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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')
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]]
probs = entail_contradiction_logits.softmax(dim=1)
contra_prob = round(probs[:,0].item() * 100,2)
neut_prob = round(probs[:,1].item() * 100,2)
entail_prob = round(probs[:,2].item() * 100,2)
return contra_prob, neut_prob, entail_prob
# gr.Interface(fn=zs, inputs=["text", "text"], outputs=["text","text","text"]).launch()
with gr.Blocks() as demo:
with gr.Row():
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])
demo.launch() |