Instructions to use jmzk96/PCSciBERT_uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jmzk96/PCSciBERT_uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jmzk96/PCSciBERT_uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jmzk96/PCSciBERT_uncased") model = AutoModelForMaskedLM.from_pretrained("jmzk96/PCSciBERT_uncased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7cd755b4f72e606a05bb355799712b714e82f6912791c3205799332565a04802
- Size of remote file:
- 440 MB
- SHA256:
- 973dc1846ec855b2886454c4d128260b70bab3fba2fc6149d19d7f5583511884
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