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@@ -30,10 +30,9 @@ To benchmark these embeddings we reported our BiLSTMs performance on joint ner a
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  #### Author
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  This model has been released along side with the article: Named Entity Recognition and News Article Classification: A Lightweight Approach.
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- ```
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  To use this model please cite the following:
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  ```
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-
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  @ARTICLE{11148234,
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  author={Katranis, Ioannis and Troussas, Christos and Krouska, Akrivi and Mylonas, Phivos and Sgouropoulou, Cleo},
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  journal={IEEE Access},
@@ -45,4 +44,4 @@ To use this model please cite the following:
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  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},
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  doi={10.1109/ACCESS.2025.3605709}}
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- ```
 
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  #### Author
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  This model has been released along side with the article: Named Entity Recognition and News Article Classification: A Lightweight Approach.
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+
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  To use this model please cite the following:
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  ```
 
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  @ARTICLE{11148234,
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  author={Katranis, Ioannis and Troussas, Christos and Krouska, Akrivi and Mylonas, Phivos and Sgouropoulou, Cleo},
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  journal={IEEE Access},
 
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  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},
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  doi={10.1109/ACCESS.2025.3605709}}
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+ ```