Update README.md

README.md

@@ -7,7 +7,7 @@ datasets:
 - c4
 ---
 
-# BERT base model (uncased)
+# FNet base model
 
 Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was 
 introduced in [this paper](https://arxiv.org/abs/2105.03824) and first released in [this repository](https://github.com/google-research/f_net).
@@ -82,8 +82,10 @@ output = model(**encoded_input)
 Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. However, the model's MLM accuracy may also affect answers. Given below are some example where gender-bias could be expected:
 
 ```python
->>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
+>>> from transformers import FNetForMaskedLM, FNetTokenizer, pipeline
+>>> tokenizer = FNetTokenizer.from_pretrained("google/fnet-base")
+>>> model = FNetForMaskedLM.from_pretrained("google/fnet-base")
+>>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
 >>> unmasker("The man worked as a [MASK].")
 
 [