interneuronai/classifying_member_activity_levels_distilbert_dataset
收藏Hugging Face2024-05-23 更新2024-06-12 收录
下载链接：
https://hf-mirror.com/datasets/interneuronai/classifying_member_activity_levels_distilbert_dataset
下载链接
链接失效反馈官方服务：
资源简介：
---
{}
---
### Classifying Member Activity Levels
**Description:** Categorize members based on their activity levels, such as low, medium, and high, to enable tailored engagement and retention strategies.
## How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/classifying_member_activity_levels_distilbert"
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
数据集名称： Classifying Member Activity Levels
数据集描述： 该数据集用于根据会员的活动水平（如低、中、高）进行分类，以实施针对性的参与和留存策略。
使用方法：
- 导入必要的库和模型。
- 设置模型和分词器的名称。
- 定义文本分类函数
classify_text(text)，该函数接受文本输入，使用分词器处理文本，并通过模型进行分类预测。 - 调用函数并输出分类结果。
示例代码： python from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/classifying_member_activity_levels_distilbert" 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))
