
# TiRoBERTa: RoBERTa Pretrained for the Tigrinya Language

We pretrain a RoBERTa base model for Tigrinya on a dataset of 40 million tokens trained for 40 epochs.

Contained in this repo is the original pretrained Flax model that was trained on a TPU v3.8 and it's corresponding PyTorch version.


## Hyperparameters

The hyperparameters corresponding to model sizes mentioned above are as follows:

| Model Size | L  | AH | HS  | FFN  | P    | Seq  |
|------------|----|----|-----|------|------|------|
| BASE       | 12 | 12 | 768 | 3072 | 125M | 512  |

(L = number of layers; AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters; Seq = maximum sequence length.)

### Framework versions

- Transformers 4.12.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3


## Citation

If you use this model in your product or research, please cite as follows:

```
@article{Fitsum2021TiPLMs,
  author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
  title={Monolingual Pre-trained Language Models for Tigrinya},
  year=2021,
  publisher={WiNLP 2021 at EMNLP 2021}
}
```

{"language": "ti", "widget": [{"text": "\u12d3\u1255\u121a \u1218\u1295\u12a5\u1230\u12ed \u12a4\u122d\u1275\u122b <mask> \u1270\u122b\u12a5\u12e9"}]}
transformers pytorch jax safetensors roberta fill-mask ti autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us 

TiRoBERTa: RoBERTa Pretrained for the Tigrinya Language
=======================================================


We pretrain a RoBERTa base model for Tigrinya on a dataset of 40 million tokens trained for 40 epochs.


Contained in this repo is the original pretrained Flax model that was trained on a TPU v3.8 and it's corresponding PyTorch version.


Hyperparameters
---------------


The hyperparameters corresponding to model sizes mentioned above are as follows:



(L = number of layers; AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters; Seq = maximum sequence length.)


### Framework versions


* Transformers 4.12.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3


If you use this model in your product or research, please cite as follows:



### Framework versions


* Transformers 4.12.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3


If you use this model in your product or research, please cite as follows:
TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Framework versions


* Transformers 4.12.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3


If you use this model in your product or research, please cite as follows:
passage: TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Framework versions


* Transformers 4.12.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3


If you use this model in your product or research, please cite as follows: