GreekNewsWord2Vec

Model description

This space contains the static cbow word2vec models along with their embedding matrices, trained on:

• 20,000 Greek news articles from the GreekNews-20k dataset
• 70,000 Greek news articles from the News Articles in Greek dataset
• 93,000 Greek Wikipedia articles from the IMISLab GreekWikipedia dataset
• 9,000 aritcles from the CGL Modern Greek Texts Corpora: newspaper corpus "Ta Nea"

Hyperparameters

The following hyperparameters were used to train the word2vec models

• window=5
• sg=0(CBOWmode)
• cbow_mean=1
• workers=8
• negative=10
• sample=1e-4
• epochs=50

Model performance

To benchmark these embeddings we reported our BiLSTMs performance on joint ner and classification on the GreekNews-20k dataset along with the WordSim353's Pearson/Spearman correlations.

Author

This model has been released along side with the article: Named Entity Recognition and News Article Classification: A Lightweight Approach.

To use this model please cite the following:

@ARTICLE{11148234,
  author={Katranis, Ioannis and Troussas, Christos and Krouska, Akrivi and Mylonas, Phivos and Sgouropoulou, Cleo},
  journal={IEEE Access}, 
  title={Named Entity Recognition and News Article Classification: A Lightweight Approach}, 
  year={2025},
  volume={13},
  number={},
  pages={155031-155046},
  keywords={Accuracy;Transformers;Pipelines;Named entity recognition;Computational modeling;Vocabulary;Tagging;Real-time systems;Benchmark testing;Training;Distilled transformer;edge-deployable model;multiclass news-topic classification;named entity recognition},
  doi={10.1109/ACCESS.2025.3605709}}