地球资源数据云——数据资源详情

深度自然语言处理

发布时间：2026-03-17 14:31:04资源ID：2032002577288564738资源类型：免费

该数据集《Deep - NLP》主要用于多分类任务，数据形态以图像为主，应用场景偏向安全检测。 题目说明：natural language processing 任务类型：图像多分类。 建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：Sheet_1.csv, Sheet_2.csv。 What's In The Deep - NLP Dataset? Sheet_1.csv contains 80 user responses, in the response_text column, to a therapy chatbot. Bot said: 'Describe a time when you have acted as a resource for someone else'. User responded. If a response is 'not flagged', the user can continue talking to the bot. If it is 'flagged', the user is referred to help. Sheet_2.csv contains 125 resumes, in the resume_text column. Resumes were queried from Indeed.com with keyword 'data scientist', location 'Vermont'. If a resume is 'not flagged', the applicant can submit a modified resume version at a later date. If it is 'flagged', the applicant is invited to interview. What Do I Do With This? There are two sets of data here - resumes and responses. Split the data into a train set and a test set to test the accuracy of your classifier. Bonus points for using the same classifier for both problems.

深度自然语言处理

摘要概览

该数据集《Deep - NLP》主要用于多分类任务，数据形态以图像为主，应用场景偏向安全检测。 题目说明：natural language processing

任务类型：图像多分类。

建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。

评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。

可用文件：Sheet_1.csv, Sheet_2.csv。

What's In The Deep - NLP Dataset?

Sheet_1.csv contains 80 user responses, in the response_text column, to a therapy chatbot. Bot said: 'Describe a time when you have acted as a resource for someone else'. User responded. If a response is 'not flagged', the user can continue talking to the bot. If it is 'flagged', the user is referred to help.

Sheet_2.csv contains 125 resumes, in the resume_text column. Resumes were queried from Indeed.com with keyword 'data scientist', location 'Vermont'. If a resume is 'not flagged', the applicant can submit a modified resume version at a later date. If it is 'flagged', the applicant is invited to interview.

What Do I Do With This?

There are two sets of data here - resumes and responses. Split the data into a train set and a test set to test the accuracy of your classifier. Bonus points for using the same classifier for both problems.