Instructions to use jmzk96/PCSciBERT_cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jmzk96/PCSciBERT_cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jmzk96/PCSciBERT_cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jmzk96/PCSciBERT_cased") model = AutoModelForMaskedLM.from_pretrained("jmzk96/PCSciBERT_cased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8dfdffca706eea6bce231eabf4aff63dfc0120e7c2cd621c962bbe28b8e0d65
- Size of remote file:
- 440 MB
- SHA256:
- c8f3635d00c82f48f46b26a3b038a84ab4a216ff2d16955695ad9705e2558a84
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