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Update README.md

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  ---
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- language: "nl"
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- thumbnail: "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png"
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  tags:
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  - Dutch
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  - Flemish
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  - RoBERTa
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  - RobBERT
 
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  license: mit
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  datasets:
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  - oscar
@@ -14,7 +15,7 @@ datasets:
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  - europarl-mono
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  - conll2002
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  widget:
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- - text: "Hallo, ik ben RobBERT, een <mask> taalmodel van de KU Leuven."
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  ---
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  <p align="center">
@@ -53,7 +54,7 @@ RobBERT uses the [RoBERTa](https://arxiv.org/abs/1907.11692) architecture and pr
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  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.
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- 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/)).
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  ```python
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  from transformers import RobertaTokenizer, RobertaForSequenceClassification
 
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  ---
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+ language: nl
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+ thumbnail: https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png
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  tags:
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  - Dutch
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  - Flemish
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  - RoBERTa
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  - RobBERT
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+ - BERT
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  license: mit
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  datasets:
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  - oscar
 
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  - europarl-mono
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  - conll2002
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  widget:
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+ - text: Hallo, ik ben RobBERT, een <mask> taalmodel van de KU Leuven.
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  ---
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  <p align="center">
 
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  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.
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+ 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/)).
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  ```python
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  from transformers import RobertaTokenizer, RobertaForSequenceClassification