Instructions to use Jzz/FidicBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jzz/FidicBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Jzz/FidicBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Jzz/FidicBERT") model = AutoModelForMaskedLM.from_pretrained("Jzz/FidicBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jzz/FidicBERT: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Jzz/FidicBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jzz/FidicBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jzz/FidicBERT/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- c8e45f2717b218d140265a4832e36a04834a5b152a1c296b71b89421b3b31063
- Size of remote file:
- 499 MB
- SHA256:
- bddca50e3e641be99f9a779d2b3c181f7d042f6b5842829be5424cc021593152
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