基于Python+Flask提供抽取文档向量的服务源码+部署文档+全部数据资料 高分项目

## 文档向量化服务 doc-feature ### 一、项目贡献 1. 基于 [企鹅号](https://om.qq.com) 自媒体发布的 1000 多万篇中文文章语料，利用 CBOW 训练 word2vec 模型，得到 300 维的词向量； 2. 基于 Flask 框架提供一个简单的抽取文档向量的服务； ### 二、模型训练 1. 本次训练使用参数 | 参数名 | 取值 | | - | :-: | | size | 300 | | window | 5 | | min_count | 3 | | sg | 0 | 2. 参数含义 - size: 指定词向量的维度 - window: 指定句子中当前词语和待预测词语之间的最大距离 - min_count: 指定最小词频，忽略语料中总出现次数低于该值的词语 - sg: 指定训练算法，1 表示 "skip-gram"，0 表示 " CBOW" 3. 使用示例 1) 设置 locale ，在终端正确输入中文字符， ```export LANG=zh_CN.UTF-8``` 2) 载入 word2vec 模型  3) 示例   ### 三、文档向量化服务 1. 在目录 vec_server 中，提供了一个简单的利用 word2vec 抽取文档向量的 HTTP 服务 2. 基于 Flask 框架，只支持 POST 请求： - 参数名 ```doc```，输入为英文空格分割的文档词语列表； - 响应内容为 JSON 格式，其中```result``` 字段表示 300 维的向量； 3. CURL 请求示例 ```sh curl -XPOST -d 'doc=王丹妮 黑龙江省 绥化市 宝山镇 患有 白血病 年 花 43 万 钱治空 房子 卖 写下 死亡 日记 记录 抗 癌 经历 月 15 号 11 岁 生日 写道 祈求 死神 早日 带走 无钱 医治 真的 太 痛苦 月前 乙图 故事 关注 网友 高度 关注 一位 好心 神秘 人一次 捐赠 22 万 医疗费 图为 回到 北京 治疗 丹妮' 'http://host:ip/vectorize' ``` ```json 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