This model is based on distilbert-base-uncased-finetuned-sst-2-english and performs Sentiment analysis on English text (positive/negative classification).

This model was uploaded as part of a machine learning assignment demonstrating model deployment to Hugging Face Hub.

Quick Start

python

from transformers import pipeline

# Load the model
classifier = pipeline("sentiment-analysis", model="Divi15/sentiment-classifier-demo-5729")

# Make predictions
result = classifier("I love machine learning!")
print(result)
# Expected output: [{'label': 'POSITIVE', 'score': 0.9991}]

Model Details

Model Type: Text Classification
Base Model: distilbert-base-uncased-finetuned-sst-2-english
Task: Sentiment Analysis (Binary Classification)
Language: English
License: Apache 2.0

Intended Use

This model is intended for:

Educational purposes
Sentiment analysis of English text
Binary classification tasks (positive/negative sentiment)

Training Data

Dataset: Stanford Sentiment Treebank (SST-2)
Training Examples: ~67K sentences
Classes: 2 (positive, negative)

Performance

Accuracy: ~91-92% on SST-2 test set
F1 Score: ~0.91-0.92

Usage Examples

Basic Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("Divi15/sentiment-classifier-demo-5729")
model = AutoModelForSequenceClassification.from_pretrained("Divi15/sentiment-classifier-demo-5729")

classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

# Test examples
examples = [
    "I absolutely love this!",
    "This is terrible.",
    "It's okay, nothing special."
]

for text in examples:
    result = classifier(text)
    print(f"Text: {text}")
    print(f"Result: {result}")
    print()

Batch Processing

python

texts = [
    "Great product, highly recommended!",
    "Poor quality, very disappointed.",
    "Average performance, could be better."
]

results = classifier(texts)
for text, result in zip(texts, results):
    print(f"{text} -> {result['label']} ({result['score']:.3f})")

Limitations

Trained primarily on movie reviews and may not generalize well to other domains
Binary classification only (positive/negative)
English language only
May exhibit biases present in the training data

Ethical Considerations

This model should not be used to make decisions that significantly impact individuals
Consider potential biases when applying to different demographic groups
Sentiment analysis can be subjective and context-dependent

Citation

bibtex

@misc{sentiment_classifier_demo_5729_2024,
  title={Sentiment Classifier Demo 5729},
  author={Your Name},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/Divi15/sentiment-classifier-demo-5729}
}

Model Card Authors

This model card was created as part of an educational assignment on model deployment and sharing.

Last updated: 2025-09-08