Instructions to use ZZ99/NBME_TAPT_deberta_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZZ99/NBME_TAPT_deberta_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ZZ99/NBME_TAPT_deberta_base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ZZ99/NBME_TAPT_deberta_base") model = AutoModelForMaskedLM.from_pretrained("ZZ99/NBME_TAPT_deberta_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f992c5a7e4aae5890cb01a0df364e0009c1939d78d022925a629d50a00596c1c
- Size of remote file:
- 2.99 kB
- SHA256:
- 4bb2ff58d1811e599ef806b6ea47e7d7484ef0fcf4a30096a6c5831ed645d086
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