README.md

metadata

title: TransformersPipelinePlayground
emoji: 💻
colorFrom: green
colorTo: indigo
sdk: gradio
sdk_version: 5.19.0
app_file: app.py
pinned: false
license: mit

Transformers Pipeline Playground 🎡🤖

Welcome to the Transformers Pipeline Playground! This project provides an interactive interface to explore and experiment with various transformer models using Hugging Face’s transformers library. Whether you’re a seasoned NLP practitioner or just getting started, this playground offers a hands-on experience with state-of-the-art models.

Features ✨

• Interactive Model Exploration: Load and test different transformer models directly in your browser.
• User-Friendly Interface: Utilizes Gradio to create an accessible web-based UI.
• Flexible Pipeline Selection: Choose from a variety of pipelines such as text generation, sentiment analysis, and more.

Installation 🛠️

To set up the Transformers Pipeline Playground locally, follow these steps: 1. Clone the Repository:

git clone https://github.com/canstralian/transformers-pipeline-playground.git cd transformers-pipeline-playground

2.	Install Dependencies:

It’s recommended to use a virtual environment:

python3 -m venv env source env/bin/activate # On Windows, use env\Scripts\activate

Then, install the required packages:

pip install -r requirements.txt

Usage 🚀

After installing the dependencies, you can launch the application with:

python app.py

This will start a local server. Open your browser and navigate to the displayed URL to access the interface.

How It Works 🧠

The application leverages Hugging Face’s transformers library to load pre-trained models and create pipelines for various NLP tasks. The user interface is built with Gradio, providing an easy way to interact with the models.

Contributing 🤝

Contributions are welcome! If you have ideas for improvements or new features, feel free to open an issue or submit a pull request.

License 📄

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

Note: Remember, with great transformer power comes great responsibility. Use the models ethically and consider the implications of their outputs.

Check out the configuration reference at https://huggingface.co./docs/hub/spaces-config-reference