Kumshe/

Hausa-sentiment-analysis

Model Description

Intended Use

• Primary Use Case: Sentiment analysis for Hausa social media content, such as tweets or Facebook posts.
• Target Users: NLP researchers, businesses analyzing social media, and developers building sentiment analysis tools for Hausa language content.
• Example Usage:
  python

  1from transformers import AutoTokenizer, AutoModelForSequenceClassification 2 3# Load the model and tokenizer 4tokenizer = AutoTokenizer.from_pretrained("Kumshe/Hausa-sentiment-analysis") 5model = AutoModelForSequenceClassification.from_pretrained("Kumshe/Hausa-sentiment-analysis") 6 7# Encode the input text 8inputs = tokenizer("Your Hausa text here", return_tensors="pt") 9 10# Get model predictions 11outputs = model(**inputs)

Model Architecture

• Base Model: BERT (Bidirectional Encoder Representations from Transformers)
• Pre-trained Model: bert-base-cased from Hugging Face Transformers library.
• Fine-Tuned Model: Fine-tuned for 40 epochs on a Hausa sentiment dataset.

Training Data

• Data Source: The model was trained on a dataset containing 35,000 examples from social media platforms such as Twitter and Facebook.
• Data Split:
  • Training Set: 80% of the data
  • Validation Set: 20% of the data

Training Details

• Number of Epochs: 40
• Batch Size:
  • Per device training batch size: 32
  • Per device evaluation batch size: 64
• Learning Rate Schedule: Warm-up steps: 10, Weight decay: 0.01
• Optimizer: AdamW
• Training Hardware: Trained on Kaggle using 2 NVIDIA T4 GPUs.

Evaluation Metrics

• Evaluation Loss: 0.6265
• Accuracy: 73.47%
• F1 Score: 73.47%
• Precision: 73.54%
• Recall: 73.47%

Model Performance

Limitations

• The model may not generalize well to other types of Hausa text outside of social media (e.g., formal writing or literature).
• Performance may degrade on text containing slang or regional dialects not well-represented in the training data.
• The model is biased towards the examples in the training dataset; biases in the data may affect predictions.

Ethical Considerations

• Sentiment analysis models can potentially amplify biases present in the training data.
• Use cautiously in sensitive applications to avoid unintended consequences.
• Consider the impact on privacy and data protection laws, especially when analyzing social media content.

License

Citation

@misc{Kumshe2024HausaSentimentAnalysis,
  author = {Umar Muhammad Mustapha Kumshe},
  title = {Hausa Sentiment Analysis},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Kumshe/Hausa-sentiment-analysis}},
}

Contributions