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 training_args.bin from Jzz/FidicBERT: direct link, hf CLI and curl.
- Browser
- Download file 2.61 kB
-
https://huggingface.co/Jzz/FidicBERT/resolve/main/training_args.bin
- Command line
-
hf download hf://Jzz/FidicBERT/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jzz/FidicBERT/resolve/main/training_args.bin
2.61 kB
- Xet hash:
- 4aba6e30b7470024b28f224a52ba40665b8716aa1da20e7ed77ae51ada1d6c98
- Size of remote file:
- 2.61 kB
- SHA256:
- 86d5a1e9b1ed051c8e6271904cc371dd840a071c4d1676612744bb998eafbe29
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