Instructions to use suzuki-2001/BERT-K5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suzuki-2001/BERT-K5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="suzuki-2001/BERT-K5")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("suzuki-2001/BERT-K5") model = AutoModelForMaskedLM.from_pretrained("suzuki-2001/BERT-K5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b59b4ba3ee269d8280fda86135ee2ff837914d027acf2fa021312d8c6d10431
- Size of remote file:
- 4.86 kB
- SHA256:
- dbd4651f7e9eabc238dc69b66ab2e48edf18288ae0b8fcfa6d27450ea3c51bc6
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