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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("icelab/spacebert_CR") model = AutoModelForTokenClassification.from_pretrained("icelab/spacebert_CR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# spacebert_CR
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## Model desciption
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This is
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The
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https://colab.research.google.com/drive/1EGh9bdxq6RqIzbvKuptAWvmIBG2EQJzJ?usp=sharing
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### BibTeX entry and citation info
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# spacebert_CR
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### Model desciption
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This is fine-tuned further SpaceSciBERT model 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](https://github.com/strath-ace/smart-nlp/blob/master/SpaceTransformers/CR/CR_ECSS_dataset.json) dataset is available for download and consists of 874 unique manual annotated ECSS requirements.
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The notebookfor fine-tuning can be assesed in Google Colab:
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[](https://colab.research.google.com/drive/1EGh9bdxq6RqIzbvKuptAWvmIBG2EQJzJ?usp=sharing)
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### BibTeX entry and citation info
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