Instructions to use CLASS-MATE/BERT-MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLASS-MATE/BERT-MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="CLASS-MATE/BERT-MLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CLASS-MATE/BERT-MLM") model = AutoModelForMaskedLM.from_pretrained("CLASS-MATE/BERT-MLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 082cddf99805568bbf64d770993c4aae8e0b666e43e712471cf25ecee84c83ee
- Size of remote file:
- 329 MB
- SHA256:
- 7ab1d0c022ada85974a76c833812b627f152cb9c2ab277b70f2ee2839482772b
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