Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language:
+- 'no'
+- nb
+- nn
+- se
+inference: false
+tags:
+- BERT
+- GPT-BERT
+- NorBERT
+- Norwegian
+- encoder
+- decoder
+license: apache-2.0
+---
+
+<img src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=12.5%>
+
+
+# NorBERT 4 large
+
+The fourth generation of NorBERT models mainly improves their efficiency, but also performance and flexibility.
+
+<img src="https://huggingface.co/ltg/norbert4-large/resolve/main/model_performance.png" width=100%>
+
+- **Made to encode long texts**: these models were trained on 16384-token-long texts, the sliding-window attention can then generalize to even longer sequences.
+- **Fast and memory-efficient training and inference**: using FlashAttention2 with unpadding, the new generation of NorBERT models can process the long texts with ease.
+- **Better performance**: better quality of training corpora and carefully tuned training settings leads to an improved performance over NorBERT 3.
+- **BERT as well as GPT**: the models can flexibly function as both bidirectional encoders (BERT) or unidirectional decoders (GPT), which makes them very flexible to any downstream use.
+- **Trained from scratch**: the model is trained from scratch on 600B tokens of Norwegian Bokmål, Nynorsk and Northern Sámi. We used the HPLT 2.0 corpus, FineWeb2 and Mímir Core.
+- **Permissable license**: the checkpoints are distributed freely under Apache 2.0, anyone can use our models.
+
+> [!TIP]
+> We recommend installing Flash Attention 2 and `torch.compile`-ing your models to get the highest training and inference efficiency.
+
+
+
+## All sizes of the NorBERT4 family:
+- [NorBERT 4 xsmall (17M)](https://huggingface.co/ltg/norbert4-xsmall)
+- [NorBERT 4 small (40M)](https://huggingface.co/ltg/norbert4-small)
+- [NorBERT 4 base (149M)](https://huggingface.co/ltg/norbert4-base)
+- [NorBERT 4 large (360M)](https://huggingface.co/ltg/norbert4-large)
+- [NorBERT 4 xlarge (987M)](https://huggingface.co/ltg/norbert4-xlarge)
+
+
+## Example usage (bidirectional encoding)
+
+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, AutoModelForMaskedLM
+
+# Import model
+tokenizer = AutoTokenizer.from_pretrained(
+    "ltg/norbert4-large"
+)
+model = AutoModelForMaskedLM.from_pretrained(
+    "ltg/norbert4-large",
+    trust_remote_code=True
+)
+
+# Tokenize text (with a mask token inside)
+input_text = tokenizer(
+    f"Nå ønsker de seg en{tokenizer.mask_token} bolig.",
+    return_tensors="pt"
+)
+
+# Inference
+with torch.inference_mode:
+    output_p = model(**input_text)
+
+# Unmask the text
+output_text = torch.where(
+    input_text.input_ids == tokenizer.mask_token_id,
+    output_p.logits.argmax(-1),
+    input_text.input_ids
+)
+
+# Decoding; should output: '<s>Nå ønsker de seg en ny bolig.'
+print(tokenizer.decode(output_text[0].tolist()))
+```
+
+## Example usage (text generation)
+
+NorBERT now also supports unidirectional text decoding, it can generate text like any other GPT model:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Import model
+tokenizer = AutoTokenizer.from_pretrained(
+    "ltg/norbert4-large"
+)
+model = AutoModelForCausalLM.from_pretrained(
+    "ltg/norbert4-large",
+    trust_remote_code=True
+)
+
+# Define zero-shot translation prompt template
+prompt = """Engelsk: {0}
+Bokmål:"""
+
+# Define tokens that should end the generation (any token with a newline)
+eos_token_ids = [
+    token_id
+    for token_id in range(tokenizer.vocab_size)
+    if '\n' in tokenizer.decode([token_id])
+]
+
+# Generation function
+@torch.inference_mode()
+def generate(text):
+    text = prompt.format(text)
+    input_ids = tokenizer(text, return_tensors='pt').input_ids
+    prediction = model.generate(
+        input_ids,
+        max_new_tokens=64,
+        do_sample=False,
+        eos_token_id=eos_token_ids
+    )
+    return tokenizer.decode(prediction[0, input_ids.size(1):]).strip()
+
+# Example usage
+generate("I'm a model that can generate text!")
+```
+
+The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForCausalLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
+
+## Contact
+
+David Samuel: `[email protected]`
+
+## Cite us
+
+```bibtex
+@inproceedings{charpentier-samuel-2024-bert,
+    title = "{GPT} or {BERT}: why not both?",
+    author = "Charpentier, Lucas Georges Gabriel  and
+      Samuel, David",
+    booktitle = "The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning",
+    month = nov,
+    year = "2024",
+    address = "Miami, FL, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.conll-babylm.24/",
+    pages = "262--283"
+}
+```
+
+```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"
+}
+```