Sara-1990/distilbert-lora-sst2

DistilBERT fine-tuned on SST-2 with LoRA

Fine-tuned distilbert-base-uncased for binary sentiment classification using parameter-efficient LoRA (r=8) via the PEFT library.

Accuracy: ~92% on SST-2 validation set
Trainable parameters: ~0.8% of total

Usage

from transformers import pipeline classifier = pipeline("text-classification", model="Sara-1990/distilbert-lora-sst2") classifier("The paper results are surprisingly strong.")

Training details

• Base model: distilbert-base-uncased
• LoRA rank: 8, alpha: 16
• Epochs: 3, batch size: 16
• Hardware: [Apple MacBook Air CPU]

Links

• GitHub repo