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:
- 179ba51179370c6005e5b50588b420987f0b5081dc0c98379743ee3cd08dbf10
- Size of remote file:
- 1.11 GB
- SHA256:
- b31af50d466b237a2037d016209273b2ab2973355e6296d95f03212c2e203236
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