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