|
from transformers import pipeline |
|
import gradio as gr |
|
|
|
classifier = pipeline("zero-shot-classification", model="DeepPavlov/xlm-roberta-large-en-ru-mnli") |
|
|
|
def wrap_classifier(text, labels, template): |
|
labels = labels.split(",") |
|
outputs = classifier(text, labels, hypothesis_template=template) |
|
return outputs["labels"][0] |
|
|
|
gr.Interface( |
|
fn=wrap_classifier, |
|
title="Zero-shot Classification", |
|
inputs=[ |
|
gr.inputs.Textbox(lines=5, label="Text to classify"), |
|
gr.inputs.Textbox(lines=1, label="Candidate labels separated with commas"), |
|
gr.inputs.Textbox(lines=1, label="Template", default="This sentence is about {}.", placeholder="This sentence is about {}.") |
|
], |
|
outputs=[ |
|
gr.outputs.Textbox(label="Label") |
|
], |
|
).launch() |