Fine-tuning can improve a Bengali model on a target task, but a higher score alone does not prove that the model became more useful. A sound benchmark must compare the same model, data, preprocessing, and evaluation protocol before and after training. It should also reveal whether gains hold across Bengali scripts, dialects, domains, and challenging examples.
This guide presents a reproducible workflow with Hugging Face Transformers and Datasets. It is designed for classification, token classification, question answering, and generation projects, with practical safeguards for Bengali text.
Define the benchmark before training
Start by writing down the task, success metric, evaluation split, and acceptable trade-offs. Do not choose metrics after seeing the results.
For example:
- Text classification: macro-F1, per-class recall, accuracy, and confusion matrix.
- Named entity recognition: entity-level precision, recall, and F1 rather than token accuracy alone.
- Question answering: exact match and token-level F1, with Bengali normalization documented.
- Translation or generation: chrF, BLEU, COMET where appropriate, plus human review for meaning and fluency.
- Summarisation: ROUGE alongside factuality and omission checks.
Bengali evaluation needs additional care because spelling variants, punctuation, Unicode composition, transliterated Bengali, and code-mixed Bengali-English text can affect exact-match metrics. Decide whether normalization will be applied, and use the identical normalization function for both checkpoints.
If the project involves adapting a multilingual model to regional-language data, review best practices for fine-tuning LLMs on custom data before preparing the experiment.
Build a reliable Bengali evaluation set
Use three separate splits:
- Training set: used only for parameter updates.
- Validation set: used for model selection and hyperparameter decisions.
- Test set: held back until the final before-and-after comparison.
The test set should represent actual use, not merely the training distribution. Include formal Bengali, conversational text, social-media spelling, news or government language, code-mixed inputs, short queries, long passages, and difficult examples containing names and loanwords.
Prevent leakage aggressively. Remove duplicate or near-duplicate texts across splits, ensure multiple versions of the same document stay in one split, and avoid evaluating on data used to create prompts or labels. If the model was pretrained on a public corpus that overlaps with your test data, record that limitation rather than presenting the score as a clean generalisation result.
Keep a small challenge set separate from the main test set. It can target negation, honorifics, dates, numbers, rare Bengali words, dialectal variation, and ambiguous entities. This often explains practical changes that aggregate scores conceal.
Load the base model and record the baseline
Use a task-specific checkpoint and tokenizer from the Hugging Face Hub. Verify that the tokenizer handles Bengali text correctly and that padding, truncation, maximum length, and label mappings are fixed before evaluation.
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_id = "your-org/your-bengali-checkpoint"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(
model_id,
num_labels=num_labels,
id2label=id2label,
label2id=label2id,
)For a fair baseline, evaluate the untouched checkpoint with the same test loader and metric code that will be used after fine-tuning. Save more than the headline score:
- model revision or commit hash;
- Transformers, Datasets, PyTorch, and Python versions;
- tokenizer configuration and preprocessing code;
- hardware and inference precision;
- random seed and batch size;
- latency, throughput, and peak memory if deployment matters.
With Trainer, supply a compute_metrics function and evaluate before training. For production-sensitive work, also measure performance on CPU or the target accelerator. A fine-tuned model that gains one F1 point but doubles latency may not be an improvement.
Fine-tune under controlled conditions
Change only the variables required by the experiment. Keep the base checkpoint, data splits, tokenizer, sequence length, metric implementation, and evaluation batch size fixed. Set seeds, log the complete training configuration, and save the best checkpoint according to the validation metric.
from transformers import TrainingArguments, Trainer
args = TrainingArguments(
output_dir="./bengali-run",
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="f1",
greater_is_better=True,
learning_rate=2e-5,
num_train_epochs=3,
per_device_train_batch_size=16,
per_device_eval_batch_size=32,
weight_decay=0.01,
seed=42,
report_to="tensorboard",
)
trainer = Trainer(
model=model,
args=args,
train_dataset=train_dataset,
eval_dataset=validation_dataset,
processing_class=tokenizer,
compute_metrics=compute_metrics,
)
trainer.train()For limited hardware, parameter-efficient methods such as LoRA can reduce memory requirements, but compare them with full fine-tuning under a clearly stated budget. If you are adapting an instruction model rather than an encoder classifier, fine-tuning Llama for Indian regional languages offers useful context on data quality and language-specific adaptation.
Run the identical post-training evaluation
Evaluate the selected fine-tuned checkpoint once on the untouched test set. Do not tune thresholds, edit labels, or change preprocessing after looking at test results. Store baseline and final predictions with stable example IDs so every change can be traced.
A simple result table should include:
| Measure | Before | After | Difference |
|---|---:|---:|---:|
| Macro-F1 | | | |
| Accuracy | | | |
| Bengali challenge-set F1 | | | |
| Inference latency | | | |
| Peak memory | | | |
Report absolute differences as well as relative percentage changes. For imbalanced Bengali datasets, macro-F1 and per-class recall are usually more informative than accuracy. Include confidence intervals using bootstrap resampling, or use paired tests based on the same examples. A small score increase may be noise, especially with a small test set.
Analyse errors, not just scores
Export examples where the prediction changed between checkpoints. Group them into useful categories:
- Bengali spelling and Unicode variants;
- code-mixing and transliteration;
- negation, politeness, and tense;
- named entities, numbers, and dates;
- long-context truncation;
- ambiguous or incomplete queries;
- harmful, biased, or culturally inappropriate outputs.
Inspect both recovered errors and new regressions. Fine-tuning can improve the target domain while damaging general Bengali ability or performance on minority classes. Compare results by document length, source, class, dialectal features, and confidence band. For generative models, sample outputs using fixed prompts and assess factuality, instruction following, repetition, and preservation of Bengali meaning.
Make the benchmark reproducible
Publish or retain the evaluation script, dataset version, label definitions, normalization rules, and model hashes. Use a model card to document intended use, known limitations, Bengali varieties covered, and whether the data contains code-mixed or synthetic text. Track experiments in TensorBoard or an equivalent system, but do not treat a training curve as a substitute for a held-out test.
If the model will run on a phone, edge device, or low-cost Indian cloud instance, add quantised inference measurements; AI model optimisation for mobile devices covers the deployment trade-offs that benchmark reports often omit. For larger deployments, compare the fine-tuned model with the cost and latency of deploying large language models locally.
A practical decision rule
Ship the fine-tuned model only when it shows a meaningful, statistically credible gain on the primary task, improves or preserves the Bengali challenge set, and meets latency, memory, safety, and robustness requirements. If the main score rises while rare-class recall or code-mixed performance falls, treat the result as a trade-off—not a clear success.
Benchmarking is most valuable when it changes a decision. A disciplined before-and-after comparison tells you whether the data, training method, and checkpoint are genuinely improving Bengali performance, and where the next investment should go.