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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cjvt/sleng-bert") model = AutoModelForMaskedLM.from_pretrained("cjvt/sleng-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cjvt/sleng-bert: direct link, hf CLI and curl.
- Browser
- Download file 467 MB
-
https://huggingface.co/cjvt/sleng-bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cjvt/sleng-bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cjvt/sleng-bert/resolve/main/pytorch_model.bin
467 MB
- Xet hash:
- 761f0d9476b4ddde5a201f1f85879c8a0d0f18fae2b30e9cffe929230ff7b95a
- Size of remote file:
- 467 MB
- SHA256:
- 6a7b1b3e94569c1da0d4409daa6b38daf048bc1feb149845b97eead1a8fc777e
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