brunellaquaye/hausa-sentiment-analysis

# Hausa Sentiment Analysis Pipeline This project demonstrates a simple, modern workflow for sentiment analysis on Hausa text using pretrained transformer models and the Hausa Sentiments Corpus from Hugging Face. ## Features - Uses Hugging Face `transformers` and `datasets` for easy, reproducible NLP workflows - Fine-tunes a pretrained Hausa language model (`mangaphd/HausaBERTa`) - Evaluates model performance (accuracy, F1) - Predicts sentiment for new Hausa text samples ## Dataset - **Name:** Hausa Sentiments Corpus - **Source:** Hugging Face Datasets - **Splits:** Train, Test, Validation - **Labels:** 0 = Negative, 1 = Neutral, 2 = Positive ## Quickstart 1. **Install dependencies** (in the notebook or terminal): ```bash pip install transformers datasets torch scikit-learn ``` 2. **Open and run the notebook:** - `hausa_sentiment_pipeline.ipynb` - The notebook will: - Load the Hausa Sentiments Corpus - Tokenize the text using a pretrained Hausa model - Fine-tune the model on the training set - Evaluate on the test set - Predict sentiment for new Hausa sentences ## Model - **Pretrained Model:** `mangaphd/HausaBERTa` - **Task:** Sequence classification (sentiment analysis) - **Metrics:** Accuracy, F1 (weighted) ## Example Prediction ```python sample_texts = [ "Ina son wannan fim din sosai!", # I love this movie! "Ban ji dadin wannan ba.", # I didn't like this. ] predictions = predict_sentiment(sample_texts) print('Predictions:', predictions) ``` ## Results - Typical accuracy and F1 scores will be printed after training. - You can easily swap in other Hausa models from Hugging Face if desired. ## Reproducibility - All steps are in the notebook for transparency and easy modification. - No complex dependencies or custom modules required. ## References - Hausa Sentiments Corpus on Hugging Face - HausaBERTa Model - Hugging Face Transformers Documentation --- For questions or improvements, please open an issue or pull request.