Instructions to use cjvt/sleng-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cjvt/sleng-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="cjvt/sleng-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cjvt/sleng-bert") model = AutoModelForMaskedLM.from_pretrained("cjvt/sleng-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 761f0d9476b4ddde5a201f1f85879c8a0d0f18fae2b30e9cffe929230ff7b95a
- Size of remote file:
- 467 MB
- SHA256:
- 6a7b1b3e94569c1da0d4409daa6b38daf048bc1feb149845b97eead1a8fc777e
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