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: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 32
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 100000

Citation Information

If you use this model in your work, please cite the following paper. Additionally, if you require more details on training and performance, refer to the paper:

bibtex

@misc{gurgurov2025smallmodelsbigimpact,
    title={Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages}, 
    author={Daniil Gurgurov and Ivan Vykopal and Josef van Genabith and Simon Ostermann},
    year={2025},
    eprint={2502.10140},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2502.10140}, 
}

DGurgurov/mbert_amh-ethi

amh-Ethi

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Citation Information

DGurgurov/
mbert_amh-ethi