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import gradio as gr
from transformers import BartForSequenceClassification, BartTokenizer
# model = pipeline("text-generation")
# following https://joeddav.github.io/blog/2020/05/29/ZSL.html
tokenizer_bart = BartTokenizer.from_pretrained('facebook/bart-large-mnli')
model_bart_sq = BartForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
title = "Stance Detection using Zero Shot"
description1 = "Welcome to the side where the grass is greener."
description2 = "This is a simple tool which was created with an aim to stance towards a given entity in a sentence. However, this is not the only use case of it!"
description3 = "What did I do with it? Check out this [blog post](https://rachithaiyappa.github.io/science/Zero-Shot-for-Stance-Detection/) to see how it performs on some [SemEval](https://semeval.github.io/) tasks."
def zs(premise,hypothesis):
input_ids = tokenizer_bart.encode(premise, hypothesis, return_tensors='pt')
logits = model_bart_sq(input_ids)[0]
# entail_contradiction_logits = logits[:,[0,1,2]]
entail_contradiction_logits = logits[:,[0,2]]
probs = entail_contradiction_logits.softmax(dim=1)
contra_prob = round(probs[:,0].item(),4)
# neut_prob = round(probs[:,1].item(),4)
entail_prob = round(probs[:,1].item(),4)
# return contra_prob, neut_prob, entail_prob
return contra_prob, entail_prob
# gr.Interface(fn=zs, inputs=["text", "text"], outputs=["text","text","text"]).launch()
with gr.Blocks() as demo:
gr.Markdown(f" # {title}")
gr.Markdown(f" ## {description1}")
gr.Markdown(f"{description2}")
gr.Markdown(f"{description3}")
with gr.Row():
# premise = gr.Textbox(label="Premise",placeholder = "Roger Federer is an amazing tennis player.")
# hypothesis = gr.Textbox(label="Hypothesis", placeholder = "The stance to Roger Federer is positive.")
premise = gr.Textbox(label="Premise")
hypothesis = gr.Textbox(label="Hypothesis")
with gr.Row():
greet_btn = gr.Button("Compute")
with gr.Row():
entailment = gr.Textbox(label="Entailment Probability")
contradiction = gr.Textbox(label="Contradiction Probability")
# neutral = gr.Textbox(label="Neutral Probability")
# greet_btn.click(fn=zs, inputs=[premise,hypothesis], outputs=[contradiction,neutral,entailment])
greet_btn.click(fn=zs, inputs=[premise,hypothesis], outputs=[contradiction,entailment])
gr.Examples(
fn = zs,
examples = [["Roger Federer is an amazing tennis player.","The stance to Roger Federer is positive."],
["NETPLACE is an awesome gathering,", "We should do this more often."]],
inputs = [premise,hypothesis]
)
demo.launch() |