Instructions to use medspaner/mdeberta-v3-base-re-ct-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use medspaner/mdeberta-v3-base-re-ct-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, DebertaV2ForRelationExtraction tokenizer = AutoTokenizer.from_pretrained("medspaner/mdeberta-v3-base-re-ct-v2") model = DebertaV2ForRelationExtraction.from_pretrained("medspaner/mdeberta-v3-base-re-ct-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4b914ec5ab295fbee3b0e0fe62f22febb3c09d34dd3778745e0de034a76df33
- Size of remote file:
- 4.31 MB
- SHA256:
- 13c8d666d62a7bc4ac8f040aab68e942c861f93303156cc28f5c7e885d86d6e3
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