| import gradio as gr | |
| title = "T0pp" | |
| description = "Gradio Demo for T0pp, T0* is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. Can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://huggingface.co/bigscience/T0pp' target='_blank'>Hugging face model page</a> | <a href='https://github.com/bigscience-workshop/promptsource' target='_blank'>Github Repo</a> | <a href='https://arxiv.org/abs/2110.08207' target='_blank'>Multitask Prompted Training Enables Zero-Shot Task Generalization</a></p>" | |
| examples = [ | |
| ['Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy'],["It's rainy today but it will stop in a few hours, when should I go for my run?"],["How many hydrogen atoms are in a water molecule?"] | |
| ] | |
| gr.Interface.load("huggingface/bigscience/T0pp", inputs=gr.inputs.Textbox(lines=5, label="Input Text"),title=title,description=description,article=article, examples=examples,enable_queue=True).launch() |