Updated readme

README.md

@@ -42,8 +42,8 @@ By default, RobBERT-2023 has the masked language model head used in training. Th
 
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
-tokenizer = AutoTokenizer.from_pretrained("pdelobelle/robbert-2023-dutch-large")
-model = AutoModelForSequenceClassification.from_pretrained("pdelobelle/robbert-2023-dutch-large")
+tokenizer = AutoTokenizer.from_pretrained("DTAI-KULeuven/robbert-2023-dutch-large")
+model = AutoModelForSequenceClassification.from_pretrained("DTAI-KULeuven/robbert-2023-dutch-large")
 ```
 
 You can then use most of [HuggingFace's BERT-based notebooks](https://huggingface.co/transformers/v4.1.1/notebooks.html) for finetuning RobBERT-2022 on your type of Dutch language dataset.
@@ -54,8 +54,8 @@ You can then use most of [HuggingFace's BERT-based notebooks](https://huggingfac
 There is a wide variety of Dutch BERT-based models available for fine-tuning on your tasks.
 Here's a quick summary to find the one that suits your need:
 
-- **(this model)** [pdelobelle/robbert-2023-dutch-large](https://huggingface.co/pdelobelle/robbert-2023-dutch-large): The RobBERT-2023 is the first Dutch large (355M parameters) model. It is trained on OSCAR2023 with a new tokenizer, using [our Tik-to-Tok method](https://arxiv.org/pdf/2310.03477.pdf). 
-- [pdelobelle/robbert-2023-dutch-base](https://huggingface.co/pdelobelle/robbert-2023-dutch-base): The RobBERT-2023 is a new RobBERT model on the OSCAR2023 dataset with a completely new tokenizer. It is helpful for tasks that rely on words and/or information about more recent events.
+- **(this model)** [DTAI-KULeuven/robbert-2023-dutch-large](https://huggingface.co/DTAI-KULeuven/robbert-2023-dutch-large): The RobBERT-2023 is the first Dutch large (355M parameters) model. It is trained on OSCAR2023 with a new tokenizer, using [our Tik-to-Tok method](https://arxiv.org/pdf/2310.03477.pdf). 
+- [DTAI-KULeuven/robbert-2023-dutch-base](https://huggingface.co/DTAI-KULeuven/robbert-2023-dutch-base): The RobBERT-2023 is a new RobBERT model on the OSCAR2023 dataset with a completely new tokenizer. It is helpful for tasks that rely on words and/or information about more recent events.
 - [DTAI-KULeuven/robbert-2022-dutch-base](https://huggingface.co/DTAI-KULeuven/robbert-2022-dutch-base): The RobBERT-2022 is a further pre-trained RobBERT model on the OSCAR2022 dataset. It is helpful for tasks that rely on words and/or information about more recent events.
 - [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base): The RobBERT model has for years been the best performing BERT-like model for most language tasks. It is trained on a large Dutch webcrawled dataset (OSCAR) and uses the superior [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta) architecture, which robustly optimized the original [BERT model](https://huggingface.co/docs/transformers/model_doc/bert).
 - [DTAI-KULeuven/robbertje-1-gb-merged](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-mergedRobBERTje): The RobBERTje model is a distilled version of RobBERT and about half the size and four times faster to perform inference on. This can help deploy more scalable language models for your language task 
@@ -71,7 +71,7 @@ The pretraining depends on the model, for RobBERT-2023 this is based on [our Tik
 ## Name Origin of RobBERT
 
 Most BERT-like models have the word *BERT* in their name (e.g. [RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html), [ALBERT](https://arxiv.org/abs/1909.11942), [CamemBERT](https://camembert-model.fr/), and [many, many others](https://huggingface.co/models?search=bert)).
-As such, we queried our newly trained model using its masked language model to name itself *\\<mask\\>bert* using [all](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Mijn+naam+is+%3Cmask%3Ebert.) [kinds](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Hallo%2C+ik+ben+%3Cmask%3Ebert.) [of](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Leuk+je+te+ontmoeten%2C+ik+heet+%3Cmask%3Ebert.) [prompts](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Niemand+weet%2C+niemand+weet%2C+dat+ik+%3Cmask%3Ebert+heet.), and it consistently called itself RobBERT.
+As such, we queried our original RobBERT model using its masked language model to name itself *\\<mask\\>bert* using [all](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Mijn+naam+is+%3Cmask%3Ebert.) [kinds](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Hallo%2C+ik+ben+%3Cmask%3Ebert.) [of](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Leuk+je+te+ontmoeten%2C+ik+heet+%3Cmask%3Ebert.) [prompts](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Niemand+weet%2C+niemand+weet%2C+dat+ik+%3Cmask%3Ebert+heet.), and it consistently called itself RobBERT.
 We thought it was really quite fitting, given that RobBERT is a [*very* Dutch name](https://en.wikipedia.org/wiki/Robbert) *(and thus clearly a Dutch language model)*, and additionally has a high similarity to its root architecture, namely [RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html).
 
 Since *"rob"* is a Dutch words to denote a seal, we decided to draw a seal and dress it up like [Bert from Sesame Street](https://muppet.fandom.com/wiki/Bert) for the [RobBERT logo](https://github.com/iPieter/RobBERT/blob/master/res/robbert_logo.png).