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Harsh-Gupta/Sentiment-Analysis-BERT-sst2

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🤖 LoRA-BERT for Sentiment Analysis (SST-2)

This is a lightweight, parameter-efficient BERT model fine-tuned with LoRA (Low-Rank Adaptation) for binary sentiment classification on the SST-2 dataset.

💡 Model Highlights

—✅ Fine-tuned using LoRA (r=8, α=16) on top of bert-base-uncased
—✅ Trained on SST2
—✅ Achieves ~91.17% validation accuracy
—✅ Lightweight: only LoRA adapter weights are updated

📊 Results

Epoch	Training Loss	Validation Loss	Accuracy
1	0.3030	0.2467	89.91%
2	0.1972	0.2424	90.94%
3	0.2083	0.2395	91.17%
4	0.1936	0.2464	90.94%
5	0.1914	0.2491	90.83%

Early stopping could be applied from Epoch 3 based on validation metrics.

🛠️ Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel, PeftConfig

model_id = "Harsh-Gupta/bert-lora-sentiment"

# Load PEFT config + model
config = PeftConfig.from_pretrained(model_id)
base_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(base_model, model_id)

# Tokenizer
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Predict
text = "This movie was absolutely amazing!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
    outputs = model(**inputs)
    probs = outputs.logits.softmax(dim=-1)
    pred = probs.argmax().item()

LoRA Configuration

python

LoraConfig(
    r=32,
    lora_alpha=4,
    target_modules=["query", "value"],
    lora_dropout=0.1,
    bias="none",
    task_type="SEQ_CLS"
)

🔍 Intended Use

—Sentiment classification for binary text (positive/negative)

—Can be adapted to other domains: movie reviews, product reviews, tweets

🧠 Author

—Harsh Gupta
—MCA, Jawaharlal Nehru University (JNU)
—GitHub: 2003Harsh