WordO - The Word Generator
Model Details
Model Name: WordO - The Word Generator
Version: 1.0
Model Type: Fine-tuned Language Model
Base Model: Llama3
Fine-tuned On: English Word, Meaning, and Usage Examples dataset
Release Date: 06-06-2024
Overview
WordO - The Word Generator is a fine-tuned version of Llama3, designed to excel in generating proper and exact English words for given statements. This model was trained using the English Word, Meaning, and Usage Examples dataset to improve its performance on this specific task.
Training Data
- Dataset Used: English Word, Meaning, and Usage Examples dataset
- Dataset Reference: Muniru Oladele Idris
- Training Duration: 643.9604 seconds
Model Performance
Training Metrics
- Epochs: 1.0
- Global Steps: 450
- Gradient Norm: 3.61397
- Learning Rate: 0.0
- Training Loss: 1.3715
- Training Runtime: 643.9604 seconds
- Training Samples per Second: 1.398
- Training Steps per Second: 0.699
- Total FLOPs: 877,460,167,606,272.0
Evaluation Metrics
- Evaluation Loss: 1.78476
- Evaluation Runtime: 25.8005 seconds
- Evaluation Samples per Second: 3.876
- Evaluation Steps per Second: 3.876
Intended Use
WordO - The Word Generator is intended for use in generating proper and exact English words for given statements. It is particularly effective in contexts where precise word choice is crucial, such as writing assistance, language learning, and editing.
Potential Use Cases
- Writing assistance tools
- Language learning applications
- Automated editing software
Limitations
While WordO - The Word Generator performs well in generating proper and exact English words, it has certain limitations:
- Context Understanding: The model may not always fully understand the context of a statement, leading to less accurate word generation.
- Biases: The model may exhibit biases present in the training data.
- Complexity: The model may struggle with highly complex or nuanced statements.
Ethical Considerations
When using WordO - The Word Generator, consider the following ethical aspects:
- Biases: The model may exhibit biases present in the training data. Efforts have been made to mitigate these biases, but users should remain vigilant.
- Privacy: Ensure that the data used with the model complies with privacy regulations and ethical guidelines.
- Fairness: Be aware of potential fairness issues and strive to use the model responsibly.
Acknowledgements
Special thanks to
Abid Ali Awan and to
Muniru Oladele Idris for the dataset English Word, Meaning, and Usage Examples.
