Updated README.md (Professional Model Card)

README.md

@@ -1,40 +1,43 @@
----
-license: mit
-language:
-- en
-metrics:
-- accuracy
-- f1
-base_model:
-- distilbert/distilbert-base-uncased-finetuned-sst-2-english
----
-# Quick DistilBERT IMDB Sentiment Classifier
-
-This is a fine-tuned DistilBERT model for **sentiment analysis** on the IMDB movie reviews dataset.  
-The model classifies reviews as **positive** or **negative**.
-
-## Model Details
-- **Base model**: `distilbert-base-uncased`
-- **Dataset**: IMDB (cleaned train/test splits)
-- **Task**: Sentiment classification (binary)
-- **Framework**: Hugging Face Transformers
-
-## Training
-- Optimized DistilBERT on IMDB dataset
-- Used standard text classification head
-- Training args saved in `training_args.bin`
-
-## Evaluation
-Accuracy and F1-score on the IMDB test set:  
-(Add numbers from your `eval_report.txt` here)
-
-Misclassified examples are available in `misclassified_examples.csv`.
-
-## How to Use
+# 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 pipeline
+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)
 
-model_id = "SuganyaP/quick-distilbert-imdb"
-classifier = pipeline("sentiment-analysis", model=model_id)
+inputs = tokenizer("This movie was excellent!", return_tensors="pt")
+outputs = model(**inputs)
+prediction = torch.argmax(outputs.logits).item()
 
-print(classifier("This movie was excellent!"))
+print("Positive" if prediction == 1 else "Negative")