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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline | |
import gradio as gr | |
def text_sentiments(text): | |
model_name = "distilbert-base-uncased-finetuned-sst-2-english" | |
model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
classifier = pipeline(task= "sentiment-analysis", model = model, tokenizer = tokenizer) | |
result = classifier(text) | |
label = result[0]["label"] | |
score = result[0]["score"] * 100 | |
return f"Sentiment is : {label} and Confidence is : {score: 0.2f} %" | |
gr.Interface(fn = text_sentiments, | |
inputs = gr.inputs.Textbox(label = "Input Text"), | |
outputs = gr.outputs.Textbox(), | |
title = "Sentiment Classification with Bert", | |
).launch() | |