ALI-USER/bert-lora-newsgroups

Model Card for LoRA-finetuned BERT

This is a BERT-base-uncased model fine-tuned using LoRA (Low-Rank Adaptation) via PEFT. It is optimized for efficient adaptation to NLP tasks like text classification and named entity recognition with minimal extra parameters.

Model Details

• Developed by: Ali Assi
• Language(s): English
• Finetuned from: bert-base-uncased

Uses

• Direct Use: news classification
• Downstream Use: Transfer learning, NLP pipelines, domain adaptation

Getting Started

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel

# Load base model and tokenizer
base_model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = AutoModelForSequenceClassification.from_pretrained(base_model_name)

# Load LoRA adapter
lora_model = PeftModel.from_pretrained(model, "ALI-USER/bert-lora-newsgroups")

# Inference
text = "Hello world!"
inputs = tokenizer(text, return_tensors="pt")
outputs = lora_model(**inputs)
logits = outputs.logits