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| import gradio as gr | |
| from qasrl_model_pipeline import QASRL_Pipeline | |
| model = "kleinay/qanom-seq2seq-model-baseline" | |
| pipeline = QASRL_Pipeline(model) | |
| description = f"""This is a demo of the '{model}' model, which fine-tuned a Seq2Seq pretrained model on the QANom task.""" | |
| title="QANom Parser Demo" | |
| examples = [["The doctor was interested in Luke 's <p> treatment .", True, "treat"], | |
| ["The Veterinary student was interested in Luke 's <p> treatment of sea animals .", True, "treat"]] | |
| input_sent_box_label = "Insert sentence here. Mark the predicate by adding the token '<p>' before it." | |
| verb_form_inp_placeholder = "e.g. 'decide' for the nominalization 'decision', 'teach' for 'teacher', etc." | |
| links = """<p style='text-align: center'> | |
| <a href='https://www.qasrl.org' target='_blank'>QASRL Website</a> | <a href='https://huggingface.co/spaces/kleinay/qanom-seq2seq-demo' target='_blank'>Model Repo at Huggingface Hub</a> | |
| </p>""" | |
| def call(sentence, is_nominal, verb_form): | |
| predicate_marker="<p>" | |
| if predicate_marker not in sentence: | |
| raise ValueError("You must highlight one word of the sentence as a predicate using preceding '<p>'.") | |
| if not verb_form: | |
| if is_nominal: | |
| raise ValueError("You should provide the verbal form of the nominalization") | |
| toks = sentence.split(" ") | |
| pred_idx = toks.index(predicate_marker) | |
| predicate = toks(pred_idx+1) | |
| verb_form=predicate | |
| pipe_out = pipeline(sentence, | |
| predicate_marker=predicate_marker, | |
| predicate_type="nominal" if is_nominal else "verbal", | |
| verb_form=verb_form) | |
| return pipe_out[0]["QAs"], pipe_out[0]["generated_text"] | |
| iface = gr.Interface(fn=call, | |
| inputs=[gr.inputs.Textbox(placeholder=input_sent_box_label, label="Sentence", lines=4), | |
| gr.inputs.Checkbox(default=True, label="Is Nominalization?"), | |
| gr.inputs.Textbox(placeholder=verb_form_inp_placeholder, label="Verbal form (for nominalizations)", default='')], | |
| outputs=[gr.outputs.JSON(label="Model Output - QASRL"), gr.outputs.Textbox(label="Raw output sequence")], | |
| title=title, | |
| description=description, | |
| article=links, | |
| examples=examples ) | |
| iface.launch() |