Instructions to use icelab/spacebert_CR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use icelab/spacebert_CR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="icelab/spacebert_CR")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("icelab/spacebert_CR") model = AutoModelForTokenClassification.from_pretrained("icelab/spacebert_CR") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("icelab/spacebert_CR")
model = AutoModelForTokenClassification.from_pretrained("icelab/spacebert_CR")Quick Links
spacebert_CR
Model desciption
This is a fine-tuned SpaceSciBERT model, for a Concept Recognition task, from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The fine-tuning dataset is available for download and consists of 874 unique manual annotated ECSS requirements.
The notebookfor fine-tuning can be accessed in Google Colab:
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} }
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="icelab/spacebert_CR")