Update README.md

README.md

@@ -15,25 +15,36 @@ pipeline_tag: text2text-generation
 
 # NorT5 x-small
 
+<img src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=12.5%>
+
+The official release of a new generation of NorT5 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:
-- [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)
+
+
+## Encoder-only NorBERT siblings:
+- [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs)
+- [NorBERT 3 small (40M)](https://huggingface.co/ltg/norbert3-small)
+- [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base)
+- [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large)
 
 
 ## Example usage
 
-This model currently needs a custom wrapper from `modeling_nort5.py`. Then you can use it like this:
+This model currently needs a custom wrapper from `modeling_nort5.py`, you should therefore load the model with `trust_remote_code=True`.
+
 
 ```python
 import torch
-from transformers import AutoTokenizer
-from modeling_norbert import NorT5ForConditionalGeneration
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-tokenizer = AutoTokenizer.from_pretrained("path/to/folder")
-t5 = NorT5ForConditionalGeneration.from_pretrained("path/to/folder")
+tokenizer = AutoTokenizer.from_pretrained("ltg/nort5-xs")
+t5 = AutoModelForSeq2SeqLM.from_pretrained("ltg/nort5-xs")
 
 
 # MASKED LANGUAGE MODELING
@@ -59,4 +70,32 @@ output_tensor = model.generate(input_tensor, max_new_tokens=50, num_beams=4, do_
 tokenizer.decode(output_tensor.squeeze())
 
 # should output: [BOS]ˈoppvarming, det vil si at det skjer en endring i temperaturen i et medium, f.eks. en ovn eller en radiator, slik at den blir varmere eller kaldere, eller at den blir varmere eller kaldere, eller at den blir
+```
+
+
+The following classes are currently implemented: `AutoModel`, `AutoModelForSeq2SeqLM`.
+
+## 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.",
+}
+
 ```