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ALI-USER/
bert-lora-newsgroups

google-bert/bert-base-uncased

adapter

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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

python

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