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:
- e22c0b180cf63eaaea88366e2f160cba40b2a5511c18a83b167ca09f94326663
- Size of remote file:
- 16.4 MB
- SHA256:
- 293c40096fbf7f2a490bcd92c560c9ef25ee1db66fc14379ceda125086ae0242
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