
### SpaceSciBERT

This is one of the 3 further pre-trained models from the SpaceTransformers family presented in [SpaceTransformers: Language Modeling for Space Systems](https://ieeexplore.ieee.org/document/9548078). The original Git repo is [strath-ace/smart-nlp](https://github.com/strath-ace/smart-nlp).

The further pre-training corpus includes publications abstracts, books, and Wikipedia pages related to space systems. Corpus size is 14.3 GB. SpaceSciBERT was further pre-trained on this domain-specific corpus from [SciBERT-SciVocab (uncased)](https://huggingface.co/allenai/scibert_scivocab_uncased). In our paper, it is then fine-tuned for a Concept Recognition task.

### BibTeX entry and citation info

```
@ARTICLE{
9548078,  
author={Berquand, Audrey and Darm, Paul and Riccardi, Annalisa},  
journal={IEEE Access},   
title={SpaceTransformers: Language Modeling for Space Systems},   
year={2021},  
volume={9},  
number={},  
pages={133111-133122},  
doi={10.1109/ACCESS.2021.3115659}
}
```
{"language": "en", "license": "mit"}
transformers pytorch bert fill-mask en license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 


### SpaceSciBERT

This is one of the 3 further pre-trained models from the SpaceTransformers family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp.

The further pre-training corpus includes publications abstracts, books, and Wikipedia pages related to space systems. Corpus size is 14.3 GB. SpaceSciBERT was further pre-trained on this domain-specific corpus from SciBERT-SciVocab (uncased). In our paper, it is then fine-tuned for a Concept Recognition task.

### BibTeX entry and citation info


### SpaceSciBERT

This is one of the 3 further pre-trained models from the SpaceTransformers family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp.

The further pre-training corpus includes publications abstracts, books, and Wikipedia pages related to space systems. Corpus size is 14.3 GB. SpaceSciBERT was further pre-trained on this domain-specific corpus from SciBERT-SciVocab (uncased). In our paper, it is then fine-tuned for a Concept Recognition task. ### BibTeX entry and citation info
TAGS
#transformers #pytorch #bert #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### SpaceSciBERT

This is one of the 3 further pre-trained models from the SpaceTransformers family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp.

The further pre-training corpus includes publications abstracts, books, and Wikipedia pages related to space systems. Corpus size is 14.3 GB. SpaceSciBERT was further pre-trained on this domain-specific corpus from SciBERT-SciVocab (uncased). In our paper, it is then fine-tuned for a Concept Recognition task. ### BibTeX entry and citation info
passage: TAGS
#transformers #pytorch #bert #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### SpaceSciBERT

This is one of the 3 further pre-trained models from the SpaceTransformers family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp.

The further pre-training corpus includes publications abstracts, books, and Wikipedia pages related to space systems. Corpus size is 14.3 GB. SpaceSciBERT was further pre-trained on this domain-specific corpus from SciBERT-SciVocab (uncased). In our paper, it is then fine-tuned for a Concept Recognition task.### BibTeX entry and citation info