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Update README.md

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Patrick von Platen 2021-09-06 08:07:18 +00:00 committed by huggingface-web
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@ -82,9 +82,9 @@ You can use this model directly with a pipeline for masked language modeling:
Here is how to use this model to get the features of a given text in PyTorch: Here is how to use this model to get the features of a given text in PyTorch:
```python ```python
from transformers import BertTokenizer, TFBertModel from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-cased') tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = TFBertModel.from_pretrained("bert-base-cased") model = BertModel.from_pretrained("bert-base-cased")
text = "Replace me by any text you'd like." text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt') encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input) output = model(**encoded_input)
@ -93,9 +93,9 @@ output = model(**encoded_input)
and in TensorFlow: and in TensorFlow:
```python ```python
from transformers import BertTokenizer, BertModel from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-cased') tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = BertModel.from_pretrained("bert-base-cased") model = TFBertModel.from_pretrained("bert-base-cased")
text = "Replace me by any text you'd like." text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf') encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input) output = model(encoded_input)