RoBERTa: BERT Done Right - When and How to Use It for Classification

RoBERTa improves on BERT through better pre-training - dynamic masking, no next-sentence prediction, larger batches, and more data - delivering consistent GLUE leaderboard advantages for classification tasks.

Mahmudul Haque Qudrati

CEO & ML Engineer

April 8, 2026

7 min read

// tags

#roberta#bert#text-classification#nlp#fine-tuning

FIG. ART-33

7 min read

“

RoBERTa: BERT Done Right - When and How to Use It for Classification

// reading plan

sections

320

words

min read

// LLMs & Language Models

Understanding Transformer Attention Mechanisms: Self-Attention vs Cross-Attention

A mathematical and visual walkthrough of multi-head attention, self-attention, and encoder-decoder cross-attention inside language models.

11 min read

// Machine Learning

ONNX: Export Any ML Model and Run It Anywhere

Multi-Label Classification

For multi-label tasks (a text belonging to multiple categories simultaneously), change the loss function:

import torch
from transformers import RobertaForSequenceClassification

model = RobertaForSequenceClassification.from_pretrained("roberta-base", num_labels=10, problem_type="multi_label_classification")
# Trainer handles BCEWithLogitsLoss automatically when labels are float tensors
# Labels should be float tensors: [0.0, 1.0, 0.0, 1.0, ...]

Few-Shot Classification With SetFit

For low-data scenarios (<50 labeled examples), SetFit outperforms RoBERTa fine-tuning by using contrastive sentence transformer training:

from setfit import SetFitModel, SetFitTrainer
from datasets import Dataset

train_data = Dataset.from_dict({
    "text": ["positive example 1", "negative example 1", ...],  # 8-16 examples per class
    "label": [1, 0, ...]
})

model = SetFitModel.from_pretrained("sentence-transformers/paraphrase-mpnet-base-v2")
trainer = SetFitTrainer(model=model, train_dataset=train_data)
trainer.train()

SetFit requires no GPU for fine-tuning with small datasets and reaches full RoBERTa fine-tune quality with 50x less data.

Production ONNX Export

Export fine-tuned RoBERTa to ONNX following the same pattern as DistilBERT. RoBERTa-base in ONNX on CPU achieves 15-25ms latency per request - acceptable for most classification use cases without GPU infrastructure.

RoBERTa: BERT Done Right - When and How to Use It for Classification

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RoBERTa: BERT Done Right - When and How to Use It for Classification

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What RoBERTa Fixed in BERT

Fine-Tuning in Under 30 Lines

Multi-Label Classification

Few-Shot Classification With SetFit

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The workspace your team
actually needs