## TwiBERT
## Model Description
TwiBERT is a pre-trained language model specifically designed for the Twi language, which is widely spoken in Ghana, 
West Africa. This model has 61 million parameters, 6 layers, 6 attention heads, 768 hidden units, and a feed-forward size of 3072. 
To optimize its performance, TwiBERT was trained using a combination of the Asanti Twi Bible and a dataset 
sourced through crowdsourcing efforts. 



## Limitations:

The model was trained on a relatively limited dataset (approximately 5MB), 
which may hinder its ability to learn intricate contextual embeddings and effectively generalize. 
Additionally, the dataset's focus on the Bible could potentially introduce a strong religious bias in the model's output. 


## How to use it

You can use TwiBERT by finetuning it on a downtream task. 
The example code below illustrates how you can use the TwiBERT model on a downtream task:

```python
>>> from transformers import AutoTokenizer, AutoModelForTokenClassification
>>> model = AutoModelForTokenClassification.from_pretrained("sakrah/TwiBERT")
>>> tokenizer = AutoTokenizer.from_pretrained("sakrah/TwiBERT")
```

{"language": "twi", "license": "mit"}
transformers pytorch roberta fill-mask twi license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #twi #license-mit #autotrain_compatible #endpoints_compatible #region-us 

## TwiBERT
## Model Description
TwiBERT is a pre-trained language model specifically designed for the Twi language, which is widely spoken in Ghana, 
West Africa. This model has 61 million parameters, 6 layers, 6 attention heads, 768 hidden units, and a feed-forward size of 3072. 
To optimize its performance, TwiBERT was trained using a combination of the Asanti Twi Bible and a dataset 
sourced through crowdsourcing efforts. 



## Limitations:

The model was trained on a relatively limited dataset (approximately 5MB), 
which may hinder its ability to learn intricate contextual embeddings and effectively generalize. 
Additionally, the dataset's focus on the Bible could potentially introduce a strong religious bias in the model's output. 


## How to use it

You can use TwiBERT by finetuning it on a downtream task. 
The example code below illustrates how you can use the TwiBERT model on a downtream task:



## TwiBERT ## Model Description
TwiBERT is a pre-trained language model specifically designed for the Twi language, which is widely spoken in Ghana, 
West Africa. This model has 61 million parameters, 6 layers, 6 attention heads, 768 hidden units, and a feed-forward size of 3072. 
To optimize its performance, TwiBERT was trained using a combination of the Asanti Twi Bible and a dataset 
sourced through crowdsourcing efforts. ## Limitations:

The model was trained on a relatively limited dataset (approximately 5MB), 
which may hinder its ability to learn intricate contextual embeddings and effectively generalize. 
Additionally, the dataset's focus on the Bible could potentially introduce a strong religious bias in the model's output. ## How to use it

You can use TwiBERT by finetuning it on a downtream task. 
The example code below illustrates how you can use the TwiBERT model on a downtream task:
TAGS
#transformers #pytorch #roberta #fill-mask #twi #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ## TwiBERT ## Model Description
TwiBERT is a pre-trained language model specifically designed for the Twi language, which is widely spoken in Ghana, 
West Africa. This model has 61 million parameters, 6 layers, 6 attention heads, 768 hidden units, and a feed-forward size of 3072. 
To optimize its performance, TwiBERT was trained using a combination of the Asanti Twi Bible and a dataset 
sourced through crowdsourcing efforts. ## Limitations:

The model was trained on a relatively limited dataset (approximately 5MB), 
which may hinder its ability to learn intricate contextual embeddings and effectively generalize. 
Additionally, the dataset's focus on the Bible could potentially introduce a strong religious bias in the model's output. ## How to use it

You can use TwiBERT by finetuning it on a downtream task. 
The example code below illustrates how you can use the TwiBERT model on a downtream task:
TAGS
#transformers #pytorch #roberta #fill-mask #twi #license-mit #autotrain_compatible #endpoints_compatible #region-us 
## TwiBERT## Model Description
TwiBERT is a pre-trained language model specifically designed for the Twi language, which is widely spoken in Ghana, 
West Africa. This model has 61 million parameters, 6 layers, 6 attention heads, 768 hidden units, and a feed-forward size of 3072. 
To optimize its performance, TwiBERT was trained using a combination of the Asanti Twi Bible and a dataset 
sourced through crowdsourcing efforts.## Limitations:

The model was trained on a relatively limited dataset (approximately 5MB), 
which may hinder its ability to learn intricate contextual embeddings and effectively generalize. 
Additionally, the dataset's focus on the Bible could potentially introduce a strong religious bias in the model's output.## How to use it

You can use TwiBERT by finetuning it on a downtream task. 
The example code below illustrates how you can use the TwiBERT model on a downtream task: