Instructions to use suzuki-2001/BERT-K4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suzuki-2001/BERT-K4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="suzuki-2001/BERT-K4")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("suzuki-2001/BERT-K4") model = AutoModelForMaskedLM.from_pretrained("suzuki-2001/BERT-K4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3f8966ece10f99958bd4e2f133addff1df2dba75553d3b43f0fb1e96e8d0266
- Size of remote file:
- 4.86 kB
- SHA256:
- 936be3c1da505a157ba40490da41b58403f811c0fc057b3894c76c2c2cd61644
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.