interneuronai/customer_feedback_analysis_bert_dataset
收藏Hugging Face2024-05-10 更新2024-06-12 收录
下载链接：
https://hf-mirror.com/datasets/interneuronai/customer_feedback_analysis_bert_dataset
下载链接
链接失效反馈官方服务：
资源简介：
---
{}
---
### Customer Feedback Analysis
**Description:** Classify customer feedback based on sentiment and topic to identify improvement areas and strengthen customer engagement.
## How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/customer_feedback_analysis_bert"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def classify_text(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
outputs = model(**inputs)
predictions = outputs.logits.argmax(-1)
return predictions.item()
text = "Your text here"
print("Category:", classify_text(text))
提供机构：
interneuronai
原始信息汇总
数据集概述
名称： Customer Feedback Analysis
描述： 该数据集用于分类客户反馈，基于情感和主题来识别改进领域并增强客户参与度。
使用方法：
- 导入必要的库和模型。
- 设置模型名称为 "interneuronai/customer_feedback_analysis_bert"。
- 使用AutoModelForSequenceClassification和AutoTokenizer从预设模型名称加载模型和分词器。
- 定义函数classify_text(text)来分类文本。
- 输入文本，调用函数输出分类结果。
