---
base_model: bert-base-uncased
library_name: peft
tags:
  - base_model:adapter:bert-base-uncased
  - lora
  - classification
  - fine tuning
  - transfer learning
---

# 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
```