Divi15/sentiment-classifier-demo-5729

Sentiment Classifier Demo 5729

Model Description

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

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

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

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

@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.

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Last updated: 2025-09-08