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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() | |