使用Gensim的公司
It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
使用Gensim的公司列表
curl --request POST \
--url "https://api.theirstack.com/v1/companies/search" \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer <api_key>" \
-d "{
\"company_technology_slug_or\": [
\"gensim\"
]
}"Technology
is any of
Gensim
员工数491k收入$157BRetailGensim高置信度
员工数168Software DevelopmentGensim高置信度
员工数2.4k收入$150MSoftware DevelopmentGensim高置信度
员工数310收入$13MSoftware DevelopmentGensim高置信度
员工数52IT Services and IT ConsultingGensim高置信度
员工数16Legal ServicesGensim高置信度
员工数36IT Services and IT ConsultingGensim高置信度
员工数284Software DevelopmentGensim高置信度
员工数57收入$50MDefense and Space ManufacturingGensim高置信度
员工数222收入$16MGovernment AdministrationGensim高置信度
我们拥有关于794家公司和用户使用Gensim的数据。我们的Gensim用户和客户列表可供下载，并附有重要的公司信息，包括行业分类、组织规模、地理位置、融资轮次和收入数据等。
技术使用统计和市场份额
如何定位 Gensim 用户
- 如何自定义此列表？
您可以根据地理位置、行业、公司规模、收入、技术使用、职位发布等需求自定义此数据。您可以将数据下载为Excel或CSV格式。
- 如何在公司采用此技术时收到通知？
您可以为此数据设置提醒。您可以通过选择您感兴趣的技术开始，当有新公司使用该技术时，您将收到收件箱中的提醒。
- 如何将此数据导入我的CRM？
您可以将此数据导出为Excel文件，然后导入到您的CRM中。您也可以将数据导出到API。
常见问题
我们的数据来源于从数百万家公司收集的职位发布。我们监控公司网站、招聘网站和其他招聘平台上的这些发布。分析职位发布提供了一种可靠的方法来了解公司正在使用的技术，包括他们使用的内部工具。
我们每天刷新数据，以确保您访问的是最新的信息。这一频繁的更新过程保证了我们的洞察和情报反映行业的最新发展和趋势。
Gensim is a popular open-source Python library designed for unsupervised topic modeling and natural language processing tasks. It provides an easy-to-use interface for various statistical semantics, document similarity analysis, text summarization, and more. Gensim stands out for its efficiency in handling large text datasets and its scalability in processing and analyzing textual data.
Gensim falls under the category of NLP (Natural Language Processing) and sentiment analysis tools. NLP involves the interaction between computers and human language, enabling machines to understand, interpret, and generate human language. Sentiment analysis, on the other hand, focuses on identifying and categorizing opinions expressed in text data to determine the sentiment conveyed, such as positive, negative, or neutral.
Founded in 2009 by Radim Řehůřek, Gensim stemmed from the motivation to provide a simple and efficient solution for processing and analyzing textual data for various NLP tasks. Over the years, Gensim has gained significant traction in the AI community due to its robust capabilities and ease of use.
Currently, Gensim holds a notable market share within the NLP and sentiment analysis category, being widely adopted by researchers, data scientists, and industry professionals. With the increasing demand for advanced NLP applications in various industries like finance, healthcare, and marketing, Gensim is poised to experience further growth in the future. Its continuous development and adaptability to evolving NLP trends make it a competitive choice for those seeking powerful text processing tools.