Instructions to use suzuki-2001/BERT-K8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suzuki-2001/BERT-K8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="suzuki-2001/BERT-K8")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("suzuki-2001/BERT-K8") model = AutoModelForMaskedLM.from_pretrained("suzuki-2001/BERT-K8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60d937765923ba232f23212bacca1f8cf6edf6d8dda1f5c749c670d62bf7550c
- Size of remote file:
- 1.09 GB
- SHA256:
- b2646a24be7d4e93fd1aedf7100c76ef81884507a7eda509706c0e4f3affd0b3
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