README.md

metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - sentiment-analysis
  - text-classification
  - pytorch
  - distilbert
  - fine-tuned
datasets:
  - imdb
language:
  - en
pipeline_tag: text-classification
widget:
  - text: This movie is absolutely amazing! I loved every minute of it.
    example_title: Positive Example
  - text: Terrible film, complete waste of time and money.
    example_title: Negative Example

DistilBERT Sentiment Analysis Model

This model is a fine-tuned version of distilbert-base-uncased for 2-class sentiment analysis (Positive, Negative) on movie reviews.

🎯 Model Description

• Model Type: Text Classification
• Base Architecture: DistilBERT (Distilled BERT)
• Language: English
• Task: Sentiment Analysis
• Classes: 2 (Negative, Positive)
• Parameters: ~66M
• Model Size: ~250MB

🚀 Quick Start

Using Transformers Pipeline

from transformers import pipeline

# Load the model
classifier = pipeline("sentiment-analysis", 
                     model="your-username/sentiment-analysis-distilbert")

# Single prediction
result = classifier("This movie is fantastic!")
print(result)
# Output: [{'label': 'POSITIVE', 'score': 0.9987}]

# Batch prediction
texts = [
    "Amazing cinematography and great acting!",
    "Boring and predictable storyline.",
    "It was an okay movie, nothing extraordinary."
]
results = classifier(texts)
for text, result in zip(texts, results):
    print(f"Text: {text}")
    print(f"Sentiment: {result['label']} (Confidence: {result['score']:.3f})")

Using AutoModel and AutoTokenizer

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "your-username/sentiment-analysis-distilbert"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare input
text = "This movie exceeded my expectations!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    
# Get predicted class
predicted_class = torch.argmax(predictions, dim=-1).item()
confidence = predictions[0][predicted_class].item()

labels = ["NEGATIVE", POSITIVE"]
print(f"Sentiment: {labels[predicted_class]} (Confidence: {confidence:.3f})")

📊 Training Details

Dataset

• Source: IMDB Movie Reviews Dataset
• Training Samples: 5,000 (balanced: 1,667 per class)
• Evaluation Samples: 1,000
• Data Split: 80% train, 20% validation
• Preprocessing: Tokenization with DistilBERT tokenizer, max length 256

Training Configuration

• Base Model: distilbert-base-uncased
• Training Framework: PyTorch + Transformers
• Optimizer: AdamW
• Learning Rate: 2e-5
• Batch Size: 8
• Epochs: 3
• Warmup Steps: 100
• Weight Decay: 0.01
• Max Sequence Length: 256 tokens

Hardware

• Platform: Google Colab
• GPU: Tesla T4 (15GB VRAM)
• Training Time: ~30-45 minutes

📈 Performance

Metric	Score
Training Accuracy	~95%
Validation Accuracy	~93%
Training Loss	0.12
Validation Loss	0.18

Class Distribution

• Negative: 33.3% (2500 samples)
• Positive: 33.3% (2500 samples)

🎯 Intended Use

Primary Use Cases

• Movie Review Analysis: Classify sentiment of movie reviews
• Product Review Sentiment: Analyze customer feedback
• Social Media Monitoring: Track sentiment in posts and comments
• Content Moderation: Identify negative sentiment in user-generated content

Suitable Domains

• Entertainment and media reviews
• E-commerce product feedback
• Social media posts
• Customer service interactions
• General English text sentiment analysis

⚠️ Limitations and Biases

Known Limitations

• Domain Specificity: Primarily trained on movie reviews, may not generalize well to other domains
• Language: English only, no multilingual support
• Context Length: Limited to 256 tokens, longer texts are truncated
• Cultural Bias: May reflect biases present in IMDB dataset

Potential Biases

• Genre Bias: May perform differently across movie genres
• Temporal Bias: Training data may reflect sentiment patterns from specific time periods
• Demographic Bias: May not equally represent all demographic groups' sentiment expressions

Not Recommended For

• Non-English text
• Highly specialized domains (medical, legal, technical)
• Real-time critical applications
• Texts longer than 256 tokens without preprocessing
• Sarcasm or irony detection

🔧 Technical Specifications

Model Architecture

DistilBERT Base
├── Transformer Layers: 6
├── Hidden Size: 768
├── Attention Heads: 12
├── Intermediate Size: 3072
└── Classification Head: Linear(768 → 3)

Input Format

• Text Encoding: UTF-8
• Tokenization: WordPiece
• Special Tokens: [CLS], [SEP]
• Max Length: 256 tokens
• Padding: Right padding with [PAD] tokens

Output Format

{
    'label': 'POSITIVE',  # One of: NEGATIVE, POSITIVE
    'score': 0.9987       # Confidence score (0-1)
}

📝 Citation

If you use this model in your research or applications, please cite:

@misc{sentiment-analysis-distilbert,
  title={Fine-tuned DistilBERT for Sentiment Analysis},
  author={Your Name},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/your-username/sentiment-analysis-distilbert}
}

📄 License

This model is released under the Apache 2.0 License. See the LICENSE file for details.

🤝 Contributing

Issues and pull requests are welcome! Please feel free to:

• Report bugs or issues
• Suggest improvements
• Share your use cases
• Contribute to documentation

🙏 Acknowledgments

• Hugging Face for the Transformers library and model hosting
• Google Research for the original BERT and DistilBERT models
• Stanford AI Lab for the IMDB dataset
• Google Colab for providing free GPU resources for training

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This model was created as part of a sentiment analysis fine-tuning project using modern NLP techniques and best practices.