Modelpublic

Asmatullah-AI-Engineer/distilbert-imdb-sentiment

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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Model Card

language: en license: apache-2.0 tags:

—text-classification
—sentiment-analysis
—distilbert
—fine-tuned datasets:
—imdb metrics:
—accuracy
—f1 ---

DistilBERT IMDb Sentiment Classifier

A fine-tuned DistilBERT model for binary sentiment analysis on movie reviews.

Model Description

This model was fine-tuned from distilbert-base-uncased on 5,000 IMDb movie reviews for 3 epochs. It classifies text as POSITIVE or NEGATIVE sentiment.

Training Data

—Source: IMDb Large Movie Review Dataset (stored in SQLite, queried with pandas)
—Train: 5,000 samples | Validation: 1,000 samples
—Label balance: approximately 50% positive, 50% negative

Evaluation Results

Metric	Score
Accuracy	88.4%	<- replace with your actual score
F1 Score	0.893	<- replace with your actual score

Baseline Comparison

Model	Accuracy
TF-IDF + Logistic Regression	86.4%
DistilBERT (this model)	92.3%

Intended Use

Product review analysis, feedback classification, general English sentiment tasks.

Limitations and Bias

—Trained only on English movie reviews performance on other domains may vary
—May not handle Urdu, Roman Urdu, or code-switched text well
—Sarcasm with no obvious negative words may be misclassified
—Very short texts (under 5 words) have lower confidence scores

How to Use

python from transformers import pipeline classifier = pipeline('text-classification', model='YOUR-USERNAME/distilbert-imdb-sentiment') result = classifier('This movie was absolutely incredible!')