Fine-tuning can improve a Tamil model—or simply make it better at memorising a narrow dataset. A credible benchmark separates those outcomes by evaluating the same model task, test set, metrics, and inference settings before and after training.
This guide presents a reproducible Hugging Face workflow for classification, token classification, generation, and translation projects. It also covers Tamil-specific issues such as Unicode normalisation, code-mixing, dialect variation, transliterated Tamil, and data leakage.
Define the benchmark before training
Start with a written evaluation plan. Record the base checkpoint, dataset version, task definition, label mapping, maximum sequence length, decoding settings, software versions, and hardware. Create a locked test set before fine-tuning; do not tune against it.
Use three splits where possible:
- Training set: used to update model weights.
- Validation set: used for hyperparameter selection and early stopping.
- Test set: opened only for the final before-and-after comparison.
Your test set should represent the production problem, not just clean literary Tamil. Include formal and conversational Tamil, spelling variation, punctuation, code-mixed Tamil-English, social-media text, and relevant regional or domain vocabulary. Keep near-duplicates and translated copies in one split to prevent inflated scores.
If your project involves adapting a larger model, review best practices for fine-tuning LLMs on custom data before preparing the training run.
Select a suitable Tamil checkpoint
Choose a model whose tokenizer and pretraining data are appropriate for Tamil. Multilingual checkpoints can be useful baselines, while Tamil-focused encoders or instruction-tuned models may provide stronger language coverage. Do not assume that a model with “Tamil” in its name supports your task head or tokenizer configuration.
Check:
- Tamil character coverage and tokenisation of representative sentences.
- Support for Tamil punctuation, numerals, pulli marks, and combining characters.
- Maximum context length and memory requirements.
- Licence, model-card limitations, and permitted commercial use.
- Whether the checkpoint is an encoder, causal language model, or sequence-to-sequence model.
Run a small tokenisation audit before benchmarking. Compare token counts for Tamil, Tamil-English code-mixed text, and transliterated Tamil. Excessive fragmentation can reduce performance even when the model appears multilingual.
Normalise Tamil data carefully
Unicode handling is a benchmark variable, not a minor preprocessing detail. Store the original text, then create a documented normalised version using a consistent Unicode form. Avoid aggressive cleaning that removes meaningful punctuation, emojis, hashtags, or spelling signals found in deployment data.
Apply exactly the same preprocessing before baseline evaluation and after fine-tuning. Keep separate fields for:
- Original text.
- Unicode-normalised text.
- Optional punctuation or whitespace cleanup.
- Language and script labels.
- Domain, source, dialect, and code-mixing metadata.
A useful benchmark reports results by slice, not only as one aggregate score. For example, compare formal Tamil, colloquial Tamil, code-mixed text, and transliterated inputs separately.
Benchmark the base model
Load the checkpoint and evaluate it on the locked test set using deterministic settings. For classification, use the same label mapping that will be used during training. For generation, record the prompt template, maximum output length, temperature, top-p, repetition penalty, and stopping rules.
A minimal Transformers evaluation pattern for classification looks like this:
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer
checkpoint = "your-org/your-tamil-checkpoint"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(
checkpoint,
num_labels=num_labels,
id2label=id2label,
label2id=label2id,
)
trainer = Trainer(
model=model,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
)
baseline_metrics = trainer.evaluate(test_dataset)
print(baseline_metrics)Save more than the final score. Export predictions, confidence scores or logits, example IDs, runtime, peak memory, and the exact commit or dataset hash. This makes it possible to investigate regressions rather than merely observe them.
For imbalanced labels, report macro-F1, weighted-F1, per-class precision and recall, and a confusion matrix. Accuracy alone can conceal poor performance on minority Tamil categories.
Fine-tune without contaminating the comparison
Fine-tune a fresh copy of the base checkpoint, not the already evaluated model after ad hoc experimentation. Fix random seeds where practical and run more than one seed for small datasets. Record learning rate, batch size, gradient accumulation, number of epochs, warm-up, weight decay, evaluation frequency, and checkpoint-selection rule.
Use the validation set for model selection. Do not repeatedly inspect test predictions and then change the training configuration; that turns the test set into another validation set.
Fine-tuning quality depends heavily on label consistency. Audit ambiguous Tamil examples, duplicate records, machine-translated text, and annotator disagreement. A smaller, carefully reviewed dataset often provides a more trustworthy improvement than a larger noisy one.
Evaluate the fine-tuned model identically
Reload the saved best checkpoint and run the same evaluation function on the unchanged test set. Keep preprocessing, batch size where relevant, padding strategy, label order, and metric implementation constant.
Calculate absolute and relative changes:
- Absolute gain: fine-tuned score minus baseline score.
- Relative gain: absolute gain divided by the baseline score.
- Slice gain: improvement for each domain, dialect, script, or text-length group.
- Cost change: latency, throughput, memory, and model size difference.
For generation, automatic metrics are not enough. Use task-appropriate measures such as chrF or sacreBLEU for translation, ROUGE for summarisation, and exact match or token-level F1 for structured extraction. Add human review for fluency, faithfulness, Tamil grammaticality, unwanted English insertion, and harmful or culturally inappropriate outputs. When evaluating translation, see how the same methodology applies to fine-tuning large language models for Sanskrit translation, while adapting the language-specific test design.
Analyse errors, not just score changes
Create a before-and-after error table with the input, reference label or answer, both predictions, confidence, and error category. Prioritise examples where the fine-tuned model regressed.
Common Tamil failure modes include:
- Confusing related characters or mishandling Unicode combinations.
- Losing meaning in colloquial or dialect-specific expressions.
- Treating Tamil-English code-mixing as noise.
- Overfitting to source-specific vocabulary or annotation style.
- Hallucinating when the prompt contains unfamiliar names or numerals.
- Improving the majority class while weakening minority classes.
Use bootstrap confidence intervals or paired significance tests when the dataset is large enough. A two-point F1 increase may not be meaningful on a small test set. Report the number of examples and uncertainty alongside every headline result.
Publish a useful benchmark report
A strong report should include the model identifiers, dataset licence and version, split counts, preprocessing rules, training configuration, evaluation code, metrics, confidence intervals, slice results, and known limitations. Publish predictions only when privacy and licensing permit it; redact personal information from Tamil social or customer data.
If the final model must run on a phone or low-cost server, measure deployment performance after fine-tuning. Quantisation and other optimisation steps can affect accuracy, latency, and memory, so use the same Tamil regression suite when applying AI model optimisation for mobile devices. For local inference, compare the benchmarked checkpoint with the deployment artefact rather than assuming they behave identically; the guide to deploying large language models locally is a useful next step.
Practical decision rule
Ship the fine-tuned model only when it improves the target metric on the locked test set, does not create unacceptable regressions in important slices, and meets latency and memory limits. If aggregate performance improves but code-mixed, dialectal, or minority-class results fall, retrain with better coverage or use a targeted evaluation gate.
Benchmarking is most valuable when it becomes a repeatable regression suite. Version the test set, run it on every new checkpoint, and track both quality and operating cost. That gives Tamil AI teams evidence they can trust rather than a single optimistic score.