Update README.md

README.md

@@ -14,6 +14,10 @@ license: cc-by-4.0
 
 # NorBERT 3 xs
 
+<img src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=12.5%>
+
+The official release of a new generation of NorBERT language models described in paper [**NorBench — A Benchmark for Norwegian Language Models**](https://arxiv.org/abs/2305.03880). Plese read the paper to learn more details about the model.
+
 
 ## Other sizes:
 - [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs)
@@ -21,18 +25,23 @@ license: cc-by-4.0
 - [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base)
 - [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large)
 
+## Generative NorT5 siblings:
+- [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-xs")
+model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-xs", 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")
@@ -43,4 +52,29 @@ 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
+
+```bibtex
+@inproceedings{samuel-etal-2023-norbench,
+    title = "{N}or{B}ench {--} A Benchmark for {N}orwegian Language Models",
+    author = "Samuel, David  and
+      Kutuzov, Andrey  and
+      Touileb, Samia  and
+      Velldal, Erik  and
+      {\O}vrelid, Lilja  and
+      R{\o}nningstad, Egil  and
+      Sigdel, Elina  and
+      Palatkina, Anna",
+    booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
+    month = may,
+    year = "2023",
+    address = "T{\'o}rshavn, Faroe Islands",
+    publisher = "University of Tartu Library",
+    url = "https://aclanthology.org/2023.nodalida-1.61",
+    pages = "618--633",
+    abstract = "We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the different benchmark tests of NorBench.",
+}
+
+```