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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."]], | |
| inputs = [premise,hypothesis] | |
| ) | |
| demo.launch() |