Instructions to use cjvt/sloberta-sleng with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cjvt/sloberta-sleng with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="cjvt/sloberta-sleng")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cjvt/sloberta-sleng") model = AutoModelForMaskedLM.from_pretrained("cjvt/sloberta-sleng", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9d56a6acfe821b64609d12c690151e2e5be8ec79c7b1bc7bf80071e681f0b2d
- Size of remote file:
- 234 MB
- SHA256:
- c86db419a34f6c0e2d88e8933a800452fc3dfbeadc4862eb7551ae57e150ec8d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.