metadata
language:
- multilingual
- ny
- kg
- kmb
- rw
- ln
- lua
- lg
- nso
- rn
- st
- sw
- ss
- ts
- tn
- tum
- umb
- xh
- zu
- fr
- en
license: apache-2.0
How to use
You can use this model directly with a pipeline for masked language modeling:
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='aioxlabs/toumbert')
>>> unmasker("rais wa [MASK] ya tanzania.")
Here is how to use this model to get the features of a given text in PyTorch:
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('aioxlabs/toumbert')
model = BertModel.from_pretrained("aioxlabs/toumbert")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
and in TensorFlow:
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('aioxlabs/toumbert')
model = TFBertModel.from_pretrained("aioxlabs/toumbert")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)