Instructions to use suzuki-2001/BERT-K6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suzuki-2001/BERT-K6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="suzuki-2001/BERT-K6")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("suzuki-2001/BERT-K6") model = AutoModelForMaskedLM.from_pretrained("suzuki-2001/BERT-K6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db1dd4b549ed9b5968fa159711cebfb3bbea1fac66e50aa628eac6a85723930b
- Size of remote file:
- 4.86 kB
- SHA256:
- 8b836c4ad9bfe75617fdaa72a4c3d8ef9455e5db183bf3d38d04666a0a2db2a2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.