README.md

---
language: en
license: mit
datasets:
- imdb
metrics:
- accuracy
- f1
pipeline_tag: text-classification
base_model: distilbert/distilbert-base-uncased
library_name: transformers
tags:
- fine-tune-model
- sentiment-analysis
- distilbert
- text-classification
- imdb
---

# DistilBERT IMDB Sentiment Classifier

## Overview
This repository contains a fine-tuned DistilBERT model for binary sentiment classification on the IMDB movie reviews dataset. The model predicts whether a given review expresses positive or negative sentiment. It is intended as a lightweight, reproducible NLP model suitable for demonstrations, small-scale applications, and experimentation.

## Base Model
- Model: distilbert-base-uncased  
- Framework: Hugging Face Transformers  
- Task: Text Classification (Binary Sentiment)

## Training Details
- Dataset: IMDB movie review dataset (train/test split)
- Objective: Binary sentiment classification
- Optimization:
  - Adam optimizer  
  - Learning rate scheduling  
  - Early stopping  
- Regularization:
  - Dropout applied as per DistilBERT architecture
  - Gradient clipping

## Evaluation Metrics
The model was evaluated using standard binary classification metrics:
- Accuracy
- Precision
- Recall
- F1-score

## Inference Example

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "SuganyaP/quick-distilbert-imdb"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

inputs = tokenizer("This movie was excellent!", return_tensors="pt")
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits).item()

print("Positive" if prediction == 1 else "Negative")