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Why Manglish benchmarking needs a dedicated protocol

Manglish—Malayalam written in Latin script, often mixed with English—is not adequately measured by a generic Malayalam or English benchmark. A single message may combine Malayalam transliteration, English words, emojis, abbreviations, code-switching, regional slang, and inconsistent spelling. For an Indian product, these details affect search, moderation, customer support, translation, speech interfaces, and chat quality.

A useful benchmark therefore measures more than an aggregate score. It should show which varieties the model handles, which tasks it supports, how much latency and memory it requires, and where it fails for real users. The workflow below is designed for models hosted on the Hugging Face Hub and can be adapted to classifiers, causal language models, encoder-decoder systems, and embedding models.

Define the task before choosing metrics

Start with a narrow evaluation contract. “Manglish performance” is not one task. Typical benchmark tracks include:

• Intent classification: route support messages, queries, or complaints to the correct category.
• Sentiment and emotion: identify positive, negative, neutral, frustrated, or mixed sentiment.
• Named-entity recognition: detect people, organisations, places, products, and dates.
• Text normalisation: convert noisy Latin-script Malayalam into a consistent representation or Malayalam script.
• Translation: translate Manglish to Malayalam, English, or both.
• Generation and question answering: assess factuality, relevance, safety, and instruction following.
• Embeddings and retrieval: test whether semantically similar Manglish messages are close in vector space.

Use a separate test set for every task. Accuracy can be acceptable for balanced intent labels, but it is misleading for imbalanced moderation or entity-extraction data. For classification, report macro-F1, per-class precision and recall, and a confusion matrix. For sequence labelling, use entity-level F1 rather than token accuracy. For translation or normalisation, combine automated scores with human ratings; BLEU or chrF can reveal lexical overlap, but neither proves that the output preserves meaning.

If your work covers several Indian languages, compare the protocol with guidance on benchmarking NLP models for Telugu and Sanskrit, while keeping Manglish-specific splits and annotations intact.

Build a representative, leakage-resistant dataset

A benchmark is only as credible as its test data. Collect examples from the product environment you intend to serve—such as public comments, opt-in support conversations, search queries, or synthetic prompts reviewed by native speakers. Do not publish personal information or scrape private messages. Remove phone numbers, email addresses, account IDs, and other identifiers before annotation.

Stratify the evaluation set by the factors that matter in practice:

• Malayalam-heavy, English-heavy, and balanced code-switching
• Latin transliteration styles, including phonetic and abbreviated spellings
• Formal, conversational, slang, and misspelled text
• Urban and non-urban usage, age groups, and relevant regional vocabulary
• Short messages, long messages, emojis, hashtags, punctuation, and Roman numerals
• Domain terms such as payments, healthcare, education, retail, or government services

Keep a locked test set that is never used for prompt selection, fine-tuning, or threshold tuning. Split by user, conversation, source, or time period—not only by random rows. Near-duplicates from the same conversation can otherwise appear in both training and testing, producing inflated scores. Run n-gram or embedding similarity checks to identify overlap with training data and document any contamination discovered.

For supervised tasks, use at least two annotators familiar with Malayalam and Manglish. Define spelling, code-switching, sarcasm, offensive language, and ambiguous cases in an annotation guide. Record agreement and adjudication decisions. A small, carefully labelled test set is more valuable than a large noisy one.

Set up a reproducible Hugging Face evaluation run

Install pinned dependencies and record the model revision, dataset revision, Python version, hardware, decoding parameters, and random seed. A minimal environment might include:

pip install transformers datasets evaluate accelerate scikit-learn sacrebleu

Load a Hub model and dataset without silently changing revisions:

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "org/model-name"
revision = "main"  # Prefer a commit hash for published results

tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
model = AutoModelForSequenceClassification.from_pretrained(model_id, revision=revision)
data = load_dataset("org/manglish-benchmark", revision=revision)

For causal or encoder-decoder models, use the correct model class and specify generation settings such as max_new_tokens, temperature, top-p, and number of beams. Evaluate in inference mode, batch inputs where possible, and measure throughput and peak memory alongside quality. A model that gains one F1 point but requires unsuitable GPU capacity may not be the right choice for an Indian startup’s deployment budget. For deployment considerations, see how to deploy large language models locally.

Choose metrics that expose real weaknesses

Report overall results and slices. Recommended measures include:

• Classification: macro-F1, weighted-F1, accuracy, calibration error, and per-class recall.
• Generation: exact match where appropriate, ROUGE, chrF, BERTScore, and human ratings for correctness, fluency, and meaning preservation.
• Translation or normalisation: chrF is often useful for spelling variation; inspect adequacy separately from surface-form similarity.
• Retrieval: recall@k, precision@k, mean reciprocal rank, and nDCG.
• Operations: tokens per second, median and p95 latency, peak RAM/VRAM, and cost per 1,000 requests.

For generative models, automated judges can help triage but should not be the sole evaluator—especially when Malayalam transliteration, cultural context, or safety is involved. Use blinded human review on a sample, report the rubric, and include confidence intervals or bootstrap intervals when comparing models. Small score differences may not be statistically or practically meaningful.

Run error analysis, not just leaderboard scoring

After scoring, examine false positives, false negatives, unsupported translations, hallucinated entities, and unsafe completions. Group errors by spelling pattern, code-switch ratio, domain, and message length. Check whether a model is merely copying English words, over-normalising regional expressions, or treating transliterated Malayalam as English noise.

Create a compact error taxonomy—for example, transliteration ambiguity, tokenisation failure, sarcasm, named-entity confusion, code-switch boundary, and cultural context. Include representative examples with sensitive content redacted. This makes the next data or fine-tuning iteration measurable rather than anecdotal. Work on related Indian-language model development can also inform transfer-learning choices; compare approaches in fine-tuning large language models for Sanskrit translation.

Publish a benchmark card teams can reproduce

A useful report should include the dataset licence and collection period, language definition, annotation process, splits, contamination checks, model commit, prompt or preprocessing code, hardware, decoding configuration, metrics, confidence intervals, and known limitations. Upload the benchmark dataset or evaluation harness to Hugging Face when licensing permits, and use a model or dataset card to state intended use and risks.

Do not present one number as proof of language competence. As of 2026, Manglish remains underrepresented in many public evaluations, so clearly label results as in-domain, out-of-domain, or human-reviewed. A transparent baseline—such as a multilingual encoder, a strong English model, and a Malayalam-capable model—helps readers understand whether improvements come from Manglish data, model size, or evaluation artefacts.

Practical checklist

Before publishing results, confirm that you have:

• Defined the task, users, and success threshold.
• Used a representative, consent-aware dataset with a locked test split.
• Checked duplicates and train-test contamination.
• Reported slice-level quality, not only an average score.
• Measured latency, memory, and cost on realistic hardware.
• Added human review for translation and generation.
• Released code, configuration, revisions, and limitations.

A disciplined benchmark turns Manglish from an informal edge case into an engineering target. It gives Indian builders evidence to choose, fine-tune, and deploy models responsibly—and makes future improvements comparable.