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from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# 加载微调的模型和tokenizer
model_name = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def classify_text(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class = torch.argmax(logits, dim=1).item()
return f"Predicted class: {predicted_class}"
import gradio as gr
interface = gr.Interface(
fn=classify_text,
inputs="text",
outputs="text",
title="BERT Text Classifier"
)
interface.launch()
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