Instructions to use SU-FMI-AI/multiclinner-enigma-es-procedure-RigoBERTa-Clinical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SU-FMI-AI/multiclinner-enigma-es-procedure-RigoBERTa-Clinical with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SU-FMI-AI/multiclinner-enigma-es-procedure-RigoBERTa-Clinical")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("SU-FMI-AI/multiclinner-enigma-es-procedure-RigoBERTa-Clinical") model = AutoModelForTokenClassification.from_pretrained("SU-FMI-AI/multiclinner-enigma-es-procedure-RigoBERTa-Clinical", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 414c4da1a974d3e503762aa4031dac135599559d095cdf4700ea3224f1950300
- Size of remote file:
- 5.27 kB
- SHA256:
- 0f2f3f9ed44e44438923232d64d7e1ab8af6394b26adb1906a892e83904e8e37
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