---
language:
  - el
tags:
  - text
  - language-modeling
datasets:
  - dataset/wiki_oscar_combined_normalized_uncased
metrics:
  - accuracy
model-index:
  - name: greek-longformer-base-4096
    results:
      - task:
          name: Masked Language Modeling
          type: fill-mask
        dataset:
          name: dataset/wiki_oscar_combined_normalized_uncased
          type: dataset/wiki_oscar_combined_normalized_uncased
          split: None
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7765486725663717
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Greek Longformer

A Greek version of the Longformer Language Model.

This model is a (from scratch) Greek Longformer model based on the configuration of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096), and trained on the combined datasets from the [Greek Wikipedia](https://huggingface.co/datasets/wikipedia) and the Greek part of [OSCAR](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301).
It achieves the following results on the evaluation set:

- Loss: 1.1080
- Accuracy: 0.7765

## Pre-training corpora

The pre-training corpora of `greek-longformer-base-4096` include:

- The Greek part of [Wikipedia](https://el.wikipedia.org/wiki/Βικιπαίδεια:Αντίγραφα_της_βάσης_δεδομένων),
- The Greek part of [OSCAR](https://traces1.inria.fr/oscar/), a cleansed version of [Common Crawl](https://commoncrawl.org).

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6.0

### Training results

### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2

## Citing & Authors

The model has been officially released with the article "From Pre-training to Meta-Learning: A journey in Low-Resource-Language Representation Learning".
Dimitrios Zaikis and Ioannis Vlahavas.
In: IEEE Access.

If you use the model, please cite the following:

```bibtex

@ARTICLE{10288436,
    author =  {Zaikis, Dimitrios and Vlahavas, Ioannis},
    journal = {IEEE Access},
    title =   {From Pre-training to Meta-Learning: A journey in Low-Resource-Language Representation Learning},
    year =    {2023},
    volume =  {},
    number =  {},
    pages =   {1-1},
    doi =     {10.1109/ACCESS.2023.3326337}
  }

```