Instructions to use Jeska/BertjeWDialDataALL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeska/BertjeWDialDataALL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Jeska/BertjeWDialDataALL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Jeska/BertjeWDialDataALL") model = AutoModelForMaskedLM.from_pretrained("Jeska/BertjeWDialDataALL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jeska/BertjeWDialDataALL: direct link, hf CLI and curl.
- Browser
- Download file 2.86 kB
-
https://huggingface.co/Jeska/BertjeWDialDataALL/resolve/main/training_args.bin
- Command line
-
hf download hf://Jeska/BertjeWDialDataALL/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jeska/BertjeWDialDataALL/resolve/main/training_args.bin
2.86 kB
- Xet hash:
- 493dda2153db1fc5c4c5f56fba52800ea88a85fc1c110aee57af11ba461d0b54
- Size of remote file:
- 2.86 kB
- SHA256:
- 71ec58b6e6a330fd4d0e5db3360d32828d2dab0366acfcd0d6a8d4069c3f212e
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