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--- |
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language: |
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- en |
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- fr |
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thumbnail: "https://www.warpy.io/_next/static/media/tgshell_icon.4fa45b6d.svg" |
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tags: |
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- text-generation |
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- t5 |
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license: "apache-2.0" |
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datasets: |
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- wikitext |
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- bookcorpus |
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metrics: |
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- perplexity |
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- bleu |
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base_model: "t5" |
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--- |
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# tgs-model |
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# Terminal Generative Shell (tgs) Model |
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## TGS Model implements NL2Bash: Natural Language Interface to Linux Bash |
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## Citation |
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If you use this software, please cite it as follows: |
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```bibtex |
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@inproceedings{LinWZE2018:NL2Bash, |
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author = {Xi Victoria Lin and Chenglong Wang and Luke Zettlemoyer and Michael D. Ernst}, |
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title = {NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System}, |
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booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation {LREC} 2018, Miyazaki (Japan), 7-12 May, 2018.}, |
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year = {2018} |
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} |
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``` |
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## Description |
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This project is based on the NL2Bash dataset, constructed from the GitHub repository [nl2bash](https://github.com/TellinaTool/nl2bash). The objective is to create a semantic parser that translates natural language commands into executable Bash commands using deep learning techniques. |
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## Installation |
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### Prerequisites |
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- Python 3.6+ |
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- PyTorch |
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- Transformers library |
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- PyTorch Lightning |
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- Scikit-learn |
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- Sentencepiece |
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### Setup |
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Clone the repository and install the required packages: |
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```bash |
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git clone https://github.com/your-repo/nl2bash.git |
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cd nl2bash |
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pip install -r requirements.txt |
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``` |
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## Usage |
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1. **Import Libraries** |
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Import all the necessary libraries including PyTorch, Transformers, and PyTorch Lightning. |
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2. **Load Data** |
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Load the NL2Bash dataset JSON file and inspect its structure. |
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3. **Preprocess Data** |
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Convert the data into a suitable format for training, including tokenization. |
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4. **Model Initialization** |
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Initialize the T5 model and tokenizer, and set up the necessary configurations. |
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5. **Training** |
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Train the model using PyTorch Lightning with specified callbacks and checkpointing. |
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6. **Validation and Testing** |
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Validate and test the model on the NL2Bash dataset. |
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7. **Model Inference** |
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Use the trained model to translate natural language commands to Bash commands. |
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## Example |
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Here's a quick example to get you started: |
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```python |
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from your_module import NL2BashModel, generate_answer |
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model = NL2BashModel.load_from_checkpoint('path_to_checkpoint') |
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tokenizer = YourTokenizer.from_pretrained('path_to_tokenizer') |
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question = "Prints process tree of a current process with id numbers and parent processes." |
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answer = generate_answer(question, model, tokenizer) |
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print(answer) |
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``` |
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## Training Analysis |
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The T5 model was fine-tuned for the NL2Bash task. The training process showed the following characteristics: |
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- Training Loss: Demonstrated a consistent decrease over time, indicating effective learning and adaptation to the training data. |
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- Validation Loss: Also decreased, suggesting good generalization to unseen data. |
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- Stability: The training process was stable, without significant fluctuations in loss values. |
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- Overfitting: No evidence of overfitting was observed, as both training and validation losses decreased concurrently. |
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This analysis provides confidence in the model's ability to learn and generalize from the NL2Bash dataset effectively. |
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### Bias Analysis |
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tgs_model `v0.1.0` has a bias towards the NL2Bash dataset. The nl2bashdataset has a huge amount of `find` command on top of the others. |
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For the `v0.2.0` model, we will be treating the NL2Bash dataset as a biased dataset. We will be using the [NL2Bash-2](https://github.com/TellinaTool/nl2bash-2) dataset. The dataset is a more balanced dataset with more commands. |
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## Contributing |
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Contributions to improve NL2Bash are welcome. Please read `CONTRIBUTING.md` for guidelines on how to contribute. |
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## License |
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This project is licensed under the MIT License - see the `LICENSE` file for details. |
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