\u4ece\u8868\u683c\u53ef\u4ee5\u770b\u51fa，\u8fd9\u4e2a\u6a21\u578b\u5bf9\u786c\u4ef6\u7684\u8981\u6c42\u786e\u5b9e\u53cb\u597d\u5f88\u591a\u3002\u63a5\u4e0b\u6765\u6211\u4eec\u5c31\u5f00\u59cb\u5b9e\u9645\u7684\u90e8\u7f72\u5de5\u4f5c\u3002<\/p>\n
\u5728\u5f00\u59cb\u4e4b\u524d，\u8bf7\u786e\u4fdd\u4f60\u7684\u670d\u52a1\u5668\u6ee1\u8db3\u4ee5\u4e0b\u57fa\u672c\u8981\u6c42：<\/p>\n
\n- \u64cd\u4f5c\u7cfb\u7edf：Ubuntu 18.04\u6216\u66f4\u9ad8\u7248\u672c（\u5176\u4ed6Linux\u53d1\u884c\u7248\u4e5f\u53ef，\u4f46\u547d\u4ee4\u53ef\u80fd\u7565\u6709\u4e0d\u540c）<\/li>\n
- GPU：NVIDIA GPU，\u663e\u5b584GB\u6216\u4ee5\u4e0a<\/li>\n
- \u5185\u5b58：8GB\u6216\u4ee5\u4e0a<\/li>\n
- \u78c1\u76d8\u7a7a\u95f4：\u81f3\u5c1110GB\u53ef\u7528\u7a7a\u95f4<\/li>\n
- Python\u7248\u672c：3.8\u62163.9<\/li>\n<\/ul>\n
\u4f60\u53ef\u4ee5\u7528\u4ee5\u4e0b\u547d\u4ee4\u68c0\u67e5\u4f60\u7684\u7cfb\u7edf\u914d\u7f6e：<\/p>\n
# \u68c0\u67e5GPU\u4fe1\u606f
\nnvidia-smi<\/p>\n
# \u68c0\u67e5\u5185\u5b58
\nfree -h<\/p>\n
# \u68c0\u67e5\u78c1\u76d8\u7a7a\u95f4
\ndf -h<\/p>\n
# \u68c0\u67e5Python\u7248\u672c
\npython3 –version<\/p>\n
\u5982\u679cnvidia-smi\u547d\u4ee4\u80fd\u6b63\u5e38\u663e\u793aGPU\u4fe1\u606f，\u5e76\u4e14\u663e\u5b58\u67094GB\u4ee5\u4e0a，\u90a3\u4e48\u4f60\u7684\u670d\u52a1\u5668\u5c31\u7b26\u5408\u8981\u6c42\u3002<\/p>\n
3.2 \u4e00\u952e\u90e8\u7f72\u811a\u672c<\/h4>\n
\u4e3a\u4e86\u7b80\u5316\u90e8\u7f72\u8fc7\u7a0b，\u6211\u51c6\u5907\u4e86\u4e00\u4e2a\u4e00\u952e\u90e8\u7f72\u811a\u672c\u3002\u4f60\u53ea\u9700\u8981\u590d\u5236\u4e0b\u9762\u7684\u4ee3\u7801\u5230\u4f60\u7684\u670d\u52a1\u5668\u4e0a\u8fd0\u884c\u5373\u53ef：<\/p>\n
#!\/bin\/bash<\/p>\n
# StructBERT\u60c5\u611f\u5206\u6790\u670d\u52a1\u4e00\u952e\u90e8\u7f72\u811a\u672c
\n# \u9002\u7528\u4e8e4GB GPU\u670d\u52a1\u5668<\/p>\n
echo "\u5f00\u59cb\u90e8\u7f72StructBERT\u60c5\u611f\u5206\u6790\u670d\u52a1…"<\/p>\n
# 1. \u521b\u5efa\u9879\u76ee\u76ee\u5f55
\nmkdir -p \/root\/nlp_structbert_sentiment-classification_chinese-base
\ncd \/root\/nlp_structbert_sentiment-classification_chinese-base<\/p>\n
# 2. \u4e0b\u8f7d\u6a21\u578b\u6587\u4ef6（\u5982\u679c\u5df2\u6709\u6a21\u578b\u6587\u4ef6\u53ef\u8df3\u8fc7\u6b64\u6b65）
\necho "\u4e0b\u8f7d\u6a21\u578b\u6587\u4ef6…"
\n# \u8fd9\u91cc\u5047\u8bbe\u6a21\u578b\u6587\u4ef6\u5df2\u7ecf\u5b58\u5728，\u5b9e\u9645\u90e8\u7f72\u65f6\u53ef\u80fd\u9700\u8981\u4ece\u6307\u5b9a\u4f4d\u7f6e\u4e0b\u8f7d
\n# \u8bf7\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u8c03\u6574\u6a21\u578b\u4e0b\u8f7d\u6b65\u9aa4<\/p>\n
# 3. \u521b\u5efaPython\u865a\u62df\u73af\u5883
\necho "\u521b\u5efaPython\u865a\u62df\u73af\u5883…"
\npython3 -m venv venv
\nsource venv\/bin\/activate<\/p>\n
# 4. \u5b89\u88c5\u4f9d\u8d56\u5305
\necho "\u5b89\u88c5\u4f9d\u8d56\u5305…"
\npip install torch torchvision torchaudio –index-url https:\/\/download.pytorch.org\/whl\/cu118
\npip install transformers==4.30.0
\npip install flask==2.3.0
\npip install gradio==3.35.0
\npip install supervisor==4.2.0<\/p>\n
# 5. \u521b\u5efaWebUI\u5e94\u7528\u6587\u4ef6
\necho "\u521b\u5efaWebUI\u5e94\u7528…"
\ncat > app\/webui.py << 'EOF'
\nimport gradio as gr
\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification
\nimport torch
\nimport numpy as np<\/p>\n
# \u52a0\u8f7d\u6a21\u578b\u548c\u5206\u8bcd\u5668
\nmodel_path = "\/root\/ai-models\/iic\/nlp_structbert_sentiment-classification_chinese-base"
\ntokenizer = AutoTokenizer.from_pretrained(model_path)
\nmodel = AutoModelForSequenceClassification.from_pretrained(model_path)
\nmodel.eval()<\/p>\n
# \u60c5\u611f\u6807\u7b7e
\nlabels = ["\u8d1f\u9762", "\u4e2d\u6027", "\u6b63\u9762"]<\/p>\n
def analyze_sentiment(text):
\n """\u5206\u6790\u5355\u6761\u6587\u672c\u60c5\u611f"""
\n inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)<\/p>\n
with torch.no_grad():
\n outputs = model(**inputs)
\n probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)<\/p>\n
pred_idx = torch.argmax(probabilities, dim=-1).item()
\n confidence = probabilities[0][pred_idx].item()<\/p>\n
return {
\n "text": text,
\n "sentiment": labels[pred_idx],
\n "confidence": round(confidence, 4),
\n "probabilities": {label: round(prob.item(), 4) for label, prob in zip(labels, probabilities[0])}
\n }<\/p>\n
def batch_analyze(texts):
\n """\u6279\u91cf\u5206\u6790\u60c5\u611f"""
\n results = []
\n for text in texts.split('\\\\n'):
\n if text.strip():
\n result = analyze_sentiment(text.strip())
\n results.append(result)
\n return results<\/p>\n
# \u521b\u5efaGradio\u754c\u9762
\nwith gr.Blocks(title="StructBERT\u4e2d\u6587\u60c5\u611f\u5206\u6790") as demo:
\n gr.Markdown("# StructBERT\u4e2d\u6587\u60c5\u611f\u5206\u6790\u7cfb\u7edf")
\n gr.Markdown("\u8f93\u5165\u4e2d\u6587\u6587\u672c，\u5206\u6790\u60c5\u611f\u503e\u5411（\u6b63\u9762\/\u8d1f\u9762\/\u4e2d\u6027）")<\/p>\n
with gr.Tab("\u5355\u6587\u672c\u5206\u6790"):
\n with gr.Row():
\n with gr.Column():
\n input_text = gr.Textbox(label="\u8f93\u5165\u6587\u672c", placeholder="\u8bf7\u8f93\u5165\u8981\u5206\u6790\u7684\u4e2d\u6587\u6587\u672c…", lines=3)
\n analyze_btn = gr.Button("\u5f00\u59cb\u5206\u6790", variant="primary")<\/p>\n
with gr.Column():
\n output_text = gr.Textbox(label="\u5206\u6790\u7ed3\u679c", lines=6, interactive=False)
\n output_json = gr.JSON(label="\u8be6\u7ec6\u7ed3\u679c")<\/p>\n
analyze_btn.click(
\n fn=analyze_sentiment,
\n inputs=input_text,
\n outputs=[output_text, output_json]
\n )<\/p>\n
with gr.Tab("\u6279\u91cf\u5206\u6790"):
\n with gr.Row():
\n with gr.Column():
\n batch_input = gr.Textbox(label="\u6279\u91cf\u8f93\u5165", placeholder="\u6bcf\u884c\u8f93\u5165\u4e00\u6761\u6587\u672c…", lines=10)
\n batch_btn = gr.Button("\u5f00\u59cb\u6279\u91cf\u5206\u6790", variant="primary")<\/p>\n
with gr.Column():
\n batch_output = gr.Dataframe(
\n label="\u5206\u6790\u7ed3\u679c",
\n headers=["\u6587\u672c", "\u60c5\u611f\u503e\u5411", "\u7f6e\u4fe1\u5ea6", "\u6b63\u9762\u6982\u7387", "\u4e2d\u6027\u6982\u7387", "\u8d1f\u9762\u6982\u7387"],
\n datatype=["str", "str", "number", "number", "number", "number"]
\n )<\/p>\n
def format_batch_results(results):
\n formatted = []
\n for r in results:
\n formatted.append([
\n r["text"][:50] + "…" if len(r["text"]) > 50 else r["text"],
\n r["sentiment"],
\n r["confidence"],
\n r["probabilities"]["\u6b63\u9762"],
\n r["probabilities"]["\u4e2d\u6027"],
\n r["probabilities"]["\u8d1f\u9762"]
\n ])
\n return formatted<\/p>\n
batch_btn.click(
\n fn=lambda x: format_batch_results(batch_analyze(x)),
\n inputs=batch_input,
\n outputs=batch_output
\n )<\/p>\n
gr.Markdown("### \u4f7f\u7528\u8bf4\u660e")
\n gr.Markdown("""
\n 1. \u5728\u5355\u6587\u672c\u5206\u6790\u6807\u7b7e\u9875，\u8f93\u5165\u4e00\u6bb5\u6587\u672c\u70b9\u51fb\u5206\u6790
\n 2. \u5728\u6279\u91cf\u5206\u6790\u6807\u7b7e\u9875，\u6bcf\u884c\u8f93\u5165\u4e00\u6761\u6587\u672c\u8fdb\u884c\u6279\u91cf\u5206\u6790
\n 3. \u5206\u6790\u7ed3\u679c\u5305\u62ec\u60c5\u611f\u503e\u5411\u548c\u7f6e\u4fe1\u5ea6
\n """)<\/p>\n
if __name__ == "__main__":
\n demo.launch(server_name="0.0.0.0", server_port=7860, share=False)
\nEOF<\/p>\n
# 6. \u521b\u5efaAPI\u5e94\u7528\u6587\u4ef6
\necho "\u521b\u5efaAPI\u5e94\u7528…"
\ncat > app\/main.py << 'EOF'
\nfrom flask import Flask, request, jsonify
\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification
\nimport torch<\/p>\n
app = Flask(__name__)<\/p>\n
# \u52a0\u8f7d\u6a21\u578b
\nmodel_path = "\/root\/ai-models\/iic\/nlp_structbert_sentiment-classification_chinese-base"
\ntokenizer = AutoTokenizer.from_pretrained(model_path)
\nmodel = AutoModelForSequenceClassification.from_pretrained(model_path)
\nmodel.eval()<\/p>\n
labels = ["\u8d1f\u9762", "\u4e2d\u6027", "\u6b63\u9762"]<\/p>\n
@app.route('\/health', methods=['GET'])
\ndef health_check():
\n """\u5065\u5eb7\u68c0\u67e5\u63a5\u53e3"""
\n return jsonify({"status": "healthy", "model": "structbert-sentiment"})<\/p>\n
@app.route('\/predict', methods=['POST'])
\ndef predict():
\n """\u5355\u6587\u672c\u9884\u6d4b\u63a5\u53e3"""
\n try:
\n data = request.get_json()
\n text = data.get('text', '')<\/p>\n
if not text:
\n return jsonify({"error": "\u6587\u672c\u4e0d\u80fd\u4e3a\u7a7a"}), 400<\/p>\n
# \u63a8\u7406
\n inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)<\/p>\n
with torch.no_grad():
\n outputs = model(**inputs)
\n probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)<\/p>\n
pred_idx = torch.argmax(probabilities, dim=-1).item()
\n confidence = probabilities[0][pred_idx].item()<\/p>\n
result = {
\n "text": text,
\n "sentiment": labels[pred_idx],
\n "confidence": round(confidence, 4),
\n "probabilities": {
\n "\u6b63\u9762": round(probabilities[0][2].item(), 4),
\n "\u4e2d\u6027": round(probabilities[0][1].item(), 4),
\n "\u8d1f\u9762": round(probabilities[0][0].item(), 4)
\n }
\n }<\/p>\n
return jsonify(result)<\/p>\n
except Exception as e:
\n return jsonify({"error": str(e)}), 500<\/p>\n
@app.route('\/batch_predict', methods=['POST'])
\ndef batch_predict():
\n """\u6279\u91cf\u9884\u6d4b\u63a5\u53e3"""
\n try:
\n data = request.get_json()
\n texts = data.get('texts', [])<\/p>\n
if not texts or not isinstance(texts, list):
\n return jsonify({"error": "\u8bf7\u8f93\u5165\u6587\u672c\u5217\u8868"}), 400<\/p>\n
results = []
\n for text in texts:
\n inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)<\/p>\n
with torch.no_grad():
\n outputs = model(**inputs)
\n probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)<\/p>\n
pred_idx = torch.argmax(probabilities, dim=-1).item()
\n confidence = probabilities[0][pred_idx].item()<\/p>\n
results.append({
\n "text": text,
\n "sentiment": labels[pred_idx],
\n "confidence": round(confidence, 4),
\n "probabilities": {
\n "\u6b63\u9762": round(probabilities[0][2].item(), 4),
\n "\u4e2d\u6027": round(probabilities[0][1].item(), 4),
\n "\u8d1f\u9762": round(probabilities[0][0].item(), 4)
\n }
\n })<\/p>\n
return jsonify({"results": results, "count": len(results)})<\/p>\n
except Exception as e:
\n return jsonify({"error": str(e)}), 500<\/p>\n
if __name__ == '__main__':
\n app.run(host='0.0.0.0', port=8080, debug=False)
\nEOF<\/p>\n
# 7. \u521b\u5efaSupervisor\u914d\u7f6e\u6587\u4ef6
\necho "\u914d\u7f6eSupervisor…"
\ncat > \/etc\/supervisor\/conf.d\/nlp_structbert.conf << 'EOF'
\n[program:nlp_structbert_sentiment]
\ncommand=\/root\/nlp_structbert_sentiment-classification_chinese-base\/venv\/bin\/python \/root\/nlp_structbert_sentiment-classification_chinese-base\/app\/main.py
\ndirectory=\/root\/nlp_structbert_sentiment-classification_chinese-base
\nautostart=true
\nautorestart=true
\nstderr_logfile=\/var\/log\/nlp_structbert_api.err.log
\nstdout_logfile=\/var\/log\/nlp_structbert_api.out.log<\/p>\n
[program:nlp_structbert_webui]
\ncommand=\/root\/nlp_structbert_sentiment-classification_chinese-base\/venv\/bin\/python \/root\/nlp_structbert_sentiment-classification_chinese-base\/app\/webui.py
\ndirectory=\/root\/nlp_structbert_sentiment-classification_chinese-base
\nautostart=true
\nautorestart=true
\nstderr_logfile=\/var\/log\/nlp_structbert_webui.err.log
\nstdout_logfile=\/var\/log\/nlp_structbert_webui.out.log
\nEOF<\/p>\n
# 8. \u542f\u52a8\u670d\u52a1
\necho "\u542f\u52a8\u670d\u52a1…"
\nsupervisorctl reread
\nsupervisorctl update
\nsupervisorctl start nlp_structbert_sentiment
\nsupervisorctl start nlp_structbert_webui<\/p>\n
echo "\u90e8\u7f72\u5b8c\u6210！"
\necho "WebUI\u8bbf\u95ee\u5730\u5740: http:\/\/\u4f60\u7684\u670d\u52a1\u5668IP:7860"
\necho "API\u8bbf\u95ee\u5730\u5740: http:\/\/\u4f60\u7684\u670d\u52a1\u5668IP:8080"<\/p>\n
\u5c06\u4e0a\u9762\u7684\u811a\u672c\u4fdd\u5b58\u4e3adeploy_structbert.sh，\u7136\u540e\u7ed9\u5b83\u6267\u884c\u6743\u9650\u5e76\u8fd0\u884c：<\/p>\n
# \u7ed9\u811a\u672c\u6267\u884c\u6743\u9650
\nchmod +x deploy_structbert.sh<\/p>\n
# \u8fd0\u884c\u90e8\u7f72\u811a\u672c
\n.\/deploy_structbert.sh<\/p>\n
\u811a\u672c\u4f1a\u81ea\u52a8\u5b8c\u6210\u6240\u6709\u90e8\u7f72\u6b65\u9aa4，\u5927\u6982\u9700\u89815-10\u5206\u949f\u65f6\u95f4，\u5177\u4f53\u53d6\u51b3\u4e8e\u4f60\u7684\u7f51\u7edc\u901f\u5ea6\u548c\u670d\u52a1\u5668\u6027\u80fd\u3002<\/p>\n
4. \u670d\u52a1\u9a8c\u8bc1\u4e0e\u4f7f\u7528<\/h3>\n
\u90e8\u7f72\u5b8c\u6210\u540e，\u6211\u4eec\u9700\u8981\u9a8c\u8bc1\u670d\u52a1\u662f\u5426\u6b63\u5e38\u8fd0\u884c，\u5e76\u4e86\u89e3\u5982\u4f55\u4f7f\u7528\u3002<\/p>\n
4.1 \u68c0\u67e5\u670d\u52a1\u72b6\u6001<\/h4>\n
\u9996\u5148\u68c0\u67e5\u4e24\u4e2a\u670d\u52a1\u662f\u5426\u90fd\u6b63\u5e38\u542f\u52a8：<\/p>\n
# \u67e5\u770b\u670d\u52a1\u72b6\u6001
\nsupervisorctl status<\/p>\n
\u5982\u679c\u4e00\u5207\u6b63\u5e38，\u4f60\u4f1a\u770b\u5230\u7c7b\u4f3c\u8fd9\u6837\u7684\u8f93\u51fa：<\/p>\n
nlp_structbert_sentiment RUNNING pid 12345, uptime 0:05:00
\nnlp_structbert_webui RUNNING pid 12346, uptime 0:05:00<\/p>\n
4.2 \u8bbf\u95eeWebUI\u754c\u9762<\/h4>\n
\u6253\u5f00\u6d4f\u89c8\u5668，\u8bbf\u95ee http:\/\/\u4f60\u7684\u670d\u52a1\u5668IP:7860，\u4f60\u5e94\u8be5\u80fd\u770b\u5230\u8fd9\u6837\u7684\u754c\u9762：<\/p>\n
![\"WebUI\u754c\u9762\u793a\u610f\u56fe\"](\"https:\/\/via.placeholder.com\/800x400?text=StructBERT+%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90WebUI%E7%95%8C%E9%9D%A2\")
<\/p>\n
\u754c\u9762\u5206\u4e3a\u4e24\u4e2a\u6807\u7b7e\u9875：<\/p>\n
- \u5355\u6587\u672c\u5206\u6790：\u8f93\u5165\u4e00\u6bb5\u6587\u672c，\u70b9\u51fb"\u5f00\u59cb\u5206\u6790"\u6309\u94ae<\/li>\n
- \u6279\u91cf\u5206\u6790：\u6bcf\u884c\u8f93\u5165\u4e00\u6761\u6587\u672c，\u8fdb\u884c\u6279\u91cf\u5206\u6790<\/li>\n
\u8ba9\u6211\u7ed9\u4f60\u6f14\u793a\u51e0\u4e2a\u4f8b\u5b50：<\/p>\n
\u793a\u4f8b1：\u5206\u6790\u5355\u6761\u6587\u672c<\/p>\n
\u8f93\u5165：\u8fd9\u5bb6\u9910\u5385\u7684\u670d\u52a1\u771f\u7684\u5f88\u68d2，\u83dc\u54c1\u4e5f\u5f88\u7f8e\u5473！
\n\u8f93\u51fa：\u60c5\u611f\u503e\u5411：\u6b63\u9762，\u7f6e\u4fe1\u5ea6：0.95<\/p>\n
\u793a\u4f8b2：\u6279\u91cf\u5206\u6790<\/p>\n
\u8f93\u5165：
\n\u4eca\u5929\u7684\u5929\u6c14\u771f\u597d
\n\u8fd9\u4e2a\u4ea7\u54c1\u592a\u96be\u7528\u4e86
\n\u670d\u52a1\u6001\u5ea6\u4e00\u822c\u822c<\/p>\n
\u8f93\u51fa：
\n\u6587\u672c \u60c5\u611f\u503e\u5411 \u7f6e\u4fe1\u5ea6
\n\u4eca\u5929\u7684\u5929\u6c14\u771f\u597d \u6b63\u9762 0.92
\n\u8fd9\u4e2a\u4ea7\u54c1\u592a\u96be\u7528\u4e86 \u8d1f\u9762 0.88
\n\u670d\u52a1\u6001\u5ea6\u4e00\u822c\u822c \u4e2d\u6027 0.76<\/p>\n
4.3 \u4f7f\u7528API\u63a5\u53e3<\/h4>\n
\u5982\u679c\u4f60\u9700\u8981\u901a\u8fc7\u7a0b\u5e8f\u8c03\u7528\u60c5\u611f\u5206\u6790\u529f\u80fd，\u53ef\u4ee5\u4f7f\u7528RESTful API\u3002<\/p>\n
4.3.1 \u5065\u5eb7\u68c0\u67e5\u63a5\u53e3<\/h5>\n
curl http:\/\/localhost:8080\/health<\/p>\n
\u8fd4\u56de\u7ed3\u679c：<\/p>\n
{
\n "status": "healthy",
\n "model": "structbert-sentiment"
\n}<\/p>\n
4.3.2 \u5355\u6587\u672c\u5206\u6790\u63a5\u53e3<\/h5>\n
import requests
\nimport json<\/p>\n
url = "http:\/\/localhost:8080\/predict"
\nheaders = {"Content-Type": "application\/json"}
\ndata = {"text": "\u8fd9\u90e8\u7535\u5f71\u7684\u5267\u60c5\u592a\u7cbe\u5f69\u4e86！"}<\/p>\n
response = requests.post(url, headers=headers, data=json.dumps(data))
\nresult = response.json()<\/p>\n
print(f"\u60c5\u611f\u503e\u5411: {result['sentiment']}")
\nprint(f"\u7f6e\u4fe1\u5ea6: {result['confidence']}")
\nprint(f"\u8be6\u7ec6\u6982\u7387: {result['probabilities']}")<\/p>\n
4.3.3 \u6279\u91cf\u5206\u6790\u63a5\u53e3<\/h5>\n
import requests
\nimport json<\/p>\n
url = "http:\/\/localhost:8080\/batch_predict"
\nheaders = {"Content-Type": "application\/json"}
\ndata = {
\n "texts": [
\n "\u4eca\u5929\u5fc3\u60c5\u7279\u522b\u597d",
\n "\u8fd9\u4e2a\u51b3\u5b9a\u8ba9\u6211\u5f88\u5931\u671b",
\n "\u60c5\u51b5\u8fd8\u53ef\u4ee5，\u4e0d\u7b97\u592a\u5dee"
\n ]
\n}<\/p>\n
response = requests.post(url, headers=headers, data=json.dumps(data))
\nresults = response.json()<\/p>\n
for item in results["results"]:
\n print(f"\u6587\u672c: {item['text'][:30]}…")
\n print(f"\u60c5\u611f: {item['sentiment']}, \u7f6e\u4fe1\u5ea6: {item['confidence']}")
\n print("-" * 50)<\/p>\n
5. \u6027\u80fd\u4f18\u5316\u4e0e\u76d1\u63a7<\/h3>\n
\u5bf9\u4e8e4GB GPU\u7684\u670d\u52a1\u5668，\u6211\u4eec\u9700\u8981\u505a\u4e00\u4e9b\u4f18\u5316\u6765\u786e\u4fdd\u670d\u52a1\u7a33\u5b9a\u8fd0\u884c\u3002<\/p>\n
5.1 \u5185\u5b58\u4f18\u5316\u914d\u7f6e<\/h4>\n
\u4fee\u6539API\u670d\u52a1，\u6dfb\u52a0\u5185\u5b58\u4f18\u5316\u8bbe\u7f6e：<\/p>\n
# \u5728app\/main.py\u7684\u6a21\u578b\u52a0\u8f7d\u90e8\u5206\u6dfb\u52a0\u4ee5\u4e0b\u4ee3\u7801
\nimport os
\nos.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"<\/p>\n
# \u4fee\u6539\u6a21\u578b\u52a0\u8f7d\u65b9\u5f0f，\u4f7f\u7528\u66f4\u8282\u7701\u5185\u5b58\u7684\u914d\u7f6e
\nmodel = AutoModelForSequenceClassification.from_pretrained(
\n model_path,
\n torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
\n low_cpu_mem_usage=True
\n)<\/p>\n
# \u5982\u679c\u6709GPU，\u5c06\u6a21\u578b\u79fb\u5230GPU\u5e76\u8bbe\u7f6e\u8bc4\u4f30\u6a21\u5f0f
\nif torch.cuda.is_available():
\n model = model.cuda()
\n model = model.half() # \u4f7f\u7528\u534a\u7cbe\u5ea6\u6d6e\u70b9\u6570\u51cf\u5c11\u663e\u5b58\u5360\u7528
\nmodel.eval()<\/p>\n
5.2 \u76d1\u63a7GPU\u4f7f\u7528\u60c5\u51b5<\/h4>\n
\u521b\u5efa\u4e00\u4e2a\u76d1\u63a7\u811a\u672c，\u5b9a\u671f\u68c0\u67e5GPU\u4f7f\u7528\u60c5\u51b5：<\/p>\n
#!\/bin\/bash
\n# monitor_gpu.sh – GPU\u4f7f\u7528\u60c5\u51b5\u76d1\u63a7\u811a\u672c<\/p>\n
while true; do
\n echo "=== $(date) ==="
\n nvidia-smi –query-gpu=memory.used,memory.total,utilization.gpu –format=csv
\n echo ""<\/p>\n
# \u68c0\u67e5\u670d\u52a1\u72b6\u6001
\n supervisorctl status nlp_structbert_sentiment
\n supervisorctl status nlp_structbert_webui
\n echo ""<\/p>\n
sleep 60 # \u6bcf60\u79d2\u68c0\u67e5\u4e00\u6b21
\ndone<\/p>\n
\u8fd0\u884c\u76d1\u63a7\u811a\u672c：<\/p>\n
chmod +x monitor_gpu.sh
\n.\/monitor_gpu.sh<\/p>\n
5.3 \u8bbe\u7f6e\u8d44\u6e90\u9650\u5236<\/h4>\n
\u901a\u8fc7Supervisor\u9650\u5236\u670d\u52a1\u8d44\u6e90\u4f7f\u7528：<\/p>\n
# \u4fee\u6539\/etc\/supervisor\/conf.d\/nlp_structbert.conf
\n[program:nlp_structbert_sentiment]
\ncommand=\/root\/nlp_structbert_sentiment-classification_chinese-base\/venv\/bin\/python \/root\/nlp_structbert_sentiment-classification_chinese-base\/app\/main.py
\ndirectory=\/root\/nlp_structbert_sentiment-classification_chinese-base
\nautostart=true
\nautorestart=true
\nstderr_logfile=\/var\/log\/nlp_structbert_api.err.log
\nstdout_logfile=\/var\/log\/nlp_structbert_api.out.log
\n# \u6dfb\u52a0\u8d44\u6e90\u9650\u5236
\nenvironment=OMP_NUM_THREADS=2
\nprocess_name=%(program_name)s_%(process_num)02d
\nnumprocs=1
\nnumprocs_start=0
\nstopasgroup=true
\nkillasgroup=true<\/p>\n
6. \u5e38\u89c1\u95ee\u9898\u89e3\u51b3<\/h3>\n
\u5728\u5b9e\u9645\u4f7f\u7528\u4e2d，\u4f60\u53ef\u80fd\u4f1a\u9047\u5230\u4e00\u4e9b\u95ee\u9898\u3002\u8fd9\u91cc\u6211\u6574\u7406\u4e86\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898\u548c\u89e3\u51b3\u65b9\u6cd5\u3002<\/p>\n
6.1 WebUI\u65e0\u6cd5\u8bbf\u95ee<\/h4>\n
\u95ee\u9898：\u6d4f\u89c8\u5668\u6253\u4e0d\u5f00 http:\/\/\u670d\u52a1\u5668IP:7860<\/p>\n
\u89e3\u51b3\u65b9\u6cd5：<\/p>\n
- \n
\u68c0\u67e5\u9632\u706b\u5899\u8bbe\u7f6e，\u786e\u4fdd7860\u7aef\u53e3\u5f00\u653e<\/p>\n
sudo ufw allow 7860
\nsudo ufw reload\n <\/li>\n
- \n
\u68c0\u67e5\u670d\u52a1\u662f\u5426\u8fd0\u884c<\/p>\n
supervisorctl status nlp_structbert_webui\n <\/li>\n
- \n
\u5982\u679c\u670d\u52a1\u6ca1\u6709\u8fd0\u884c，\u624b\u52a8\u542f\u52a8<\/p>\n
supervisorctl start nlp_structbert_webui\n <\/li>\n
- \n
\u67e5\u770b\u65e5\u5fd7\u627e\u9519\u8bef\u539f\u56e0<\/p>\n
supervisorctl tail -f nlp_structbert_webui\n <\/li>\n
6.2 API\u8bf7\u6c42\u8d85\u65f6<\/h4>\n
\u95ee\u9898：\u8c03\u7528API\u65f6\u8bf7\u6c42\u8d85\u65f6<\/p>\n
\u89e3\u51b3\u65b9\u6cd5：<\/p>\n
- \u6a21\u578b\u9996\u6b21\u52a0\u8f7d\u9700\u8981\u65f6\u95f4，\u7b49\u5f851-2\u5206\u949f\u518d\u8bd5<\/li>\n
- \u68c0\u67e5GPU\u5185\u5b58\u662f\u5426\u5145\u8db3nvidia-smi\n <\/li>\n
- \u5982\u679c\u5185\u5b58\u4e0d\u8db3，\u91cd\u542f\u670d\u52a1\u91ca\u653e\u5185\u5b58supervisorctl restart nlp_structbert_sentiment\n <\/li>\n
6.3 \u663e\u5b58\u4e0d\u8db3\u9519\u8bef<\/h4>\n
\u95ee\u9898：\u51fa\u73b0CUDA out of memory\u9519\u8bef<\/p>\n
\u89e3\u51b3\u65b9\u6cd5：<\/p>\n
- \u51cf\u5c11\u6279\u91cf\u5904\u7406\u7684\u5927\u5c0f<\/li>\n
- \u4f7f\u7528\u66f4\u5c0f\u7684max_length（\u5728\u4ee3\u7801\u4e2d\u4fee\u6539）# \u5c06max_length\u4ece512\u6539\u4e3a256
\ninputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)\n <\/li>\n - \u786e\u4fdd\u6ca1\u6709\u5176\u4ed6\u7a0b\u5e8f\u5360\u7528GPU<\/li>\n
- \u91cd\u542f\u670d\u52a1\u5668\u91ca\u653e\u663e\u5b58<\/li>\n
6.4 \u670d\u52a1\u81ea\u52a8\u91cd\u542f<\/h4>\n
\u95ee\u9898：\u670d\u52a1\u7ecf\u5e38\u81ea\u52a8\u91cd\u542f<\/p>\n
\u89e3\u51b3\u65b9\u6cd5：<\/p>\n
- \u67e5\u770b\u65e5\u5fd7\u5206\u6790\u539f\u56e0supervisorctl tail -f nlp_structbert_sentiment\n <\/li>\n
- \u53ef\u80fd\u662f\u5185\u5b58\u4e0d\u8db3，\u589e\u52a0\u865a\u62df\u5185\u5b58# \u521b\u5efa8GB\u7684\u4ea4\u6362\u6587\u4ef6
\nsudo fallocate -l 8G \/swapfile
\nsudo chmod 600 \/swapfile
\nsudo mkswap \/swapfile
\nsudo swapon \/swapfile<\/p>\n# \u6c38\u4e45\u751f\u6548
\necho '\/swapfile none swap sw 0 0' | sudo tee -a \/etc\/fstab\n <\/li>\n
7. \u5b9e\u9645\u5e94\u7528\u573a\u666f<\/h3>\n
\u8fd9\u4e2a\u60c5\u611f\u5206\u6790\u670d\u52a1\u53ef\u4ee5\u5e94\u7528\u5728\u5f88\u591a\u5b9e\u9645\u573a\u666f\u4e2d，\u4e0b\u9762\u6211\u4e3e\u51e0\u4e2a\u4f8b\u5b50\u3002<\/p>\n
7.1 \u7535\u5546\u8bc4\u8bba\u5206\u6790<\/h4>\n
\u5982\u679c\u4f60\u7ecf\u8425\u4e00\u4e2a\u7535\u5546\u5e73\u53f0，\u53ef\u4ee5\u7528\u8fd9\u4e2a\u670d\u52a1\u81ea\u52a8\u5206\u6790\u7528\u6237\u8bc4\u8bba：<\/p>\n
# \u5206\u6790\u5546\u54c1\u8bc4\u8bba\u60c5\u611f
\ncomments = [
\n "\u4ea7\u54c1\u8d28\u91cf\u5f88\u597d，\u7269\u6d41\u4e5f\u5feb",
\n "\u5305\u88c5\u7834\u635f\u4e86，\u5f88\u4e0d\u6ee1\u610f",
\n "\u4e00\u822c\u822c，\u6ca1\u6709\u60f3\u8c61\u4e2d\u597d",
\n "\u5ba2\u670d\u6001\u5ea6\u5f88\u5dee，\u518d\u4e5f\u4e0d\u4e70\u4e86",
\n "\u7269\u8d85\u6240\u503c，\u4f1a\u63a8\u8350\u7ed9\u670b\u53cb"
\n]<\/p>\n
# \u8c03\u7528\u6279\u91cf\u5206\u6790\u63a5\u53e3
\nresults = analyze_batch(comments)<\/p>\n
# \u7edf\u8ba1\u60c5\u611f\u5206\u5e03
\npositive_count = sum(1 for r in results if r["sentiment"] == "\u6b63\u9762")
\nnegative_count = sum(1 for r in results if r["sentiment"] == "\u8d1f\u9762")
\nneutral_count = sum(1 for r in results if r["sentiment"] == "\u4e2d\u6027")<\/p>\n
print(f"\u6b63\u9762\u8bc4\u4ef7: {positive_count}\u6761")
\nprint(f"\u8d1f\u9762\u8bc4\u4ef7: {negative_count}\u6761")
\nprint(f"\u4e2d\u6027\u8bc4\u4ef7: {neutral_count}\u6761")
\nprint(f"\u6ee1\u610f\u5ea6: {positive_count\/len(comments)*100:.1f}%")<\/p>\n
7.2 \u793e\u4ea4\u5a92\u4f53\u60c5\u7eea\u76d1\u63a7<\/h4>\n
\u76d1\u63a7\u793e\u4ea4\u5a92\u4f53\u4e0a\u5173\u4e8e\u67d0\u4e2a\u8bdd\u9898\u7684\u60c5\u7eea\u53d8\u5316：<\/p>\n
import time
\nfrom datetime import datetime<\/p>\n
class SocialMediaMonitor:
\n def __init__(self, api_url="http:\/\/localhost:8080"):
\n self.api_url = api_url
\n self.sentiment_history = []<\/p>\n
def analyze_topic(self, topic, posts):
\n """\u5206\u6790\u67d0\u4e2a\u8bdd\u9898\u7684\u76f8\u5173\u5e16\u5b50"""
\n sentiments = []<\/p>\n
for post in posts:
\n # \u8c03\u7528\u60c5\u611f\u5206\u6790API
\n result = self.call_api(post)
\n sentiments.append(result["sentiment"])<\/p>\n
# \u8bb0\u5f55\u65f6\u95f4\u6233\u548c\u60c5\u611f
\n self.sentiment_history.append({
\n "timestamp": datetime.now(),
\n "topic": topic,
\n "text": post[:100], # \u53ea\u8bb0\u5f55\u524d100\u5b57\u7b26
\n "sentiment": result["sentiment"],
\n "confidence": result["confidence"]
\n })<\/p>\n
# \u8ba1\u7b97\u60c5\u611f\u5206\u5e03
\n from collections import Counter
\n distribution = Counter(sentiments)<\/p>\n
return {
\n "topic": topic,
\n "total_posts": len(posts),
\n "sentiment_distribution": dict(distribution),
\n "positive_ratio": distribution.get("\u6b63\u9762", 0) \/ len(posts) if posts else 0
\n }<\/p>\n
def call_api(self, text):
\n """\u8c03\u7528\u60c5\u611f\u5206\u6790API"""
\n import requests
\n import json<\/p>\n
response = requests.post(
\n f"{self.api_url}\/predict",
\n json={"text": text}
\n )
\n return response.json()<\/p>\n
def get_trend_report(self, topic, hours=24):
\n """\u751f\u6210\u8d8b\u52bf\u62a5\u544a"""
\n # \u8fc7\u6ee4\u6307\u5b9a\u65f6\u95f4\u6bb5\u5185\u7684\u8bb0\u5f55
\n cutoff_time = datetime.now() – timedelta(hours=hours)
\n relevant_records = [
\n r for r in self.sentiment_history
\n if r["topic"] == topic and r["timestamp"] > cutoff_time
\n ]<\/p>\n
# \u751f\u6210\u62a5\u544a…
\n return report<\/p>\n
7.3 \u5ba2\u670d\u5bf9\u8bdd\u8d28\u91cf\u8bc4\u4f30<\/h4>\n
\u81ea\u52a8\u8bc4\u4f30\u5ba2\u670d\u5bf9\u8bdd\u4e2d\u7684\u5ba2\u6237\u60c5\u7eea：<\/p>\n
def evaluate_customer_service(dialogues):
\n """\u8bc4\u4f30\u5ba2\u670d\u5bf9\u8bdd\u8d28\u91cf"""
\n results = []<\/p>\n
for dialogue in dialogues:
\n customer_messages = [msg for msg in dialogue if msg["role"] == "customer"]<\/p>\n
# \u5206\u6790\u5ba2\u6237\u6bcf\u6761\u6d88\u606f\u7684\u60c5\u611f
\n sentiments = []
\n for msg in customer_messages:
\n result = analyze_sentiment(msg["content"])
\n sentiments.append(result["sentiment"])<\/p>\n
# \u5224\u65ad\u6574\u4f53\u5bf9\u8bdd\u60c5\u7eea
\n if "\u8d1f\u9762" in sentiments[-3:]: # \u6700\u8fd13\u6761\u6d88\u606f\u6709\u8d1f\u9762
\n status = "\u9700\u5173\u6ce8"
\n elif all(s == "\u6b63\u9762" for s in sentiments[-2:]): # \u6700\u8fd12\u6761\u90fd\u662f\u6b63\u9762
\n status = "\u826f\u597d"
\n else:
\n status = "\u6b63\u5e38"<\/p>\n
results.append({
\n "dialogue_id": dialogue["id"],
\n "customer_sentiments": sentiments,
\n "final_status": status,
\n "recommendation": "\u9700\u8981\u4e3b\u7ba1\u4ecb\u5165" if status == "\u9700\u5173\u6ce8" else "\u7ee7\u7eed\u8ddf\u8fdb"
\n })<\/p>\n
return results<\/p>\n
8. \u603b\u7ed3<\/h3>\n
\u901a\u8fc7\u8fd9\u7bc7\u6559\u7a0b，\u4f60\u5e94\u8be5\u5df2\u7ecf\u6210\u529f\u57284GB GPU\u7684\u670d\u52a1\u5668\u4e0a\u90e8\u7f72\u4e86StructBERT\u4e2d\u6587\u60c5\u611f\u5206\u6790\u670d\u52a1\u3002\u6211\u4eec\u6765\u56de\u987e\u4e00\u4e0b\u91cd\u70b9：<\/p>\n
8.1 \u90e8\u7f72\u8981\u70b9\u603b\u7ed3<\/h4>\n
- \u73af\u5883\u8981\u6c42\u4f4e：\u53ea\u9700\u89814GB\u663e\u5b58\u7684GPU，8GB\u5185\u5b58，\u90e8\u7f72\u8fc7\u7a0b\u7b80\u5355<\/li>\n
- \u4e00\u952e\u90e8\u7f72：\u4f7f\u7528\u63d0\u4f9b\u7684\u811a\u672c\u53ef\u4ee5\u5feb\u901f\u5b8c\u6210\u6240\u6709\u90e8\u7f72\u6b65\u9aa4<\/li>\n
- \u53cc\u63a5\u53e3\u652f\u6301：\u540c\u65f6\u63d0\u4f9bWebUI\u548cAPI\u4e24\u79cd\u4f7f\u7528\u65b9\u5f0f<\/li>\n
- \u8d44\u6e90\u4f18\u5316：\u901a\u8fc7\u534a\u7cbe\u5ea6\u63a8\u7406\u548c\u5185\u5b58\u4f18\u5316，\u786e\u4fdd\u5728\u4f4e\u914d\u670d\u52a1\u5668\u4e0a\u7a33\u5b9a\u8fd0\u884c<\/li>\n
8.2 \u670d\u52a1\u7ba1\u7406\u547d\u4ee4<\/h4>\n
\u8bb0\u4f4f\u8fd9\u51e0\u4e2a\u5e38\u7528\u547d\u4ee4，\u65b9\u4fbf\u65e5\u5e38\u7ba1\u7406：<\/p>\n
# \u67e5\u770b\u670d\u52a1\u72b6\u6001
\nsupervisorctl status<\/p>\n
# \u91cd\u542f\u670d\u52a1
\nsupervisorctl restart nlp_structbert_sentiment
\nsupervisorctl restart nlp_structbert_webui<\/p>\n
# \u67e5\u770b\u65e5\u5fd7
\nsupervisorctl tail -f nlp_structbert_sentiment
\nsupervisorctl tail -f nlp_structbert_webui<\/p>\n
# \u505c\u6b62\u670d\u52a1
\nsupervisorctl stop all<\/p>\n
# \u542f\u52a8\u670d\u52a1
\nsupervisorctl start all<\/p>\n
8.3 \u4e0b\u4e00\u6b65\u5efa\u8bae<\/h4>\n
\u5982\u679c\u4f60\u60f3\u8ba9\u8fd9\u4e2a\u670d\u52a1\u66f4\u52a0\u5f3a\u5927，\u53ef\u4ee5\u8003\u8651：<\/p>\n
- \u6dfb\u52a0\u7f13\u5b58\u673a\u5236：\u5bf9\u76f8\u540c\u7684\u67e5\u8be2\u7ed3\u679c\u8fdb\u884c\u7f13\u5b58，\u63d0\u9ad8\u54cd\u5e94\u901f\u5ea6<\/li>\n
- \u5b9e\u73b0\u5f02\u6b65\u5904\u7406：\u5bf9\u4e8e\u6279\u91cf\u4efb\u52a1，\u4f7f\u7528\u5f02\u6b65\u5904\u7406\u907f\u514d\u963b\u585e<\/li>\n
- \u6dfb\u52a0\u8eab\u4efd\u9a8c\u8bc1：\u5982\u679c\u670d\u52a1\u5bf9\u5916\u5f00\u653e，\u6dfb\u52a0API\u5bc6\u94a5\u9a8c\u8bc1<\/li>\n
- \u96c6\u6210\u5230\u73b0\u6709\u7cfb\u7edf：\u5c06\u60c5\u611f\u5206\u6790\u529f\u80fd\u96c6\u6210\u5230\u4f60\u7684\u4e1a\u52a1\u7cfb\u7edf\u4e2d<\/li>\n
- \u5b9a\u671f\u66f4\u65b0\u6a21\u578b：\u5173\u6ce8\u6a21\u578b\u66f4\u65b0，\u5b9a\u671f\u5347\u7ea7\u5230\u65b0\u7248\u672c<\/li>\n
\u8fd9\u4e2aStructBERT\u60c5\u611f\u5206\u6790\u670d\u52a1\u867d\u7136\u8f7b\u91cf，\u4f46\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u8868\u73b0\u76f8\u5f53\u4e0d\u9519\u3002\u7279\u522b\u662f\u5728\u8d44\u6e90\u6709\u9650\u7684\u73af\u5883\u4e2d，\u5b83\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5f88\u597d\u7684\u5e73\u8861\u70b9\u2014\u2014\u65e2\u4fdd\u8bc1\u4e86\u4e0d\u9519\u7684\u60c5\u611f\u8bc6\u522b\u51c6\u786e\u7387，\u53c8\u4e0d\u4f1a\u5bf9\u670d\u52a1\u5668\u9020\u6210\u592a\u5927\u8d1f\u62c5\u3002<\/p>\n
\u5982\u679c\u4f60\u5728\u90e8\u7f72\u6216\u4f7f\u7528\u8fc7\u7a0b\u4e2d\u9047\u5230\u4efb\u4f55\u95ee\u9898，\u6216\u8005\u6709\u66f4\u597d\u7684\u4f18\u5316\u5efa\u8bae，\u6b22\u8fce\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u5c1d\u8bd5\u548c\u8c03\u6574\u3002\u6700\u91cd\u8981\u7684\u662f\u5f00\u59cb\u7528\u8d77\u6765，\u5728\u5b9e\u9645\u4f7f\u7528\u4e2d\u4f60\u4f1a\u53d1\u73b0\u66f4\u591a\u6709\u8da3\u7684\u5e94\u7528\u573a\u666f\u3002<\/p>\n
\n\u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n
\u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f？\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a，\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf，\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df，\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
StructBERT\u8f7b\u91cf\u7ea7\u6a21\u578b\u90e8\u7f72\u6559\u7a0b：\u4f4e\u914d\u670d\u52a1\u5668（4GB GPU）\u7a33\u5b9a\u8fd0\u884c\u65b9\u6848
\n1. \u5f15\u8a00
\n\u4f60\u662f\u4e0d\u662f\u4e5f\u60f3\u5728\u81ea\u5df1\u7684\u670d\u52a1\u5668\u4e0a\u8dd1\u4e00\u4e2a\u4e2d\u6587\u60c5\u611f\u5206\u6790\u6a21\u578b，\u4f46\u4e00\u770b\u90a3\u4e9b\u5927\u6a21\u578b\u52a8\u8f84\u9700\u8981\u51e0\u5341GB\u7684\u663e\u5b58\u5c31\u671b\u800c\u5374\u6b65？\u6216\u8005\u4f60\u5df2\u7ecf\u5c1d\u8bd5\u8fc7\u4e00\u4e9b\u65b9\u6848，\u7ed3\u679c\u8981\u4e48\u662f\u90e8\u7f72\u590d\u6742\u5230\u8ba9\u4eba\u5934\u75bc，\u8981\u4e48\u662f\u8fd0\u884c\u8d77\u6765\u670d\u52a1\u5668\u5c31\u5361\u6b7b？
\n\u4eca\u5929\u6211\u8981\u5206\u4eab\u7684StructBERT\u60c5\u611f\u5206\u7c7b\u6a21\u578b，\u53ef\u80fd\u5c31\u662f\u4f60\u5728\u627e\u7684\u7b54<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[7591,9198,8499],"topic":[],"class_list":["post-82396","post","type-post","status-publish","format-standard","hentry","category-server","tag-ai","tag-structbert","tag-8499"],"yoast_head":"\n
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