使用SpaCy的公司
It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.
使用SpaCy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\": [
\"spacy\"
]
}"Technology
is any of
SpaCy
员工数24kInformation ServicesSpaCy高置信度
员工数15k收入$1.8BFinancial ServicesSpaCy高置信度
员工数218IT Services and IT ConsultingSpaCy高置信度
员工数857收入$200MIT Services and IT ConsultingSpaCy高置信度
员工数492收入$10MSoftware DevelopmentSpaCy高置信度
员工数71收入$5.6MData Infrastructure and AnalyticsSpaCy高置信度
员工数117k收入$35BFinancial ServicesSpaCy高置信度
员工数145收入$32MIT Services and IT ConsultingSpaCy高置信度
员工数3kSoftware DevelopmentSpaCy高置信度
员工数547IT Services and IT ConsultingSpaCy高置信度
我们拥有关于5,905家公司和用户使用SpaCy的数据。我们的SpaCy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 SpaCy 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。
SpaCy is a leading open-source library for Natural Language Processing (NLP) in Python. It provides efficient tools and a simple API for various NLP tasks such as tokenization, POS tagging, named entity recognition, and dependency parsing. SpaCy is known for its speed and accuracy, making it a popular choice among developers and researchers working with large volumes of text data. The library is designed to be user-friendly, enabling users to quickly implement complex NLP pipelines with minimal effort.
SpaCy falls under the category of NLP and Sentiment Analysis technology, offering a wide range of functionalities to analyze and process text data. From extracting entities and relationships to performing sentiment analysis on text, SpaCy caters to diverse NLP tasks required in industries like e-commerce, social media, and customer service.
Founded in 2015 by Matthew Honnibal and Ines Montani, SpaCy aimed to provide a modern and efficient NLP tool to the community. With a focus on speed and usability, the developers set out to create a library that could handle large-scale text processing tasks with ease. Since its inception, SpaCy has gained widespread adoption in both academia and industry, becoming a go-to choice for NLP projects.
In terms of market share, SpaCy has been steadily growing in popularity within the NLP technology landscape. With its robust features and continuous updates, SpaCy is well-positioned to maintain and even increase its market share in the future. As the demand for NLP solutions continues to rise across various sectors, SpaCy's versatile capabilities are likely to drive its growth trajectory forward.