Update README.md

README.md

@@ -1,11 +1,12 @@
 ---
-language: "nl"
-thumbnail: "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png"
+language: nl
+thumbnail: https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png
 tags:
 - Dutch
 - Flemish
 - RoBERTa
 - RobBERT
+- BERT
 license: mit
 datasets:
 - oscar
@@ -14,7 +15,7 @@ datasets:
 - europarl-mono
 - conll2002
 widget:
-- text: "Hallo, ik ben RobBERT, een <mask> taalmodel van de KU Leuven."
+- text: Hallo, ik ben RobBERT, een <mask> taalmodel van de KU Leuven.
 ---
 
 <p align="center"> 
@@ -53,7 +54,7 @@ RobBERT uses the [RoBERTa](https://arxiv.org/abs/1907.11692) architecture and pr
 
 By default, RobBERT has the masked language model head used in training. This can be used as a zero-shot way to fill masks in sentences. It can be tested out for free on [RobBERT's Hosted infererence API of Huggingface](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=De+hoofdstad+van+Belgi%C3%AB+is+%3Cmask%3E.). You can also create a new prediction head for your own task by using any of HuggingFace's [RoBERTa-runners](https://huggingface.co/transformers/v2.7.0/examples.html#language-model-training), [their fine-tuning notebooks](https://huggingface.co/transformers/v4.1.1/notebooks.html) by changing the model name to `pdelobelle/robbert-v2-dutch-base`, or use the original fairseq [RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta) training regimes.
 
-Use the following code to download the base model and finetune it yourself, or use one of our finetuned models (documented on  [our project site](https://people.cs.kuleuven.be/~pieter.delobelle/robbert/)).
+Use the following code to download the base model and finetune it yourself, or use one of our finetuned models (documented on  [our project site](https://pieter.ai/robbert/)).
 
 ```python
 from transformers import RobertaTokenizer, RobertaForSequenceClassification