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07aec03 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
model = AutoModelForSequenceClassification.from_pretrained("knightscode139/bert-base-cased-imdb-sentiment")
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
def predict(text):
inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits[0], dim=0)
return {"Negative": float(probs[0]), "Positive": float(probs[1])}
interface = gr.Interface(
fn=predict,
inputs=gr.Textbox(lines=5, placeholder="Enter movie review..."),
outputs=gr.Label(num_top_classes=2),
title="IMDB Sentiment Classifier",
description="Fine-tuned BERT on IMDB reviews - 92.8% test accuracy"
)
interface.launch()
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