| | --- |
| | language: |
| | - 'no' |
| | - nb |
| | - nn |
| | inference: false |
| | tags: |
| | - BERT |
| | - NorBERT |
| | - Norwegian |
| | - encoder |
| | license: cc-by-4.0 |
| | --- |
| | |
| | # NorBERT 3 small |
| |
|
| | <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://aclanthology.org/2023.nodalida-1.61/). Plese read the paper to learn more details about the model. |
| |
|
| |
|
| | ## Other sizes: |
| | - [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) |
| |
|
| | ## 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`, you should therefore load the model with `trust_remote_code=True`. |
| |
|
| | ```python |
| | import torch |
| | from transformers import AutoTokenizer, AutoModelForMaskedLM |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("ltg/norbert3-small") |
| | model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-small", 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") |
| | output_p = model(**input_text) |
| | output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids) |
| | |
| | # should output: '[CLS] Nå ønsker de seg en ny bolig.[SEP]' |
| | print(tokenizer.decode(output_text[0].tolist())) |
| | ``` |
| |
|
| | 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.", |
| | } |
| | |
| | ``` |
