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--- |
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license: apache-2.0 |
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datasets: |
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- XiangPan/waimai_10k |
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language: |
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- zh |
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metrics: |
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- accuracy |
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base_model: |
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- google-bert/bert-base-chinese |
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--- |
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# Introduction |
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This model is trained based on the **base_model:google-bert/bert-base-chinese** and **datasets:XiangPan/waimai_10k** for sentiment analysis of reviews on a food delivery platform. It is designed to quickly identify negative reviews, allowing merchants to make targeted improvements efficiently. |
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# How to use |
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```bash |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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import torch |
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# 设备设置 |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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# 加载预训练的模型和分词器 |
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model_name = "zzz16/Public-analysis" # 确保该模型路径正确 |
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tokenizer_name = "bert-base-chinese" |
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model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device) |
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) |
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# 输入文本 |
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text = "这个外卖平台的服务很差劲，配送慢，食物也冷了。" |
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# 使用分词器进行编码，将文本转化为模型输入的格式 |
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inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt") |
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inputs = {key: value.to(device) for key, value in inputs.items()} # 迁移到设备上 |
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# 使用模型进行预测 |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# 获取模型的输出结果 |
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logits = outputs.logits |
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predicted_class = torch.argmax(logits, dim=-1) |
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# 输出预测的类别 |
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print(f"预测类别: {predicted_class.item()}") |
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``` |
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# 合作 |
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我们在研发针对商家/企业/平台的外卖、舆情分析部署，主要针对商家/企业/平台进行舆情把控、情感分析，以进行针对性、快速应对和解决问题，如果您的公司想要体验或者是合作可以联系我们：3022656072@qq.com **邮件最好用中文！英文垃圾邮件太多，可能会回复不及时** |
