Instructions to use Zichuu/spert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zichuu/spert with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, SpERT tokenizer = AutoTokenizer.from_pretrained("Zichuu/spert") model = SpERT.from_pretrained("Zichuu/spert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Zichuu/spert: direct link, hf CLI and curl.
- Browser
- Download file 381 Bytes
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https://huggingface.co/Zichuu/spert/resolve/main/README.md
- Command line
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hf download hf://Zichuu/spert/README.md
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curl -L -o README.md https://huggingface.co/Zichuu/spert/resolve/main/README.md
381 Bytes
| # SpERT | |
| SpERT is the Relation Extraction model [(SpERT)Span-based Entity and Relation Transformer](https://github.com/lavis-nlp/spert).This is the model trained with CoNLL04 Dataset. | |
| ## Use | |
| ## References | |
| ``` | |
| Markus Eberts, Adrian Ulges. Span-based Joint Entity and Relation Extraction with Transformer Pre-training. 24th European Conference on Artificial Intelligence, 2020. | |
| ``` |