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
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, and trained on the combined datasets from the Greek Wikipedia and the Greek part of OSCAR. 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,
- The Greek part of OSCAR, a cleansed version of Common Crawl.
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:
@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}
}