---
tags:
- pytorch
- sentiment-analysis
- yoruba
- cnn
- afriberta
---

# Yoruba Sentiment Analysis with CNN and Afriberta

This repository contains a PyTorch model for sentiment analysis of Yoruba text. The model utilizes a Convolutional Neural Network (CNN) architecture on top of a pre-trained Afriberta model, specifically "Davlan/naija-twitter-sentiment-afriberta-large".

## Model Description

The model consists of the following components:

- **Afriberta Base:** The pre-trained Afriberta model serves as a powerful feature extractor for Yoruba text.
- **CNN Layers:** Multiple 1D convolutional layers with varying kernel sizes capture local patterns and n-gram features from the Afriberta embeddings.
- **Max Pooling:** Max pooling layers extract the most salient features from the convolutional outputs.
- **Dropout:** Dropout regularization helps prevent overfitting.
- **Fully Connected Layer:** A final fully connected layer maps the concatenated pooled features to sentiment classes.

## Intended Uses & Limitations

This model is designed for sentiment analysis of Yoruba text and can be applied to various use cases, such as:

- **Social Media Monitoring:** Analyze sentiment expressed in Yoruba tweets or social media posts.
- **Customer Feedback Analysis:** Understand customer sentiment towards products or services in Yoruba.
- **Opinion Mining:** Extract opinions and sentiments from Yoruba text data.

**Limitations:**

- The model's performance may be limited by the size and quality of the training data.
- It may not generalize well to domains significantly different from the training data.
- As with any language model, there's a risk of bias and potential for misuse.

## Training and Evaluation Data

The model was trained on a dataset of Yoruba tweets annotated with sentiment labels. The dataset was split into training, validation, and test sets.

## Training Procedure

The model was trained using the following steps:

1. **Data Preprocessing:** Text data was tokenized using the Afriberta tokenizer.
2. **Model Initialization:** The SentimentCNNModel was initialized with the pre-trained Afriberta model and CNN layers.
3. **Optimization:** The model was trained using the Adam optimizer and cross-entropy loss.
4. **Early Stopping:** Training was stopped early based on validation loss to prevent overfitting.

## Evaluation Results

The model achieved the following performance on the test set:

- **Test Loss:** [0.6707]
- **F1-Score:** [0.8095]

## How to Use

1. **Install Dependencies:** Ensure you have PyTorch and Transformers installed:

   ```bash
   pip install torch transformers
   ```

2. **Load the Model:** You can load the model using the Hugging Face `transformers` library:

   ```python
   from transformers import AutoModelForSequenceClassification, AutoTokenizer

   model_name = "Testys/cnn_sent_yor"
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   model = AutoModelForSequenceClassification.from_pretrained(model_name)
   ```

3. **Make Predictions:** Use the tokenizer to prepare your input text and the model to get predictions:

   ```python
   def predict(text):
       inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
       outputs = model(**inputs)
       return torch.softmax(outputs.logits, dim=1).items()

   sample_text = "Your Yoruba text here"
   prediction = predict(sample_text)
   print("Sentiment:", prediction)
   ```

## Citing the Model

If you use this model in your research, please cite it using the following format:

```bibtex
@misc{your_model_name,
  author = {Your Name},
  title = {Yoruba Sentiment Analysis with CNN and Afriberta},
  year = {2024},
  publisher = {Hugging Face's Model Hub},
  journal = {Hugging Face's Model Hub},
  howpublished = {\\url{https://huggingface.co/your_model_name}}
}
```

## License

This model is open-sourced under the MIT license. The license allows commercial use, modification, distribution, and private use. 

## Contact Information

For any queries regarding the model, feel free to reach out via GitHub or direct email:
- **GitHub:** [https://github.com/dev-tyta]
- **Email:** [hardeykoryar@gmail.com]

```