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.
Sentences
Vocabulary
Dimension
min_count
OOV
WS-353 Pearson
WS-353 Similarity
NER MicroF1%
Class Acc%
Total model parameters (M)
4564417
94865
128
44
42.4
0.42
0.40
85
76
13.7
4564417
140631
72
27
36.2
0.39
0.39
84
76
11.9
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}}
