Instructions to use suzuki-2001/BERT-K3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suzuki-2001/BERT-K3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="suzuki-2001/BERT-K3")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("suzuki-2001/BERT-K3") model = AutoModelForMaskedLM.from_pretrained("suzuki-2001/BERT-K3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b19e65531b5b04d8c54d27f9f2342ae475fd2ec2b2a5bc9d8e9b34d8f11e7dc
- Size of remote file:
- 4.86 kB
- SHA256:
- b7a602cdb75b218f785dd58b93a374a7d7cec1504bb1ccffbddacd763200508d
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