| import gradio as gr |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch |
|
|
| model_name = "distilbert-base-uncased" |
| model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2) |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
|
| def classify_skill(text): |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| prediction = torch.argmax(outputs.logits, dim=1).item() |
| return "软技能" if prediction == 1 else "技术技能" |
|
|
| gr.Interface( |
| fn=classify_skill, |
| inputs="text", |
| outputs="text", |
| title="技能分类器", |
| description="输入一句话，系统将判断它描述的是技术技能还是软技能" |
| ).launch() |
