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umbermalik/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

MetricScore
Accuracy88.9%<- replace with your actual score
F1 Score0.888<- replace with your actual score

Baseline Comparison

ModelAccuracy
TF-IDF + Logistic Regression86.4%
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!')
# Output: [{'label': 'POSITIVE', 'score': 0.997}]