readme: ---
  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:
  
  ```python
  >>> 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:
  
  ```python
  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:
  
  ```python
  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)
  ```