
# BERT Base for Tigrinya Language

We pre-train a BERT base-uncased model for Tigrinya on a dataset of 40 million tokens trained for 40 epochs.

This repo contains the original pre-trained Flax model that was trained on a TPU v3.8 and its corresponding PyTorch version.

## Hyperparameters

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

| Model Size | L  | AH | HS  | FFN  | P    | Seq  |
|------------|----|----|-----|------|------|------|
| BASE       | 12 | 12 | 768 | 3072 | 110M | 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.)


## 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 \u12f0\u1242\u12a3\u1295\u1235\u1275\u12ee [MASK] \u1265\u130d\u1265\u122a \u1270\u122b\u12a5\u12e9"}]}
transformers pytorch jax bert fill-mask ti autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #jax #bert #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us 

BERT Base for Tigrinya Language
===============================


We pre-train a BERT base-uncased model for Tigrinya on a dataset of 40 million tokens trained for 40 epochs.


This repo contains the original pre-trained Flax model that was trained on a TPU v3.8 and its corresponding PyTorch version.


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


The hyperparameters corresponding to the 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.)


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



TAGS
#transformers #pytorch #jax #bert #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #jax #bert #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us 
