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Multilingual-Zero-Shot-Sentiment-Classification / app.py

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Update app.py

2fe7971 about 2 years ago

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	from transformers import AutoModelForMaskedLM , AutoTokenizer
	import torch
	model_path="bert-base-multilingual-uncased"
	tokenizer = AutoTokenizer.from_pretrained(model_path)
	# load Prompting class
	from prompt import Prompting
	prompting= Prompting(model=model_path)
	prompt= ". Because it was "+ prompting.tokenizer.mask_token +"."

	def predict(text):
	THRESHOLD = prompting.compute_tokens_prob(prompt, token_list1=["good"], token_list2= ["bad"])[0].item()
	res=prompting.compute_tokens_prob(text+prompt, token_list1=["good"], token_list2= ["bad"])
	if res[0] > THRESHOLD:
	return {"POSITIVE":(res[0].item()-THRESHOLD)/ (1-THRESHOLD)}, (res[0].item()-THRESHOLD)/ (1-THRESHOLD)
	return {"NEGATIVE":(THRESHOLD-res[0].item())/THRESHOLD},(THRESHOLD-res[0].item())/THRESHOLD

	import gradio as gr
	iface = gr.Interface(fn=predict, inputs=["text"], outputs=["label","number"]).launch()