How to Benchmark a Hindi Model Before and After Fine-Tuning

Hindi model fine-tuning should end with evidence, not intuition. A model that produces more fluent Hindi may still become less accurate, less robust to code-mixing, or more expensive to run. This guide shows how to benchmark a Hindi model before and after fine-tuning on Hugging Face using a fixed evaluation set, task-appropriate metrics, reproducible configurations, and qualitative error analysis.

The workflow applies to encoder models used for classification and retrieval, causal language models used for generation, and instruction-tuned systems. It is especially relevant for Indian-language builders working with limited labelled data, Devanagari variation, Hinglish, and domain-specific terminology.

Define the comparison before training

Write down the claim you want to test. For example: “Fine-tuning improves Hindi intent classification on customer-support queries without reducing performance on general Hindi.” This determines the datasets and metrics you need.

Keep these conditions identical between the baseline and fine-tuned runs:

Test examples and labels
Tokenisation and text normalisation
Prompt or input template
Decoding parameters such as temperature, top-p, and maximum tokens
Hardware and evaluation batch size where latency is measured
Metric implementation and aggregation method

Create three data splits: training, validation, and a locked test set. Do not tune hyperparameters against the test set. If your data comes from users, split by customer, document, or conversation—not only by row—to reduce near-duplicate leakage.

For a broader Hindi model selection exercise, compare your candidate with open-source small language models for Hindi, but keep the same test protocol for every model.

Build a Hindi-specific evaluation set

A large corpus is not automatically a useful benchmark. Your test set should reflect the traffic the model will see in production. Include examples across:

Formal and conversational Hindi
Devanagari, Romanised Hindi, and Hinglish where relevant
Regional vocabulary and spelling variation
Short queries, long documents, and noisy user text
Named entities, numbers, dates, currency, and mixed English terms
The domains in which the model will be deployed

For generation tasks, use prompts that represent real product behaviour rather than isolated textbook sentences. For classification, preserve the natural class distribution but also report macro-F1 so minority classes are visible. Consider adding a small challenge set containing ambiguity, negation, code-switching, and rare words.

Document dataset provenance, licensing, annotator instructions, label definitions, and disagreements. Never place test answers in prompts, training files, retrieval indexes, or system instructions.

Install a reproducible Hugging Face stack

A typical environment in 2026 includes transformers, datasets, evaluate, accelerate, and task-specific libraries:

pip install -U transformers datasets evaluate accelerate scikit-learn sacrebleu rouge-score

Pin package versions in requirements.txt or a lockfile. Record the model revision, tokenizer revision, dataset revision, random seed, GPU type, precision, and evaluation command. Hugging Face Hub model cards and dataset cards are useful places to publish this metadata.

For fine-tuning decisions, also follow the data-quality and validation practices in best practices for fine-tuning LLMs on custom data. Benchmarking cannot rescue contaminated or poorly labelled training data.

Measure the baseline correctly

Use the original pretrained or instruction-tuned checkpoint before changing its weights. For a sequence-classification model, use AutoModelForSequenceClassification; AutoModel alone does not provide a task head or an evaluate() method.

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer

checkpoint = "your-org/your-hindi-checkpoint"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)

def tokenize(batch):
    return tokenizer(batch["text"], truncation=True, max_length=256)

data = load_dataset("your-org/hindi-eval")
tokenized = data.map(tokenize, batched=True)

trainer = Trainer(model=model, tokenizer=tokenizer)
baseline = trainer.predict(tokenized["test"])
print(baseline.metrics)

For a causal model, calculate perplexity only on the intended language-modelling text and mask padding correctly. Perplexity is not a substitute for task accuracy: it can improve while instruction following or factuality worsens.

Save baseline predictions, not only aggregate scores. They are essential for paired error analysis after fine-tuning.

Select metrics that match the task

Use multiple metrics when a single score can hide regressions:

Classification: accuracy, macro-F1, weighted-F1, per-class recall, and a confusion matrix.
Named-entity recognition: entity-level precision, recall, and F1, with exact span matching.
Retrieval: recall@k, precision@k, and mean reciprocal rank; report Hindi and Hinglish queries separately.
Translation: SacreBLEU and chrF, plus human review for adequacy and fluency. chrF is often useful when morphology and spelling variation matter.
Summarisation: ROUGE as a reference signal, supplemented by factuality, coverage, and human preference checks.
Generation: exact-match or structured-field accuracy where possible, refusal or safety rates, repetition rate, and human-rated usefulness.
Operations: latency, throughput, peak memory, model size, and cost per request.

For imbalanced Hindi datasets, macro-F1 and per-class results should be first-class outputs. Report confidence intervals or bootstrap intervals when the test set is small. A two-point increase may not be meaningful if it lies within sampling noise.

Fine-tune without changing the experiment

Fine-tune a copy of the baseline checkpoint and retain the exact preprocessing pipeline. Log learning rate, batch size, gradient accumulation, number of steps, sequence length, warm-up, optimiser, seed, and stopping criterion. Evaluate on validation data during training, but run the final comparison once on the locked test set.

If compute is limited, parameter-efficient methods such as LoRA or QLoRA can reduce memory requirements. Compare them against the same baseline and disclose quantisation settings. If you are adapting a multilingual model to Hindi or other Indian languages, review the trade-offs discussed in fine-tuning Llama for Indian regional languages.

Avoid selecting the checkpoint with the best test score. Select using validation performance, then evaluate the chosen checkpoint on the untouched test set.

Run a paired before-and-after evaluation

Use identical examples and align predictions by a stable example ID. Produce a table containing:

| Measure | Baseline | Fine-tuned | Change |
|---|---:|---:|---:|
| Accuracy or macro-F1 | | | |
| Minority-class recall | | | |
| Challenge-set score | | | |
| Latency per request | | | |
| Peak memory | | | |

Calculate change = fine_tuned - baseline, but interpret it alongside confidence intervals and per-category results. A higher overall score is not sufficient if performance falls sharply on Romanised Hindi, rare intents, or safety-critical examples.

For generative models, use deterministic decoding for the primary comparison, then test a fixed set of production decoding settings. Store prompts, outputs, model revision, and decoding parameters so another engineer can reproduce the result.

Analyse errors, not just scores

Create four buckets: fixed errors, new regressions, unchanged errors, and ambiguous labels. Inspect examples from each bucket with a Hindi-speaking reviewer. Look for:

Devanagari spelling and normalisation failures
Confusion between closely related intents
Incorrect handling of negation or politeness
Hallucinated names, dates, prices, or locations
Memorisation of training phrases
Unwanted translation into English
Repetition, truncation, or unsafe completions

Slice results by script, length, domain, region, and class. This often reveals that an apparent improvement comes from one dominant slice. For deployment on phones or edge devices, measure the same model after compression using an AI model optimisation for mobile devices workflow.

Define a release gate

Set acceptance criteria before reviewing the results. A practical gate might require:

At least a specified macro-F1 improvement on the target task
No more than a defined regression on the general Hindi challenge set
Minimum recall for critical classes
No increase in unsafe or fabricated outputs
Latency and memory within the product budget
Reproducible results across at least two seeds when feasible

Publish a short benchmark report with dataset versions, scripts, hardware, scores, confidence intervals, known limitations, and representative examples. Treat the benchmark as a living regression suite: add production failures only after removing personal data and documenting their provenance.

Common mistakes to avoid

Calling model.evaluate() on a raw Transformers model
Comparing different test sets or tokenisation rules
Reporting only accuracy on an imbalanced dataset
Fine-tuning on examples copied into evaluation prompts
Using BLEU or ROUGE as a complete measure of quality
Ignoring Romanised Hindi and code-mixed inputs
Relying on one random seed or one aggregate score
Publishing results without model and dataset revisions

A disciplined before-and-after benchmark turns fine-tuning from a subjective demo into an auditable engineering decision. For Hindi systems, the strongest evaluation combines reproducible Hugging Face scripts, task-specific metrics, script and domain slices, human review, and operational measurements. That evidence tells you not only whether the model improved, but where, why, and whether it is ready for deployment.

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