Tr Core News Md

🚀 土耳其语中型TrSpaCy管道模型

这是一个用于TrSpaCy的土耳其语中型管道模型，包含了tok2vec、tagger、morphologizer、lemmatizer、parser、ner等组件，可用于词性标注、形态分析、命名实体识别等自然语言处理任务。

✨ 主要特性

• 多任务支持：支持词性标注（TAG）、词形还原（LEMMA）、命名实体识别（NER）等多种自然语言处理任务。
• 丰富的标签体系：提供了详细的标签体系，涵盖了多种词性和形态信息。
• 良好的性能表现：在多个任务上取得了较好的准确率和F1值。

📦 安装指南

由于安装工具的更改，模型的轮子名称有所变化。可以使用以下命令安装该模型：

pip install https://huggingface.co/turkish-nlp-suite/tr_core_news_md/resolve/main/tr_core_news_md-1.0-py3-none-any.whl

💻 使用示例

如果你想快速开始使用该模型，可以访问以下Colab笔记本，其中包含了pip安装、将模型加载到spaCy以及一个简单的使用示例： 点击访问Colab笔记本

📚 详细文档

模型信息

标签体系

准确率

🔧 技术细节

该模型基于TrSpaCy构建，使用了多个组件来完成不同的自然语言处理任务。其中，tok2vec组件用于将文本转换为向量表示，tagger组件用于词性标注，morphologizer组件用于形态分析，lemmatizer组件用于词形还原，parser组件用于句法分析，ner组件用于命名实体识别。模型在多个公开数据集上进行了训练，以提高其在不同任务上的性能。

📄 许可证

该模型采用cc-by-sa-4.0许可证。

相关视频教程

有几个与spaCy土耳其语模型相关的Youtube视频系列，列表如下：

• Hızlı spaCy Türkçe Tarifleri
• Hızlı Bankacılık FAQ Chatbotu
• spaCy modeli nasıl yapılır?
• Baştan sonra Türkçe dilbilimi

引用与研究论文

如果你想在自己的工作中使用该模型，请引用以下论文 A Diverse Set of Freely Available Linguistic Resources for Turkish：

@inproceedings{altinok-2023-diverse,
    title = "A Diverse Set of Freely Available Linguistic Resources for {T}urkish",
    author = "Altinok, Duygu",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.768",
    pages = "13739--13750",
    abstract = "This study presents a diverse set of freely available linguistic resources for Turkish natural language processing, including corpora, pretrained models and education material. Although Turkish is spoken by a sizeable population of over 80 million people, Turkish linguistic resources for natural language processing remain scarce. In this study, we provide corpora to allow practitioners to build their own applications and pretrained models that would assist industry researchers in creating quick prototypes. The provided corpora include named entity recognition datasets of diverse genres, including Wikipedia articles and supplement products customer reviews. In addition, crawling e-commerce and movie reviews websites, we compiled several sentiment analysis datasets of different genres. Our linguistic resources for Turkish also include pretrained spaCy language models. To the best of our knowledge, our models are the first spaCy models trained for the Turkish language. Finally, we provide various types of education material, such as video tutorials and code examples, that can support the interested audience on practicing Turkish NLP. The advantages of our linguistic resources are three-fold: they are freely available, they are first of their kind, and they are easy to use in a broad range of implementations. Along with a thorough description of the resource creation process, we also explain the position of our resources in the Turkish NLP world.",
}