transformers pytorch tf safetensors bert feature-extraction text language-modeling pretraining greek-media domain-adaptation fill-mask el endpoints_compatible region:us
---
language:
- el
metrics:
- accuracy
pipeline_tag: fill-mask
tags:
- text
- language-modeling
- bert
- pretraining
- greek-media
- domain-adaptation
model-index:
- name: greek-media-bert-base-uncased
  results: []
---


# Greek Media BERT (uncased)

This model is a domain-adapted version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on Greek media centric data.

## Model description

Details will be updated soon.

## Intended uses & limitations

Details will be updated soon.

## Training and evaluation data

Details will be updated soon.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results
Details will be updated soon.

### Framework versions

- Transformers 4.21.0.dev0
- Pytorch 1.12.0+cu116
- Tensorflow 2.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1

### Citation

The model has been officially released with the article "PIMA: Parameter-shared Intelligent Media Analytics Framework for Low Resource Language. 
Dimitrios Zaikis, Nikolaos Stylianou and Ioannis Vlahavas. 
In the Special Issue: New Techniques of Machine Learning and Deep Learning in Text Classification, Applied Sciences Journal. 
2023" (https://www.mdpi.com/2174928).

If you use the model, please cite the following:

```bibtex
@Article{app13053265,
  AUTHOR = {Zaikis, Dimitrios and Stylianou, Nikolaos and Vlahavas, Ioannis},
  TITLE = {PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages},
  JOURNAL = {Applied Sciences},
  VOLUME = {13},
  YEAR = {2023},
  NUMBER = {5},
  ARTICLE-NUMBER = {3265},
  URL = {https://www.mdpi.com/2076-3417/13/5/3265},
  ISSN = {2076-3417},
  DOI = {10.3390/app13053265}
}
```