such as terminology control, fallback handling, and evaluation beyond a single benchmark.

FAQ

Is FLORES enough to evaluate a Telugu translation system?
No. FLORES is an excellent controlled benchmark, but it should be supplemented with representative in-domain data and human review.

Should I report BLEU or chrF?
Report both where possible. BLEU supports historical comparison, while chrF is often more informative for morphologically rich languages and spelling variation.

Can I use Hugging Face pipelines for benchmarking?
Yes, for quick checks. For a serious benchmark, explicit batching and generation code gives better control over decoding, timing, alignment, and reproducibility.

How should I compare two models?
Run both on the identical split with identical preprocessing and decoding settings, then compare metric differences, bootstrap intervals, runtime, and human-rated errors—not just the highest headline score.

Apply for AI Grants India