Instructions to use gjonesQ02/WO_MaskedModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gjonesQ02/WO_MaskedModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gjonesQ02/WO_MaskedModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gjonesQ02/WO_MaskedModel") model = AutoModelForMaskedLM.from_pretrained("gjonesQ02/WO_MaskedModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gjonesQ02/WO_MaskedModel: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/gjonesQ02/WO_MaskedModel/resolve/main/training_args.bin
- Command line
-
hf download hf://gjonesQ02/WO_MaskedModel/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gjonesQ02/WO_MaskedModel/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- c36d373c23e6f4db7dd7f6fa6d7015d1485730bc9589980009ddb71f4603ea7c
- Size of remote file:
- 4.66 kB
- SHA256:
- 319015555ad320df86eb40de4b7d6c78cbed68414df95a843cade7ba9f5a381c
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