Instructions to use dobbytk/KSL-BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dobbytk/KSL-BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dobbytk/KSL-BERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dobbytk/KSL-BERT") model = AutoModelForMaskedLM.from_pretrained("dobbytk/KSL-BERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d986a0032103cc4ac080ec82eb45a4b67c56dee1b796daad0a0212a3fa122d6f
- Size of remote file:
- 439 MB
- SHA256:
- 6a3cbedc64d14b333253218dfc694ee25d242b4ae83c99c9097f78ea7d6a3a75
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.