Update README.md

README.md

@@ -26,23 +26,22 @@ The official release of a new generation of NorBERT language models described in
 - [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large)
 
 ## Generative NorT5 siblings:
-- [NorT5 xs (15M)](https://huggingface.co/ltg/nort5-xs)
-- [NorT5 small (40M)](https://huggingface.co/ltg/nort5-small)
-- [NorT5 base (123M)](https://huggingface.co/ltg/nort5-base)
-- [NorT5 large (323M)](https://huggingface.co/ltg/nort5-large)
+- [NorT5 xs (32M)](https://huggingface.co/ltg/nort5-xs)
+- [NorT5 small (88M)](https://huggingface.co/ltg/nort5-small)
+- [NorT5 base (228M)](https://huggingface.co/ltg/nort5-base)
+- [NorT5 large (808M)](https://huggingface.co/ltg/nort5-large)
 
 
 ## Example usage
 
-This model currently needs a custom wrapper from `modeling_norbert.py`. Then you can use it like this:
+This model currently needs a custom wrapper from `modeling_norbert.py`, you should therefore load the model with ``trust_remote_code=True''.
 
 ```python
 import torch
-from transformers import AutoTokenizer
-from modeling_norbert import NorbertForMaskedLM
+from transformers import AutoTokenizer, AutoModelForMaskedLM
 
-tokenizer = AutoTokenizer.from_pretrained("path/to/folder")
-bert = NorbertForMaskedLM.from_pretrained("path/to/folder")
+tokenizer = AutoTokenizer.from_pretrained("ltg/norbert3-base")
+model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-base", trust_remote_code=True)
 
 mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
 input_text = tokenizer("Nå ønsker de seg en[MASK] bolig.", return_tensors="pt")
@@ -53,7 +52,7 @@ output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argma
 print(tokenizer.decode(output_text[0].tolist()))
 ```
 
-The following classes are currently implemented: `NorbertForMaskedLM`, `NorbertForSequenceClassification`, `NorbertForTokenClassification`, `NorbertForQuestionAnswering` and `NorbertForMultipleChoice`.
+The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
 
 ## Cite us