Mental Health Diagnosis Bert

🚀 心理健康診斷BERT模型

本模型對Bio_ClinicalBERT進行微調，以根據患者陳述預測心理健康診斷結果。它可以將文本分為5個類別：

• 焦慮
• 抑鬱
• 自殺傾向
• 壓力
• 正常

🚀 快速開始

💻 使用示例

基礎用法

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn.functional as F

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/mental-health-diagnosis-bert")
model = AutoModelForSequenceClassification.from_pretrained("YOUR_USERNAME/mental-health-diagnosis-bert")

# Prepare text
text = "I've been feeling very anxious and worried all the time."
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)
    probabilities = F.softmax(outputs.logits, dim=1)

# Map prediction to label
label_mapping = {0: "Anxiety", 1: "Normal", 2: "Depression", 3: "Suicidal", 4: "Stress"}
predicted_class = torch.argmax(probabilities, dim=1).item()
prediction = label_mapping[predicted_class]
confidence = probabilities[0][predicted_class].item()

print(f"Prediction: {prediction}, Confidence: {confidence:.2f}")

📄 許可證

本項目採用MIT許可證。