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:
- 5d2b111f724fbe7c8b85da774a14b086d31a839f8ab840b836b3ea6d18c2f8e8
- Size of remote file:
- 557 MB
- SHA256:
- ec0b44c8c4542e52426d6e79f74e7149c6044b55785b42d3c91512387bc140db
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