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 pytorch_model.bin from Jeska/BertjeWDialDataALL: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/Jeska/BertjeWDialDataALL/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jeska/BertjeWDialDataALL/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jeska/BertjeWDialDataALL/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- d1dd781f9d55814a716889bffca4348f6eed679237045293f7a2015c1f70cb9a
- Size of remote file:
- 437 MB
- SHA256:
- 9d496f2d12f658edae1ba3fec025f3c8b55be34887bbaf2b4bbb098483224abf
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