FPRT/SurgerySort
0
1---2title: SurgerySort3emoji: 📊4colorFrom: purple5colorTo: yellow6sdk: streamlit7sdk_version: 1.26.08app_file: app.py9pinned: false10---11 12# Clinical Text Classification App13 1415 16## Introduction17 18This is a clinical text classification app that uses a pre-trained language model to classify clinical text into two categories: "surgery" or "non-surgery". The app is designed to help healthcare professionals quickly sort through large volumes of clinical text and identify posts related to surgical interventions. The app is built using Streamlit and leverages the Hugging Face Transformers library for fine-tuning the language model.19 20## Installation21 221. Clone this repository to your local machine.232. Install the required dependencies using the following command:24 ```bash25 pip install -r requirements.txt26 ```27 28## Usage29 301. Run the app using the following command:31 ```bash32 streamlit run app.py33 ```342. The app will open in your default web browser.353. Enter clinical text data in the provided text input or upload a CSV file containing clinical text and labels.364. Click the "Submit" button to initiate the text classification process.375. The app will display the predicted labels for each input text and provide visualizations of the training process.38 39## Features40 41- Text classification: The app uses a pre-trained language model to classify clinical text into "surgery" or "non-surgery" categories.42- User-friendly interface: The app provides an intuitive interface for entering text data and visualizing classification results.43- Efficient training: The app leverages the Hugging Face Transformers library for efficient model training and fine-tuning.44 45## Known Issues46 47- **Performance**: Processing large volumes of text data may slow down the app. Consider breaking down large text data into smaller chunks.48- **Data Storage**: The app may encounter performance issues if data access and storage methods are inefficient. Optimize data storage and retrieval mechanisms for better performance.49 50## Future Enhancements51 52- **Model Selection**: Allow users to choose from a variety of pre-trained models for text classification.53- **Data Preprocessing**: Implement advanced data preprocessing techniques to handle noisy or unstructured clinical text data.54- **Real-time Inference**: Enable real-time text classification for dynamic data streams.55- **Model Interpretability**: Provide insights into the model's decision-making process for better transparency.56 57## License58 59This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.60 61## Acknowledgments62 63- This app was built using the Streamlit framework and the Hugging Face Transformers library.64 65---66*Note: This readme provides a high-level overview of the app. For detailed instructions and information, refer to the app's documentation and comments in the source code.*67 68Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference69