## Quickstart
**Release 2.0** (February 7, 2022)

Trained on the very large corpus of Norwegian (C4 + NCC, about 15 billion word tokens).
Features a 50 000 words vocabulary and was trained using Whole Word Masking.

Download the model here:
* Cased Norwegian BERT Base 2.0 (NorBERT 2): [221.zip](http://vectors.nlpl.eu/repository/20/221.zip)

More about NorBERT training corpora, training procedure and evaluation benchmarks: http://norlm.nlpl.eu/

Associated code: https://github.com/ltgoslo/NorBERT

Check this paper for more details:

_Andrey Kutuzov, Jeremy Barnes, Erik Velldal, Lilja Øvrelid, Stephan Oepen. [Large-Scale Contextualised Language Modelling for Norwegian](https://aclanthology.org/2021.nodalida-main.4/), NoDaLiDa'21 (2021)_

NorBERT was trained as a part of NorLM, a joint initiative of the projects [EOSC-Nordic](https://www.eosc-nordic.eu/) (European Open Science Cloud),
coordinated by the [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) (LTG) at the University of Oslo.

The computations were performed on resources provided by UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway.

## NorBERT-3
In 2023, we released a new family of *NorBERT-3* language models for Norwegian. In general, we now recommend using these models:

- [NorBERT 3 xs](https://huggingface.co/ltg/norbert3-xs) (15M parameters)
- [NorBERT 3 small](https://huggingface.co/ltg/norbert3-small) (40M parameters)
- [NorBERT 3 base](https://huggingface.co/ltg/norbert3-base) (123M parameters)
- [NorBERT 3 large](https://huggingface.co/ltg/norbert3-large) (323M parameters)

NorBERT-3 is described in detail in this paper:
[NorBench – A Benchmark for Norwegian Language Models](https://aclanthology.org/2023.nodalida-1.61/) (Samuel et al., NoDaLiDa 2023)

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transformers pytorch tf safetensors bert fill-mask norwegian no license:cc-by-4.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #norwegian #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

## Quickstart
Release 2.0 (February 7, 2022)

Trained on the very large corpus of Norwegian (C4 + NCC, about 15 billion word tokens).
Features a 50 000 words vocabulary and was trained using Whole Word Masking.

Download the model here:
* Cased Norwegian BERT Base 2.0 (NorBERT 2): URL

More about NorBERT training corpora, training procedure and evaluation benchmarks: URL

Associated code: URL

Check this paper for more details:

_Andrey Kutuzov, Jeremy Barnes, Erik Velldal, Lilja Øvrelid, Stephan Oepen. Large-Scale Contextualised Language Modelling for Norwegian, NoDaLiDa'21 (2021)_

NorBERT was trained as a part of NorLM, a joint initiative of the projects EOSC-Nordic (European Open Science Cloud),
coordinated by the Language Technology Group (LTG) at the University of Oslo.

The computations were performed on resources provided by UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway.

## NorBERT-3
In 2023, we released a new family of *NorBERT-3* language models for Norwegian. In general, we now recommend using these models:

- NorBERT 3 xs (15M parameters)
- NorBERT 3 small (40M parameters)
- NorBERT 3 base (123M parameters)
- NorBERT 3 large (323M parameters)

NorBERT-3 is described in detail in this paper:
NorBench – A Benchmark for Norwegian Language Models (Samuel et al., NoDaLiDa 2023)

## Quickstart
Release 2.0 (February 7, 2022)

Trained on the very large corpus of Norwegian (C4 + NCC, about 15 billion word tokens).
Features a 50 000 words vocabulary and was trained using Whole Word Masking.

Download the model here:
* Cased Norwegian BERT Base 2.0 (NorBERT 2): URL

More about NorBERT training corpora, training procedure and evaluation benchmarks: URL

Associated code: URL

Check this paper for more details:

_Andrey Kutuzov, Jeremy Barnes, Erik Velldal, Lilja Øvrelid, Stephan Oepen. Large-Scale Contextualised Language Modelling for Norwegian, NoDaLiDa'21 (2021)_

NorBERT was trained as a part of NorLM, a joint initiative of the projects EOSC-Nordic (European Open Science Cloud),
coordinated by the Language Technology Group (LTG) at the University of Oslo.

The computations were performed on resources provided by UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway. ## NorBERT-3
In 2023, we released a new family of *NorBERT-3* language models for Norwegian. In general, we now recommend using these models:

- NorBERT 3 xs (15M parameters)
- NorBERT 3 small (40M parameters)
- NorBERT 3 base (123M parameters)
- NorBERT 3 large (323M parameters)

NorBERT-3 is described in detail in this paper:
NorBench – A Benchmark for Norwegian Language Models (Samuel et al., NoDaLiDa 2023)
TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #norwegian #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ## Quickstart
Release 2.0 (February 7, 2022)

Trained on the very large corpus of Norwegian (C4 + NCC, about 15 billion word tokens).
Features a 50 000 words vocabulary and was trained using Whole Word Masking.

Download the model here:
* Cased Norwegian BERT Base 2.0 (NorBERT 2): URL

More about NorBERT training corpora, training procedure and evaluation benchmarks: URL

Associated code: URL

Check this paper for more details:

_Andrey Kutuzov, Jeremy Barnes, Erik Velldal, Lilja Øvrelid, Stephan Oepen. Large-Scale Contextualised Language Modelling for Norwegian, NoDaLiDa'21 (2021)_

NorBERT was trained as a part of NorLM, a joint initiative of the projects EOSC-Nordic (European Open Science Cloud),
coordinated by the Language Technology Group (LTG) at the University of Oslo.

The computations were performed on resources provided by UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway. ## NorBERT-3
In 2023, we released a new family of *NorBERT-3* language models for Norwegian. In general, we now recommend using these models:

- NorBERT 3 xs (15M parameters)
- NorBERT 3 small (40M parameters)
- NorBERT 3 base (123M parameters)
- NorBERT 3 large (323M parameters)

NorBERT-3 is described in detail in this paper:
NorBench – A Benchmark for Norwegian Language Models (Samuel et al., NoDaLiDa 2023)
passage: TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #norwegian #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
## Quickstart
Release 2.0 (February 7, 2022)

Trained on the very large corpus of Norwegian (C4 + NCC, about 15 billion word tokens).
Features a 50 000 words vocabulary and was trained using Whole Word Masking.

Download the model here:
* Cased Norwegian BERT Base 2.0 (NorBERT 2): URL

More about NorBERT training corpora, training procedure and evaluation benchmarks: URL

Associated code: URL

Check this paper for more details:

_Andrey Kutuzov, Jeremy Barnes, Erik Velldal, Lilja Øvrelid, Stephan Oepen. Large-Scale Contextualised Language Modelling for Norwegian, NoDaLiDa'21 (2021)_

NorBERT was trained as a part of NorLM, a joint initiative of the projects EOSC-Nordic (European Open Science Cloud),
coordinated by the Language Technology Group (LTG) at the University of Oslo.

The computations were performed on resources provided by UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway.## NorBERT-3
In 2023, we released a new family of *NorBERT-3* language models for Norwegian. In general, we now recommend using these models:

- NorBERT 3 xs (15M parameters)
- NorBERT 3 small (40M parameters)
- NorBERT 3 base (123M parameters)
- NorBERT 3 large (323M parameters)

NorBERT-3 is described in detail in this paper:
NorBench – A Benchmark for Norwegian Language Models (Samuel et al., NoDaLiDa 2023)