This model is a fine-tuned version of distilbert-base-uncased on the IMDB movie reviews dataset. It achieves an accuracy of 100.0% on the test set.

Model Description

This is a sentiment analysis model specifically trained on movie reviews. It can classify text as either positive or negative sentiment.

Intended Uses & Limitations

Intended Uses:

Sentiment analysis of movie reviews
General sentiment classification of English text
Educational purposes and research

Limitations:

Trained specifically on movie reviews, may not generalize well to other domains
English language only
Binary classification (positive/negative) - no neutral sentiment

Training Procedure

Training Data

The model was fine-tuned on the IMDB movie reviews dataset:

Training samples: 2250
Validation samples: 250
Test samples: 500

Training Hyperparameters

Learning rate: 2e-05
Train batch size: 16
Eval batch size: 16
Number of epochs: 3
Optimizer: AdamW
Weight decay: 0.01

Results

Metric	Value
Accuracy	1.0000

Usage

python

from transformers import pipeline

# Load the model
classifier = pipeline("text-classification", model="Mallikarjunareddy/malli_finetuned_model")

# Classify text
result = classifier("This movie was absolutely amazing!")
print(result)
# Output: [{'label': 'LABEL_1', 'score': 0.9998}]
# LABEL_0 = Negative, LABEL_1 = Positive

Model Performance

The model shows strong performance on movie review sentiment analysis:

Test Accuracy: 100.0%
Baseline (random guessing): 50.0%
Improvement: +50.0 percentage points

Citation

@misc{malli_finetuned_model_2024,
  author = {Your Name},
  title = {malli_finetuned_model: Fine-tuned IMDB Sentiment Analysis},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Mallikarjunareddy/malli_finetuned_model}}
}