Instructions to use Jeska/BertjeWDialDataALLQonly07 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeska/BertjeWDialDataALLQonly07 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Jeska/BertjeWDialDataALLQonly07")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Jeska/BertjeWDialDataALLQonly07") model = AutoModelForMaskedLM.from_pretrained("Jeska/BertjeWDialDataALLQonly07", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jeska/BertjeWDialDataALLQonly07: direct link, hf CLI and curl.
- Browser
- Download file 2.93 kB
-
https://huggingface.co/Jeska/BertjeWDialDataALLQonly07/resolve/main/training_args.bin
- Command line
-
hf download hf://Jeska/BertjeWDialDataALLQonly07/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jeska/BertjeWDialDataALLQonly07/resolve/main/training_args.bin
2.93 kB
- Xet hash:
- a34b92a8fbbca2c01feeade693f542ddb1bfe5bae8ee55334ddf3c33adcfeeb9
- Size of remote file:
- 2.93 kB
- SHA256:
- 18715bbde1b5b25618ff249df58c52da8af3eaf0d8e2194f74fe492bcd1094fc
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