Tr Core News Md

🚀 土耳其語中型TrSpaCy管道模型

這是一個用於TrSpaCy的土耳其語中型管道模型，包含了tok2vec、tagger、morphologizer、lemmatizer、parser、ner等組件，可用於詞性標註、形態分析、命名實體識別等自然語言處理任務。

✨ 主要特性

• 多任務支持：支持詞性標註（TAG）、詞形還原（LEMMA）、命名實體識別（NER）等多種自然語言處理任務。
• 豐富的標籤體系：提供了詳細的標籤體系，涵蓋了多種詞性和形態信息。
• 良好的性能表現：在多個任務上取得了較好的準確率和F1值。

📦 安裝指南

由於安裝工具的更改，模型的輪子名稱有所變化。可以使用以下命令安裝該模型：

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

如果你想快速開始使用該模型，可以訪問以下Colab筆記本，其中包含了pip安裝、將模型加載到spaCy以及一個簡單的使用示例： 點擊訪問Colab筆記本

📚 詳細文檔

模型信息

標籤體系

準確率

🔧 技術細節

該模型基於TrSpaCy構建，使用了多個組件來完成不同的自然語言處理任務。其中，tok2vec組件用於將文本轉換為向量表示，tagger組件用於詞性標註，morphologizer組件用於形態分析，lemmatizer組件用於詞形還原，parser組件用於句法分析，ner組件用於命名實體識別。模型在多個公開數據集上進行了訓練，以提高其在不同任務上的性能。

📄 許可證

該模型採用cc-by-sa-4.0許可證。

相關視頻教程

有幾個與spaCy土耳其語模型相關的Youtube視頻系列，列表如下：

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

引用與研究論文

如果你想在自己的工作中使用該模型，請引用以下論文 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.",
}