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| import gradio as gr | |
| title = "ELECTRA" | |
| description = "Gradio Demo for ELECTRA. To use it, simply add your text, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2003.10555' target='_blank'>ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators</a></p>" | |
| examples = [ | |
| ["My name is Sarah and I live in London","electra_large_discriminator_squad2_512","Where do I live?"] | |
| ] | |
| io1 = gr.Interface.load("huggingface/ahotrod/electra_large_discriminator_squad2_512") | |
| io2 = gr.Interface.load("huggingface/deepset/electra-base-squad2") | |
| def inference(context, model,question): | |
| if model == "electra_large_discriminator_squad2_512": | |
| outlabel = io1(context,question) | |
| else: | |
| outlabel = io2(context,question) | |
| return outlabel | |
| gr.Interface( | |
| inference, | |
| [gr.inputs.Textbox(label="Context",lines=10),gr.inputs.Dropdown(choices=["electra_large_discriminator_squad2_512","electra-base-squad2"], type="value", default="electra_large_discriminator_squad2_512", label="model"),gr.inputs.Textbox(label="Question Answering")], | |
| [gr.outputs.Textbox(label="Output")], | |
| examples=examples, | |
| article=article, | |
| title=title, | |
| description=description).launch(enable_queue=True) | |